From 5bf2b94dd4fb74378b78604023b31512fec55f8f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 16 Feb 2024 19:05:56 +0200 Subject: [PATCH 001/401] cmake : fix VULKAN and ROCm builds (#5525) * cmake : fix VULKAN and ROCm builds * cmake : fix (cont) * vulkan : fix compile warnings ggml-ci * cmake : fix ggml-ci * cmake : minor ggml-ci --- CMakeLists.txt | 379 +++++++++++++++++++++++++----------------------- ggml-vulkan.cpp | 12 +- 2 files changed, 205 insertions(+), 186 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f8c7f9978488a..2a922fdb316aa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -112,17 +112,14 @@ option(LLAMA_MPI "llama: use MPI" option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) +option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ON) - # add perf arguments option(LLAMA_PERF "llama: enable perf" OFF) -if (LLAMA_PERF) - add_definitions(-DGGML_PERF) -endif() # Required for relocatable CMake package include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) @@ -130,6 +127,7 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) # # Compile flags # + if (LLAMA_SYCL) set(CMAKE_CXX_STANDARD 17) else() @@ -140,6 +138,7 @@ set(CMAKE_CXX_STANDARD_REQUIRED true) set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD_REQUIRED true) set(THREADS_PREFER_PTHREAD_FLAG ON) + find_package(Threads REQUIRED) include(CheckCXXCompilerFlag) @@ -151,17 +150,17 @@ endif() if (NOT MSVC) if (LLAMA_SANITIZE_THREAD) add_compile_options(-fsanitize=thread) - link_libraries(-fsanitize=thread) + link_libraries (-fsanitize=thread) endif() if (LLAMA_SANITIZE_ADDRESS) add_compile_options(-fsanitize=address -fno-omit-frame-pointer) - link_libraries(-fsanitize=address) + link_libraries (-fsanitize=address) endif() if (LLAMA_SANITIZE_UNDEFINED) add_compile_options(-fsanitize=undefined) - link_libraries(-fsanitize=undefined) + link_libraries (-fsanitize=undefined) endif() endif() @@ -298,14 +297,17 @@ if (LLAMA_BLAS) endif() message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") + add_compile_options(${BLAS_LINKER_FLAGS}) + add_compile_definitions(GGML_USE_OPENBLAS) + if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel")) add_compile_definitions(GGML_BLAS_USE_MKL) endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) + set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) else() message(WARNING "BLAS not found, please refer to " "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" @@ -330,9 +332,6 @@ if (LLAMA_CUBLAS) set(GGML_SOURCES_CUDA ggml-cuda.cu) add_compile_definitions(GGML_USE_CUBLAS) -# if (LLAMA_CUDA_CUBLAS) -# add_compile_definitions(GGML_CUDA_CUBLAS) -# endif() if (LLAMA_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV) endif() @@ -387,15 +386,20 @@ if (LLAMA_MPI) find_package(MPI) if (MPI_C_FOUND) message(STATUS "MPI found") + set(GGML_HEADERS_MPI ggml-mpi.h) - set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h) + set(GGML_SOURCES_MPI ggml-mpi.c) + add_compile_definitions(GGML_USE_MPI) add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) + if (NOT MSVC) add_compile_options(-Wno-cast-qual) endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES}) set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS}) + # Even if you're only using the C header, C++ programs may bring in MPI # C++ functions, so more linkage is needed if (MPI_CXX_FOUND) @@ -427,31 +431,28 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") - add_library(ggml-vulkan OBJECT ggml-vulkan.cpp ggml-vulkan.h) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) - endif() - target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) + set(GGML_HEADERS_VULKAN ggml-vulkan.h) + set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) add_compile_definitions(GGML_USE_VULKAN) if (LLAMA_VULKAN_CHECK_RESULTS) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS) + add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) endif() if (LLAMA_VULKAN_DEBUG) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG) + add_compile_definitions(GGML_VULKAN_DEBUG) endif() if (LLAMA_VULKAN_VALIDATE) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE) + add_compile_definitions(GGML_VULKAN_VALIDATE) endif() if (LLAMA_VULKAN_RUN_TESTS) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS) + add_compile_definitions(GGML_VULKAN_RUN_TESTS) endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} Vulkan::Vulkan) else() message(WARNING "Vulkan not found") endif() @@ -463,43 +464,45 @@ if (LLAMA_HIPBLAS) if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang") message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang") endif() + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") endif() - find_package(hip) - find_package(hipblas) - find_package(rocblas) + find_package(hip REQUIRED) + find_package(hipblas REQUIRED) + find_package(rocblas REQUIRED) - if (${hipblas_FOUND} AND ${hip_FOUND}) - message(STATUS "HIP and hipBLAS found") - add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) - if (LLAMA_HIP_UMA) - add_compile_definitions(GGML_HIP_UMA) - endif() - add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON) - endif() - if (LLAMA_CUDA_FORCE_DMMV) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV) - endif() - if (LLAMA_CUDA_FORCE_MMQ) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ) - endif() - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) - target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) - target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas) + message(STATUS "HIP and hipBLAS found") - if (LLAMA_STATIC) - message(FATAL_ERROR "Static linking not supported for HIP/ROCm") - endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm) - else() - message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm") + set(GGML_HEADERS_ROCM ggml-cuda.h) + set(GGML_SOURCES_ROCM ggml-cuda.cu) + + add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) + + if (LLAMA_HIP_UMA) + add_compile_definitions(GGML_HIP_UMA) + endif() + + if (LLAMA_CUDA_FORCE_DMMV) + add_compile_definitions(GGML_CUDA_FORCE_DMMV) + endif() + + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) + add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) + + set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) + + if (LLAMA_STATIC) + message(FATAL_ERROR "Static linking not supported for HIP/ROCm") endif() + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device PUBLIC hip::host roc::rocblas roc::hipblas) endif() if (LLAMA_SYCL) @@ -509,10 +512,14 @@ if (LLAMA_SYCL) #todo: AOT find_package(IntelSYCL REQUIRED) + + message(STATUS "SYCL found") + + add_compile_definitions(GML_USE_SYCL) + if (LLAMA_SYCL_F16) add_compile_definitions(GGML_SYCL_F16) endif() - add_compile_definitions(GGML_USE_SYCL) add_compile_options(-I./) #include DPCT add_compile_options(-I/${SYCL_INCLUDE_DIR}) @@ -521,7 +528,7 @@ if (LLAMA_SYCL) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib") - set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) + set(GGML_HEADERS_SYCL ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) if (WIN32) @@ -540,61 +547,61 @@ if (LLAMA_KOMPUTE) endif() function(compile_shader) - set(options) - set(oneValueArgs) - set(multiValueArgs SOURCES) - cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - foreach(source ${compile_shader_SOURCES}) - get_filename_component(filename ${source} NAME) - set(spv_file ${filename}.spv) - add_custom_command( - OUTPUT ${spv_file} - DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp - COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} - COMMENT "Compiling ${source} to ${spv_file}" - ) - - get_filename_component(RAW_FILE_NAME ${spv_file} NAME) - set(FILE_NAME "shader${RAW_FILE_NAME}") - string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) - string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) - string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") - set(OUTPUT_HEADER_FILE "${HEADER_FILE}") - message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") - if(CMAKE_GENERATOR MATCHES "Visual Studio") - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" - ) - else() + set(options) + set(oneValueArgs) + set(multiValueArgs SOURCES) + cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + foreach(source ${compile_shader_SOURCES}) + get_filename_component(filename ${source} NAME) + set(spv_file ${filename}.spv) add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" - ) - endif() - endforeach() + OUTPUT ${spv_file} + DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp + COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} + COMMENT "Compiling ${source} to ${spv_file}" + ) + + get_filename_component(RAW_FILE_NAME ${spv_file} NAME) + set(FILE_NAME "shader${RAW_FILE_NAME}") + string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) + string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) + string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") + set(OUTPUT_HEADER_FILE "${HEADER_FILE}") + message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") + if(CMAKE_GENERATOR MATCHES "Visual Studio") + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" + ) + else() + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" + ) + endif() + endforeach() endfunction() if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") @@ -604,66 +611,66 @@ if (LLAMA_KOMPUTE) # Compile our shaders compile_shader(SOURCES - kompute-shaders/op_scale.comp - kompute-shaders/op_scale_8.comp - kompute-shaders/op_add.comp - kompute-shaders/op_addrow.comp - kompute-shaders/op_mul.comp - kompute-shaders/op_silu.comp - kompute-shaders/op_relu.comp - kompute-shaders/op_gelu.comp - kompute-shaders/op_softmax.comp - kompute-shaders/op_norm.comp - kompute-shaders/op_rmsnorm.comp - kompute-shaders/op_diagmask.comp - kompute-shaders/op_mul_mat_mat_f32.comp - kompute-shaders/op_mul_mat_f16.comp - kompute-shaders/op_mul_mat_q8_0.comp - kompute-shaders/op_mul_mat_q4_0.comp - kompute-shaders/op_mul_mat_q4_1.comp - kompute-shaders/op_mul_mat_q6_k.comp - kompute-shaders/op_getrows_f16.comp - kompute-shaders/op_getrows_q4_0.comp - kompute-shaders/op_getrows_q4_1.comp - kompute-shaders/op_getrows_q6_k.comp - kompute-shaders/op_rope_f16.comp - kompute-shaders/op_rope_f32.comp - kompute-shaders/op_cpy_f16_f16.comp - kompute-shaders/op_cpy_f16_f32.comp - kompute-shaders/op_cpy_f32_f16.comp - kompute-shaders/op_cpy_f32_f32.comp + kompute-shaders/op_scale.comp + kompute-shaders/op_scale_8.comp + kompute-shaders/op_add.comp + kompute-shaders/op_addrow.comp + kompute-shaders/op_mul.comp + kompute-shaders/op_silu.comp + kompute-shaders/op_relu.comp + kompute-shaders/op_gelu.comp + kompute-shaders/op_softmax.comp + kompute-shaders/op_norm.comp + kompute-shaders/op_rmsnorm.comp + kompute-shaders/op_diagmask.comp + kompute-shaders/op_mul_mat_mat_f32.comp + kompute-shaders/op_mul_mat_f16.comp + kompute-shaders/op_mul_mat_q8_0.comp + kompute-shaders/op_mul_mat_q4_0.comp + kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f16.comp + kompute-shaders/op_getrows_q4_0.comp + kompute-shaders/op_getrows_q4_1.comp + kompute-shaders/op_getrows_q6_k.comp + kompute-shaders/op_rope_f16.comp + kompute-shaders/op_rope_f32.comp + kompute-shaders/op_cpy_f16_f16.comp + kompute-shaders/op_cpy_f16_f32.comp + kompute-shaders/op_cpy_f32_f16.comp + kompute-shaders/op_cpy_f32_f32.comp ) # Create a custom target for our generated shaders add_custom_target(generated_shaders DEPENDS - shaderop_scale.h - shaderop_scale_8.h - shaderop_add.h - shaderop_addrow.h - shaderop_mul.h - shaderop_silu.h - shaderop_relu.h - shaderop_gelu.h - shaderop_softmax.h - shaderop_norm.h - shaderop_rmsnorm.h - shaderop_diagmask.h - shaderop_mul_mat_mat_f32.h - shaderop_mul_mat_f16.h - shaderop_mul_mat_q8_0.h - shaderop_mul_mat_q4_0.h - shaderop_mul_mat_q4_1.h - shaderop_mul_mat_q6_k.h - shaderop_getrows_f16.h - shaderop_getrows_q4_0.h - shaderop_getrows_q4_1.h - shaderop_getrows_q6_k.h - shaderop_rope_f16.h - shaderop_rope_f32.h - shaderop_cpy_f16_f16.h - shaderop_cpy_f16_f32.h - shaderop_cpy_f32_f16.h - shaderop_cpy_f32_f32.h + shaderop_scale.h + shaderop_scale_8.h + shaderop_add.h + shaderop_addrow.h + shaderop_mul.h + shaderop_silu.h + shaderop_relu.h + shaderop_gelu.h + shaderop_softmax.h + shaderop_norm.h + shaderop_rmsnorm.h + shaderop_diagmask.h + shaderop_mul_mat_mat_f32.h + shaderop_mul_mat_f16.h + shaderop_mul_mat_q8_0.h + shaderop_mul_mat_q4_0.h + shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q6_k.h + shaderop_getrows_f16.h + shaderop_getrows_q4_0.h + shaderop_getrows_q4_1.h + shaderop_getrows_q6_k.h + shaderop_rope_f16.h + shaderop_rope_f32.h + shaderop_cpy_f16_f16.h + shaderop_cpy_f16_f32.h + shaderop_cpy_f32_f16.h + shaderop_cpy_f32_f32.h ) # Create a custom command that depends on the generated_shaders @@ -676,8 +683,10 @@ if (LLAMA_KOMPUTE) # Add the stamp to the main sources to ensure dependency tracking set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - set(GGML_HEADERS_KOMPUTE ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + set(GGML_HEADERS_KOMPUTE ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + add_compile_definitions(GGML_USE_KOMPUTE) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute) set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR}) else() @@ -685,6 +694,18 @@ if (LLAMA_KOMPUTE) endif() endif() +if (LLAMA_CPU_HBM) + find_library(memkind memkind REQUIRED) + + add_compile_definitions(GGML_USE_CPU_HBM) + + target_link_libraries(ggml PUBLIC memkind) +endif() + +if (LLAMA_PERF) + add_compile_definitions(GGML_PERF) +endif() + function(get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -821,6 +842,7 @@ execute_process( ERROR_VARIABLE output OUTPUT_QUIET ) + if (output MATCHES "dyld-1015\.7") add_compile_definitions(HAVE_BUGGY_APPLE_LINKER) endif() @@ -830,10 +852,10 @@ endif() # feel free to update the Makefile for your architecture and send a pull request or issue message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}") if (MSVC) - string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) - message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") + string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) + message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") else () - set(CMAKE_GENERATOR_PLATFORM_LWR "") + set(CMAKE_GENERATOR_PLATFORM_LWR "") endif () if (NOT MSVC) @@ -1027,11 +1049,6 @@ endif() # ggml -if (GGML_USE_CPU_HBM) - add_definitions(-DGGML_USE_CPU_HBM) - find_library(memkind memkind REQUIRED) -endif() - add_library(ggml OBJECT ggml.c ggml.h @@ -1048,16 +1065,17 @@ add_library(ggml OBJECT ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} + ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} + ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) -target_compile_features(ggml PUBLIC c_std_11) # don't bump +target_compile_features (ggml PUBLIC c_std_11) # don't bump + target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) -if (GGML_USE_CPU_HBM) - target_link_libraries(ggml PUBLIC memkind) -endif() add_library(ggml_static STATIC $) + if (BUILD_SHARED_LIBS) set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) add_library(ggml_shared SHARED $) @@ -1073,7 +1091,8 @@ add_library(llama ) target_include_directories(llama PUBLIC .) -target_compile_features(llama PUBLIC cxx_std_11) # don't bump +target_compile_features (llama PUBLIC cxx_std_11) # don't bump + target_link_libraries(llama PRIVATE ggml ${LLAMA_EXTRA_LIBS} @@ -1124,7 +1143,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1fad24fd12df4..4a30414df2fce 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1091,7 +1091,7 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } -void ggml_vk_instance_init() { +static void ggml_vk_instance_init() { if (vk_instance_initialized) { return; } @@ -1150,7 +1150,7 @@ void ggml_vk_instance_init() { vk_instance_initialized = true; } -void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { +static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { GGML_ASSERT(idx < vk_instance.device_indices.size()); size_t dev_num = vk_instance.device_indices[idx]; #ifdef GGML_VULKAN_DEBUG @@ -4556,13 +4556,13 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { } } -GGML_CALL int ggml_vk_get_device_count() { +GGML_CALL static int ggml_vk_get_device_count() { ggml_vk_instance_init(); return vk_instance.device_indices.size(); } -GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) { ggml_vk_instance_init(); std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); @@ -4580,7 +4580,7 @@ void ggml_vk_init_cpu_assist() { std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl; - for (size_t i = 0; i < ggml_vk_get_device_count(); i++) { + for (int i = 0; i < ggml_vk_get_device_count(); i++) { ggml_vk_print_gpu_info(i); } // Initialize the first backend to make sure CPU matrix multiplications can be offloaded. @@ -5267,7 +5267,7 @@ GGML_CALL void ggml_backend_vk_get_device_description(int device, char * descrip } GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { - GGML_ASSERT(device < vk_instance.device_indices.size()); + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; From d250c9d61d4d9f7346930814cc4aef3f3673dc3e Mon Sep 17 00:00:00 2001 From: clibdev <52199778+clibdev@users.noreply.github.com> Date: Sat, 17 Feb 2024 18:28:37 +0200 Subject: [PATCH 002/401] gitignore : update for CLion IDE (#5544) --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index b84459b92e86e..62b6b8b1ab250 100644 --- a/.gitignore +++ b/.gitignore @@ -23,11 +23,13 @@ .clang-tidy .vs/ .vscode/ +.idea/ lcov-report/ gcovr-report/ build* +cmake-build-* out/ tmp/ From 6e4e973b2615f8d390b1c4f4a7e05a119078bb0f Mon Sep 17 00:00:00 2001 From: Ananta Bastola Date: Sat, 17 Feb 2024 16:03:14 -0500 Subject: [PATCH 003/401] ci : add an option to fail on compile warning (#3952) * feat(ci): add an option to fail on compile warning * Update CMakeLists.txt * minor : fix compile warnings ggml-ci * ggml : fix unreachable code warnings ggml-ci * ci : disable fatal warnings for windows, ios and tvos * ggml : fix strncpy warning * ci : disable fatal warnings for MPI build * ci : add fatal warnings to ggml-ci ggml-ci --------- Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 10 +++++++--- CMakeLists.txt | 11 +++++++++++ Makefile | 29 ++++++++++++++++++++++++++++ ci/run.sh | 2 +- examples/export-lora/export-lora.cpp | 2 -- ggml-backend.c | 1 + ggml-metal.m | 2 +- ggml.c | 15 +++++++++----- 8 files changed, 60 insertions(+), 12 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ed292d6b8935d..03d76d45560cf 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -37,6 +37,8 @@ jobs: - name: Build id: make_build + env: + LLAMA_FATAL_WARNINGS: 1 run: | CC=gcc-8 make -j $(nproc) @@ -65,7 +67,7 @@ jobs: run: | mkdir build cd build - cmake .. + cmake .. -DLLAMA_FATAL_WARNINGS=ON cmake --build . --config Release -j $(nproc) - name: Test @@ -100,7 +102,7 @@ jobs: run: | mkdir build cd build - cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} cmake --build . --config ${{ matrix.build_type }} -j $(nproc) - name: Test @@ -244,6 +246,8 @@ jobs: - name: Build id: make_build + env: + LLAMA_FATAL_WARNINGS: 1 run: | LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu) @@ -277,7 +281,7 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_METAL=OFF .. + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test diff --git a/CMakeLists.txt b/CMakeLists.txt index 2a922fdb316aa..5ea4d4f1979dc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -55,6 +55,9 @@ option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF) option(LLAMA_GPROF "llama: enable gprof" OFF) +# build +option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF) + # sanitizers option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF) option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF) @@ -142,6 +145,14 @@ set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads REQUIRED) include(CheckCXXCompilerFlag) +if (LLAMA_FATAL_WARNINGS) + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + add_compile_options(-Werror) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + add_compile_options(/WX) + endif() +endif() + # enable libstdc++ assertions for debug builds if (CMAKE_SYSTEM_NAME MATCHES "Linux") add_compile_definitions($<$:_GLIBCXX_ASSERTIONS>) diff --git a/Makefile b/Makefile index 0a2070b539df8..90179860623d0 100644 --- a/Makefile +++ b/Makefile @@ -215,6 +215,35 @@ MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmis -Werror=implicit-function-declaration MK_CXXFLAGS += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn +ifeq ($(LLAMA_FATAL_WARNINGS),1) + MK_CFLAGS += -Werror + MK_CXXFLAGS += -Werror +endif + +ifeq ($(CC_IS_CLANG), 1) + # clang options + MK_CFLAGS += -Wunreachable-code-break -Wunreachable-code-return + MK_HOST_CXXFLAGS += -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi + + ifneq '' '$(and $(CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 030800)))' + MK_CFLAGS += -Wdouble-promotion + endif + ifneq '' '$(and $(CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 070300)))' + MK_CFLAGS += -Wdouble-promotion + endif +else + # gcc options + MK_CFLAGS += -Wdouble-promotion + MK_HOST_CXXFLAGS += -Wno-array-bounds + + ifeq ($(shell expr $(CC_VER) \>= 070100), 1) + MK_HOST_CXXFLAGS += -Wno-format-truncation + endif + ifeq ($(shell expr $(CC_VER) \>= 080100), 1) + MK_HOST_CXXFLAGS += -Wextra-semi + endif +endif + # this version of Apple ld64 is buggy ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))' MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER diff --git a/ci/run.sh b/ci/run.sh index 979b4a7939137..b94658c96c5a4 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -33,7 +33,7 @@ sd=`dirname $0` cd $sd/../ SRC=`pwd` -CMAKE_EXTRA="" +CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON" if [ ! -z ${GG_BUILD_METAL} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON" diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 2f7be8a1344fa..08413f57e4c3a 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -7,8 +7,6 @@ #include #include -static const size_t tensor_alignment = 32; - struct lora_info { std::string filename; float scale; diff --git a/ggml-backend.c b/ggml-backend.c index 87eea84402e92..d019d813ad5f0 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1006,6 +1006,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, gg } } GGML_ASSERT(false && "tensor buffer type not supported by any backend"); + return -1; // silence warning } #if 0 diff --git a/ggml-metal.m b/ggml-metal.m index c1d8e2de81e02..6e76f8bedb50b 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -176,7 +176,7 @@ // MSL code // TODO: move the contents here when ready // for now it is easier to work in a separate file -//static NSString * const msl_library_source = @"see metal.metal"; +// static NSString * const msl_library_source = @"see metal.metal"; // Here to assist with NSBundle Path Hack @interface GGMLMetalClass : NSObject diff --git a/ggml.c b/ggml.c index 4e302fb7de2f4..264cfd705cd37 100644 --- a/ggml.c +++ b/ggml.c @@ -868,7 +868,7 @@ do { \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ const __m128 t1 = _mm_hadd_ps(t0, t0); \ - res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ } while (0) // TODO: is this optimal ? @@ -1149,7 +1149,7 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ - res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ } // TODO: is this optimal ? @@ -2086,6 +2086,7 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { } } #else + GGML_UNUSED(numa_flag); // TODO #endif } @@ -3219,7 +3220,7 @@ const char * ggml_get_name(const struct ggml_tensor * tensor) { } struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) { - strncpy(tensor->name, name, sizeof(tensor->name)); + strncpy(tensor->name, name, sizeof(tensor->name) - 1); tensor->name[sizeof(tensor->name) - 1] = '\0'; return tensor; } @@ -18575,7 +18576,9 @@ static enum ggml_opt_result linesearch_backtracking( (*step) *= width; } - GGML_UNREACHABLE(); + GGML_ASSERT(false && "line search failed"); + + return GGML_LINESEARCH_FAIL; } static enum ggml_opt_result ggml_opt_lbfgs( @@ -18843,7 +18846,9 @@ static enum ggml_opt_result ggml_opt_lbfgs( step[0] = 1.0; } - GGML_UNREACHABLE(); + GGML_ASSERT(false && "lbfgs failed"); + + return GGML_OPT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { From 8f1be0d42f23016cb6819dbae01126699c4bd9bc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 17 Feb 2024 23:04:16 +0200 Subject: [PATCH 004/401] ggml : add ALiBi support for ggml_soft_max_ext (#5488) * ggml : avoid recomputing alibi slopes (CPU) * llama : reuse hparams.f_max_alibi_bias in all cases ggml-ci * ggml : support alibi bias in ggml_soft_max_ext (CPU + Metal) ggml-ci * ggml : handle all SRCs (do not break on first null) ggml-ci * tests : do not use slope for large soft_max accumulates too much error ggml-ci * ggml : alternative ALiBi without extra tensor We compute the slopes in the kernel ggml-ci * cuda : add ALiBi support in ggml_soft_max_ext ggml-ci * ggml : deprecate ggml_alibi * ggml : support multi-sequence ALiBi (Metal) ggml-ci * cuda : add multi-seq ALiBi + remote F16 soft_max ggml-ci * ggml : update deprecation message * ggml : fix pos ptr when no ALiBi ggml-ci * cuda : fix performance (pow -> powf) * cuda : precompute ALiBi constants * metal : pre-compute ALiBi slopes ggml-ci * llama : init kq_pos only if needed ggml-ci * test-backend-ops : add null pos test to soft_max test-backend-ops : replace soft_max tests ggml-ci --------- Co-authored-by: slaren --- ggml-alloc.c | 6 +- ggml-backend.c | 16 +-- ggml-cuda.cu | 257 ++++++++----------------------------- ggml-metal.m | 35 +++-- ggml-metal.metal | 47 ++++++- ggml.c | 116 +++++++++++------ ggml.h | 13 +- llama.cpp | 133 ++++++++++++------- tests/test-backend-ops.cpp | 74 ++++++----- 9 files changed, 344 insertions(+), 353 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index c28c37c4fd9ff..d4123564ff0d1 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -551,7 +551,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr } for (int j = 0; j < GGML_MAX_SRC; j++) { if (graph->nodes[i]->src[j] == NULL) { - break; + continue; } if (graph->nodes[i]->src[j]->flags & GGML_TENSOR_FLAG_INPUT) { ggml_gallocr_allocate_node(galloc, graph->nodes[i]->src[j], get_node_buffer_id(node_buffer_ids, i)); @@ -787,7 +787,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } if (!ggml_gallocr_node_needs_realloc(galloc, src, node_alloc, &node_alloc->src[j])) { #ifndef NDEBUG @@ -833,7 +833,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } ggml_gallocr_init_tensor(galloc, src, node_alloc, &node_alloc->src[j]); } diff --git a/ggml-backend.c b/ggml-backend.c index d019d813ad5f0..66e8c293a9e3f 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1041,7 +1041,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st for (int i = 0; i < GGML_MAX_SRC; i++) { const struct ggml_tensor * src = tensor->src[i]; if (src == NULL) { - break; + continue; } if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer); @@ -1088,7 +1088,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } ggml_backend_t src_backend = tensor_backend(src); fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, @@ -1144,7 +1144,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } if (tensor_backend_id(src) == -1) { tensor_backend_id(src) = ggml_backend_sched_backend_id_from_cur(sched, src); @@ -1256,7 +1256,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } int src_backend_id = tensor_backend_id(src); if (src_backend_id == -1) { @@ -1315,7 +1315,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } int src_backend_id = tensor_backend_id(src); assert(src_backend_id != -1); // all inputs should be assigned by now @@ -1362,7 +1362,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } ggml_backend_t src_backend = tensor_backend(src); if (src_backend != tensor_backend /* && src_backend != NULL */) { @@ -1668,7 +1668,7 @@ static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, for (int i = 0; i < GGML_MAX_SRC; i++) { struct ggml_tensor * s = src->src[i]; if (s == NULL) { - break; + continue; } dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s); } @@ -1697,7 +1697,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te for (int i = 0; i < GGML_MAX_SRC; i++) { struct ggml_tensor * s = src->src[i]; if (s == NULL) { - break; + continue; } graph_copy_init_tensor(hash_set, node_copies, node_init, s); } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b35fcb7fdb5d2..5fd8a87e4150f 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5956,148 +5956,30 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } -template -static __global__ void soft_max_f16(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX - const int ncols_data = ncols_template == 0 ? ncols_par : ncols_template; - const int ncols_smem = GGML_PAD(ncols_data, 2*WARP_SIZE)/2; +template +static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; const int tid = threadIdx.x; const int rowx = blockIdx.x; - const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension + const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - extern __shared__ half data_soft_max_f16[]; - half * buf_iw = data_soft_max_f16 + 0; // shared memory buffer for inter-warp communication - // (shared memory) buffer to cache values between iterations: - half2 * vals = vals_smem ? (half2 *) (buf_iw + WARP_SIZE) : (half2 *) (dst + rowx*ncols_data); - // if the buffer is larger than max. shared memory per block, use dst as temp. buffer instead - // in that case col_smem == col_data must be enforced to avoid race conditions - - half2 max_val = make_half2(-INFINITY, -INFINITY); - -#pragma unroll - for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { - const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id; - const int col_smem = vals_smem ? col0 + tid : col_data; - - const int ix = rowx*ncols_data + col_data; - const int iy = rowy*ncols_data + col_data; - - half2 val; - if (need_check && col_data + 0 >= ncols_data) { - val.x = -INFINITY; - } else { - val.x = x[ix + 0]*scale + (y ? y[iy + 0] : 0.0f); - } - if (need_check && col_data + WARP_SIZE >= ncols_data) { - val.y = -INFINITY; - } else { - val.y = x[ix + WARP_SIZE]*scale + (y ? y[iy + WARP_SIZE] : 0.0f); - } - if (!need_check || col_smem < (vals_smem ? ncols_smem : ncols_data)) { - vals[col_smem] = val; - } - max_val = __hmax2(max_val, val); - } - - // find the max value in the block - max_val = warp_reduce_max(max_val); - if (block_size > WARP_SIZE) { - if (warp_id == 0) { - buf_iw[lane_id] = -INFINITY; - } - __syncthreads(); - - if (lane_id == 0) { - buf_iw[warp_id] = __hmax(max_val.x, max_val.y); - } - __syncthreads(); - - max_val = __half2half2(buf_iw[lane_id]); - max_val = warp_reduce_max(max_val); - } else { - max_val = __half2half2(__hmax(max_val.x, max_val.y)); - } - - half2 tmp = make_half2(0.0f, 0.0f); // partial sums + float slope = 0.0f; -#pragma unroll - for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { - const int col_smem = vals_smem ? col0 + tid : 2*col0 + 2*warp_id*WARP_SIZE + lane_id; - - if (ncols_template == 0 && col_smem >= (vals_smem ? ncols_smem : ncols_data)) { - break; - } - - const half2 val = h2exp(vals[col_smem] - max_val); - - tmp += val; - vals[col_smem] = val; - } - - // find the sum of exps in the block - tmp = warp_reduce_sum(tmp); - if (block_size > WARP_SIZE) { - if (warp_id == 0) { - buf_iw[lane_id] = 0.0f; - } - __syncthreads(); - - if (lane_id == 0) { - buf_iw[warp_id] = tmp.x + tmp.y; - } - __syncthreads(); - - tmp = __half2half2(buf_iw[lane_id]); - tmp = warp_reduce_sum(tmp); - } else { - tmp = __half2half2(tmp.x + tmp.y); - } - - const half2 inv_sum = make_half2(1.0f, 1.0f) / tmp; - -#pragma unroll - for (int col0 = 0; col0 < ncols_smem; col0 += block_size) { - const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id; - const int col_smem = vals_smem ? col0 + tid : col_data; - - const int idst = rowx*ncols_data + col_data; - const half2 result = vals[col_smem] * inv_sum; - - if (need_check && col_data + 0 >= ncols_data) { - return; - } - dst[idst] = result.x; + // ALiBi + if (max_bias > 0.0f) { + const int h = rowx/nrows_y; // head index - if (need_check && col_data + WARP_SIZE >= ncols_data) { - return; - } + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - dst[idst + WARP_SIZE] = result.y; + slope = powf(base, exp); } -#else - (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale; - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -} - -template -static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { - const int ncols = ncols_template == 0 ? ncols_par : ncols_template; - - const int tid = threadIdx.x; - const int rowx = blockIdx.x; - const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension - - const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; - - const int warp_id = threadIdx.x / WARP_SIZE; - const int lane_id = threadIdx.x % WARP_SIZE; extern __shared__ float data_soft_max_f32[]; float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication @@ -6117,7 +5999,8 @@ static __global__ void soft_max_f32(const float * x, const float * y, float * ds const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - const float val = x[ix]*scale + (y ? y[iy] : 0.0f); + const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + slope*pos[col]; + vals[col] = val; max_val = max(max_val, val); } @@ -7589,89 +7472,53 @@ static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols diag_mask_inf_f32<<>>(x, dst, ncols_x, rows_per_channel, n_past); } -static void soft_max_f16_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) { - int nth = WARP_SIZE; - while (nth < ncols_x/2 && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; - const dim3 block_dims(nth, 1, 1); - const dim3 block_nums(nrows_x, 1, 1); - const size_t shmem = (GGML_PAD(ncols_x, 2*WARP_SIZE) + WARP_SIZE)*sizeof(half); - static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); - if (shmem <= g_device_caps[g_main_device].smpb) { - switch (ncols_x) { - case 32: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 64: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 128: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 256: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 512: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 1024: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 2048: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - case 4096: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - default: - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - break; - } - } else { - const size_t shmem_low = WARP_SIZE*sizeof(half); - soft_max_f16<<>>(x, y, dst, ncols_x, nrows_y, scale); - } -} - -static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) { +static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { int nth = WARP_SIZE; while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; const dim3 block_dims(nth, 1, 1); const dim3 block_nums(nrows_x, 1, 1); const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + if (shmem < g_device_caps[g_main_device].smpb) { switch (ncols_x) { case 32: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 64: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 128: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 256: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 512: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 1024: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 2048: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 4096: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; default: - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; } } else { const size_t shmem_low = WARP_SIZE*sizeof(float); - soft_max_f32<<>>(x, y, dst, ncols_x, nrows_y, scale); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); } } @@ -9090,30 +8937,36 @@ static void ggml_cuda_op_soft_max( GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - const int64_t ne00 = src0->ne[0]; + const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1; + const int64_t nrows_y = src0->ne[1]; - float scale = 1.0f; - memcpy(&scale, dst->op_params, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION >= CUDART_HMAX -#ifdef GGML_CUDA_F16 - const bool use_f16_soft_max = true; -#else - const bool use_f16_soft_max = false; -#endif // GGML_CUDA_F16 -#else - const bool use_f16_soft_max = false; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && CUDART_VERSION >= CUDART_HMAX + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - if (use_f16_soft_max) { - soft_max_f16_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); - } else { - soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + // positions tensor + float * src2_dd = dst_dd; // default to avoid null checks in the kernel + cuda_pool_alloc src2_f; + + ggml_tensor * src2 = dst->src[2]; + const bool use_src2 = src2 != nullptr; + + if (use_src2) { + const bool src2_on_device = use_src2 && src2->backend == GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * src2_extra = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; + + if (src2_on_device) { + src2_dd = (float *) src2_extra->data_device[g_main_device]; + } else { + src2_dd = src2_f.alloc(ggml_nelements(src2)); + CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); + } } - (void) dst; + soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); } static void ggml_cuda_op_scale( diff --git a/ggml-metal.m b/ggml-metal.m index 6e76f8bedb50b..c0848a293e48f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -728,6 +728,7 @@ static bool ggml_metal_graph_compute( size_t offs_src0 = 0; size_t offs_src1 = 0; + size_t offs_src2 = 0; size_t offs_dst = 0; id command_buffer = command_buffers[cb_idx]; @@ -746,6 +747,7 @@ static bool ggml_metal_graph_compute( struct ggml_tensor * src0 = gf->nodes[i]->src[0]; struct ggml_tensor * src1 = gf->nodes[i]->src[1]; + struct ggml_tensor * src2 = gf->nodes[i]->src[2]; struct ggml_tensor * dst = gf->nodes[i]; switch (dst->op) { @@ -807,6 +809,7 @@ static bool ggml_metal_graph_compute( id id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; id id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; + id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); @@ -1188,7 +1191,16 @@ static bool ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } - const float scale = ((float *) dst->op_params)[0]; + const float scale = ((float *) dst->op_params)[0]; + const float max_bias = ((float *) dst->op_params)[1]; + + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -1197,11 +1209,20 @@ static bool ggml_metal_graph_compute( } else { [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; } - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + if (id_src2) { + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:4]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:6]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:7]; + [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:8]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:9]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:10]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11]; [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; @@ -1514,8 +1535,6 @@ static bool ggml_metal_graph_compute( // max size of the src1ids array in the kernel stack GGML_ASSERT(ne11 <= 512); - struct ggml_tensor * src2 = gf->nodes[i]->src[2]; - const int64_t ne20 = src2 ? src2->ne[0] : 0; const int64_t ne21 = src2 ? src2->ne[1] : 0; const int64_t ne22 = src2 ? src2->ne[2] : 0; diff --git a/ggml-metal.metal b/ggml-metal.metal index efed6ad465e78..09ebcc9e3040f 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -351,12 +351,17 @@ kernel void kernel_sum_rows( kernel void kernel_soft_max( device const float * src0, device const float * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant float & scale, - threadgroup float * buf [[threadgroup(0)]], + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, + threadgroup float * buf [[threadgroup(0)]], uint tgpig[[threadgroup_position_in_grid]], uint tpitg[[thread_position_in_threadgroup]], uint sgitg[[simdgroup_index_in_threadgroup]], @@ -368,13 +373,26 @@ kernel void kernel_soft_max( device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; device const float * pmask = src1 != src0 ? src1 + i01*ne00 : nullptr; + device const float * ppos = src2 != src0 ? src2 : nullptr; device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + float slope = 0.0f; + + // ALiBi + if (max_bias > 0.0f) { + const int64_t h = i02; + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = pow(base, exp); + } + // parallel max float lmax = -INFINITY; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f)); + lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]); } // find the max value in the block @@ -399,7 +417,7 @@ kernel void kernel_soft_max( // parallel sum float lsum = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val); + const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]) - max_val); lsum += exp_psrc0; pdst[i00] = exp_psrc0; } @@ -437,12 +455,17 @@ kernel void kernel_soft_max( kernel void kernel_soft_max_4( device const float * src0, device const float * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant float & scale, - threadgroup float * buf [[threadgroup(0)]], + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, + threadgroup float * buf [[threadgroup(0)]], uint tgpig[[threadgroup_position_in_grid]], uint tpitg[[thread_position_in_threadgroup]], uint sgitg[[simdgroup_index_in_threadgroup]], @@ -454,13 +477,25 @@ kernel void kernel_soft_max_4( device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 + i01*ne00) : nullptr; + device const float4 * ppos = src2 != src0 ? (device const float4 *)(src2) : nullptr; device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); + float slope = 0.0f; + + if (max_bias > 0.0f) { + const int64_t h = i02; + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = pow(base, exp); + } + // parallel max float4 lmax4 = -INFINITY; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f)); + lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]); } const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); @@ -486,7 +521,7 @@ kernel void kernel_soft_max_4( // parallel sum float4 lsum4 = 0.0f; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val); + const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]) - max_val); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } diff --git a/ggml.c b/ggml.c index 264cfd705cd37..e94024c62a123 100644 --- a/ggml.c +++ b/ggml.c @@ -5096,16 +5096,28 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, + struct ggml_tensor * pos, float scale, + float max_bias, bool inplace) { GGML_ASSERT(ggml_is_contiguous(a)); + if (mask) { GGML_ASSERT(ggml_is_contiguous(mask)); - GGML_ASSERT(mask->ne[2] == 1); - GGML_ASSERT(mask->ne[3] == 1); + GGML_ASSERT(ggml_is_matrix(mask)); GGML_ASSERT(ggml_can_repeat_rows(mask, a)); } + if (pos) { + GGML_ASSERT(ggml_is_vector(pos)); + GGML_ASSERT(pos->type == GGML_TYPE_F32); + GGML_ASSERT(pos->ne[0] == a->ne[0]); + } + + if (max_bias > 0.0f) { + GGML_ASSERT(pos); + } + bool is_node = false; if (a->grad) { @@ -5114,13 +5126,14 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - float params[] = { scale }; + float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = mask; + result->src[2] = pos; return result; } @@ -5128,21 +5141,23 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, 1.0f, false); + return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false); } struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, 1.0f, true); + return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true); } struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - float scale) { - return ggml_soft_max_impl(ctx, a, mask, scale, false); + struct ggml_tensor * pos, + float scale, + float max_bias) { + return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false); } // ggml_soft_max_back @@ -11495,6 +11510,7 @@ static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, + const struct ggml_tensor * src2, struct ggml_tensor * dst) { assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); @@ -11503,16 +11519,29 @@ static void ggml_compute_forward_soft_max_f32( return; } - float scale = 1.0f; - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; + GGML_TENSOR_UNARY_OP_LOCALS + const int64_t ne11 = src1 ? src1->ne[1] : 1; + // TODO: is this supposed to be ceil instead of floor? + // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 + const uint32_t n_head_kv = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); @@ -11525,6 +11554,9 @@ static void ggml_compute_forward_soft_max_f32( float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; + // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching + float * pos = src2 ? (float *) src2->data : src0->data; + for (int i1 = ir0; i1 < ir1; i1++) { float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); @@ -11538,6 +11570,16 @@ static void ggml_compute_forward_soft_max_f32( ggml_vec_acc_f32(nc, wp, mp); } + // ALiBi bias + if (max_bias > 0.0f) { + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1); + + for (int i = 0; i < nc; i++) { + wp[i] = wp[i] + slope*pos[i]; + } + } + #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); @@ -11582,11 +11624,12 @@ static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, + const struct ggml_tensor * src2, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_soft_max_f32(params, src0, src1, dst); + ggml_compute_forward_soft_max_f32(params, src0, src1, src2, dst); } break; default: { @@ -11730,22 +11773,20 @@ static void ggml_compute_forward_alibi_f32( const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - for (int64_t i = 0; i < ne0; i++) { - for (int64_t j = 0; j < ne1; j++) { - for (int64_t k = 0; k < ne2_ne3; k++) { - float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); - - // TODO: k*nb2 or k*nb3 + for (int64_t k = 0; k < ne2_ne3; k++) { + // TODO: k*nb2 or k*nb3 + float m_k; - float m_k; - - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } + if (k < n_heads_log2_floor) { + m_k = powf(m0, k + 1); + } else { + m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); + } + for (int64_t i = 0; i < ne0; i++) { + for (int64_t j = 0; j < ne1; j++) { + float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); + float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); pdst[0] = i * m_k + src[0]; } } @@ -11790,21 +11831,20 @@ static void ggml_compute_forward_alibi_f16( const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - for (int i = 0; i < ne0; i++) { - for (int j = 0; j < ne1; j++) { - for (int k = 0; k < ne2_ne3; k++) { - ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); - - // TODO: k*nb2 or k*nb3 + for (int k = 0; k < ne2_ne3; k++) { + // TODO: k*nb2 or k*nb3 + float m_k; - float m_k; + if (k < n_heads_log2_floor) { + m_k = powf(m0, k + 1); + } else { + m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); + } - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } + for (int i = 0; i < ne0; i++) { + for (int j = 0; j < ne1; j++) { + ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); + float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); // we return F32 pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]); @@ -15116,7 +15156,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_SOFT_MAX: { - ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); } break; case GGML_OP_SOFT_MAX_BACK: { diff --git a/ggml.h b/ggml.h index 270018185f397..6c1956772324c 100644 --- a/ggml.h +++ b/ggml.h @@ -1383,13 +1383,17 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); - // fused soft_max(a*scale + mask) + // fused soft_max(a*scale + mask + pos[i]*(ALiBi slope)) // mask is optional + // pos is required when max_bias > 0.0f + // max_bias = 0.0f for no ALiBi GGML_API struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - float scale); + struct ggml_tensor * pos, + float scale, + float max_bias); GGML_API struct ggml_tensor * ggml_soft_max_back( struct ggml_context * ctx, @@ -1491,12 +1495,13 @@ extern "C" { // alibi position embedding // in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_alibi( + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_alibi( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_head, - float bias_max); + float bias_max), + "use ggml_soft_max_ext instead (will be removed in Mar 2024)"); // clamp // in-place, returns view(a) diff --git a/llama.cpp b/llama.cpp index 8966c3e665916..6ac9caa957a05 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1557,12 +1557,13 @@ struct llama_hparams { uint32_t n_yarn_orig_ctx; int32_t rope_scaling_type_train; - float f_clamp_kqv; - float f_max_alibi_bias; + float f_clamp_kqv = 0.0f; + float f_max_alibi_bias = 0.0f; bool causal_attn = true; - uint32_t pooling_type = LLAMA_POOLING_NONE; + bool need_kq_pos = false; + uint32_t pooling_type = LLAMA_POOLING_NONE; bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -1923,6 +1924,7 @@ struct llama_context { struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] + struct ggml_tensor * inp_KQ_pos; // F32 [n_ctx] struct ggml_tensor * inp_K_shift; // I32 [n_ctx] struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] @@ -3054,6 +3056,11 @@ static void llm_load_hparams( case 40: model.type = e_model::MODEL_13B; break; default: model.type = e_model::MODEL_UNKNOWN; } + + if (model.type == e_model::MODEL_13B) { + // TODO: become GGUF KV parameter + hparams.f_max_alibi_bias = 8.0f; + } } break; case LLM_ARCH_STARCODER: { @@ -3081,6 +3088,9 @@ static void llm_load_hparams( case 32: model.type = e_model::MODEL_1B; break; default: model.type = e_model::MODEL_UNKNOWN; } + + // TODO: become GGUF KV parameter + hparams.f_max_alibi_bias = 8.0f; } break; case LLM_ARCH_BERT: { @@ -3126,11 +3136,12 @@ static void llm_load_hparams( case 4096: model.type = e_model::MODEL_7B; break; } break; } + + // TODO: become GGUF KV parameter + hparams.f_max_alibi_bias = 8.0f; } break; case LLM_ARCH_MPT: { - hparams.f_clamp_kqv = 0.0f; - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false); ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); @@ -3232,6 +3243,10 @@ static void llm_load_hparams( } model.ftype = ml.ftype; + + if (hparams.f_max_alibi_bias > 0.0f) { + hparams.need_kq_pos = true; + } } // TODO: This should probably be in llama.h @@ -4774,10 +4789,10 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * wo_b, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, + struct ggml_tensor * kq_pos, int64_t n_ctx, int32_t n_tokens, int32_t n_kv, - float max_alibi_bias, float kq_scale, const llm_build_cb & cb, int il) { @@ -4807,26 +4822,26 @@ static struct ggml_tensor * llm_build_kqv( ggml_mul_mat_set_prec(kq, GGML_PREC_F32); } - if (max_alibi_bias > 0.0f) { - // temporary branch until we figure out how to handle ggml_alibi through ggml_add +#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_SYCL) +#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, Kompute, and SYCL") +#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") + if (hparams.f_max_alibi_bias > 0.0f) { kq = ggml_scale(ctx, kq, kq_scale); cb(kq, "kq_scaled", il); - if (max_alibi_bias > 0.0f) { - // TODO: n_head or n_head_kv - // TODO: K-shift is likely not working - // TODO: change to ggml_add - kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias); - cb(kq, "kq_scaled_alibi", il); - } + kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias); + cb(kq, "kq_scaled_alibi", il); kq = ggml_add(ctx, kq, kq_mask); cb(kq, "kq_masked", il); kq = ggml_soft_max(ctx, kq); cb(kq, "kq_soft_max", il); - } else { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale); + } else +#endif + { + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias); cb(kq, "kq_soft_max_ext", il); } @@ -4874,11 +4889,11 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * v_cur, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, + struct ggml_tensor * kq_pos, int64_t n_ctx, int32_t n_tokens, int32_t kv_head, int32_t n_kv, - float max_alibi_bias, float kq_scale, const llm_build_cb & cb, int il) { @@ -4892,9 +4907,8 @@ static struct ggml_tensor * llm_build_kv( llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il); struct ggml_tensor * cur; - cur = llm_build_kqv(ctx, model, hparams, kv, graph, - wo, wo_b, - q_cur, kq_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, kq_scale, cb, il); + cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b, + q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); return cur; @@ -5062,7 +5076,7 @@ struct llm_build_context { } Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -5077,7 +5091,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5207,6 +5221,10 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); + // positions of the tokens in the KV cache + struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); + cb(KQ_pos, "KQ_pos", -1); + // shift the entire K-cache if needed if (do_rope_shift) { llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); @@ -5255,12 +5273,9 @@ struct llm_build_context { cb(Kcur, "Kcur", il); - // apply ALiBi for 13B model - const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5384,7 +5399,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5483,7 +5498,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5688,7 +5703,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5750,6 +5765,10 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); + // positions of the tokens in the KV cache + struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); + cb(KQ_pos, "KQ_pos", -1); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -5777,7 +5796,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5878,7 +5897,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } else { // compute Q and K and RoPE them @@ -5909,7 +5928,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5985,6 +6004,10 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); + // positions of the tokens in the KV cache + struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); + cb(KQ_pos, "KQ_pos", -1); + inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, @@ -6018,7 +6041,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6078,6 +6101,10 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); + // positions of the tokens in the KV cache + struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); + cb(KQ_pos, "KQ_pos", -1); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -6111,7 +6138,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6233,7 +6260,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6348,7 +6375,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6469,7 +6496,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6596,7 +6623,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); cb(cur, "kqv_out", il); } @@ -6699,7 +6726,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } struct ggml_tensor * sa_out = cur; @@ -6798,7 +6825,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6907,7 +6934,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -7025,7 +7052,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -7144,7 +7171,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -7276,7 +7303,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -7507,6 +7534,18 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } + if (hparams.need_kq_pos) { + const int64_t n_kv = kv_self.n; + + assert(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer)); + + float * data = (float *) lctx.inp_KQ_pos->data; + + for (int i = 0; i < n_kv; ++i) { + data[i] = float(lctx.kv_self.cells[i].pos); + } + } + if (kv_self.has_shift) { const int64_t n_ctx = cparams.n_ctx; @@ -11434,7 +11473,7 @@ struct llama_context * llama_new_context_with_model( // graph inputs { ggml_init_params init_params = { - /* .mem_size */ ggml_tensor_overhead()*7, + /* .mem_size */ ggml_tensor_overhead()*8, /* .mem_buffer */ nullptr, /* .no_alloc */ true, }; @@ -11444,6 +11483,7 @@ struct llama_context * llama_new_context_with_model( ctx->inp_embd = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch); ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); + ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx); ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); @@ -11452,6 +11492,7 @@ struct llama_context * llama_new_context_with_model( ggml_set_name(ctx->inp_embd, "inp_embd"); ggml_set_name(ctx->inp_pos, "inp_pos"); ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask"); + ggml_set_name(ctx->inp_KQ_pos, "inp_KQ_pos"); ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); ggml_set_name(ctx->inp_mean, "inp_mean"); ggml_set_name(ctx->inp_cls, "inp_cls"); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 9af8517d950db..30a7d1f5ab3e2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1085,24 +1085,32 @@ struct test_diag_mask_inf : public test_case { struct test_soft_max : public test_case { const ggml_type type; const std::array ne; - const float scale; const bool mask; + const float scale; + const float max_bias; std::string vars() override { - return VARS_TO_STR4(type, ne, scale, mask); + return VARS_TO_STR5(type, ne, mask, scale, max_bias); } test_soft_max(ggml_type type = GGML_TYPE_F32, std::array ne = {10, 10, 10, 10}, + bool mask = false, float scale = 1.0f, - bool mask = false) - : type(type), ne(ne), scale(scale), mask(mask) {} + float max_bias = 0.0f) + : type(type), ne(ne), mask(mask), scale(scale), max_bias(max_bias) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * b = nullptr; - if (mask) { b = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]); } - ggml_tensor * out = ggml_soft_max_ext(ctx, a, b, scale); + ggml_tensor * mask = nullptr; + if (this->mask) { + mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]); + } + ggml_tensor * pos = nullptr; + if (max_bias > 0.0f) { + pos = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]); + } + ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, pos, scale, max_bias); return out; } }; @@ -1147,30 +1155,6 @@ struct test_rope : public test_case { } }; -// GGML_OP_ALIBI -struct test_alibi : public test_case { - const ggml_type type; - const std::array ne; - int n_past; - int n_head; - float bias_max; - - std::string vars() override { - return VARS_TO_STR5(type, ne, n_past, n_head, bias_max); - } - - test_alibi(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 10}, - int n_past = 512, int n_head = 10, float bias_max = 0.5f) - : type(type), ne(ne), n_past(n_past), n_head(n_head), bias_max(bias_max) {} - - ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * out = ggml_alibi(ctx, a, n_past, n_head, bias_max); - return out; - } -}; - // GGML_OP_POOL2D struct test_pool2d : public test_case { enum ggml_op_pool pool_type; @@ -1488,7 +1472,7 @@ struct test_moe : public test_case { ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur); - ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, 1.0f/sqrtf(n_embd)); + ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, nullptr, 1.0f/sqrtf(n_embd), 0.0f); // select experts ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok); @@ -1617,7 +1601,6 @@ struct test_llm : public test_case { ggml_cpy(ctx, v_cur_t, v_cache_view); } - // if max_alibi_bias > 0 then apply ALiBi struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, struct ggml_tensor * k_l, @@ -1636,7 +1619,7 @@ struct test_llm : public test_case { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); - kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale); + kq = ggml_soft_max_ext(ctx, kq, kq_mask, nullptr, kq_scale, 0.0f); // split cached v into n_head heads struct ggml_tensor * v = @@ -2083,6 +2066,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 1}, 5)); test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 10}, 5)); +#if 0 std::uniform_int_distribution<> dist_ne1(1, 50); int exponent = 1; while (exponent < (1 << 17)) { @@ -2091,14 +2075,29 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op for (int n = 0; n < 10; ++n) { int64_t ne0 = dist_ne0(rng); int64_t ne1 = dist_ne1(rng); - test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1})); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f)); } exponent <<= 1; } +#endif + for (bool mask : {false, true}) { + for (float max_bias : {0.0f, 8.0f}) { + for (float scale : {1.0f, 0.1f}) { + for (int64_t ne0 : {16, 1024}) { + for (int64_t ne1 : {16, 1024}) { + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias)); + } + } + } + } + } - test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, 0.1f)); - test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, 0.1f, true)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 8.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f)); for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B @@ -2113,7 +2112,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2) } - test_cases.emplace_back(new test_alibi()); test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); From c8e0d7efeb7634ecc2e9832e879ab9fca4510e71 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 18 Feb 2024 00:17:07 +0000 Subject: [PATCH 005/401] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/f8e2ebd66d097614d51a56a755450d4ae1632df1' (2024-02-07) → 'github:NixOS/nixpkgs/5863c27340ba4de8f83e7e3c023b9599c3cb3c80' (2024-02-16) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 239d0686c44fc..47d6448b5ceb9 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1707268954, - "narHash": "sha256-2en1kvde3cJVc3ZnTy8QeD2oKcseLFjYPLKhIGDanQ0=", + "lastModified": 1708118438, + "narHash": "sha256-kk9/0nuVgA220FcqH/D2xaN6uGyHp/zoxPNUmPCMmEE=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "f8e2ebd66d097614d51a56a755450d4ae1632df1", + "rev": "5863c27340ba4de8f83e7e3c023b9599c3cb3c80", "type": "github" }, "original": { From bd2d4e393b2b7d2a1b2e201058e26017c9728ead Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 18 Feb 2024 18:16:55 +0200 Subject: [PATCH 006/401] 1.5 bit quantization (#5453) * iq1_s: WIP basics * iq1_s: CUDA is working * iq1_s: scalar CPU dot product * iq1_s: WIP AVX2 dot product - something is not right * Fix tests * Fix shadow warnings * Fix after merge with latest master * iq1_s: AVX2 finally works * iq1_s: ARM_NEON dot product. Works, but not very fast * iq1_s: better grid * iq1_s: use IQ2_XXS for attn_output At a cost of 0.04 extra bpw this gives a big improvement in PPL. * iq1_s: Metal basics Dequantize works, but not dot product * iq1_s: Metal works, but quite slow As usual, Apple Silicon does not like the code I write. * iq1_s: Tests * iq1_s: slightly faster dot product --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 6 +- ggml-backend.c | 2 +- ggml-cuda.cu | 224 ++++++++++- ggml-metal.m | 29 +- ggml-metal.metal | 337 +++++++++++++++++ ggml-quants.c | 657 +++++++++++++++++++++++++++++++-- ggml-quants.h | 14 +- ggml.c | 44 ++- ggml.h | 2 + llama.cpp | 16 +- llama.h | 1 + tests/test-backend-ops.cpp | 2 +- 12 files changed, 1286 insertions(+), 48 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 4a5c504e31c9c..ea7ba50c96eb6 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -23,6 +23,7 @@ static const std::vector QUANT_OPTIONS = { { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, + { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, @@ -287,9 +288,10 @@ int main(int argc, char ** argv) { } } - if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) && imatrix_data.empty()) { + if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || + params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) { fprintf(stderr, "\n===============================================================================================\n"); - fprintf(stderr, "Please do not use IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); + fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); fprintf(stderr, "===============================================================================================\n\n\n"); return 1; } diff --git a/ggml-backend.c b/ggml-backend.c index 66e8c293a9e3f..5076d9e5ec325 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -756,7 +756,7 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_CPY: - return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS; // missing type_traits.from_float + return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float case GGML_OP_MUL_MAT: return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; default: diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5fd8a87e4150f..933ebbc4eff72 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -517,6 +517,15 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#define QR1_S 8 +#define QI1_S (QK_K / (4*QR1_S)) +typedef struct { + half d; + uint8_t qs[QK_K/8]; + uint8_t scales[QK_K/16]; +} block_iq1_s; +static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -1681,6 +1690,137 @@ static const __device__ uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +static const __device__ uint64_t iq1s_grid[512] = { + 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, + 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, + 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, + 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, + 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, + 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, + 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, + 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, + 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, + 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, + 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, + 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, + 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, + 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, + 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, + 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, + 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, + 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, + 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, + 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, + 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, + 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, + 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, + 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, + 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, + 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, + 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, + 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, + 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, + 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, + 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, + 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, + 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, + 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, + 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, + 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, + 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, + 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, + 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, + 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, + 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, + 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, + 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, + 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, + 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, + 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, + 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, + 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, + 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, + 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, + 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, + 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, + 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, + 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, + 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, + 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, + 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, + 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, + 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, + 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, + 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, + 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, + 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, + 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, + 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, + 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, + 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, + 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, + 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, + 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, + 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, + 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, + 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, + 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, + 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, + 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, + 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, + 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, + 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, + 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, + 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, + 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, + 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, + 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, + 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, + 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, + 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, + 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, + 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, + 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, + 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, + 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, + 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, + 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, + 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, + 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, + 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, + 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, + 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, + 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, + 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, + 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, + 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, + 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, + 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, + 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, + 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, + 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, + 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, + 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, + 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, + 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, + 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, + 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, + 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, + 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, + 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, + 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, + 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, + 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, + 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, + 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, + 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, + 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, + 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, + 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, + 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, +}; + static const __device__ uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -1823,6 +1963,29 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds } +template +static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq1_s * x = (const block_iq1_s *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const int i8 = 4*ib+il; + uint8_t h = x[i].scales[i8/2] >> 4*(i8%2); + const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5))); + const float d = (float)x[i].d * (2*(h & 7) + 1); + for (int j = 0; j < 8; ++j) y[j] = d * grid[j]; +#else + assert(false); +#endif + +} + + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -4522,6 +4685,49 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( #endif } +static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 + const block_iq1_s * bq1 = (const block_iq1_s *) vbq; + + const int ib32 = iqs; + int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; + const uint8_t h1 = bq1->scales[2*ib32+0]; + const uint8_t h2 = bq1->scales[2*ib32+1]; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const int * q8 = (const int *)bq8_1[ib32].qs; + const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); + const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); + const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); + const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); + for (int j = 0; j < 2; ++j) { + sumi1 = __dp4a(q8[j+0], grid1[j], sumi1); + sumi2 = __dp4a(q8[j+2], grid2[j], sumi2); + sumi3 = __dp4a(q8[j+4], grid3[j], sumi3); + sumi4 = __dp4a(q8[j+6], grid4[j], sumi4); + } +#else + const int8_t * q8 = bq8_1[ib32].qs; + const int8_t * grid1 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); + const int8_t * grid2 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); + const int8_t * grid3 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); + const int8_t * grid4 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j+ 0] * grid1[j]; + sumi2 += q8[j+ 8] * grid2[j]; + sumi3 += q8[j+16] * grid3[j]; + sumi4 += q8[j+24] * grid4[j]; + } +#endif + const float d = (float)bq1->d * __low2float(bq8_1[ib32].ds); + return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) + + sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1)); +#else + assert(false); + return 0.f; +#endif +} + template static __device__ __forceinline__ void mul_mat_q( @@ -6561,6 +6767,12 @@ static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq3_xxs<<>>(vx, y); } +template +static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq1_s<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -6600,6 +6812,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq2_xs_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -6635,6 +6849,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq2_xs_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -8378,6 +8594,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -8401,6 +8618,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -8498,6 +8716,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ1_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; default: GGML_ASSERT(false); break; @@ -11214,7 +11436,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons return false; } ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS) { + if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ1_S) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index c0848a293e48f..f3c1fff8fed94 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -61,6 +61,7 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, @@ -83,6 +84,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, @@ -101,6 +103,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, @@ -116,6 +119,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, @@ -131,6 +135,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, @@ -433,6 +438,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); @@ -455,6 +461,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); @@ -473,6 +480,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); @@ -488,6 +496,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); @@ -503,6 +512,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); @@ -1318,6 +1328,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } @@ -1452,6 +1463,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; } break; + case GGML_TYPE_IQ1_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1486,7 +1503,7 @@ static bool ggml_metal_graph_compute( if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { + src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S) { // || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -1592,6 +1609,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } @@ -1729,6 +1747,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; } break; + case GGML_TYPE_IQ1_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1779,7 +1803,7 @@ static bool ggml_metal_graph_compute( if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { + src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S) { // || src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { @@ -1833,6 +1857,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 09ebcc9e3040f..a009621113893 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2525,6 +2525,13 @@ typedef struct { } block_iq3_xxs; // 98 bytes / block for QK_K = 256, so 3.0625 bpw +typedef struct { + half d; + uint8_t qs[QK_K/8]; + uint8_t scales[QK_K/16]; +} block_iq1_s; + + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -3782,6 +3789,137 @@ constexpr constant static uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +#define NGRID_IQ1S 512 +constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = { + 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, + 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, + 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, + 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, + 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, + 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, + 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, + 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, + 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, + 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, + 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, + 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, + 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, + 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, + 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, + 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, + 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, + 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, + 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, + 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, + 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, + 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, + 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, + 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, + 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, + 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, + 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, + 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, + 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, + 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, + 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, + 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, + 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, + 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, + 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, + 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, + 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, + 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, + 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, + 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, + 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, + 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, + 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, + 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, + 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, + 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, + 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, + 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, + 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, + 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, + 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, + 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, + 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, + 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, + 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, + 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, + 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, + 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, + 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, + 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, + 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, + 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, + 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, + 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, + 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, + 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, + 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, + 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, + 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, + 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, + 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, + 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, + 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, + 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, + 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, + 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, + 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, + 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, + 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, + 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, + 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, + 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, + 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, + 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, + 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, + 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, + 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, + 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, + 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, + 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, + 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, + 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, + 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, + 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, + 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, + 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, + 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, + 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, + 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, + 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, + 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, + 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, + 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, + 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, + 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, + 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, + 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, + 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, + 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, + 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, + 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, + 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, + 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, + 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, + 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, + 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, + 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, + 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, + 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, + 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, + 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, + 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, + 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, + 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, + 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, + 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, + 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, +}; constexpr constant static uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, @@ -4208,6 +4346,123 @@ kernel void kernel_mul_mv_iq3_xxs_f32( kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq1_s_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[16]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + +#if QK_K == 256 + const int ix = tiisg/2; + const int il = tiisg%2; + + device const float * y4 = y + 32 * ix + 16 * il; + + for (int ib32 = ix; ib32 < nb32; ib32 += 16) { + + for (int i = 0; i < 16; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq1_s * xr = x + ibl; + device const uint8_t * qs = xr->qs + 4 * ib + 2 * il; + device const uint8_t * sc = xr->scales + 2 * ib + il; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); + constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); + + float2 sum = {0}; + for (int j = 0; j < 8; ++j) { + sum[0] += yl[j+ 0] * grid1[j]; + sum[1] += yl[j+ 8] * grid2[j]; + } + sumf[row] += (float)dh[0] * (sum[0] * (2*(sc[0] & 7) + 1) + sum[1] * (2*((sc[0] >> 4) & 7) + 1)); + + dh += nb*sizeof(block_iq1_s)/2; + qs += nb*sizeof(block_iq1_s); + sc += nb*sizeof(block_iq1_s); + } + + y4 += 16 * 32; + } +#else + // TODO +#endif + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + } + } +} + +[[host_name("kernel_mul_mv_iq1_s_f32")]] +kernel void kernel_mul_mv_iq1_s_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); +} + //============================= templates and their specializations ============================= @@ -4553,6 +4808,22 @@ void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x } } +template +void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + device const uint8_t * qs = xb->qs + 2*il; + device const uint8_t * sc = xb->scales + il; + const float dl1 = d * (2*(sc[0] & 7) + 1); + const float dl2 = d * (2*((sc[0] >> 4) & 7) + 1); + constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); + constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); + for (int i = 0; i < 8; ++i) { + reg[i/4+0][i%4] = dl1 * grid1[i]; + reg[i/4+2][i%4] = dl2 * grid2[i]; + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -5095,6 +5366,7 @@ template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -5134,6 +5406,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -5185,6 +5458,7 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -6152,3 +6426,66 @@ kernel void kernel_mul_mv_id_iq3_xxs_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq1_s_f32")]] +kernel void kernel_mul_mv_id_iq1_s_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq1_s_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index f44377f455a91..48f5294e1e788 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3480,6 +3480,139 @@ static const uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +#define NGRID_IQ2XXS 512 +static const uint64_t iq1s_grid[NGRID_IQ2XXS] = { + 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, + 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, + 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, + 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, + 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, + 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, + 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, + 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, + 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, + 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, + 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, + 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, + 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, + 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, + 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, + 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, + 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, + 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, + 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, + 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, + 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, + 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, + 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, + 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, + 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, + 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, + 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, + 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, + 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, + 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, + 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, + 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, + 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, + 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, + 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, + 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, + 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, + 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, + 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, + 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, + 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, + 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, + 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, + 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, + 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, + 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, + 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, + 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, + 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, + 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, + 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, + 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, + 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, + 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, + 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, + 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, + 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, + 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, + 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, + 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, + 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, + 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, + 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, + 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, + 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, + 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, + 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, + 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, + 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, + 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, + 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, + 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, + 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, + 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, + 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, + 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, + 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, + 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, + 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, + 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, + 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, + 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, + 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, + 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, + 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, + 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, + 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, + 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, + 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, + 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, + 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, + 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, + 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, + 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, + 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, + 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, + 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, + 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, + 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, + 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, + 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, + 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, + 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, + 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, + 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, + 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, + 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, + 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, + 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, + 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, + 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, + 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, + 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, + 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, + 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, + 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, + 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, + 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, + 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, + 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, + 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, + 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, + 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, + 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, + 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, + 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, + 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, + +}; + static const uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -3578,6 +3711,49 @@ void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y } } +// ====================== 1.5625 bpw (de)-quantization + +void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + float db[4]; + uint16_t idx[4]; + //const int8_t * grid[4]; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + const uint8_t * sc = x[i].scales; + const uint8_t * qs = x[i].qs; + + for (int i8 = 0; i8 < QK_K/8; i8 += 4) { + idx[0] = qs[0] | ((sc[0] & 0x08) << 5); + idx[1] = qs[1] | ((sc[0] & 0x80) << 1); + idx[2] = qs[2] | ((sc[1] & 0x08) << 5); + idx[3] = qs[3] | ((sc[1] & 0x80) << 1); + //grid[0] = (const int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); + //grid[1] = (const int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); + //grid[2] = (const int8_t *)(iq1s_grid + (qs[2] | ((sc[1] & 0x08) << 5))); + //grid[3] = (const int8_t *)(iq1s_grid + (qs[3] | ((sc[1] & 0x80) << 1))); + db[0] = d * (2*(sc[0] & 7) + 1); + db[1] = d * (2*((sc[0] >> 4) & 7) + 1); + db[2] = d * (2*(sc[1] & 7) + 1); + db[3] = d * (2*((sc[1] >> 4) & 7) + 1); + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]); + for (int j = 0; j < 8; ++j) { + //y[j] = db[l] * grid[l][j]; + y[j] = db[l] * grid[j]; + } + y += 8; + } + qs += 4; + sc += 2; + } + } +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -3679,7 +3855,7 @@ static inline __m128i get_scale_shuffle(int i) { } #endif -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -3690,8 +3866,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bx); - UNUSED(by); + UNUSED(bbx); + UNUSED(bby); UNUSED(bs); const block_q4_0 * restrict x = vx; @@ -4046,7 +4222,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { const int qk = QK8_1; const int nb = n / qk; @@ -4057,8 +4233,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bx); - UNUSED(by); + UNUSED(bbx); + UNUSED(bby); UNUSED(bs); const block_q4_1 * restrict x = vx; @@ -4264,7 +4440,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -4272,8 +4448,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(qk == QK5_0); assert(nrc == 1); UNUSED(nrc); - UNUSED(bx); - UNUSED(by); + UNUSED(bbx); + UNUSED(bby); UNUSED(bs); const block_q5_0 * restrict x = vx; @@ -4555,7 +4731,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { const int qk = QK8_1; const int nb = n / qk; @@ -4563,8 +4739,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r assert(qk == QK5_1); assert(nrc == 1); UNUSED(nrc); - UNUSED(bx); - UNUSED(by); + UNUSED(bbx); + UNUSED(bby); UNUSED(bs); const block_q5_1 * restrict x = vx; @@ -4859,7 +5035,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -4870,8 +5046,8 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bx); - UNUSED(by); + UNUSED(bbx); + UNUSED(bby); UNUSED(bs); const block_q8_0 * restrict x = vx; @@ -9107,6 +9283,178 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void #endif } +#ifdef __AVX2__ +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = _mm256_sign_epi8(x, x); + const __m256i sy = _mm256_sign_epi8(y, x); + return _mm256_maddubs_epi16(ax, sy); +} +#endif + +void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq1_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + + const uint8x16_t m8 = vdupq_n_u8(0x08); + const uint8x16_t m7 = vdupq_n_u8(0x07); + const uint8x16_t m1 = vdupq_n_u8(0x01); + const int32x4_t vzero = vdupq_n_s32(0); + + uint16_t gindex[8]; + uint16x8x2_t vindex; + int8x16x4_t q1b; + int8x16x4_t q8b; + uint16x8x4_t scales; + int32x4x2_t sumi; + int32x4x2_t dotq; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * sc = x[i].scales; + + sumi.val[0] = sumi.val[1] = vzero; + + for (int i128 = 0; i128 < QK_K/128; ++i128) { + const uint8x16_t ql = vld1q_u8(qs); qs += 16; + const uint8x8_t tm1 = vld1_u8 (sc); sc += 8; + const uint8x8_t tm2 = vshr_n_u8(tm1, 4); + const uint8x16_t qh = vcombine_u8(vzip1_u8(tm1, tm2), vzip2_u8(tm1, tm2)); + const uint8x16_t hbit = vandq_u8(qh, m8); + vindex.val[0] = vorrq_u16(vmovl_u8(vget_low_u8 (ql)), vshlq_n_u16(vmovl_u8(vget_low_u8 (hbit)), 5)); + vindex.val[1] = vorrq_u16(vmovl_u8(vget_high_u8(ql)), vshlq_n_u16(vmovl_u8(vget_high_u8(hbit)), 5)); + const uint8x16_t scales8 = vorrq_u8(vshlq_n_u8(vandq_u8(qh, m7), 1), m1); + scales.val[0] = vmovl_u8(vget_low_u8 (scales8)); + scales.val[1] = vmovl_u8(vget_high_u8 (scales8)); + + for (int l = 0; l < 2; ++l) { + vst1q_u16(gindex+0, vindex.val[l]); + q1b.val[0] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[0])), vld1_s8((const void *)(iq1s_grid+gindex[1]))); + q1b.val[1] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[2])), vld1_s8((const void *)(iq1s_grid+gindex[3]))); + q1b.val[2] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[4])), vld1_s8((const void *)(iq1s_grid+gindex[5]))); + q1b.val[3] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[6])), vld1_s8((const void *)(iq1s_grid+gindex[7]))); + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + dotq.val[0] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(vzero, q1b.val[1], q8b.val[1])); + dotq.val[1] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(vzero, q1b.val[3], q8b.val[3])); + + sumi.val[0] = vmlaq_s32(sumi.val[0], dotq.val[0], vreinterpretq_s32_u32(vmovl_u16(vget_low_u16 (scales.val[l])))); + sumi.val[1] = vmlaq_s32(sumi.val[1], dotq.val[1], vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales.val[l])))); + } + } + + sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * vaddvq_s32(vaddq_s32(sumi.val[0], sumi.val[1])); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i m8 = _mm_set1_epi8(0x08); + const __m128i m7 = _mm_set1_epi8(0x07); + const __m128i m1 = _mm_set1_epi8(0x01); + const __m128i shuffle_h = _mm_set_epi8(15, 7, 14, 6, 13, 5, 12, 4, 11, 3, 10, 2, 9, 1, 8, 0); + const __m128i shuffle_s[4] = { + _mm_set_epi32(0x03030303, 0x02020202, 0x01010101, 0x00000000), + _mm_set_epi32(0x07070707, 0x06060606, 0x05050505, 0x04040404), + _mm_set_epi32(0x0b0b0b0b, 0x0a0a0a0a, 0x09090909, 0x08080808), + _mm_set_epi32(0x0f0f0f0f, 0x0e0e0e0e, 0x0d0d0d0d, 0x0c0c0c0c) + }; + + uint64_t aux64; + + __m256i v_gindex; + const uint16_t * gindex = (const uint16_t *)&v_gindex; + + __m256 accum = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * sc = x[i].scales; + + __m256i sumi = _mm256_setzero_si256(); + for (int i128 = 0; i128 < QK_K/128; ++i128) { + const __m128i ql = _mm_loadu_si128((const __m128i*)qs); qs += 16; + memcpy(&aux64, sc, 8); sc += 8; + const __m128i qh = _mm_shuffle_epi8(_mm_set_epi64x(aux64 >> 4, aux64), shuffle_h); + const __m256i hbit = _mm256_cvtepu8_epi16(_mm_and_si128(qh, m8)); + v_gindex = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); + const __m128i scales = _mm_or_si128(_mm_slli_epi16(_mm_and_si128(qh, m7), 1), m1); + + for (int i32 = 0; i32 < 4; ++i32) { + const __m256i q8b = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q1b = _mm256_set_epi64x(iq1s_grid[gindex[4*i32+3]], iq1s_grid[gindex[4*i32+2]], + iq1s_grid[gindex[4*i32+1]], iq1s_grid[gindex[4*i32+0]]); + const __m256i dot = mul_add_epi8(q1b, q8b); + const __m256i s16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, shuffle_s[i32])); + const __m256i p = _mm256_madd_epi16(s16, dot); + sumi = _mm256_add_epi32(sumi, p); + } + + } + + accum = _mm256_fmadd_ps(_mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)), _mm256_cvtepi32_ps(sumi), accum); + + } + + *s = hsum_float_8(accum); + +#else + + int db[4]; + uint16_t idx[4]; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * sc = x[i].scales; + + int sumi = 0; + for (int i32 = 0; i32 < QK_K/32; ++i32) { + idx[0] = qs[0] | ((sc[0] & 0x08) << 5); + idx[1] = qs[1] | ((sc[0] & 0x80) << 1); + idx[2] = qs[2] | ((sc[1] & 0x08) << 5); + idx[3] = qs[3] | ((sc[1] & 0x80) << 1); + db[0] = (2*(sc[0] & 7) + 1); + db[1] = (2*((sc[0] >> 4) & 7) + 1); + db[2] = (2*(sc[1] & 7) + 1); + db[3] = (2*((sc[1] >> 4) & 7) + 1); + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]); + int suml = 0; + for (int j = 0; j < 8; ++j) suml += q8[j] * grid[j]; + sumi += db[l] * suml; + q8 += 8; + } + qs += 4; + sc += 2; + } + + sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * sumi; + } + + *s = sumf; + +#endif + +} + // ================================ IQ2 quantization ============================================= typedef struct { @@ -9115,14 +9463,22 @@ typedef struct { uint16_t * neighbours; } iq2_entry_t; -static iq2_entry_t iq2_data[2] = { +static iq2_entry_t iq2_data[3] = { + {NULL, NULL, NULL}, {NULL, NULL, NULL}, {NULL, NULL, NULL}, }; -static inline int iq2_data_index(int grid_size) { - GGML_ASSERT(grid_size == 256 || grid_size == 512); - return grid_size == 256 ? 0 : 1; +static inline int iq2_data_index(enum ggml_type type) { + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + return type == GGML_TYPE_IQ2_XXS ? 0 : + type == GGML_TYPE_IQ2_XS ? 1 : 2; +} + +static inline int iq2_grid_size(enum ggml_type type) { + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + return type == GGML_TYPE_IQ2_XXS ? 256 : + type == GGML_TYPE_IQ2_XS ? 512 : 512; } static int iq2_compare_func(const void * left, const void * right) { @@ -9131,12 +9487,13 @@ static int iq2_compare_func(const void * left, const void * right) { return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0; } -void iq2xs_init_impl(int grid_size) { - const int gindex = iq2_data_index(grid_size); +void iq2xs_init_impl(enum ggml_type type) { + const int gindex = iq2_data_index(type); + const int grid_size = iq2_grid_size(type); if (iq2_data[gindex].grid) { return; } - static const uint16_t kgrid_256[256] = { + static const uint16_t kgrid_2bit_256[256] = { 0, 2, 5, 8, 10, 17, 20, 32, 34, 40, 42, 65, 68, 80, 88, 97, 100, 128, 130, 138, 162, 257, 260, 272, 277, 320, 388, 408, 512, 514, 546, 642, 1025, 1028, 1040, 1057, 1060, 1088, 1090, 1096, 1120, 1153, 1156, 1168, 1188, 1280, 1282, 1288, @@ -9154,7 +9511,7 @@ void iq2xs_init_impl(int grid_size) { 33888, 34048, 34118, 34196, 34313, 34368, 34400, 34818, 35076, 35345, 36868, 36880, 36900, 36928, 37025, 37142, 37248, 37445, 37888, 37922, 37956, 38225, 39041, 39200, 40962, 41040, 41093, 41225, 41472, 42008, 43088, 43268, }; - static const uint16_t kgrid_512[512] = { + static const uint16_t kgrid_2bit_512[512] = { 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, 73, 80, 82, 85, 88, 97, 100, 128, 130, 133, 136, 145, 148, 153, 160, 257, 260, 262, 265, 272, 274, 277, 280, 282, 289, 292, 320, 322, 325, 328, 337, 340, @@ -9188,9 +9545,45 @@ void iq2xs_init_impl(int grid_size) { 40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048, 42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690, }; + static const uint16_t kgrid_1bit_512[512] = { + 10, 33, 41, 85, 132, 134, 160, 162, 277, 337, 340, 345, 357, 405, 516, 545, + 553, 598, 641, 650, 681, 1042, 1044, 1097, 1169, 1176, 1320, 1345, 1365, 1378, 1434, 1444, + 1545, 1617, 1642, 1685, 2053, 2080, 2089, 2133, 2176, 2182, 2208, 2214, 2306, 2384, 2393, 2440, + 2453, 2581, 2664, 2690, 2721, 4117, 4161, 4182, 4184, 4261, 4357, 4369, 4372, 4377, 4390, 4422, + 4432, 4437, 4449, 4457, 4485, 4497, 4505, 4629, 4677, 4696, 4774, 5205, 5217, 5225, 5386, 5397, + 5409, 5445, 5457, 5460, 5461, 5462, 5465, 5472, 5477, 5525, 5545, 5650, 5668, 5717, 5729, 5769, + 5777, 6212, 6234, 6244, 6293, 6424, 6482, 6485, 6502, 6505, 6529, 6538, 6565, 6656, 6682, 6788, + 6806, 6820, 8218, 8224, 8226, 8232, 8277, 8326, 8354, 8469, 8521, 8530, 8549, 8596, 8737, 8794, + 9221, 9253, 9348, 9369, 9380, 9474, 9557, 9633, 9732, 9753, 9793, 9830, 9862, 9880, 10240, 10272, + 10282, 10321, 10406, 10517, 10530, 10566, 10585, 10645, 10896, 16466, 16468, 16473, 16485, 16646, 16660, 16665, + 16725, 16793, 16806, 16914, 16969, 16977, 16996, 17028, 17057, 17408, 17416, 17434, 17493, 17512, 17578, 17685, + 17696, 17733, 17745, 17748, 17749, 17750, 17753, 17765, 17794, 17813, 17946, 17984, 18005, 18072, 18453, 18529, + 18569, 18722, 18756, 18762, 18773, 18794, 18833, 18853, 18945, 19026, 19033, 19077, 20489, 20497, 20500, 20517, + 20565, 20586, 20610, 20633, 20757, 20769, 20776, 20805, 20817, 20820, 20821, 20822, 20825, 20837, 20864, 20872, + 20885, 20896, 21002, 21029, 21077, 21146, 21510, 21525, 21573, 21585, 21588, 21589, 21590, 21593, 21605, 21653, + 21665, 21765, 21777, 21780, 21781, 21782, 21785, 21797, 21825, 21828, 21829, 21830, 21833, 21840, 21841, 21842, + 21844, 21846, 21848, 21849, 21850, 21857, 21860, 21861, 21862, 21865, 21893, 21905, 21908, 21909, 21910, 21913, + 21925, 22024, 22037, 22085, 22097, 22100, 22101, 22102, 22105, 22117, 22165, 22545, 22566, 22568, 22594, 22608, + 22613, 22676, 22697, 22793, 22805, 22853, 22865, 22868, 22869, 22870, 22873, 22885, 22933, 22946, 23046, 23072, + 23125, 23209, 24597, 24640, 24665, 24673, 24725, 24833, 24840, 24869, 24917, 24934, 24965, 25001, 25108, 25110, + 25152, 25184, 25192, 25234, 25616, 25618, 25625, 25685, 25704, 25738, 25744, 25770, 25877, 25897, 25925, 25937, + 25940, 25941, 25942, 25945, 25957, 25986, 26005, 26186, 26197, 26276, 26632, 26634, 26725, 26757, 26770, 26885, + 26965, 26976, 26986, 27032, 27153, 27174, 27200, 27208, 27240, 27269, 27282, 27290, 32778, 32800, 32802, 32808, + 32810, 32853, 32904, 32922, 32930, 32932, 33105, 33110, 33112, 33125, 33157, 33280, 33288, 33301, 33312, 33320, + 33424, 33797, 33829, 33858, 34068, 34133, 34146, 34176, 34217, 34306, 34342, 34441, 34454, 34468, 34832, 34918, + 34965, 34984, 35094, 35137, 35161, 35208, 35232, 35332, 35338, 35368, 35429, 36932, 36934, 36953, 37009, 37125, + 37136, 37138, 37145, 37157, 37205, 37220, 37258, 37290, 37444, 37446, 37465, 37478, 37525, 37905, 37968, 37973, + 38040, 38054, 38145, 38154, 38165, 38180, 38186, 38213, 38225, 38228, 38229, 38230, 38233, 38245, 38293, 38485, + 38504, 38530, 38938, 38985, 38993, 39012, 39040, 39173, 39192, 39253, 39265, 39301, 39316, 39322, 39442, 39497, + 39504, 39590, 40970, 40984, 40992, 41002, 41045, 41120, 41128, 41237, 41289, 41297, 41317, 41364, 41366, 41514, + 41557, 41633, 41989, 42021, 42056, 42068, 42074, 42113, 42242, 42265, 42274, 42325, 42340, 42402, 42501, 42512, + 42533, 42624, 42632, 42666, 43040, 43093, 43106, 43168, 43176, 43264, 43286, 43345, 43429, 43590, 43618, 43680, + }; + const int kmap_size = 43692; - const int nwant = 2; - const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512; + const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; + const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 : + type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : kgrid_1bit_512; uint64_t * kgrid_q2xs; int * kmap_q2xs; uint16_t * kneighbors_q2xs; @@ -9286,9 +9679,9 @@ void iq2xs_init_impl(int grid_size) { free(dist2); } -void iq2xs_free_impl(int grid_size) { - GGML_ASSERT(grid_size == 256 || grid_size == 512 || grid_size == 1024); - const int gindex = iq2_data_index(grid_size); +void iq2xs_free_impl(enum ggml_type type) { + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + const int gindex = iq2_data_index(type); if (iq2_data[gindex].grid) { free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; free(iq2_data[gindex].map); iq2_data[gindex].map = NULL; @@ -9322,7 +9715,7 @@ static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const u static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { - const int gindex = iq2_data_index(256); + const int gindex = iq2_data_index(GGML_TYPE_IQ2_XXS); const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; const int * kmap_q2xs = iq2_data[gindex].map; @@ -9495,7 +9888,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { - const int gindex = iq2_data_index(512); + const int gindex = iq2_data_index(GGML_TYPE_IQ2_XS); const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; const int * kmap_q2xs = iq2_data[gindex].map; @@ -10132,3 +10525,207 @@ void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * re assert(k % QK_K == 0); quantize_row_iq3_xxs_impl(x, y, k, NULL); } + +// =================================== 1.5 bpw =================================================== + +static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid, + const float * restrict xval, const float * restrict weight, float * scale, int8_t * restrict L, int ngrid) { + int num_neighbors = neighbours[0]; + GGML_ASSERT(num_neighbors > 0); + float best_score = 0; + int grid_index = -1; + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 8; ++i) { + float q = (pg[i] - 3)/2; + float w = weight[i]; + sumqx += w*q*xval[i]; + sumq2 += w*q*q; + } + if (sumqx > 0 && sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { + *scale = sumqx/sumq2; best_score = *scale * sumqx; + grid_index = neighbours[j]; + } + } + if (grid_index < 0) { + for (int i = 0; i < ngrid; ++i) { + const int8_t * grid_i = (const int8_t *)(grid + i); + float sumqx = 0, sumq2 = 0; + for (int j = 0; j < 8; ++j) { + float w = weight[j]; + float q = (grid_i[j] - 3)/2; + sumqx += w*q*xval[j]; + sumq2 += w*q*q; + } + if (sumqx > 0 && sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { + *scale = sumqx/sumq2; best_score = *scale*sumqx; + grid_index = i; + } + } + } + if (grid_index < 0) { + printf("Oops, did not find grid point\n"); + printf("Have %d neighbours\n", num_neighbors); + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 8; ++i) { + float q = (pg[i] - 3)/2; + float w = weight[i]; + sumqx += w*q*xval[i]; + sumq2 += w*q*q; + } + printf(" neighbour %d: sumqx = %g sumq2 = %g\n", j, (double)sumqx, (double)sumq2); + } + } + GGML_ASSERT(grid_index >= 0); + //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + *scale *= 1.05f; // This is a fudge factor. Don't ask me why it improves the result. + //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + const int8_t * pg = (const int8_t *)(grid + grid_index); + for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2; + return grid_index; +} + +static int iq1_sort_helper(const void * left, const void * right) { + const float * l = left; + const float * r = right; + return *l < *r ? -1 : *l > *r ? 1 : 0; +} + +static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(GGML_TYPE_IQ1_S); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights && "missing quantization weights"); + GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + const int nbl = n/256; + + block_iq1_s * y = vy; + + float scales[QK_K/8]; + float weight[8]; + int8_t L[8]; + float sumx[9]; + float sumw[9]; + float pairs[16]; + int * idx = (int *)(pairs + 1); + uint8_t hbit[QK_K/8]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(y[ibl].qs, 0, QK_K/8); + memset(y[ibl].scales, 0, QK_K/16); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/8; ++ib) { + const float * xb = xbl + 8*ib; + const float * qw = quant_weights + QK_K*ibl + 8*ib; + for (int i = 0; i < 8; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + float max = fabsf(xb[0]); + for (int i = 1; i < 8; ++i) max = MAX(max, fabsf(xb[i])); + if (!max) { + scales[ib] = 0; + memset(L, 1, 8); + continue; + } + // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem. + // With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two + // boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights + // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and + // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale + // for each possible and score for each split. + for (int j = 0; j < 8; ++j) { + pairs[2*j] = xb[j]; + idx[2*j] = j; + } + qsort(pairs, 8, 2*sizeof(float), iq1_sort_helper); + { + sumx[0] = sumw[0] = 0; + for (int j = 0; j < 8; ++j) { + int i = idx[2*j]; + sumx[j+1] = sumx[j] + weight[i]*xb[i]; + sumw[j+1] = sumw[j] + weight[i]; + } + } + float best_score = 0, scale = max; + int besti1 = 0, besti2 = 0; + for (int i1 = 0; i1 <= 8; ++i1) { + for (int i2 = i1; i2 <= 8; ++i2) { + float sumqx = -(sumx[i1] - sumx[0]) + (sumx[8] - sumx[i2]); + float sumq2 = (sumw[i1] - sumw[0]) + (sumw[8] - sumw[i2]); + if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { + scale = sumqx/sumq2; best_score = scale*sumqx; + besti1 = i1; besti2 = i2; + } + } + } + for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; + for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; + for (int j = besti2; j < 8; ++j) L[idx[2*j]] = 2; + if (scale < 0) { + for (int j = 0; j < 8; ++j) L[j] = 2 - L[j]; + scale = -scale; + } + // Now we check if the solution found above corresponds to a grid point and, if not, use a neighbouring + // grid point that minimizes SSD. + uint16_t u = 0; + for (int j = 0; j < 8; ++j) u |= (L[j] << 2*j); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq1_find_best_neighbour(neighbours, kgrid_q2xs, xb, weight, &scale, L, NGRID_IQ2XXS); + GGML_ASSERT(grid_index >= 0); + } + y[ibl].qs[ib] = grid_index & 255; + hbit[ib] = grid_index >> 8; + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, QK_K/8); + continue; + } + + float d = max_scale/15; + y[ibl].d = GGML_FP32_TO_FP16(d*1.085f); // 1.085f is another fudge factor. Don't ask me why it is needed. + float id = 1/d; + for (int ib = 0; ib < QK_K/8; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(7, l)); + if (hbit[ib]) l |= 8; + y[ibl].scales[ib/2] |= (l << 4*(ib%2)); + } + } +} + +size_t quantize_iq1_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq1_s); + } + return nrow * nblock * sizeof(block_iq1_s); +} diff --git a/ggml-quants.h b/ggml-quants.h index 68f09b1e12f25..ad381cfab022d 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -191,6 +191,13 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +typedef struct { + ggml_fp16_t d; + uint8_t qs[QK_K/8]; + uint8_t scales[QK_K/16]; +} block_iq1_s; +static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); + #ifdef __cplusplus extern "C" { #endif @@ -243,6 +250,7 @@ void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRI void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -259,6 +267,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -266,6 +275,7 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); @@ -276,8 +286,8 @@ size_t quantize_q4_1 (const float * src, void * dst, int nrows, int n_per_row, size_t quantize_q5_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -void iq2xs_init_impl(int grid_size); -void iq2xs_free_impl(int grid_size); +void iq2xs_init_impl(enum ggml_type type); +void iq2xs_free_impl(enum ggml_type type); void iq3xs_init_impl(int grid_size); void iq3xs_free_impl(int grid_size); diff --git a/ggml.c b/ggml.c index e94024c62a123..aefcda6d42a0d 100644 --- a/ggml.c +++ b/ggml.c @@ -673,6 +673,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ1_S] = { + .type_name = "iq1_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq1_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq1_s, + .from_float = NULL, + .from_float_reference = NULL, + .vec_dot = ggml_vec_dot_iq1_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2267,6 +2279,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; + case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7677,6 +7690,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; @@ -7944,6 +7958,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; @@ -8064,6 +8079,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: default: { GGML_ASSERT(false); @@ -10830,6 +10846,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: { ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; @@ -11010,6 +11027,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: default: { GGML_ASSERT(false); @@ -11207,6 +11225,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -11880,6 +11899,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -11957,6 +11977,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -19136,8 +19157,9 @@ void ggml_quantize_init(enum ggml_type type) { ggml_critical_section_start(); switch (type) { - case GGML_TYPE_IQ2_XXS: iq2xs_init_impl(256); break; - case GGML_TYPE_IQ2_XS: iq2xs_init_impl(512); break; + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; default: // nothing break; @@ -19149,8 +19171,10 @@ void ggml_quantize_init(enum ggml_type type) { void ggml_quantize_free(void) { ggml_critical_section_start(); - iq2xs_free_impl(256); - iq2xs_free_impl(512); + iq2xs_free_impl(GGML_TYPE_IQ2_XXS); + iq2xs_free_impl(GGML_TYPE_IQ2_XS); + iq2xs_free_impl(GGML_TYPE_IQ1_S); + iq3xs_free_impl(256); ggml_critical_section_end(); } @@ -19285,7 +19309,8 @@ size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * bool ggml_quantize_requires_imatrix(enum ggml_type type) { return type == GGML_TYPE_IQ2_XXS || - type == GGML_TYPE_IQ2_XS; + type == GGML_TYPE_IQ2_XS || + type == GGML_TYPE_IQ1_S; } size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, @@ -19410,6 +19435,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ1_S: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq1_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index 6c1956772324c..004d09c700f32 100644 --- a/ggml.h +++ b/ggml.h @@ -354,6 +354,7 @@ extern "C" { GGML_TYPE_IQ2_XXS = 16, GGML_TYPE_IQ2_XS = 17, GGML_TYPE_IQ3_XXS = 18, + GGML_TYPE_IQ1_S = 19, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -391,6 +392,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index 6ac9caa957a05..5cfebb3b1c99c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2526,6 +2526,7 @@ struct llama_model_loader { case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; + case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2875,6 +2876,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw"; default: return "unknown, may not work"; } @@ -10312,20 +10314,20 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { new_type = GGML_TYPE_Q5_K; } else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } } else if (name == "token_embd.weight") { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { new_type = GGML_TYPE_Q2_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = GGML_TYPE_Q4_K; } - } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { if (name.find("attn_v.weight") != std::string::npos) { if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; else new_type = GGML_TYPE_Q2_K; @@ -10335,6 +10337,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (qs.i_ffn_down < qs.n_ffn_down/8) new_type = GGML_TYPE_Q2_K; ++qs.i_ffn_down; } + else if (name.find("attn_output.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS; + } } else if (name.find("attn_v.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; @@ -10468,7 +10473,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || - new_type == GGML_TYPE_IQ3_XXS) { + new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -10483,6 +10488,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; @@ -10525,6 +10531,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ2_XS: quantized_type = GGML_TYPE_IQ2_XS; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S ; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } @@ -10698,6 +10705,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS || + new_type == GGML_TYPE_IQ1_S || (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { LLAMA_LOG_ERROR("\n\n============================================================\n"); LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); diff --git a/llama.h b/llama.h index f4ec6ea6394a3..5a97abcc94d7c 100644 --- a/llama.h +++ b/llama.h @@ -100,6 +100,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 30a7d1f5ab3e2..ef37c5af275fe 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1917,7 +1917,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, - GGML_TYPE_IQ3_XXS, + GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, }; // unary ops From fc0c8d286a533363a9a663510b62af85ffad58b3 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sun, 18 Feb 2024 17:19:23 +0100 Subject: [PATCH 007/401] llava : update surgery script to not remove tensors (#5536) This commit updates the surgery script to not remove the tensors from the model file. For this to work the `--skip-unknown` flag is added as an argument to the convert.py script in README.md. The motivation for this change is that the surgery script currently removes the projector tensors from the model file. If the model was checked out from a repository, the model file will have been updated and have to be checked out again to reset this effect. If this can be avoided I think it would be preferable. I did not perform this change for BakLLaVA models as I am not sure how that part works. --- examples/llava/README.md | 2 +- examples/llava/llava-surgery.py | 6 +----- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 57eb4293254dd..e42db6e5ad3cf 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -53,7 +53,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-pa 5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py ../llava-v1.5-7b +python ./convert.py ../llava-v1.5-7b --skip-unknown ``` Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py index 0a61efdfe14d1..8b7a62fbaa145 100644 --- a/examples/llava/llava-surgery.py +++ b/examples/llava/llava-surgery.py @@ -19,10 +19,6 @@ projector = {name: checkpoint[name].float() for name in mm_tensors} torch.save(projector, f"{args.model}/llava.projector") -# remove these tensors from the checkpoint and save it again -for name in mm_tensors: - del checkpoint[name] - # BakLLaVA models contain CLIP tensors in it clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")] if len(clip_tensors) > 0: @@ -39,7 +35,7 @@ f.write("{}\n") -torch.save(checkpoint, path) + torch.save(checkpoint, path) print("Done!") print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") From 5d3de51f972055702a1859186fe7acb8f0b43dc4 Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Sun, 18 Feb 2024 16:20:12 +0000 Subject: [PATCH 008/401] ggml, common, examples, tests : fixed type arguments in printf (#5528) --- common/common.cpp | 4 +- examples/batched-bench/batched-bench.cpp | 2 +- examples/batched/batched.cpp | 2 +- .../convert-llama2c-to-ggml.cpp | 38 +++++++++---------- examples/perplexity/perplexity.cpp | 2 +- .../train-text-from-scratch.cpp | 14 +++---- ggml.c | 4 +- tests/test-grammar-parser.cpp | 20 +++++----- tests/test-llama-grammar.cpp | 4 +- 9 files changed, 45 insertions(+), 45 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 3a92d3797492f..9ffc3951f6dee 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1741,7 +1741,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed); + fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); @@ -1750,7 +1750,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency()); + fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 55dfd97843895..b4b8a38e1bcf5 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -159,7 +159,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index eab636692e7d1..9be7eb56bcd8a 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -92,7 +92,7 @@ int main(int argc, char ** argv) { const int n_ctx = llama_n_ctx(ctx); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); + LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 4d41e17793f43..8209dcb642bee 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -325,14 +325,14 @@ struct train_params { }; static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab: %d\n", __func__, params->n_vocab); - printf("%s: n_ctx: %d\n", __func__, params->n_ctx); - printf("%s: n_embd: %d\n", __func__, params->n_embd); - printf("%s: n_mult: %d\n", __func__, params->n_mult); - printf("%s: n_head: %d\n", __func__, params->n_head); - printf("%s: n_ff: %d\n", __func__, params->n_ff); - printf("%s: n_layer: %d\n", __func__, params->n_layer); - printf("%s: n_rot: %d\n", __func__, params->n_rot); + printf("%s: n_vocab: %u\n", __func__, params->n_vocab); + printf("%s: n_ctx: %u\n", __func__, params->n_ctx); + printf("%s: n_embd: %u\n", __func__, params->n_embd); + printf("%s: n_mult: %u\n", __func__, params->n_mult); + printf("%s: n_head: %u\n", __func__, params->n_head); + printf("%s: n_ff: %u\n", __func__, params->n_ff); + printf("%s: n_layer: %u\n", __func__, params->n_layer); + printf("%s: n_rot: %u\n", __func__, params->n_rot); } static void init_model(struct my_llama_model * model) { @@ -350,25 +350,25 @@ static void init_model(struct my_llama_model * model) { model->train_tokens = 0; model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab); + printf("[%s:GG] Allocating [%u] x [%u] = [%u] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab); model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - printf("[%s:GG] Allocating [%d] float space for model->norm\n",__func__,n_embd); + printf("[%s:GG] Allocating [%u] float space for model->norm\n",__func__,n_embd); model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab); // printing the per-layer allocations here so we dont print in the for loop. - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wq for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wk for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wv for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wo for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\n",__func__,n_embd, n_layer); + printf("[%s:GG] Allocating [%u] float space for layer.ffn_norm for [%u] layers\n",__func__,n_embd, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer); - printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w1 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w2 for [%u] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer); + printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w3 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); ggml_set_name(model->tok_embeddings, "tok_embeddings.weight"); ggml_set_name(model->norm, "norm.weight"); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 67d2d3293a327..74dcc642a876e 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1623,7 +1623,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { uint32_t n_ctx; in.read((char *)&n_ctx, sizeof(n_ctx)); if (n_ctx > llama_n_ctx(ctx)) { - fprintf(stderr, "%s: %s has been computed with %d, while the current context is %d. Increase it with -c and retry\n", + fprintf(stderr, "%s: %s has been computed with %u, while the current context is %d. Increase it with -c and retry\n", __func__, params.logits_file.c_str(), n_ctx, params.n_ctx); } diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index bfdf124d7af19..e78ab185d89f3 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -111,13 +111,13 @@ static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down"; static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up"; static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab: %d\n", __func__, params->n_vocab); - printf("%s: n_ctx: %d\n", __func__, params->n_ctx); - printf("%s: n_embd: %d\n", __func__, params->n_embd); - printf("%s: n_head: %d\n", __func__, params->n_head); - printf("%s: n_ff: %d\n", __func__, params->n_ff); - printf("%s: n_layer: %d\n", __func__, params->n_layer); - printf("%s: n_rot: %d\n", __func__, params->n_rot); + printf("%s: n_vocab: %u\n", __func__, params->n_vocab); + printf("%s: n_ctx: %u\n", __func__, params->n_ctx); + printf("%s: n_embd: %u\n", __func__, params->n_embd); + printf("%s: n_head: %u\n", __func__, params->n_head); + printf("%s: n_ff: %u\n", __func__, params->n_ff); + printf("%s: n_layer: %u\n", __func__, params->n_layer); + printf("%s: n_rot: %u\n", __func__, params->n_rot); } static void set_param_model(struct my_llama_model * model) { diff --git a/ggml.c b/ggml.c index aefcda6d42a0d..8224652a9cc50 100644 --- a/ggml.c +++ b/ggml.c @@ -17909,7 +17909,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context * ptr += ggml_nbytes(tensor); - fprintf(stderr, "%s: loaded leaf %d: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); + fprintf(stderr, "%s: loaded leaf %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); } } @@ -18012,7 +18012,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context * result->nodes[i] = tensor; - fprintf(stderr, "%s: loaded node %d: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); + fprintf(stderr, "%s: loaded node %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); } } } diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp index a0b5b043df868..91939e276df07 100644 --- a/tests/test-grammar-parser.cpp +++ b/tests/test-grammar-parser.cpp @@ -38,8 +38,8 @@ term ::= [0-9]+)"""; // pretty print error message before asserting if (expected_pair.first != key || expected_pair.second != value) { - fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second); - fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value); + fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second); + fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value); fprintf(stderr, "expected_pair != actual_pair\n"); } @@ -96,9 +96,9 @@ term ::= [0-9]+)"""; // pretty print error message before asserting if (expected_element.type != element.type || expected_element.value != element.value) { - fprintf(stderr, "index: %d\n", index); - fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value); - fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value); + fprintf(stderr, "index: %u\n", index); + fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value); + fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value); fprintf(stderr, "expected_element != actual_element\n"); } @@ -144,8 +144,8 @@ term ::= [0-9]+)"""; // pretty print error message before asserting if (expected_pair.first != key || expected_pair.second != value) { - fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second); - fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value); + fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second); + fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value); fprintf(stderr, "expected_pair != actual_pair\n"); } @@ -235,9 +235,9 @@ term ::= [0-9]+)"""; // pretty print error message before asserting if (expected_element.type != element.type || expected_element.value != element.value) { - fprintf(stderr, "index: %d\n", index); - fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value); - fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value); + fprintf(stderr, "index: %u\n", index); + fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value); + fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value); fprintf(stderr, "expected_element != actual_element\n"); } diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 16ebe753fe98f..27ca4d2656c5d 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -180,8 +180,8 @@ int main() if (expected_element.type != element->type || expected_element.value != element->value) { fprintf(stderr, "index: %d\n", index); - fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value); - fprintf(stderr, "actual_element: %d, %d\n", element->type, element->value); + fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value); + fprintf(stderr, "actual_element: %d, %u\n", element->type, element->value); fprintf(stderr, "expected_element != actual_element\n"); } From 1dcc3fde004787e6fc4d84c9de0bb34cd2901a3e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 18:21:52 +0200 Subject: [PATCH 009/401] common : fix ub (#5530) --- common/common.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 9ffc3951f6dee..489462b5a1a78 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1801,7 +1801,8 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { if (cs_curr[j] < 0) { continue; } if (seqs.find(cs_curr[j]) == seqs.end()) { if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } - seqs[cs_curr[j]] = seqs.size(); + const size_t sz = seqs.size(); + seqs[cs_curr[j]] = sz; } } if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } From 66c1968f7a2e895675425e875b6589f1233a1b52 Mon Sep 17 00:00:00 2001 From: Daniel Hiltgen Date: Sun, 18 Feb 2024 08:23:16 -0800 Subject: [PATCH 010/401] server : graceful server shutdown (#5244) This updates the server queue to support graceful shutdown of the server on signals. --- examples/server/server.cpp | 23 ++++++++++++++++++++++- examples/server/utils.hpp | 20 +++++++++++++++++--- 2 files changed, 39 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a0b46970b83a9..7800c6e7e67e8 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -28,6 +28,7 @@ #include #include #include +#include using json = nlohmann::json; @@ -2511,6 +2512,9 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con } } +std::function shutdown_handler; +inline void signal_handler(int signal) { shutdown_handler(signal); } + int main(int argc, char **argv) { #if SERVER_VERBOSE != 1 @@ -3128,8 +3132,25 @@ int main(int argc, char **argv) std::placeholders::_2, std::placeholders::_3 )); - llama.queue_tasks.start_loop(); + shutdown_handler = [&](int) { + llama.queue_tasks.terminate(); + }; + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) + struct sigaction sigint_action; + sigint_action.sa_handler = signal_handler; + sigemptyset (&sigint_action.sa_mask); + sigint_action.sa_flags = 0; + sigaction(SIGINT, &sigint_action, NULL); +#elif defined (_WIN32) + auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { + return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false; + }; + SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); +#endif + llama.queue_tasks.start_loop(); + svr.stop(); t.join(); llama_backend_free(); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 5485489627d5d..0ee670dbadca5 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -220,6 +220,7 @@ inline std::string format_chatml(std::vector messages) struct llama_server_queue { int id = 0; std::mutex mutex_tasks; + bool running; // queues std::vector queue_tasks; std::vector queue_tasks_deferred; @@ -278,9 +279,18 @@ struct llama_server_queue { queue_tasks_deferred.clear(); } - // Start the main loop. This call is blocking - [[noreturn]] + // end the start_loop routine + void terminate() { + { + std::unique_lock lock(mutex_tasks); + running = false; + } + condition_tasks.notify_all(); + } + + // Start the main loop. void start_loop() { + running = true; while (true) { // new task arrived LOG_VERBOSE("have new task", {}); @@ -324,8 +334,12 @@ struct llama_server_queue { { std::unique_lock lock(mutex_tasks); if (queue_tasks.empty()) { + if (!running) { + LOG_VERBOSE("ending start_loop", {}); + return; + } condition_tasks.wait(lock, [&]{ - return !queue_tasks.empty(); + return (!queue_tasks.empty() || !running); }); } } From 36376abe05a12a8cb3af548a4af9b8d0e2e69597 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 18 Feb 2024 17:30:09 +0100 Subject: [PATCH 011/401] server : --n-predict option document and cap to max value (#5549) * server: document --n-predict * server: ensure client request cannot override n_predict if set * server: fix print usage LF in new --n-predict option --- examples/server/README.md | 1 + examples/server/server.cpp | 15 ++++++++++++++- 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 249368749ff07..fe5cd8d5d1382 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -39,6 +39,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. - `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` +- `-n, --n-predict`: Set the maximum tokens to predict (default: -1) ## Build diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7800c6e7e67e8..7aa706e953079 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -159,6 +159,7 @@ struct llama_client_slot int32_t n_decoded = 0; int32_t n_remaining = -1; int32_t i_batch = -1; + int32_t n_predict = -1; int32_t num_prompt_tokens = 0; int32_t num_prompt_tokens_processed = 0; @@ -410,6 +411,7 @@ struct llama_server_context slot.id = i; slot.n_ctx = n_ctx_slot; + slot.n_predict = params.n_predict; LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); @@ -546,6 +548,15 @@ struct llama_server_context slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) { + // Might be better to reject the request with a 400 ? + LOG_WARNING("Max tokens to predict exceeds server configuration", { + {"params.n_predict", slot->params.n_predict}, + {"slot.n_predict", slot->n_predict}, + }); + slot->params.n_predict = slot->n_predict; + } + // infill if (data.count("input_prefix") != 0) { @@ -1053,6 +1064,7 @@ struct llama_server_context return json { {"n_ctx", slot.n_ctx}, + {"n_predict", slot.n_predict}, {"model", params.model_alias}, {"seed", slot.params.seed}, {"temperature", slot.sparams.temp}, @@ -1915,13 +1927,14 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); printf("\n"); + printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf(" --chat-template FORMAT_NAME"); - printf(" set chat template, possible valus is: llama2, chatml (default %s)", sparams.chat_template.c_str()); + printf(" set chat template, possible value is: llama2, chatml (default %s)", sparams.chat_template.c_str()); printf("\n"); } From e75c6279d1c8e7abb82a331f5de7124eed402de2 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 18 Feb 2024 17:31:28 +0100 Subject: [PATCH 012/401] server : enhanced health endpoint (#5548) * server: enrich health endpoint with available slots, return 503 if not slots are available * server: document new status no slot available in the README.md --- examples/server/README.md | 1 + examples/server/server.cpp | 31 +++++++++++++++++++++++++++++-- 2 files changed, 30 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fe5cd8d5d1382..5e3ae833bef57 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -136,6 +136,7 @@ node index.js - `{"status": "loading model"}` if the model is still being loaded. - `{"status": "error"}` if the model failed to load. - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below. + - `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available - **POST** `/completion`: Given a `prompt`, it returns the predicted completion. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7aa706e953079..8145af867292b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2578,8 +2578,35 @@ int main(int argc, char **argv) server_state current_state = state.load(); switch(current_state) { case SERVER_STATE_READY: - res.set_content(R"({"status": "ok"})", "application/json"); - res.status = 200; // HTTP OK + if (llama.all_slots_are_idle) { + res.set_content(R"({"status": "ok"})", "application/json"); + res.status = 200; // HTTP OK + } else { + int available_slots = 0; + int processing_slots = 0; + for (llama_client_slot & slot : llama.slots) { + if (slot.available()) { + available_slots++; + } else { + processing_slots++; + } + } + if (available_slots > 0) { + json health = { + {"status", "ok"}, + {"slots_idle", available_slots}, + {"slots_processing", processing_slots}}; + res.set_content(health.dump(), "application/json"); + res.status = 200; // HTTP OK + } else { + json health = { + {"status", "no slot available"}, + {"slots_idle", available_slots}, + {"slots_processing", processing_slots}}; + res.set_content(health.dump(), "application/json"); + res.status = 503; // HTTP Service Unavailable + } + } break; case SERVER_STATE_LOADING_MODEL: res.set_content(R"({"status": "loading model"})", "application/json"); From f3f28c5395cd25b371617981b341616dbdd31e85 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 19:17:00 +0200 Subject: [PATCH 013/401] cmake : fix GGML_USE_SYCL typo (#5555) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5ea4d4f1979dc..0c29b5d09bd2b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -526,7 +526,7 @@ if (LLAMA_SYCL) message(STATUS "SYCL found") - add_compile_definitions(GML_USE_SYCL) + add_compile_definitions(GGML_USE_SYCL) if (LLAMA_SYCL_F16) add_compile_definitions(GGML_SYCL_F16) From 689a091bbe0537ee9abff3e15a1d74f5f3561165 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 19:38:06 +0200 Subject: [PATCH 014/401] sampling : do not set min_keep to n_probs (#5564) --- common/sampling.cpp | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 53013138a9eb4..611c327bb7447 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -121,7 +121,7 @@ static void sampler_queue( struct llama_context * ctx_main, const llama_sampling_params & params, llama_token_data_array & cur_p, - size_t & min_keep) { + size_t min_keep) { const float temp = params.temp; const float dynatemp_range = params.dynatemp_range; const float dynatemp_exponent = params.dynatemp_exponent; @@ -248,10 +248,7 @@ static llama_token llama_sampling_sample_impl( llama_sample_temp(ctx_main, &cur_p, temp); id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu); } else { - // temperature sampling - size_t min_keep = std::max(1, params.n_probs); - - sampler_queue(ctx_main, params, cur_p, min_keep); + sampler_queue(ctx_main, params, cur_p, 1); id = llama_sample_token(ctx_main, &cur_p); From c145f8a132b2fe1d1e65987faddbd9a40bef7a12 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 18 Feb 2024 18:39:57 +0100 Subject: [PATCH 015/401] server : slots monitoring endpoint (#5550) --- examples/server/README.md | 64 ++++++++++++++++++++++++++++++++++++++ examples/server/server.cpp | 32 +++++++++++++++++++ 2 files changed, 96 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index 5e3ae833bef57..ac5133d2404cb 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -40,6 +40,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` - `-n, --n-predict`: Set the maximum tokens to predict (default: -1) +- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. ## Build @@ -381,6 +382,69 @@ Notice that each `probs` is an array of length `n_probs`. }' ``` +- **GET** `/slots`: Returns the current slots processing state. Can be disabled with `--slots-endpoint-disable`. + +### Result JSON + +```json +[ + { + "dynatemp_exponent": 1.0, + "dynatemp_range": 0.0, + "frequency_penalty": 0.0, + "grammar": "", + "id": 0, + "ignore_eos": false, + "logit_bias": [], + "min_p": 0.05000000074505806, + "mirostat": 0, + "mirostat_eta": 0.10000000149011612, + "mirostat_tau": 5.0, + "model": "llama-2-7b-32k-instruct.Q2_K.gguf", + "n_ctx": 2048, + "n_keep": 0, + "n_predict": 100000, + "n_probs": 0, + "next_token": { + "has_next_token": true, + "n_remain": -1, + "num_tokens_predicted": 0, + "stopped_eos": false, + "stopped_limit": false, + "stopped_word": false, + "stopping_word": "" + }, + "penalize_nl": true, + "penalty_prompt_tokens": [], + "presence_penalty": 0.0, + "prompt": "Say hello to llama.cpp", + "repeat_last_n": 64, + "repeat_penalty": 1.100000023841858, + "samplers": [ + "top_k", + "tfs_z", + "typical_p", + "top_p", + "min_p", + "temperature" + ], + "seed": 42, + "state": 1, + "stop": [ + "\n" + ], + "stream": false, + "task_id": 0, + "temperature": 0.0, + "tfs_z": 1.0, + "top_k": 40, + "top_p": 0.949999988079071, + "typical_p": 1.0, + "use_penalty_prompt_tokens": false + } +] +``` + ## More examples ### Change system prompt on runtime diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8145af867292b..4f2e9c898b391 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -41,6 +41,7 @@ struct server_params int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; + bool slots_endpoint = true; }; bool server_verbose = false; @@ -1926,6 +1927,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); + printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); printf("\n"); printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); @@ -2374,6 +2376,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } + else if (arg == "--slots-endpoint-disable") + { + sparams.slots_endpoint = false; + } else if (arg == "--chat-template") { if (++i >= argc) @@ -2619,6 +2625,32 @@ int main(int argc, char **argv) } }); + if (sparams.slots_endpoint) { + svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) { + json slots; + for (llama_client_slot & slot : llama.slots) { + json slot_data = llama.get_formated_generation(slot); + slot_data["id"] = slot.id; + slot_data["task_id"] = slot.task_id; + slot_data["state"] = slot.state; + slot_data["prompt"] = slot.prompt; + slot_data["next_token"] = { + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"num_tokens_predicted", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }; + + slots.push_back(slot_data); + } + res.set_content(slots.dump(), "application/json"); + res.status = 200; // HTTP OK + }); + } + svr.set_logger(log_server_request); svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) From 5ee99c32f5e47c8d32634eff9a47fb32a24c276b Mon Sep 17 00:00:00 2001 From: Robey Holderith Date: Sun, 18 Feb 2024 11:11:16 -0800 Subject: [PATCH 016/401] common, server : surface min_keep as its own parameter (#5567) * Feature - surface min_keep as its own parameter * Updated README with min_keep param --- common/common.cpp | 1 + common/sampling.cpp | 5 ++++- common/sampling.h | 1 + examples/server/README.md | 2 ++ examples/server/public/index.html | 4 ++++ examples/server/server.cpp | 2 ++ 6 files changed, 14 insertions(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 489462b5a1a78..10ef11829cc50 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1704,6 +1704,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l } fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); + fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); diff --git a/common/sampling.cpp b/common/sampling.cpp index 611c327bb7447..de4331a1182d6 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -248,7 +248,10 @@ static llama_token llama_sampling_sample_impl( llama_sample_temp(ctx_main, &cur_p, temp); id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu); } else { - sampler_queue(ctx_main, params, cur_p, 1); + // temperature sampling + size_t min_keep = std::max(1, params.min_keep); + + sampler_queue(ctx_main, params, cur_p, min_keep); id = llama_sample_token(ctx_main, &cur_p); diff --git a/common/sampling.h b/common/sampling.h index e1279a8941ce0..95d8753942b40 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -22,6 +22,7 @@ enum class llama_sampler_type : char { typedef struct llama_sampling_params { int32_t n_prev = 64; // number of previous tokens to remember int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled float min_p = 0.05f; // 0.0 = disabled diff --git a/examples/server/README.md b/examples/server/README.md index ac5133d2404cb..809e2d37c8732 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -199,6 +199,8 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum (default: 0) + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) diff --git a/examples/server/public/index.html b/examples/server/public/index.html index b059c75f2da66..84038ddcefb43 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -234,6 +234,7 @@ mirostat_eta: 0.1, // learning rate grammar: '', n_probs: 0, // no completion_probabilities, + min_keep: 0, // min probs from each sampler, image_data: [], cache_prompt: true, api_key: '' @@ -791,6 +792,9 @@
${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
+
+ ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} +
diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 4f2e9c898b391..22c344dd45544 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -548,6 +548,7 @@ struct llama_server_context slot->params.seed = json_value(data, "seed", default_params.seed); slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot->sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) { // Might be better to reject the request with a 400 ? @@ -1093,6 +1094,7 @@ struct llama_server_context {"stream", slot.params.stream}, {"logit_bias", slot.sparams.logit_bias}, {"n_probs", slot.sparams.n_probs}, + {"min_keep", slot.sparams.min_keep}, {"grammar", slot.sparams.grammar}, {"samplers", samplers_sequence} }; From 7ad554f90e735cf2a0f612ce44f9aa4fad6ae46a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 21:39:58 +0200 Subject: [PATCH 017/401] metal : fix unused warnings (#0) --- ggml-metal.metal | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index a009621113893..d0a85a1925c68 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -4027,7 +4027,10 @@ void kernel_mul_mv_iq2_xxs_f32_impl( y4 += 32 * 32; } #else - // TODO + (void) x; + (void) y; + (void) yl; + (void) nb32; #endif for (int row = 0; row < N_DST; ++row) { @@ -4170,7 +4173,10 @@ void kernel_mul_mv_iq2_xs_f32_impl( y4 += 32 * 32; } #else - // TODO + (void) x; + (void) y; + (void) yl; + (void) nb32; #endif for (int row = 0; row < N_DST; ++row) { @@ -4306,7 +4312,10 @@ void kernel_mul_mv_iq3_xxs_f32_impl( y4 += 32 * 32; } #else - // TODO + (void) x; + (void) y; + (void) yl; + (void) nb32; #endif for (int row = 0; row < N_DST; ++row) { @@ -4424,7 +4433,10 @@ void kernel_mul_mv_iq1_s_f32_impl( y4 += 16 * 32; } #else - // TODO + (void) x; + (void) y; + (void) yl; + (void) nb32; #endif for (int row = 0; row < N_DST; ++row) { @@ -4659,6 +4671,8 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg const float dl = d * sc[0]; const float ml = min * sc[1]; #else + (void) get_scale_min_k4_just2; + q = q + 16 * (il&1); device const uint8_t * s = xb->scales; device const half2 * dh = (device const half2 *)xb->d; From b1de96824bdbeb91ea458abcb3e5478690ad0727 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 22:39:30 +0200 Subject: [PATCH 018/401] ci : fix wikitext url + compile warnings (#5569) ggml-ci --- README.md | 2 +- ci/run.sh | 4 ++-- examples/perplexity/perplexity.cpp | 4 ++-- ggml-quants.c | 6 +++--- scripts/get-wikitext-2.sh | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 0c4ee5a27470a..8c7bc2689c647 100644 --- a/README.md +++ b/README.md @@ -768,7 +768,7 @@ The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 thread #### How to run -1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research +1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip 2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` 3. Output: ``` diff --git a/ci/run.sh b/ci/run.sh index b94658c96c5a4..f3a29c2e9be0b 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -219,7 +219,7 @@ function gg_run_open_llama_3b_v2 { gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json - gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw @@ -401,7 +401,7 @@ function gg_run_open_llama_7b_v2 { gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json - gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ path_models="../models-mnt/open-llama/7B-v2" diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 74dcc642a876e..9ec989389cfad 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -309,7 +309,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens } static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { - // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research + // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval @@ -447,7 +447,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par return perplexity_v2(ctx, params); } - // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research + // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval diff --git a/ggml-quants.c b/ggml-quants.c index 48f5294e1e788..43a8f1de4dac6 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1837,9 +1837,9 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri float sigma2 = sumx2/QK_K; for (int j = 0; j < QK_K/16; ++j) { const float * restrict qw = quant_weights + QK_K * i + 16*j; - for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); - for (int l = 0; l < 16; ++l) sw[j] += weight[l]; - scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + for (int l = 0; l < QK_K/16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); + for (int l = 0; l < QK_K/16; ++l) sw[j] += weight[l]; + scales[j] = make_qkx3_quants(QK_K/16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); } float dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh index ff96f331e026e..7ca760fa61304 100755 --- a/scripts/get-wikitext-2.sh +++ b/scripts/get-wikitext-2.sh @@ -1,6 +1,6 @@ #!/bin/bash -wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip +wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip echo "Usage:" echo "" From 14278f55d2e2c6a53022075c7f2719b71e1cd61d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Feb 2024 22:58:57 +0200 Subject: [PATCH 019/401] ggml : restore vec dot stride arg names (#5453) --- ggml-quants.c | 76 +++++++++++++++++++++++++-------------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 43a8f1de4dac6..3319d2ccf1812 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3855,7 +3855,7 @@ static inline __m128i get_scale_shuffle(int i) { } #endif -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -3866,8 +3866,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bbx); - UNUSED(bby); + UNUSED(bx); + UNUSED(by); UNUSED(bs); const block_q4_0 * restrict x = vx; @@ -4024,15 +4024,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs); - __m128i bx = _mm_and_si128(lowMask, tmp); - __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs); - bx = _mm_sub_epi8(bx, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx, by); + __m128i bx_0 = _mm_and_si128(lowMask, tmp); + __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4)); - by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); - bx = _mm_sub_epi8(bx, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx, by); + bx_0 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4)); + by_0 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_0, by_0); // Convert int32_t to float __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1)); @@ -4222,7 +4222,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { +void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_1; const int nb = n / qk; @@ -4233,8 +4233,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bbx); - UNUSED(bby); + UNUSED(bx); + UNUSED(by); UNUSED(bs); const block_q4_1 * restrict x = vx; @@ -4440,7 +4440,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { +void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -4448,8 +4448,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(qk == QK5_0); assert(nrc == 1); UNUSED(nrc); - UNUSED(bbx); - UNUSED(bby); + UNUSED(bx); + UNUSED(by); UNUSED(bs); const block_q5_0 * restrict x = vx; @@ -4618,21 +4618,21 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bx_0 = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); __m128i bxhil = _mm256_castsi256_si128(bxhi); __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); bxhil = _mm_andnot_si128(bxhil, mask); bxhih = _mm_andnot_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx); - __m128i bxh = _mm256_extractf128_si256(bx, 1); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); bxl = _mm_or_si128(bxl, bxhil); bxh = _mm_or_si128(bxh, bxhih); - bx = MM256_SET_M128I(bxh, bxl); + bx_0 = MM256_SET_M128I(bxh, bxl); - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_i8_pairs_float(bx, by); + const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0); /* Multiply q with scale and accumulate */ acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); @@ -4731,7 +4731,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_1; const int nb = n / qk; @@ -4739,8 +4739,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r assert(qk == QK5_1); assert(nrc == 1); UNUSED(nrc); - UNUSED(bbx); - UNUSED(bby); + UNUSED(bx); + UNUSED(by); UNUSED(bs); const block_q5_1 * restrict x = vx; @@ -4925,22 +4925,22 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; - __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bx_0 = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); __m128i bxhil = _mm256_castsi256_si128(bxhi); __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); bxhil = _mm_and_si128(bxhil, mask); bxhih = _mm_and_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx); - __m128i bxh = _mm256_extractf128_si256(bx, 1); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); bxl = _mm_or_si128(bxl, bxhil); bxh = _mm_or_si128(bxh, bxhih); - bx = MM256_SET_M128I(bxh, bxl); + bx_0 = MM256_SET_M128I(bxh, bxl); const __m256 dy = _mm256_set1_ps(y[i].d); - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_us8_pairs_float(bx, by); + const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); } @@ -5035,7 +5035,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r #endif } -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bbx, const void * restrict vy, size_t bby, int nrc) { +void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -5046,8 +5046,8 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r assert(nrc == 1); #endif UNUSED(nrc); - UNUSED(bbx); - UNUSED(bby); + UNUSED(bx); + UNUSED(by); UNUSED(bs); const block_q8_0 * restrict x = vx; @@ -5169,10 +5169,10 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; i++) { // load elements - vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl); - vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[i].qs, vl); + vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[i].qs, vl); - vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl); + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl); vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); From a0c2dad9d43456c677e205c6240a5f8afb0121ac Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sun, 18 Feb 2024 16:21:52 -0500 Subject: [PATCH 020/401] build : pass all warning flags to nvcc via -Xcompiler (#5570) * build : pass all warning flags to nvcc via -Xcompiler * make : fix apparent mis-merge from #3952 * make : fix incorrect GF_CC_VER for CUDA host compiler --- CMakeLists.txt | 11 ++++++----- Makefile | 28 ++-------------------------- scripts/get-flags.mk | 2 +- 3 files changed, 9 insertions(+), 32 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 0c29b5d09bd2b..f5e7f2980b04e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -778,10 +778,7 @@ endif() set(CUDA_CXX_FLAGS "") if (LLAMA_CUBLAS) - set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math) - if (NOT MSVC) - list(APPEND CUDA_FLAGS -Wno-pedantic) - endif() + set(CUDA_FLAGS -use_fast_math) if (LLAMA_ALL_WARNINGS AND NOT MSVC) set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) @@ -814,7 +811,11 @@ if (LLAMA_CUBLAS) message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") get_flags(${CUDA_CCID} ${CUDA_CCVER}) - list(APPEND CUDA_CXX_FLAGS ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later + list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later + endif() + + if (NOT MSVC) + list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) endif() endif() diff --git a/Makefile b/Makefile index 90179860623d0..f5f6d32a76735 100644 --- a/Makefile +++ b/Makefile @@ -220,30 +220,6 @@ ifeq ($(LLAMA_FATAL_WARNINGS),1) MK_CXXFLAGS += -Werror endif -ifeq ($(CC_IS_CLANG), 1) - # clang options - MK_CFLAGS += -Wunreachable-code-break -Wunreachable-code-return - MK_HOST_CXXFLAGS += -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi - - ifneq '' '$(and $(CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 030800)))' - MK_CFLAGS += -Wdouble-promotion - endif - ifneq '' '$(and $(CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 070300)))' - MK_CFLAGS += -Wdouble-promotion - endif -else - # gcc options - MK_CFLAGS += -Wdouble-promotion - MK_HOST_CXXFLAGS += -Wno-array-bounds - - ifeq ($(shell expr $(CC_VER) \>= 070100), 1) - MK_HOST_CXXFLAGS += -Wno-format-truncation - endif - ifeq ($(shell expr $(CC_VER) \>= 080100), 1) - MK_HOST_CXXFLAGS += -Wextra-semi - endif -endif - # this version of Apple ld64 is buggy ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))' MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER @@ -468,7 +444,7 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ifdef JETSON_EOL_MODULE_DETECT $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ else - $(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ + $(NVCC) $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endif # JETSON_EOL_MODULE_DETECT endif # LLAMA_CUBLAS @@ -579,7 +555,7 @@ override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) ifdef LLAMA_CUBLAS GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler include scripts/get-flags.mk -CUDA_CXXFLAGS := $(GF_CXXFLAGS) +CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic endif # diff --git a/scripts/get-flags.mk b/scripts/get-flags.mk index 596d7ead18043..a742766d1495e 100644 --- a/scripts/get-flags.mk +++ b/scripts/get-flags.mk @@ -1,6 +1,6 @@ ifeq '' '$(findstring clang,$(shell $(GF_CC) --version))' GF_CC_IS_GCC = 1 - GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null || $(GF_CC) -dumpversion; } | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }') + GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null; echo; $(GF_CC) -dumpversion; } | awk -F. '/./ { printf("%02d%02d%02d", $$1, $$2, $$3); exit }') else GF_CC_IS_CLANG = 1 ifeq '' '$(findstring Apple,$(shell $(GF_CC) --version))' From f0d1fafc029a056cd765bdae58dcaa12312e9879 Mon Sep 17 00:00:00 2001 From: bmwl Date: Sun, 18 Feb 2024 23:38:32 -0800 Subject: [PATCH 021/401] ggml : android and old glibc NUMA incompatibility bugfixes (#5557) * #ifdef out some code NUMA blocks for Android due to lack of support * added in some __ANDROID__ if def gates around numa code and forced GLIBC prior to 2.29 to use a syscall for getcpu instead of the wrapper * Changed gates on numa platform specific stuff to __gnu_linux__ to skip any platforms without glibc * harmonizing #if defined blocks for numa code to __gnu_linux__ since that's the only model that's being followed anyways --------- Co-authored-by: root --- ggml.c | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/ggml.c b/ggml.c index 8224652a9cc50..4ee2c5e11a002 100644 --- a/ggml.c +++ b/ggml.c @@ -23,6 +23,9 @@ #include #include #include +#if defined(__gnu_linux__) +#include +#endif #ifdef GGML_USE_METAL #include @@ -1971,7 +1974,7 @@ struct ggml_numa_nodes { uint32_t n_nodes; uint32_t total_cpus; // hardware threads on system uint32_t current_node; // node on which main process is execting -#ifdef __linux__ +#if defined(__gnu_linux__) cpu_set_t cpuset; // cpuset from numactl #else uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype @@ -2009,7 +2012,7 @@ inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } -#ifdef __linux__ +#if defined(__gnu_linux__) static cpu_set_t ggml_get_numa_affinity(void) { cpu_set_t cpuset; pthread_t thread; @@ -2031,7 +2034,7 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { return; } -#ifdef __linux__ +#if defined(__gnu_linux__) struct stat st; char path[256]; int rv; @@ -2063,7 +2066,13 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { // figure out which node we're on uint current_cpu; - int getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); + int getcpu_ret = 0; +#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) + getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); +#else + // old glibc doesn't have a wrapper for this call. Fall back on direct syscall + getcpu_ret = syscall(SYS_getcpu,¤t_cpu,&g_state.numa.current_node); +#endif if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { g_state.numa.n_nodes = 0; @@ -16734,7 +16743,7 @@ typedef pthread_t ggml_thread_t; #endif // Android's libc implementation "bionic" does not support setting affinity -#if defined(__linux__) && !defined(__BIONIC__) +#if defined(__gnu_linux__) static void set_numa_thread_affinity(int thread_n) { if (!ggml_is_numa()) { return; From 769a716e30ba1da46f709df1c00727d6869d30e7 Mon Sep 17 00:00:00 2001 From: Mirko185 Date: Mon, 19 Feb 2024 08:39:31 +0100 Subject: [PATCH 022/401] readme : update (#5572) Added 1.5-bit on README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8c7bc2689c647..70866e2490218 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ variety of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2 and AVX512 support for x86 architectures -- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use +- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) - Vulkan, SYCL, and (partial) OpenCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity From 3a9cb4ca6408c29423373dd6cd7aa78a58286c00 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 19 Feb 2024 09:04:45 +0100 Subject: [PATCH 023/401] cuda, metal : fix nans in soft_max (#5574) * cuda : fix nans in soft_max * metal : fix nans in soft_max --------- Co-authored-by: Georgi Gerganov --- ggml-cuda.cu | 8 ++++---- ggml-metal.metal | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 933ebbc4eff72..eef2135097881 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6205,7 +6205,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + slope*pos[col]; + const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); vals[col] = val; max_val = max(max_val, val); @@ -9170,17 +9170,17 @@ static void ggml_cuda_op_soft_max( memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // positions tensor - float * src2_dd = dst_dd; // default to avoid null checks in the kernel + float * src2_dd = nullptr; cuda_pool_alloc src2_f; ggml_tensor * src2 = dst->src[2]; const bool use_src2 = src2 != nullptr; if (use_src2) { - const bool src2_on_device = use_src2 && src2->backend == GGML_BACKEND_GPU; - ggml_tensor_extra_gpu * src2_extra = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; + const bool src2_on_device = src2->backend == GGML_BACKEND_GPU; if (src2_on_device) { + ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; src2_dd = (float *) src2_extra->data_device[g_main_device]; } else { src2_dd = src2_f.alloc(ggml_nelements(src2)); diff --git a/ggml-metal.metal b/ggml-metal.metal index d0a85a1925c68..f0d77d446755c 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -392,7 +392,7 @@ kernel void kernel_soft_max( float lmax = -INFINITY; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]); + lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)); } // find the max value in the block @@ -417,7 +417,7 @@ kernel void kernel_soft_max( // parallel sum float lsum = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]) - max_val); + const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val); lsum += exp_psrc0; pdst[i00] = exp_psrc0; } @@ -495,7 +495,7 @@ kernel void kernel_soft_max_4( float4 lmax4 = -INFINITY; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]); + lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)); } const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); @@ -521,7 +521,7 @@ kernel void kernel_soft_max_4( // parallel sum float4 lsum4 = 0.0f; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + slope*ppos[i00]) - max_val); + const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } From 11b12de39bd787c0494da0cd405958fdfedc29c4 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 19 Feb 2024 09:23:37 +0100 Subject: [PATCH 024/401] llama : add llama_chat_apply_template() (#5538) * llama: add llama_chat_apply_template * test-chat-template: remove dedundant vector * chat_template: do not use std::string for buffer * add clarification for llama_chat_apply_template * llama_chat_apply_template: add zephyr template * llama_chat_apply_template: correct docs * llama_chat_apply_template: use term "chat" everywhere * llama_chat_apply_template: change variable name to "tmpl" --- Makefile | 4 ++ llama.cpp | 117 +++++++++++++++++++++++++++++++++++ llama.h | 25 ++++++++ tests/CMakeLists.txt | 1 + tests/test-chat-template.cpp | 64 +++++++++++++++++++ 5 files changed, 211 insertions(+) create mode 100644 tests/test-chat-template.cpp diff --git a/Makefile b/Makefile index f5f6d32a76735..59352eb532597 100644 --- a/Makefile +++ b/Makefile @@ -867,3 +867,7 @@ tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o te tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-chat-template: tests/test-chat-template.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/llama.cpp b/llama.cpp index 5cfebb3b1c99c..143870645d637 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12508,6 +12508,123 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token return 0; } +// trim whitespace from the beginning and end of a string +static std::string trim(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && isspace(str[start])) { + start += 1; + } + while (end > start && isspace(str[end - 1])) { + end -= 1; + } + return str.substr(start, end - start); +} + +// Simple version of "llama_apply_chat_template" that only works with strings +// This function uses heuristic checks to determine commonly used template. It is not a jinja parser. +static int32_t llama_chat_apply_template_internal( + const std::string & tmpl, + const std::vector & chat, + std::string & dest, bool add_ass) { + // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 + std::stringstream ss; + if (tmpl.find("<|im_start|>") != std::string::npos) { + // chatml template + for (auto message : chat) { + ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n"; + } + if (add_ass) { + ss << "<|im_start|>assistant\n"; + } + } else if (tmpl.find("[INST]") != std::string::npos) { + // llama2 template and its variants + // [variant] support system message + bool support_system_message = tmpl.find("<>") != std::string::npos; + // [variant] space before + after response + bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos; + // [variant] add BOS inside history + bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos; + // [variant] trim spaces from the input message + bool strip_message = tmpl.find("content.strip()") != std::string::npos; + // construct the prompt + bool is_inside_turn = true; // skip BOS at the beginning + ss << "[INST] "; + for (auto message : chat) { + std::string content = strip_message ? trim(message->content) : message->content; + std::string role(message->role); + if (!is_inside_turn) { + is_inside_turn = true; + ss << (add_bos_inside_history ? "[INST] " : "[INST] "); + } + if (role == "system") { + if (support_system_message) { + ss << "<>\n" << content << "\n<>\n\n"; + } else { + // if the model does not support system message, we still include it in the first message, but without <> + ss << content << "\n"; + } + } else if (role == "user") { + ss << content << " [/INST]"; + } else { + ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << ""; + is_inside_turn = false; + } + } + // llama2 templates seem to not care about "add_generation_prompt" + } else if (tmpl.find("<|user|>") != std::string::npos) { + // zephyr template + for (auto message : chat) { + ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } + } else { + // template not supported + return -1; + } + dest = ss.str(); + return dest.size(); +} + +LLAMA_API int32_t llama_chat_apply_template( + const struct llama_model * model, + const char * tmpl, + const struct llama_chat_message * chat, + size_t n_msg, + bool add_ass, + char * buf, + int32_t length) { + std::string curr_tmpl(tmpl == nullptr ? "" : tmpl); + if (tmpl == nullptr) { + GGML_ASSERT(model != nullptr); + // load template from model + std::vector model_template(2048, 0); // longest known template is about 1200 bytes + std::string template_key = "tokenizer.chat_template"; + int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), curr_tmpl.size()); + if (res < 0) { + // worst case: there is no information about template, we will use chatml by default + curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal + } else { + curr_tmpl = std::string(model_template.data(), model_template.size()); + } + } + // format the chat to string + std::vector chat_vec; + chat_vec.resize(n_msg); + for (size_t i = 0; i < n_msg; i++) { + chat_vec[i] = &chat[i]; + } + std::string formatted_chat; + int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass); + if (res < 0) { + return res; + } + strncpy(buf, formatted_chat.c_str(), length); + return res; +} + struct llama_timings llama_get_timings(struct llama_context * ctx) { struct llama_timings result = { /*.t_start_ms =*/ 1e-3 * ctx->t_start_us, diff --git a/llama.h b/llama.h index 5a97abcc94d7c..77a84c18a69cf 100644 --- a/llama.h +++ b/llama.h @@ -305,6 +305,12 @@ extern "C" { int32_t n_eval; }; + // used in chat template + typedef struct llama_chat_message { + const char * role; + const char * content; + } llama_chat_message; + // Helpers for getting default parameters LLAMA_API struct llama_model_params llama_model_default_params(void); LLAMA_API struct llama_context_params llama_context_default_params(void); @@ -699,6 +705,25 @@ extern "C" { char * buf, int32_t length); + /// Apply chat template. Inspired by hf apply_chat_template() on python. + /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" + /// NOTE: This function only support some known jinja templates. It is not a jinja parser. + /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead. + /// @param chat Pointer to a list of multiple llama_chat_message + /// @param n_msg Number of llama_chat_message in this chat + /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message. + /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages) + /// @param length The size of the allocated buffer + /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template. + LLAMA_API int32_t llama_chat_apply_template( + const struct llama_model * model, + const char * tmpl, + const struct llama_chat_message * chat, + size_t n_msg, + bool add_ass, + char * buf, + int32_t length); + // // Grammar // diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 3e40a78cdeac9..10326d531e9fb 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -28,6 +28,7 @@ endfunction() llama_build_and_test_executable(test-quantize-fns.cpp) llama_build_and_test_executable(test-quantize-perf.cpp) llama_build_and_test_executable(test-sampling.cpp) +llama_build_and_test_executable(test-chat-template.cpp) llama_build_executable(test-tokenizer-0-llama.cpp) llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp new file mode 100644 index 0000000000000..9830650d4f8dd --- /dev/null +++ b/tests/test-chat-template.cpp @@ -0,0 +1,64 @@ +#include +#include +#include +#include + +#undef NDEBUG +#include + +#include "llama.h" + +int main(void) { + llama_chat_message conversation[] = { + {"system", "You are a helpful assistant"}, + {"user", "Hello"}, + {"assistant", "Hi there"}, + {"user", "Who are you"}, + {"assistant", " I am an assistant "}, + {"user", "Another question"}, + }; + size_t message_count = 6; + std::vector templates = { + // teknium/OpenHermes-2.5-Mistral-7B + "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}", + // mistralai/Mistral-7B-Instruct-v0.2 + "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", + // TheBloke/FusionNet_34Bx2_MoE-AWQ + "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <>\\\\n' + messages[idx]['content'] + '\\\\n<>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}", + // bofenghuang/vigogne-2-70b-chat + "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\\\n' + system_message + '\\\\n<>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\\\\n' + content.strip() + '\\\\n<>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}", + }; + std::vector expected_substr = { + "<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant", + "[/INST]Hi there[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + "[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + "[/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + }; + std::vector formatted_chat(1024); + int32_t res; + + // test invalid chat template + res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size()); + assert(res < 0); + + for (size_t i = 0; i < templates.size(); i++) { + std::string custom_template = templates[i]; + std::string substr = expected_substr[i]; + formatted_chat.resize(1024); + res = llama_chat_apply_template( + nullptr, + custom_template.c_str(), + conversation, + message_count, + true, + formatted_chat.data(), + formatted_chat.size() + ); + formatted_chat.resize(res); + std::string output(formatted_chat.data(), formatted_chat.size()); + std::cout << output << "\n-------------------------\n"; + // expect the "formatted_chat" to contain pre-defined strings + assert(output.find(substr) != std::string::npos); + } + return 0; +} From 4480542b2271ba1438f0daff8e5f3a74b1dc8609 Mon Sep 17 00:00:00 2001 From: NawafAlansari <72708095+NawafAlansari@users.noreply.github.com> Date: Mon, 19 Feb 2024 03:25:38 -0500 Subject: [PATCH 025/401] baby-llama : allocate graphs in ggml_context (#5573) * Fixed the baby-llama issue (see issue #4830) * minor : fix whitespaces --------- Co-authored-by: Georgi Gerganov --- examples/baby-llama/baby-llama.cpp | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index e7d2ad592e4c9..65bb238a0d565 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -1533,16 +1533,17 @@ int main(int argc, char ** argv) { int n_past = 0; - ggml_cgraph gf = {}; + struct ggml_cgraph * gf = NULL; + gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true); get_example_targets_batch(ctx0, 64*ex+0, tokens_input, targets); - struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, &gf, tokens_input, n_tokens, n_past, n_batch); + struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, gf, tokens_input, n_tokens, n_past, n_batch); // struct ggml_tensor * e = cross_entropy_loss(ctx0, targets, logits); struct ggml_tensor * e = square_error_loss(ctx0, targets, logits); - ggml_build_forward_expand(&gf, e); - ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, e); + ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1); float error_before_opt = ggml_get_f32_1d(e, 0); @@ -1552,8 +1553,8 @@ int main(int argc, char ** argv) { opt_params_lbfgs.lbfgs.n_iter = 16; ggml_opt(ctx0, opt_params_lbfgs, e); // - ggml_build_forward_expand(&gf, e); - ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, e); + ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1); float error_after_opt = ggml_get_f32_1d(e, 0); @@ -1600,13 +1601,14 @@ int main(int argc, char ** argv) { }; struct ggml_context * ctx0 = ggml_init(params); - ggml_cgraph gf = {}; + struct ggml_cgraph * gf = NULL; + gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true); int n_past = 0; - struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past); + struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, gf, tokens_input, sample_ctx, n_past); - ggml_build_forward_expand(&gf, logits); - ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, logits); + ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1); struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx); struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx); From 70847553963c85e86051d06df848236829f5f951 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 19 Feb 2024 09:31:59 +0100 Subject: [PATCH 026/401] llava : avoid changing the original BakLLaVA model (#5577) This is a follup of Commit fc0c8d286a533363a9a663510b62af85ffad58b3 ("llava : update surgery script to not remove tensors") but this time the change is to the BakLLaVA specific part of the surgery script. I've been able to test this using SkunkworksAI/BakLLaVA-1 and it works as expected using the instructions in README.md. Signed-off-by: Daniel Bevenius --- examples/llava/llava-surgery.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py index 8b7a62fbaa145..4f2da3beefff6 100644 --- a/examples/llava/llava-surgery.py +++ b/examples/llava/llava-surgery.py @@ -25,9 +25,6 @@ clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors} torch.save(clip, f"{args.model}/llava.clip") - # remove these tensors - for name in clip_tensors: - del checkpoint[name] # added tokens should be removed to be able to convert Mistral models if os.path.exists(f"{args.model}/added_tokens.json"): @@ -35,7 +32,6 @@ f.write("{}\n") - torch.save(checkpoint, path) print("Done!") print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") From f53119cec4f073b6d214195ecbe1fad3abdf2b34 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 10:34:10 +0200 Subject: [PATCH 027/401] minor : fix trailing whitespace (#5538) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 143870645d637..5de07dfa999a7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12525,7 +12525,7 @@ static std::string trim(const std::string & str) { // This function uses heuristic checks to determine commonly used template. It is not a jinja parser. static int32_t llama_chat_apply_template_internal( const std::string & tmpl, - const std::vector & chat, + const std::vector & chat, std::string & dest, bool add_ass) { // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 std::stringstream ss; From 13e2c771aa4212cd5405cf310203848d50f7f859 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Mon, 19 Feb 2024 14:45:18 +0530 Subject: [PATCH 028/401] cmake : remove obsolete sycl compile flags (#5581) * rm unwanted sycl compile options * fix bug * fix bug * format fix --- CMakeLists.txt | 7 ------- 1 file changed, 7 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f5e7f2980b04e..40a098d01d21b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -741,13 +741,6 @@ function(get_flags CCID CCVER) if (CCVER VERSION_GREATER_EQUAL 8.1.0) list(APPEND CXX_FLAGS -Wextra-semi) endif() - elseif (CCID MATCHES "Intel") - if (NOT LLAMA_SYCL) - # enable max optimization level when using Intel compiler - set(C_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) - set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) - add_link_options(-fuse-ld=lld -static-intel) - endif() endif() set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE) From 70d45af0efce9ed360e1858b827989d971dd9caf Mon Sep 17 00:00:00 2001 From: valiray <133289098+valiray@users.noreply.github.com> Date: Mon, 19 Feb 2024 02:37:10 -0800 Subject: [PATCH 029/401] readme : fix typo in README-sycl.md (#5353) --- README-sycl.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README-sycl.md b/README-sycl.md index e3a8e726e6513..dd5bf9dead5bf 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -272,7 +272,7 @@ Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). -Recommend to install to default folder: **/opt/intel/oneapi**. +Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**. Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. From 68a6b98b3c8af7e5baade3ee45fe1d2c7b9323a9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 13:41:51 +0200 Subject: [PATCH 030/401] make : fix CUDA build (#5580) --- Makefile | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 59352eb532597..29fd2ca9c07fb 100644 --- a/Makefile +++ b/Makefile @@ -97,9 +97,10 @@ endif # # keep standard at C11 and C++11 -MK_CPPFLAGS = -I. -Icommon -MK_CFLAGS = -std=c11 -fPIC -MK_CXXFLAGS = -std=c++11 -fPIC +MK_CPPFLAGS = -I. -Icommon +MK_CFLAGS = -std=c11 -fPIC +MK_CXXFLAGS = -std=c++11 -fPIC +MK_NVCCFLAGS = -std=c++11 # -Ofast tends to produce faster code, but may not be available for some compilers. ifdef LLAMA_FAST From d0e3ce51f45bd6a646da1952d7e5d143a087db3e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 14:45:41 +0200 Subject: [PATCH 031/401] ci : enable -Werror for CUDA builds (#5579) * cmake : pass -Werror through -Xcompiler ggml-ci * make, cmake : enable CUDA errors on warnings ggml-ci --- CMakeLists.txt | 33 +++++++++++++++++++-------------- Makefile | 5 ++++- ggml-cuda.cu | 50 ++++++++++++++++++++++++++------------------------ 3 files changed, 49 insertions(+), 39 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 40a098d01d21b..168b133f4e1c1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -145,14 +145,6 @@ set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads REQUIRED) include(CheckCXXCompilerFlag) -if (LLAMA_FATAL_WARNINGS) - if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") - add_compile_options(-Werror) - elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") - add_compile_options(/WX) - endif() -endif() - # enable libstdc++ assertions for debug builds if (CMAKE_SYSTEM_NAME MATCHES "Linux") add_compile_definitions($<$:_GLIBCXX_ASSERTIONS>) @@ -747,15 +739,24 @@ function(get_flags CCID CCVER) set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE) endfunction() +if (LLAMA_FATAL_WARNINGS) + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + list(APPEND C_FLAGS -Werror) + list(APPEND CXX_FLAGS -Werror) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + add_compile_options(/WX) + endif() +endif() + if (LLAMA_ALL_WARNINGS) if (NOT MSVC) - set(WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function) - set(C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes - -Werror=implicit-int -Werror=implicit-function-declaration) - set(CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn) + list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function) + list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes + -Werror=implicit-int -Werror=implicit-function-declaration) + list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn) - set(C_FLAGS ${WARNING_FLAGS} ${C_FLAGS}) - set(CXX_FLAGS ${WARNING_FLAGS} ${CXX_FLAGS}) + list(APPEND C_FLAGS ${WARNING_FLAGS}) + list(APPEND CXX_FLAGS ${WARNING_FLAGS}) get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) @@ -773,6 +774,10 @@ set(CUDA_CXX_FLAGS "") if (LLAMA_CUBLAS) set(CUDA_FLAGS -use_fast_math) + if (LLAMA_FATAL_WARNINGS) + list(APPEND CUDA_FLAGS -Werror all-warnings) + endif() + if (LLAMA_ALL_WARNINGS AND NOT MSVC) set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") diff --git a/Makefile b/Makefile index 29fd2ca9c07fb..63b4af9ba3340 100644 --- a/Makefile +++ b/Makefile @@ -217,7 +217,7 @@ MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmis MK_CXXFLAGS += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn ifeq ($(LLAMA_FATAL_WARNINGS),1) - MK_CFLAGS += -Werror + MK_CFLAGS += -Werror MK_CXXFLAGS += -Werror endif @@ -385,6 +385,9 @@ ifdef LLAMA_CUBLAS MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o MK_NVCCFLAGS += -use_fast_math +ifdef LLAMA_FATAL_WARNINGS + MK_NVCCFLAGS += -Werror all-warnings +endif # LLAMA_FATAL_WARNINGS ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT diff --git a/ggml-cuda.cu b/ggml-cuda.cu index eef2135097881..e091dbdc1b32a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -651,18 +651,18 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { return a; } -static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); - } - return a; -#else - (void) a; - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -} +//static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +//#pragma unroll +// for (int mask = 16; mask > 0; mask >>= 1) { +// a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); +// } +// return a; +//#else +// (void) a; +// NO_DEVICE_CODE; +//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +//} static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll @@ -672,18 +672,18 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { return x; } -static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); - } - return x; -#else - (void) x; - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -} +//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { +//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +//#pragma unroll +// for (int mask = 16; mask > 0; mask >>= 1) { +// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); +// } +// return x; +//#else +// (void) x; +// NO_DEVICE_CODE; +//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +//} static __device__ __forceinline__ float op_repeat(const float a, const float b) { return b; @@ -4641,10 +4641,12 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); #else + (void) ksigns64; assert(false); return 0.f; #endif #else + (void) ksigns64; assert(false); return 0.f; #endif From 890559ab28e354052e16e770155ad007fd0856e8 Mon Sep 17 00:00:00 2001 From: Didzis Gosko Date: Sun, 11 Feb 2024 16:41:41 +0200 Subject: [PATCH 032/401] metal : option to embed MSL source into compiled binary (whisper/1842) * ggml : embed Metal library source (ggml-metal.metal) into binary enable by setting WHISPER_EMBED_METAL_LIBRARY * rename the build option * rename the preprocessor directive * generate Metal library embedding assembly on-fly during build process --- ggml-metal.m | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index f3c1fff8fed94..956e323a0d053 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -277,6 +277,14 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ return NULL; } } else { +#if GGML_METAL_EMBED_LIBRARY + GGML_METAL_LOG_INFO("%s: using embedded metal library\n", __func__); + + extern const char ggml_metallib_start[]; + extern const char ggml_metallib_end[]; + + NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding]; +#else GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); NSString * sourcePath; @@ -299,6 +307,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } +#endif @autoreleasepool { // dictionary of preprocessor macros From a3145bdc305422973e25f0b066da6f469ed5dc45 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 14:53:48 +0200 Subject: [PATCH 033/401] ggml-alloc : apply ggml/731 --- ggml-alloc.c | 112 ++++++++++++++++++++++++++++++++------------------- 1 file changed, 71 insertions(+), 41 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index d4123564ff0d1..e675306c8c3f1 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -377,6 +377,9 @@ struct ggml_gallocr { struct node_alloc * node_allocs; // [n_nodes] int n_nodes; + + struct tensor_alloc * leaf_allocs; // [n_leafs] + int n_leafs; }; ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) { @@ -427,6 +430,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) { free(galloc->buffers); free(galloc->buf_tallocs); free(galloc->node_allocs); + free(galloc->leaf_allocs); free(galloc); } @@ -464,7 +468,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor for (int i = 0; i < GGML_MAX_SRC; i++) { struct ggml_tensor * parent = node->src[i]; if (parent == NULL) { - break; + continue; } // if the node's data is external, then we cannot re-use it @@ -544,22 +548,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *)); memset(galloc->hash_values, 0, galloc->hash_set.size * sizeof(struct hash_node)); - // allocate all graph inputs first to avoid overwriting them - for (int i = 0; i < graph->n_nodes; i++) { - if (graph->nodes[i]->flags & GGML_TENSOR_FLAG_INPUT) { - ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i)); - } - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (graph->nodes[i]->src[j] == NULL) { - continue; - } - if (graph->nodes[i]->src[j]->flags & GGML_TENSOR_FLAG_INPUT) { - ggml_gallocr_allocate_node(galloc, graph->nodes[i]->src[j], get_node_buffer_id(node_buffer_ids, i)); - } - } - } - // count number of children and views + // allocate all graph inputs and leafs first to avoid overwriting them for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -568,14 +558,37 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr ggml_gallocr_hash_get(galloc, view_src)->n_views += 1; } + if (node->flags & GGML_TENSOR_FLAG_INPUT) { + ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i)); + } + for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + + ggml_gallocr_hash_get(galloc, src)->n_children += 1; + + // allocate explicit inputs and leafs + if (src->flags & GGML_TENSOR_FLAG_INPUT || src->op == GGML_OP_NONE) { + ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i)); } - ggml_gallocr_hash_get(galloc, parent)->n_children += 1; } - } + } + + // allocate the remaining leafs that are unused on the graph + // these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); + + if (hn->n_children == 0) { + assert(!hn->allocated); + // since buffer ids are only given for nodes, these leafs are always allocated in the first buffer + ggml_gallocr_allocate_node(galloc, leaf, 0); + } + } // allocate tensors for (int i = 0; i < graph->n_nodes; i++) { @@ -586,7 +599,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { - break; + continue; } ggml_gallocr_allocate_node(galloc, parent, buffer_id); } @@ -598,7 +611,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { - break; + continue; } AT_PRINTF("%s", parent->name); if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { @@ -611,7 +624,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { - break; + continue; } struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); p_hn->n_children -= 1; @@ -696,6 +709,18 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c } } } + if (galloc->n_leafs < graph->n_leafs) { + free(galloc->leaf_allocs); + galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs); + GGML_ASSERT(galloc->leaf_allocs != NULL); + } + galloc->n_leafs = graph->n_leafs; + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); + galloc->leaf_allocs[i].offset = hn->offset; + galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf); + } // reallocate buffers if needed for (int i = 0; i < galloc->n_buffers; i++) { @@ -722,8 +747,8 @@ bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { return ggml_gallocr_reserve_n(galloc, graph, NULL); } -static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * node_alloc, struct tensor_alloc * tensor_alloc) { - assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[node_alloc->buffer_id], node) <= tensor_alloc->size_max); +static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) { + assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max); if (node->view_src != NULL) { if (node->buffer == NULL) { @@ -732,29 +757,20 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * // this tensor was allocated without ggml-backend return; } - ggml_backend_view_init(galloc->buffers[node_alloc->buffer_id], node); + ggml_backend_view_init(galloc->buffers[buffer_id], node); } } else { if (node->data == NULL) { assert(tensor_alloc->offset != SIZE_MAX); - assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[node_alloc->buffer_id], node) <= tensor_alloc->size_max); - void * base = ggml_backend_buffer_get_base(galloc->buffers[node_alloc->buffer_id]); + assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max); + void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]); void * addr = (char *)base + tensor_alloc->offset; - ggml_backend_tensor_alloc(galloc->buffers[node_alloc->buffer_id], node, addr); + ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr); } else { if (node->buffer == NULL) { // this tensor was allocated without ggml-backend return; } - -#ifndef NDEBUG - size_t offset = - (char *)node->data - - (char *)ggml_backend_buffer_get_base(node->buffer); - size_t size = ggml_backend_buffer_get_alloc_size(node->buffer, node); - assert(tensor_alloc->offset == SIZE_MAX || offset == tensor_alloc->offset); - assert(tensor_alloc->offset == SIZE_MAX || size <= tensor_alloc->size_max); -#endif } } } @@ -773,6 +789,13 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph return true; } + if (galloc->n_leafs != graph->n_leafs) { +#ifndef NDEBUG + fprintf(stderr, "%s: graph has different number of leafs\n", __func__); +#endif + return true; + } + for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; struct node_alloc * node_alloc = &galloc->node_allocs[i]; @@ -827,6 +850,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) } // allocate the graph tensors from the previous assignments + // nodes for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; struct node_alloc * node_alloc = &galloc->node_allocs[i]; @@ -835,9 +859,15 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) if (src == NULL) { continue; } - ggml_gallocr_init_tensor(galloc, src, node_alloc, &node_alloc->src[j]); + ggml_gallocr_init_tensor(galloc, src, node_alloc->buffer_id, &node_alloc->src[j]); } - ggml_gallocr_init_tensor(galloc, node, node_alloc, &node_alloc->dst); + ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst); + } + // leafs + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i]; + ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc); } return true; From 337c9cbd52918ae5fb9a9d9e25d7fae4e238c9f1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 14:54:21 +0200 Subject: [PATCH 034/401] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 7a23ab162eb52..733d8f95b93df 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -5070f078a67c18c11736e78316ab715ca9afde16 +818eeb8a3be99125746a90ec63af8f51516a2ec6 From 6fd413791a754598a54a366145960f2e27eec015 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 19 Feb 2024 14:02:36 +0100 Subject: [PATCH 035/401] llava : replace ggml_cpy with ggml_cont --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 98d512f67a0e2..1a02fde322334 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -618,7 +618,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3)); - cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size)); + cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size); } // attention output From 1387cf60f758efb218fa06b670182c38ff149b7b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Feb 2024 15:23:17 +0200 Subject: [PATCH 036/401] llava : remove extra cont (#5587) --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 1a02fde322334..ef9e4ba7a6b5a 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -616,7 +616,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 KQ = ggml_soft_max_inplace(ctx0, KQ); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); - KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3)); + KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size); } From 9d679f0fccd4030779ed3c7684a40122fe41806c Mon Sep 17 00:00:00 2001 From: nopperl <54780682+nopperl@users.noreply.github.com> Date: Mon, 19 Feb 2024 14:14:07 +0000 Subject: [PATCH 037/401] examples : support minItems/maxItems in JSON grammar converter (#5039) * support minLength and maxLength in JSON schema grammar converter * Update examples/json-schema-to-grammar.py --------- Co-authored-by: Georgi Gerganov --- examples/json-schema-to-grammar.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py index 2a4cb65bcfc7e..6a977f031662e 100755 --- a/examples/json-schema-to-grammar.py +++ b/examples/json-schema-to-grammar.py @@ -87,7 +87,21 @@ def visit(self, schema, name): elif schema_type == 'array' and 'items' in schema: # TODO `prefixItems` keyword item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item') - rule = f'"[" space ({item_rule_name} ("," space {item_rule_name})*)? "]" space' + list_item_operator = f'("," space {item_rule_name})' + successive_items = "" + min_items = schema.get("minItems", 0) + if min_items > 0: + first_item = f"({item_rule_name})" + successive_items = list_item_operator * (min_items - 1) + min_items -= 1 + else: + first_item = f"({item_rule_name})?" + max_items = schema.get("maxItems") + if max_items is not None and max_items > min_items: + successive_items += (list_item_operator + "?") * (max_items - min_items - 1) + else: + successive_items += list_item_operator + "*" + rule = f'"[" space {first_item} {successive_items} "]" space' return self._add_rule(rule_name, rule) else: From f24ed14ee0ce28dfe98115c378b37da144912016 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Mon, 19 Feb 2024 15:54:12 -0500 Subject: [PATCH 038/401] make : pass CPPFLAGS directly to nvcc, not via -Xcompiler (#5598) --- Makefile | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index 63b4af9ba3340..db5df1b328560 100644 --- a/Makefile +++ b/Makefile @@ -446,9 +446,9 @@ ifdef LLAMA_CUDA_CCBIN endif ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ifdef JETSON_EOL_MODULE_DETECT - $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ + $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ else - $(NVCC) $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ + $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endif # JETSON_EOL_MODULE_DETECT endif # LLAMA_CUBLAS @@ -549,9 +549,10 @@ GF_CC := $(CC) include scripts/get-flags.mk # combine build flags with cmdline overrides -override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS) -BASE_CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS) -override CXXFLAGS := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS) +override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) +override CFLAGS := $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS) +BASE_CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS) +override CXXFLAGS := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS) $(CPPFLAGS) override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS) override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) From 40c3a6c1e11040088b4a1ce0abc4651cb3011dd4 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 19 Feb 2024 23:40:26 +0100 Subject: [PATCH 039/401] cuda : ignore peer access already enabled errors (#5597) * cuda : ignore peer access already enabled errors * fix hip --- ggml-cuda.cu | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e091dbdc1b32a..6caae56b0244b 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -54,6 +54,8 @@ #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t +#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled +#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled #define cudaEventCreateWithFlags hipEventCreateWithFlags #define cudaEventDisableTiming hipEventDisableTiming #define cudaEventRecord hipEventRecord @@ -9325,9 +9327,15 @@ static void ggml_cuda_set_peer_access(const int n_tokens) { CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other)); if (can_access_peer) { if (enable_peer_access) { - CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0)); + cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); + if (err != cudaErrorPeerAccessAlreadyEnabled) { + CUDA_CHECK(err); + } } else { - CUDA_CHECK(cudaDeviceDisablePeerAccess(id_other)); + cudaError_t err = cudaDeviceDisablePeerAccess(id_other); + if (err != cudaErrorPeerAccessNotEnabled) { + CUDA_CHECK(err); + } } } } @@ -10999,10 +11007,10 @@ GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backe UNUSED(buffer); } -// unused at the moment -//static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { -// return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; -//} +static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; + UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds +} GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; @@ -11390,7 +11398,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT); - assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); + assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); assert(node->src[j]->extra != nullptr); } } From 5dde5408978eda22242b87e22e306d1c2d1a5834 Mon Sep 17 00:00:00 2001 From: Mathijs de Bruin Date: Sat, 3 Feb 2024 17:56:46 +0000 Subject: [PATCH 040/401] Allow for Vulkan build with Accelerate. Closes #5304 --- ggml.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml.c b/ggml.c index 4ee2c5e11a002..d129df50548a3 100644 --- a/ggml.c +++ b/ggml.c @@ -273,6 +273,8 @@ inline static void * ggml_calloc(size_t num, size_t size) { #include #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions #include "ggml-opencl.h" +#elif defined(GGML_USE_VULKAN) +#include "ggml-vulkan.h" #endif #elif defined(GGML_USE_OPENBLAS) #if defined(GGML_BLAS_USE_MKL) From 42f664a3825dfde13a32c3577ab66d10c56f3aa6 Mon Sep 17 00:00:00 2001 From: Mathijs de Bruin Date: Sat, 3 Feb 2024 18:00:11 +0000 Subject: [PATCH 041/401] Resolve ErrorIncompatibleDriver with Vulkan on MacOS. Refs: - https://chat.openai.com/share/7020ce72-65fc-45ec-b7be-9d9d798a5f3f - https://github.com/SaschaWillems/Vulkan/issues/954 - https://github.com/haasn/libplacebo/issues/128 - https://github.com/KhronosGroup/Vulkan-Samples/issues/476 --- ggml-vulkan.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 4a30414df2fce..e9e966dbfd350 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1109,8 +1109,10 @@ static void ggml_vk_instance_init() { #ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif + "VK_KHR_portability_enumeration", }; - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); + + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR), &app_info, layers, extensions); #ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { From d8c054517dc24f1316f3be12a98fff383e1e93e3 Mon Sep 17 00:00:00 2001 From: Mathijs de Bruin Date: Tue, 6 Feb 2024 14:39:22 +0000 Subject: [PATCH 042/401] Add preprocessor checks for Apple devices. Based on work by @rbourgeat in https://github.com/ggerganov/llama.cpp/pull/5322/files --- ggml-vulkan.cpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index e9e966dbfd350..33b8a90619989 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1109,10 +1109,15 @@ static void ggml_vk_instance_init() { #ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif +#ifdef __APPLE__ "VK_KHR_portability_enumeration", +#endif }; + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); +#ifdef __APPLE__ + instance_create_info.flags = vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; +#endif - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR), &app_info, layers, extensions); #ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { From f50db6ae0bdcb5f8593ca6ca46dfa03b177faa2f Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 10 Feb 2024 22:14:52 +0100 Subject: [PATCH 043/401] Add check for VK_KHR_portability_enumeration for MoltenVK support --- ggml-vulkan.cpp | 39 ++++++++++++++++++++++++++++++--------- 1 file changed, 30 insertions(+), 9 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 33b8a90619989..37123ac8f0c4d 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1100,24 +1100,45 @@ static void ggml_vk_instance_init() { #endif vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; - const std::vector layers = { + + const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); +#ifdef __APPLE__ + bool portability_enumeration_ext = false; + // Check for portability enumeration extension for MoltenVK support + for (const auto& properties : instance_extensions) { + if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { + portability_enumeration_ext = true; + break; + } + } + if (!portability_enumeration_ext) { + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; + } +#endif + + std::vector layers = { #ifdef GGML_VULKAN_VALIDATE "VK_LAYER_KHRONOS_validation", #endif }; - const std::vector extensions = { + std::vector extensions = { #ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif + }; #ifdef __APPLE__ - "VK_KHR_portability_enumeration", + if (portability_enumeration_ext) { + extensions.push_back("VK_KHR_portability_enumeration"); + } #endif - }; - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); #ifdef __APPLE__ - instance_create_info.flags = vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; + if (portability_enumeration_ext) { + instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; + } #endif + #ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { @@ -1175,12 +1196,12 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { vk_instance.devices[idx] = std::make_shared(); ctx->device = vk_instance.devices[idx]; ctx->device.lock()->physical_device = devices[dev_num]; - std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); + const std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); bool maintenance4_support = false; // Check if maintenance4 is supported - for (auto properties : ext_props) { + for (const auto& properties : ext_props) { if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { maintenance4_support = true; } @@ -1211,7 +1232,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { bool fp16_storage = false; bool fp16_compute = false; - for (auto properties : ext_props) { + for (const auto& properties : ext_props) { if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { fp16_storage = true; } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { From bb9dcd560a7e81265398b0d463c40f3e467daf19 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 14 Feb 2024 20:57:17 +0100 Subject: [PATCH 044/401] Refactor validation and enumeration platform checks into functions to clean up ggml_vk_instance_init() --- ggml-vulkan.cpp | 99 +++++++++++++++++++++++++++++++------------------ 1 file changed, 62 insertions(+), 37 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 37123ac8f0c4d..4e5eaff15110b 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1091,7 +1091,10 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } -static void ggml_vk_instance_init() { +static bool ggml_vk_instance_validation_ext_available(const std::vector& instance_extensions); +static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions); + +void ggml_vk_instance_init() { if (vk_instance_initialized) { return; } @@ -1102,54 +1105,40 @@ static void ggml_vk_instance_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); -#ifdef __APPLE__ - bool portability_enumeration_ext = false; - // Check for portability enumeration extension for MoltenVK support - for (const auto& properties : instance_extensions) { - if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { - portability_enumeration_ext = true; - break; - } + const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions); + const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); + + std::vector layers; + + if (validation_ext) { + layers.push_back("VK_LAYER_KHRONOS_validation"); } - if (!portability_enumeration_ext) { - std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; + std::vector extensions; + if (validation_ext) { + extensions.push_back("VK_EXT_validation_features"); } -#endif - - std::vector layers = { -#ifdef GGML_VULKAN_VALIDATE - "VK_LAYER_KHRONOS_validation", -#endif - }; - std::vector extensions = { -#ifdef GGML_VULKAN_VALIDATE - "VK_EXT_validation_features", -#endif - }; -#ifdef __APPLE__ if (portability_enumeration_ext) { extensions.push_back("VK_KHR_portability_enumeration"); } -#endif vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); -#ifdef __APPLE__ if (portability_enumeration_ext) { instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; } -#endif + std::vector features_enable; + vk::ValidationFeaturesEXT validation_features; -#ifdef GGML_VULKAN_VALIDATE - const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; - vk::ValidationFeaturesEXT validation_features = { - features_enable, - {}, - }; - validation_features.setPNext(nullptr); - instance_create_info.setPNext(&validation_features); + if (validation_ext) { + features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; + validation_features = { + features_enable, + {}, + }; + validation_features.setPNext(nullptr); + instance_create_info.setPNext(&validation_features); - std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; -#endif + std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + } vk_instance.instance = vk::createInstance(instance_create_info); memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES); @@ -5329,6 +5318,42 @@ GGML_CALL int ggml_backend_vk_reg_devices() { return vk_instance.device_indices.size(); } +// Extension availability +static bool ggml_vk_instance_validation_ext_available(const std::vector& instance_extensions) { +#ifdef GGML_VULKAN_VALIDATE + bool portability_enumeration_ext = false; + // Check for portability enumeration extension for MoltenVK support + for (const auto& properties : instance_extensions) { + if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { + return true; + } + } + if (!portability_enumeration_ext) { + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; + } +#endif + return false; + + UNUSED(instance_extensions); +} +static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions) { +#ifdef __APPLE__ + bool portability_enumeration_ext = false; + // Check for portability enumeration extension for MoltenVK support + for (const auto& properties : instance_extensions) { + if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { + return true; + } + } + if (!portability_enumeration_ext) { + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; + } +#endif + return false; + + UNUSED(instance_extensions); +} + // checks #ifdef GGML_VULKAN_CHECK_RESULTS From 22f83f0c383e12106692b8afc224d61b8993a52c Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 10 Feb 2024 22:18:33 +0100 Subject: [PATCH 045/401] Enable Vulkan MacOS CI --- .devops/nix/package.nix | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index ad23f7dd757d1..815db6a2d8c20 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -255,11 +255,11 @@ effectiveStdenv.mkDerivation ( # Configurations we don't want even the CI to evaluate. Results in the # "unsupported platform" messages. This is mostly a no-op, because # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin; + badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; # Configurations that are known to result in build failures. Can be # overridden by importing Nixpkgs with `allowBroken = true`. - broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin); + broken = (useMetalKit && !effectiveStdenv.isDarwin); description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; homepage = "https://github.com/ggerganov/llama.cpp/"; From 633782b8d949f24b619e6c68ee37b5cc79167173 Mon Sep 17 00:00:00 2001 From: Mathijs de Bruin Date: Tue, 13 Feb 2024 20:28:02 +0000 Subject: [PATCH 046/401] nix: now that we can do so, allow MacOS to build Vulkan binaries Author: Philip Taron Date: Tue Feb 13 20:28:02 2024 +0000 --- flake.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index ad2f9b2951036..dc4e503c33061 100644 --- a/flake.nix +++ b/flake.nix @@ -150,6 +150,7 @@ packages = { default = config.legacyPackages.llamaPackages.llama-cpp; + vulkan = config.packages.default.override { useVulkan = true; }; } // lib.optionalAttrs pkgs.stdenv.isLinux { opencl = config.packages.default.override { useOpenCL = true; }; @@ -157,7 +158,6 @@ mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; - vulkan = config.packages.default.override { useVulkan = true; }; } // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; From b9111bd209c7b11b0592450a6ed2e0ca545b2c84 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Tue, 20 Feb 2024 07:01:25 +0000 Subject: [PATCH 047/401] Update ggml_sycl_op_mul_mat_vec_q (#5502) * Update ggml_sycl_op_mul_mat_vec_q * Apply suggestions from code review Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> * revert suggestion on macro * fix bug * Add quant type GGML_TYPE_IQ1_S to unsupported * fix format --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 258 ++++++++++++++------------------------------------ 1 file changed, 69 insertions(+), 189 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index cd4b3a1e169c9..df182611241b0 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -9188,174 +9188,22 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, } } -static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK4_0 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK4_1 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK5_0 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK5_1 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK8_0 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); -} - -static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, - float *dst, const int ncols, - const int nrows, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q(vx, vy, dst, ncols, nrows, - item_ct1); - }); +template +static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), [= + ](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); } int get_device_index_by_id(int id){ @@ -12095,37 +11943,63 @@ inline void ggml_sycl_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + // TODO: support these quantization types + GGML_ASSERT(!(src0->type == GGML_TYPE_IQ2_XXS || + src0->type == GGML_TYPE_IQ2_XS || + src0->type == GGML_TYPE_IQ3_XXS || + src0->type == GGML_TYPE_IQ1_S)); + switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_1: - mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_0: - mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_1: - mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q8_0: - mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q2_K: - mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q3_K: - mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_K: - mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_K: - mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q6_K: - mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q_sycl_submitter( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -12145,7 +12019,7 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( const int64_t src1_ncols, const int64_t src1_padded_row_size, const dpct::queue_ptr &stream) { - GGML_TENSOR_BINARY_OP_LOCALS + GGML_TENSOR_BINARY_OP_LOCALS; const int64_t row_diff = row_high - row_low; @@ -15093,6 +14967,12 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten return false; } + if (a->type == GGML_TYPE_IQ1_S) { + return false; + } + if (a->type == GGML_TYPE_IQ3_XXS) { + return false; + } if (a->type == GGML_TYPE_IQ2_XXS) { return false; } From c0a8c6db371cb3e4379900867b948879f5842201 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Tue, 20 Feb 2024 08:48:19 +0100 Subject: [PATCH 048/401] server : health endpoint configurable failure on no slot (#5594) --- examples/server/README.md | 9 ++++--- examples/server/server.cpp | 52 +++++++++++++++++++------------------- 2 files changed, 31 insertions(+), 30 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 809e2d37c8732..f6b9c74021f30 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -134,10 +134,11 @@ node index.js ## API Endpoints - **GET** `/health`: Returns the current state of the server: - - `{"status": "loading model"}` if the model is still being loaded. - - `{"status": "error"}` if the model failed to load. - - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below. - - `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available + - 503 -> `{"status": "loading model"}` if the model is still being loaded. + - 500 -> `{"status": "error"}` if the model failed to load. + - 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below. + - 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available. + - 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available. - **POST** `/completion`: Given a `prompt`, it returns the predicted completion. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 22c344dd45544..23482ed95075a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2582,40 +2582,40 @@ int main(int argc, char **argv) res.set_header("Access-Control-Allow-Headers", "*"); }); - svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) { + svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) { server_state current_state = state.load(); switch(current_state) { - case SERVER_STATE_READY: - if (llama.all_slots_are_idle) { - res.set_content(R"({"status": "ok"})", "application/json"); + case SERVER_STATE_READY: { + int available_slots = 0; + int processing_slots = 0; + for (llama_client_slot &slot: llama.slots) { + if (slot.available()) { + available_slots++; + } else { + processing_slots++; + } + } + if (available_slots > 0) { + json health = { + {"status", "ok"}, + {"slots_idle", available_slots}, + {"slots_processing", processing_slots}}; + res.set_content(health.dump(), "application/json"); res.status = 200; // HTTP OK } else { - int available_slots = 0; - int processing_slots = 0; - for (llama_client_slot & slot : llama.slots) { - if (slot.available()) { - available_slots++; - } else { - processing_slots++; - } - } - if (available_slots > 0) { - json health = { - {"status", "ok"}, - {"slots_idle", available_slots}, - {"slots_processing", processing_slots}}; - res.set_content(health.dump(), "application/json"); - res.status = 200; // HTTP OK - } else { - json health = { - {"status", "no slot available"}, - {"slots_idle", available_slots}, - {"slots_processing", processing_slots}}; - res.set_content(health.dump(), "application/json"); + json health = { + {"status", "no slot available"}, + {"slots_idle", available_slots}, + {"slots_processing", processing_slots}}; + res.set_content(health.dump(), "application/json"); + if (req.has_param("fail_on_no_slot")) { res.status = 503; // HTTP Service Unavailable + } else { + res.status = 200; // HTTP OK } } break; + } case SERVER_STATE_LOADING_MODEL: res.set_content(R"({"status": "loading model"})", "application/json"); res.status = 503; // HTTP Service Unavailable From 8dbbd75754d43ec7b4bbe42fb287cc2553fdf0e9 Mon Sep 17 00:00:00 2001 From: Haoxiang Fei Date: Mon, 19 Feb 2024 22:58:36 -1100 Subject: [PATCH 049/401] metal : add build system support for embedded metal library (#5604) * add build support for embedded metal library * Update Makefile --------- Co-authored-by: Haoxiang Fei Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 24 ++++++++++++++++++++++++ Makefile | 18 ++++++++++++++++++ 2 files changed, 42 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 168b133f4e1c1..3c4629001a38d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -110,6 +110,7 @@ option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) +option(LLAMA_METAL_EMBED_LIBRARY "llama: embed Metal library" OFF) option(LLAMA_KOMPUTE "llama: use Kompute" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) @@ -201,6 +202,29 @@ if (LLAMA_METAL) # copy ggml-metal.metal to bin directory configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) + if (LLAMA_METAL_EMBED_LIBRARY) + enable_language(ASM) + add_compile_definitions(GGML_METAL_EMBED_LIBRARY) + + set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal") + file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") + set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s") + + add_custom_command( + OUTPUT ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY} + COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY} + DEPENDS ${METALLIB_SOURCE} + COMMENT "Generate assembly for embedded Metal library" + ) + + set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY}) + endif() + if (LLAMA_METAL_SHADER_DEBUG) # custom command to do the following: # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air diff --git a/Makefile b/Makefile index db5df1b328560..211a08d7ff186 100644 --- a/Makefile +++ b/Makefile @@ -533,11 +533,29 @@ ifdef LLAMA_METAL ifdef LLAMA_METAL_NDEBUG MK_CPPFLAGS += -DGGML_METAL_NDEBUG endif +ifdef LLAMA_METAL_EMBED_LIBRARY + MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY + OBJS += ggml-metal-embed.o +endif endif # LLAMA_METAL ifdef LLAMA_METAL ggml-metal.o: ggml-metal.m ggml-metal.h $(CC) $(CFLAGS) -c $< -o $@ + +ifdef LLAMA_METAL_EMBED_LIBRARY +ggml-metal-embed.o: ggml-metal.metal + @echo "Embedding Metal library" + $(eval TEMP_ASSEMBLY=$(shell mktemp)) + @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY) + @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY) + @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY) + @echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY) + @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY) + @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY) + @$(AS) $(TEMP_ASSEMBLY) -o $@ + @rm -f ${TEMP_ASSEMBLY} +endif endif # LLAMA_METAL ifdef LLAMA_MPI From 5207b3fbc500f89dfe528693e96540956dbaed96 Mon Sep 17 00:00:00 2001 From: Dane Madsen Date: Tue, 20 Feb 2024 21:00:23 +1100 Subject: [PATCH 050/401] readme : update UI list (#5605) * Add maid to ui list * Specify licence --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 70866e2490218..747d2e98b5a94 100644 --- a/README.md +++ b/README.md @@ -156,6 +156,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) - [semperai/amica](https://github.com/semperai/amica) - [withcatai/catai](https://github.com/withcatai/catai) +- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) --- From 9c405c9f9a7cfd23511fd6b2de05dc72481119b4 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 20 Feb 2024 15:58:27 +0100 Subject: [PATCH 051/401] Server: use llama_chat_apply_template (#5593) * server: use llama_chat_apply_template * server: remove trailing space * server: fix format_chat * server: fix help message Co-authored-by: Georgi Gerganov * server: fix formatted_chat --------- Co-authored-by: Georgi Gerganov --- examples/server/oai.hpp | 6 ++-- examples/server/server.cpp | 17 +++++----- examples/server/utils.hpp | 69 ++++++++++++++++++-------------------- llama.cpp | 2 +- 4 files changed, 45 insertions(+), 49 deletions(-) diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp index 2eca8a9fb4560..ff4ad69943552 100644 --- a/examples/server/oai.hpp +++ b/examples/server/oai.hpp @@ -15,13 +15,11 @@ using json = nlohmann::json; inline static json oaicompat_completion_params_parse( + const struct llama_model * model, const json &body, /* openai api json semantics */ const std::string &chat_template) { json llama_params; - std::string formatted_prompt = chat_template == "chatml" - ? format_chatml(body["messages"]) // OpenAI 'messages' to chatml (with <|im_start|>,...) - : format_llama2(body["messages"]); // OpenAI 'messages' to llama2 (with [INST],...) llama_params["__oaicompat"] = true; @@ -34,7 +32,7 @@ inline static json oaicompat_completion_params_parse( // https://platform.openai.com/docs/api-reference/chat/create llama_sampling_params default_sparams; llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = formatted_prompt; + llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); llama_params["temperature"] = json_value(body, "temperature", 0.0); llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 23482ed95075a..c7821eca68cba 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -37,7 +37,7 @@ struct server_params std::string hostname = "127.0.0.1"; std::vector api_keys; std::string public_path = "examples/server/public"; - std::string chat_template = "chatml"; + std::string chat_template = ""; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; @@ -1937,8 +1937,9 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); - printf(" --chat-template FORMAT_NAME"); - printf(" set chat template, possible value is: llama2, chatml (default %s)", sparams.chat_template.c_str()); + printf(" --chat-template JINJA_TEMPLATE\n"); + printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); + printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); printf("\n"); } @@ -2389,13 +2390,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - std::string value(argv[i]); - if (value != "chatml" && value != "llama2") { - fprintf(stderr, "error: chat template can be \"llama2\" or \"chatml\", but got: %s\n", value.c_str()); + if (!verify_custom_template(argv[i])) { + fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); + fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); invalid_param = true; break; } - sparams.chat_template = value; + sparams.chat_template = argv[i]; } else if (arg == "--override-kv") { @@ -2913,7 +2914,7 @@ int main(int argc, char **argv) if (!validate_api_key(req, res)) { return; } - json data = oaicompat_completion_params_parse(json::parse(req.body), sparams.chat_template); + json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); const int task_id = llama.queue_tasks.get_new_id(); llama.queue_results.add_waiting_task_id(task_id); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 0ee670dbadca5..e954fb0ef0413 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -167,50 +167,47 @@ static T json_value(const json &body, const std::string &key, const T &default_v : default_value; } -inline std::string format_llama2(std::vector messages) -{ - std::ostringstream output; - bool is_inside_turn = false; +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +inline bool verify_custom_template(const std::string & tmpl) { + llama_chat_message chat[] = {{"user", "test"}}; + std::vector buf(1); + int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size()); + return res >= 0; +} - for (auto it = messages.begin(); it != messages.end(); ++it) { - if (!is_inside_turn) { - output << "[INST] "; - } - std::string role = json_value(*it, "role", std::string("user")); - std::string content = json_value(*it, "content", std::string("")); - if (role == "system") { - output << "<>\n" << content << "\n<>\n\n"; - is_inside_turn = true; - } else if (role == "user") { - output << content << " [/INST]"; - is_inside_turn = true; - } else { - output << " " << content << " "; - is_inside_turn = false; - } +// Format given chat. If tmpl is empty, we take the template from model metadata +inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) +{ + size_t alloc_size = 0; + // vector holding all allocated string to be passed to llama_chat_apply_template + std::vector str(messages.size() * 2); + std::vector chat(messages.size()); + + for (size_t i = 0; i < messages.size(); ++i) { + auto &curr_msg = messages[i]; + str[i*2 + 0] = json_value(curr_msg, "role", std::string("")); + str[i*2 + 1] = json_value(curr_msg, "content", std::string("")); + alloc_size += str[i*2 + 1].length(); + chat[i].role = str[i*2 + 0].c_str(); + chat[i].content = str[i*2 + 1].c_str(); } - LOG_VERBOSE("format_llama2", {{"text", output.str()}}); + const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str(); + std::vector buf(alloc_size * 2); - return output.str(); -} - -inline std::string format_chatml(std::vector messages) -{ - std::ostringstream chatml_msgs; + // run the first time to get the total output length + int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); - for (auto it = messages.begin(); it != messages.end(); ++it) { - chatml_msgs << "<|im_start|>" - << json_value(*it, "role", std::string("user")) << '\n'; - chatml_msgs << json_value(*it, "content", std::string("")) - << "<|im_end|>\n"; + // if it turns out that our buffer is too small, we resize it + if ((size_t) res > buf.size()) { + buf.resize(res); + res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); } - chatml_msgs << "<|im_start|>assistant" << '\n'; - - LOG_VERBOSE("format_chatml", {{"text", chatml_msgs.str()}}); + std::string formatted_chat(buf.data(), res); + LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}}); - return chatml_msgs.str(); + return formatted_chat; } // diff --git a/llama.cpp b/llama.cpp index 5de07dfa999a7..4296eca3261e8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12602,7 +12602,7 @@ LLAMA_API int32_t llama_chat_apply_template( // load template from model std::vector model_template(2048, 0); // longest known template is about 1200 bytes std::string template_key = "tokenizer.chat_template"; - int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), curr_tmpl.size()); + int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); if (res < 0) { // worst case: there is no information about template, we will use chatml by default curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal From 4ed8e4fbef6a15afd993bfcd9ffa279841e18ef1 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 20 Feb 2024 18:30:27 +0100 Subject: [PATCH 052/401] llava : add explicit instructions for llava-1.6 (#5611) This commit contains a suggestion for the README.md in the llava example. The suggestion adds explicit instructions for how to convert a llava-1.6 model and run it using llava-cli. The motivation for this is that having explicit instructions similar to the 1.5 instructions will make it easier for users to try this out. Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 38 ++++++++++++++++++++++++++++++++------ 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index e42db6e5ad3cf..25ea967153510 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -59,14 +59,40 @@ python ./convert.py ../llava-v1.5-7b --skip-unknown Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. ## LLaVA 1.6 gguf conversion - -1) Backup your pth/safetensor model files as llava-surgery modifies them -2) Use `python llava-surgery-v2.py -C -m /path/to/hf-model` which also supports llava-1.5 variants pytorch as well as safetensor models: +1) First clone a LLaVA 1.6 model: +```console +git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b +``` +2) Backup your pth/safetensor model files as llava-surgery modifies them +3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: +```console +python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/ +``` - you will find a llava.projector and a llava.clip file in your model directory -3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config_vit.json) and rename it to config.json. -4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip-model-is-vision` +4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: +```console +mkdir vit +cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin +cp ../llava-v1.6-vicuna-7b/llava.projector vit/ +curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json +``` + +5) Create the visual gguf model: +```console +python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP -5) Everything else as usual: convert.py the hf model, quantize as needed + +6) Then convert the model to gguf format: +```console +python ./convert.py ../llava-v1.6-vicuna-7b/ +``` + +7) And finally we can run the llava-cli using the 1.6 model version: +```console +./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 +``` + **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) **note** llava-1.6 greatly benefits from batched prompt processing (defaults work) From 06bf2cf8c406e6b70dbf9b431a02fa0ad845b9df Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 20 Feb 2024 20:06:17 +0100 Subject: [PATCH 053/401] make : fix debug build with CUDA (#5616) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 211a08d7ff186..41c79c135467a 100644 --- a/Makefile +++ b/Makefile @@ -173,7 +173,7 @@ ifdef LLAMA_DEBUG MK_LDFLAGS += -g ifeq ($(UNAME_S),Linux) - MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS + MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS endif else MK_CPPFLAGS += -DNDEBUG From 6560bed3f066c876682464762cad90f1e28e3f1b Mon Sep 17 00:00:00 2001 From: CJ Pais Date: Tue, 20 Feb 2024 11:07:22 -0800 Subject: [PATCH 054/401] server : support llava 1.6 (#5553) * server: init working 1.6 * move clip_image to header * remove commented code * remove c++ style from header * remove todo * expose llava_image_embed_make_with_clip_img * fix zig build --- Makefile | 2 +- build.zig | 3 ++- examples/llava/llava.cpp | 2 +- examples/llava/llava.h | 2 ++ examples/server/server.cpp | 36 +++--------------------------------- 5 files changed, 9 insertions(+), 36 deletions(-) diff --git a/Makefile b/Makefile index 41c79c135467a..f03faf6eda0fb 100644 --- a/Makefile +++ b/Makefile @@ -719,7 +719,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) diff --git a/build.zig b/build.zig index 699738f3dd509..c0af454dc9e92 100644 --- a/build.zig +++ b/build.zig @@ -123,6 +123,7 @@ pub fn build(b: *std.build.Builder) !void { const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); const train = make.obj("train", "common/train.cpp"); const clip = make.obj("clip", "examples/llava/clip.cpp"); + const llava = make.obj("llava", "examples/llava/llava.cpp"); _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser }); _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); @@ -131,7 +132,7 @@ pub fn build(b: *std.build.Builder) !void { _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 4cb65a07b6740..1a1cf7c78bf34 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -311,7 +311,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * return true; } -static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { +bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model if (!image_embd) { fprintf(stderr, "Unable to allocate memory for image embeddings\n"); diff --git a/examples/llava/llava.h b/examples/llava/llava.h index 9e9466a5d1726..2d40f3f1d5f84 100644 --- a/examples/llava/llava.h +++ b/examples/llava/llava.h @@ -31,6 +31,8 @@ struct llava_image_embed { /** sanity check for clip <-> llava embed size match */ LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip); +LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); + /** build an image embed from image file bytes */ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); /** build an image embed from a path to an image filename */ diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c7821eca68cba..eb01729fa7a8a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -5,6 +5,7 @@ #include "oai.hpp" #include "../llava/clip.h" +#include "../llava/llava.h" #include "stb_image.h" @@ -997,43 +998,12 @@ struct llama_server_context { continue; } - clip_image_f32_batch img_res_v; - img_res_v.size = 0; - img_res_v.data = nullptr; - if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v)) - { - LOG_TEE("Error processing the given image"); - clip_free(clp_ctx); - clip_image_f32_batch_free(img_res_v); - return false; - } - if (img_res_v.size == 0) - { - LOG_TEE("Error processing the given image"); - return false; - } - - // note: assumes only one image was returned by clip_image_preprocess - clip_image_f32 * img_res = img_res_v.data; - img.image_tokens = clip_n_patches(clp_ctx); - img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); - if (!img.image_embedding) - { - LOG_TEE("Unable to allocate memory for image embeddings\n"); - clip_image_f32_batch_free(img_res_v); - clip_free(clp_ctx); - return false; - } - LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); - if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding)) - { - LOG_TEE("Unable to encode image\n"); - clip_image_f32_batch_free(img_res_v); + if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) { + LOG_TEE("Error processing the given image"); return false; } - clip_image_f32_batch_free(img_res_v); img.request_encode_image = false; } From a14679cc30c785e75d38028bae6ec39c6209ddef Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Wed, 21 Feb 2024 11:39:52 +0200 Subject: [PATCH 055/401] IQ4_NL: 4-bit non-linear quants with blocks of 32 (#5590) * iq4_nl: squash commits for easier rebase * Basics (quantize, dequantize) * CUDA dequantize and dot product * Slightly faster CUDA dot product (120 t/s) * Switch to 6-bit scales * Scalar dot product * AVX2 dot product * ARM_NEON dot product * Works on metal, but still slow * Slightly better Metal dot product * Another small Metal improvement * Metal dot product is getting there * Faster CUDA dot product * Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided * Report the actual bpw * Add _xs mix that is 4.05 bpw for non-MoE models * Remove IQ4_XS for now, slightly adjust kvalues_iq4nl * AVX2 dot product uses Q8_0 instead of Q8_K * Add to test-backend-ops * Minor fix * Also use use Q5_K for attn_output in MoE models * Fixes after merging latest master * Switching to blocks of 32 * AVX2 for blocks of 32 * Scaler dot product for blocks of 32 * ARM_NEON dot product for blocks of 32 * Metal kernels for blocks of 32 * Slightly faster Metal kernels * iq4_nl: Fix after merging with master * iq4_nl: another fix after merging with master * Use IQ4_NL instead of Q4_K when using k-quants is not possible * Fix typo that makes several tests fail * It was the ggml_vdotq thing missed inside the brackets --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 1 + ggml-cuda.cu | 98 +++++++++++++- ggml-metal.m | 35 +++++ ggml-metal.metal | 215 +++++++++++++++++++++++++++++- ggml-quants.c | 234 ++++++++++++++++++++++++++++++++- ggml-quants.h | 13 ++ ggml.c | 30 +++++ ggml.h | 2 + llama.cpp | 17 ++- llama.h | 1 + tests/test-backend-ops.cpp | 1 + 11 files changed, 640 insertions(+), 7 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index ea7ba50c96eb6..37520857f99f7 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -32,6 +32,7 @@ static const std::vector QUANT_OPTIONS = { { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, + { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.25 bpw non-linear quantization", }, { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", }, { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", }, diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 6caae56b0244b..e7c211d7d6087 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -528,6 +528,15 @@ typedef struct { } block_iq1_s; static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); +#define QK4_NL 32 +#define QR4_NL 2 +#define QI4_NL (QK4_NL / (4*QR4_NL)) +typedef struct { + half d; + uint8_t qs[QK4_NL/2]; +} block_iq4_nl; +static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -1987,6 +1996,26 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ } +static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +template +static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); + + const int tid = threadIdx.x; + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = (float)x[ib].d; + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; + y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; + } + +} static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { @@ -4732,6 +4761,56 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( #endif } +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values, + int & val1, int & val2) { + + uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32; + aux32 = q4 & 0x0f0f0f0f; + uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8); + uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8); + val1 = v1 | (v2 << 16); + aux32 = (q4 >> 4) & 0x0f0f0f0f; + v1 = values[q8[0]] | (values[q8[1]] << 8); + v2 = values[q8[2]] | (values[q8[3]] << 8); + val2 = v1 | (v2 << 16); +} +#endif + +static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + + const block_iq4_nl * bq = (const block_iq4_nl *) vbq; + +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs; + const int32_t * q8 = (const int32_t *)bq8_1->qs + iqs; + + const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) { + const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16); + get_int_from_table_16(aux, values, v1, v2); + sumi1 = __dp4a(v1, q8[l+0], sumi1); + sumi2 = __dp4a(v2, q8[l+4], sumi2); + } + +#else + const uint8_t * q4 = bq->qs + 4*iqs; + const int8_t * q8 = bq8_1->qs + 4*iqs; + + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) { + sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf]; + sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >> 4]; + } +#endif + const float d = (float)bq->d * __low2float(bq8_1->ds); + return d * (sumi1 + sumi2); +} + template static __device__ __forceinline__ void mul_mat_q( @@ -6777,6 +6856,12 @@ static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, c dequantize_block_iq1_s<<>>(vx, y); } +template +static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = (k + QK_K - 1) / QK_K; + dequantize_block_iq4_nl<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -6818,6 +6903,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: return dequantize_row_iq1_s_cuda; + case GGML_TYPE_IQ4_NL: + return dequantize_row_iq4_nl_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -6855,6 +6942,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: return dequantize_row_iq1_s_cuda; + case GGML_TYPE_IQ4_NL: + return dequantize_row_iq4_nl_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -8599,6 +8688,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -8623,6 +8713,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -8724,6 +8815,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ4_NL: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; default: GGML_ASSERT(false); break; @@ -11446,7 +11541,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons return false; } ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ1_S) { + if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index 956e323a0d053..0d4aa43093739 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -62,6 +62,7 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, @@ -85,6 +86,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, @@ -104,6 +106,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, @@ -120,6 +123,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, @@ -136,6 +140,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, @@ -448,6 +453,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); @@ -471,6 +477,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); @@ -490,6 +497,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); @@ -506,6 +514,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); @@ -522,6 +531,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); @@ -1338,6 +1348,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } @@ -1478,6 +1489,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; } break; + case GGML_TYPE_IQ4_NL: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1525,6 +1542,11 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_IQ4_NL) { + const int mem_size = 32*sizeof(float); + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1619,6 +1641,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } @@ -1762,6 +1785,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; } break; + case GGML_TYPE_IQ4_NL: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1825,6 +1854,11 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src2t == GGML_TYPE_IQ4_NL) { + const int mem_size = 32*sizeof(float); + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1867,6 +1901,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index f0d77d446755c..c223a981c246a 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2531,6 +2531,12 @@ typedef struct { uint8_t scales[QK_K/16]; } block_iq1_s; +// Non-linear quants +#define QK4_NL 32 +typedef struct { + half d; + uint8_t qs[QK4_NL/2]; +} block_iq4_nl; //====================================== dot products ========================= @@ -4384,7 +4390,6 @@ void kernel_mul_mv_iq1_s_f32_impl( const uint i13 = im/ne12; const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0; device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; @@ -4447,6 +4452,103 @@ void kernel_mul_mv_iq1_s_f32_impl( } } +constexpr constant static float kvalues_iq4nl_f[16] = { + -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f +}; + +void kernel_mul_mv_iq4_nl_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK4_NL; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + const int first_row = (r0 * 2 + sgitg) * 2; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + device const block_iq4_nl * x = (device const block_iq4_nl *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + const int ix = tiisg/2; // 0...15 + const int it = tiisg%2; // 0 or 1 + + shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + threadgroup_barrier(mem_flags::mem_threadgroup); + + float4 yl[4]; + float sumf[2]={0.f}, all_sum; + + device const float * yb = y + ix * QK4_NL + it * 8; + + uint32_t aux32[2]; + thread const uint8_t * q8 = (thread const uint8_t *)aux32; + + float4 qf1, qf2; + + for (int ib = ix; ib < nb; ib += 16) { + + device const float4 * y4 = (device const float4 *)yb; + yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; + + for (int row = 0; row < 2; ++row) { + + device const block_iq4_nl & xb = x[row*nb + ib]; + device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); + + float4 acc1 = {0.f}, acc2 = {0.f}; + + aux32[0] = q4[0] | (q4[1] << 16); + aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; + aux32[0] &= 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[0] * qf1; + acc2 += yl[1] * qf2; + + aux32[0] = q4[2] | (q4[3] << 16); + aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; + aux32[0] &= 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[2] * qf1; + acc2 += yl[3] * qf2; + + acc1 += acc2; + + sumf[row] += (float)xb.d * (acc1[0] + acc1[1] + acc1[2] + acc1[3]); + + } + + yb += 16 * QK4_NL; + } + + for (int row = 0; row < 2; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + } + } +} + [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( device const void * src0, @@ -4475,6 +4577,34 @@ kernel void kernel_mul_mv_iq1_s_f32( kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); } +[[host_name("kernel_mul_mv_iq4_nl_f32")]] +kernel void kernel_mul_mv_iq4_nl_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} //============================= templates and their specializations ============================= @@ -4838,6 +4968,21 @@ void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & } } +template +void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { + device const uint16_t * q4 = (device const uint16_t *)xb->qs; + const float d = xb->d; + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -5381,6 +5526,7 @@ template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -5421,6 +5567,7 @@ template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -5473,6 +5620,7 @@ template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -6503,3 +6651,68 @@ kernel void kernel_mul_mv_id_iq1_s_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq4_nl_f32")]] +kernel void kernel_mul_mv_id_iq4_nl_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq4_nl_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index 3319d2ccf1812..6336538f0e99e 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3754,6 +3754,26 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in } } +static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) { + assert(k % QK4_NL == 0); + const int nb = k / QK4_NL; + + for (int i = 0; i < nb; i++) { + + const uint8_t * qs = x[i].qs; + + const float d = GGML_FP16_TO_FP32(x[i].d); + for (int j = 0; j < QK4_NL/2; ++j) { + y[j+ 0] = d * kvalues_iq4nl[qs[j] & 0xf]; + y[j+QK4_NL/2] = d * kvalues_iq4nl[qs[j] >> 4]; + } + y += QK4_NL; + qs += QK4_NL/2; + } +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -9148,7 +9168,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * #endif } -// TODO void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -9452,7 +9471,100 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const *s = sumf; #endif +} + +void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK4_NL == 0); + static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); + + const block_iq4_nl * restrict x = vx; + const block_q8_0 * restrict y = vy; + + const int nb = n / QK4_NL; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + uint8x16x2_t q4bits; + int8x16x4_t q4b; + int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + float sumf = 0; + + for (int ib = 0; ib < nb; ib += 2) { + + q4bits.val[0] = vld1q_u8(x[ib+0].qs); + q4bits.val[1] = vld1q_u8(x[ib+1].qs); + q8b.val[0] = vld1q_s8(y[ib+0].qs); + q8b.val[1] = vld1q_s8(y[ib+0].qs + 16); + q8b.val[2] = vld1q_s8(y[ib+1].qs); + q8b.val[3] = vld1q_s8(y[ib+1].qs + 16); + + q4b.val[0] = vqtbl1q_s8(values, vandq_u8(q4bits.val[0], m4b)); + q4b.val[1] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = vqtbl1q_s8(values, vandq_u8(q4bits.val[1], m4b)); + q4b.val[3] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + sumf += (float)x[ib+0].d * (float)y[ib+0].d * vaddvq_s32(prod_1) + (float)x[ib+1].d * (float)y[ib+1].d * vaddvq_s32(prod_2); + + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + const __m256i mone = _mm256_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (int ib = 0; ib < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs); + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs); + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs); + const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); + const __m256i p_2 = _mm256_madd_epi16(p16_2, mone); + accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)), + _mm256_cvtepi32_ps(p_1), accum1); + accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)), + _mm256_cvtepi32_ps(p_2), accum2); + + y += 2; + x += 2; + } + + *s = hsum_float_8(_mm256_add_ps(accum1, accum2)); + +#else + float sumf = 0; + for (int ib = 0; ib < nb; ++ib) { + const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf]; + sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >> 4]; + } + sumf += d * (sumi1 + sumi2); + } + *s = sumf; +#endif } // ================================ IQ2 quantization ============================================= @@ -10729,3 +10841,123 @@ size_t quantize_iq1_s(const float * src, void * dst, int nrow, int n_per_row, in } return nrow * nblock * sizeof(block_iq1_s); } + +// ============================ 4-bit non-linear quants + +static inline int best_index_int8(int n, const int8_t * val, float x) { + if (x <= val[0]) return 0; + if (x >= val[n-1]) return n-1; + int ml = 0, mu = n-1; + while (mu-ml > 1) { + int mav = (ml+mu)/2; + if (x < val[mav]) mu = mav; else ml = mav; + } + return x - val[mu-1] < val[mu] - x ? mu-1 : mu; +} + +static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RESTRICT x, + ggml_fp16_t * dh, uint8_t * q4, + float * weight, uint8_t * L, + const int8_t * values, + const float * quant_weights) { + + const int ntry = 7; + + float sigma2 = 0; + for (int j = 0; j < QK4_NL; ++j) sigma2 += x[j]*x[j]; + sigma2 *= 2.f/QK4_NL; + + const int nb = QK4_NL/block_size; + + memset(q4, 0, QK4_NL/2); + for (int ib = 0; ib < nb; ++ib) { + dh[ib] = GGML_FP32_TO_FP16(0.f); + const float * xb = x + ib*block_size; + if (quant_weights) { + const float * qw = quant_weights + ib*block_size; + for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]); + } else { + for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j]; + } + float amax = 0, max = 0; + for (int j = 0; j < block_size; ++j) { + float ax = fabsf(xb[j]); + if (ax > amax) { + amax = ax; max = xb[j]; + } + } + if (!amax) { + continue; + } + float d = -max/values[0]; + float id = 1/d; + float sumqx = 0, sumq2 = 0; + for (int j = 0; j < block_size; ++j) { + float al = id*xb[j]; + int l = best_index_int8(16, values, al); + float q = values[l]; + float w = weight[j]; + sumqx += w*q*xb[j]; + sumq2 += w*q*q; + } + float best_id = id; + d = sumqx/sumq2; + float best = d*sumqx; + for (int itry = -ntry; itry <= ntry; ++itry) { + id = (itry + values[0])/max; + sumqx = sumq2 = 0; + for (int j = 0; j < block_size; ++j) { + float al = id*xb[j]; + int l = best_index_int8(16, values, al); + float q = values[l]; + float w = weight[j]; + sumqx += w*q*xb[j]; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + d = sumqx/sumq2; best = d * sumqx; + best_id = id; + } + } + dh[ib] = GGML_FP32_TO_FP16(d); + for (int j = 0; j < block_size; ++j) { + L[ib*block_size + j] = best_index_int8(16, values, best_id*xb[j]); + } + } + for (int i = 0; i < QK4_NL/32; ++i) { + for (int j = 0; j < 16; ++j) { + q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4); + } + } +} + +size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK4_NL == 0); + int nblock = n_per_row/QK4_NL; + char * qrow = (char *)dst; + uint8_t L[QK4_NL]; + float weight[32]; + for (int row = 0; row < nrow; ++row) { + block_iq4_nl * iq4 = (block_iq4_nl *)qrow; + for (int ibl = 0; ibl < nblock; ++ibl) { + const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL; + quantize_row_iq4_nl_impl(32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, weight, L, kvalues_iq4nl, qw); + } + src += n_per_row; + qrow += nblock*sizeof(block_iq4_nl); + } + return nrow * nblock * sizeof(block_iq4_nl); +} + +void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { + assert(k % QK4_NL == 0); + block_iq4_nl * restrict y = vy; + quantize_row_iq4_nl_reference(x, y, k); +} + +void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) { + assert(k % QK4_NL == 0); + quantize_iq4_nl(x, y, 1, k, NULL, NULL); +} + diff --git a/ggml-quants.h b/ggml-quants.h index ad381cfab022d..113623b62938a 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -198,6 +198,14 @@ typedef struct { } block_iq1_s; static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); +// Non-linear quants +#define QK4_NL 32 +typedef struct { + ggml_fp16_t d; + uint8_t qs[QK4_NL/2]; +} block_iq4_nl; +static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); + #ifdef __cplusplus extern "C" { #endif @@ -217,6 +225,7 @@ void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGM void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); +void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -232,6 +241,7 @@ void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -251,6 +261,7 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -268,6 +279,7 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -276,6 +288,7 @@ size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index d129df50548a3..91adbb0aee6bc 100644 --- a/ggml.c +++ b/ggml.c @@ -690,6 +690,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ4_NL] = { + .type_name = "iq4_nl", + .blck_size = QK4_NL, + .type_size = sizeof(block_iq4_nl), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_nl, + .from_float = quantize_row_iq4_nl, + .from_float_reference = (ggml_from_float_t)quantize_row_iq4_nl_reference, + .vec_dot = ggml_vec_dot_iq4_nl_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2291,6 +2303,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; + case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7702,6 +7715,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; @@ -7970,6 +7984,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; @@ -8091,6 +8106,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: default: { GGML_ASSERT(false); @@ -10858,6 +10874,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: { ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; @@ -11039,6 +11056,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: default: { GGML_ASSERT(false); @@ -11237,6 +11255,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -11911,6 +11930,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -11989,6 +12009,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -19455,6 +19476,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq1_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ4_NL: + { + GGML_ASSERT(start % QK4_NL == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index 004d09c700f32..bed7a36a0ee6a 100644 --- a/ggml.h +++ b/ggml.h @@ -355,6 +355,7 @@ extern "C" { GGML_TYPE_IQ2_XS = 17, GGML_TYPE_IQ3_XXS = 18, GGML_TYPE_IQ1_S = 19, + GGML_TYPE_IQ4_NL = 20, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -393,6 +394,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index 4296eca3261e8..3748d5eac8a50 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2527,6 +2527,7 @@ struct llama_model_loader { case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; + case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2877,6 +2878,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; default: return "unknown, may not work"; } @@ -10354,6 +10356,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && qs.model.hparams.n_gqa() >= 4) { + new_type = GGML_TYPE_Q5_K; + } else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; @@ -10406,6 +10411,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; } } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && !qs.has_imatrix) { + if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) { new_type = GGML_TYPE_Q5_K; @@ -10422,7 +10430,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (arch != LLM_ARCH_FALCON) { if (qs.model.hparams.n_expert == 8) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || - ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || + ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { new_type = GGML_TYPE_Q5_K; } @@ -10489,8 +10497,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: - case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; - case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: new_type = GGML_TYPE_IQ4_NL; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; @@ -10531,7 +10539,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ2_XS: quantized_type = GGML_TYPE_IQ2_XS; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S ; break; + case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break; + case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } diff --git a/llama.h b/llama.h index 77a84c18a69cf..8ba20696f8af9 100644 --- a/llama.h +++ b/llama.h @@ -101,6 +101,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ef37c5af275fe..55db42bf6e851 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1918,6 +1918,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, + GGML_TYPE_IQ4_NL, }; // unary ops From 88c46cbdac05cebd936511b1d3c74112e721615f Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Wed, 21 Feb 2024 17:52:06 +0800 Subject: [PATCH 056/401] [SYCL] conext add name (#5624) * [SYCL] conext add name * name should start with SYCL* --- ggml-sycl.cpp | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index df182611241b0..b897828f967b1 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -14642,7 +14642,8 @@ GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_bu static ggml_backend_buffer_t ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) try { - int device = (int) (intptr_t) buft->context; + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + int device = (int) buft_ctx->device; ggml_sycl_set_device(device); int device_index = get_device_index_by_id(device); @@ -14720,7 +14721,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { ggml_backend_sycl_buffer_types[i] = { /* .iface = */ ggml_backend_sycl_buffer_type_interface, - /* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i, + /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i)}, }; } ggml_backend_sycl_buffer_type_initialized = true; @@ -14782,10 +14783,6 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { // backend -struct ggml_backend_context_sycl { - int device; -}; - static const char * ggml_backend_sycl_name(ggml_backend_t backend) { return GGML_SYCL_NAME; @@ -14793,14 +14790,14 @@ static const char * ggml_backend_sycl_name(ggml_backend_t backend) { } static void ggml_backend_sycl_free(ggml_backend_t backend) { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; delete sycl_ctx; delete backend; } static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; return ggml_backend_sycl_buffer_type(sycl_ctx->device); } @@ -14809,7 +14806,7 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -14827,7 +14824,7 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -14842,7 +14839,7 @@ catch (sycl::exception const &exc) { } static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); @@ -14878,7 +14875,7 @@ static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_ba } static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; ggml_sycl_set_main_device(sycl_ctx->device); @@ -15092,8 +15089,9 @@ ggml_backend_t ggml_backend_sycl_init(int device) { // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_sycl_set_main_device(device); - ggml_backend_context_sycl * ctx = new ggml_backend_context_sycl { - /* .device = */ device + ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context { + /* .device = */ device, + /* .name = */ GGML_SYCL_NAME + std::to_string(device), }; ggml_backend_t sycl_backend = new ggml_backend { From 580111d42b3b6ad0a390bfb267d6e3077506eb31 Mon Sep 17 00:00:00 2001 From: postmasters Date: Wed, 21 Feb 2024 05:08:22 -0800 Subject: [PATCH 057/401] llama : add `gemma` model (#5631) There are couple things in this architecture: 1. Shared input and output embedding parameters. 2. Key length and value length are not derived from `n_embd`. More information about the models can be found at https://ai.google.dev/gemma. GGUFs can be downloaded from https://huggingface.co/google. --- README.md | 1 + gguf-py/gguf/constants.py | 15 ++++ llama.cpp | 170 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 186 insertions(+) diff --git a/README.md b/README.md index 747d2e98b5a94..225db8e49ce39 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,7 @@ Typically finetunes of the base models below are supported as well. - [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) - [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) +- [x] [Gemma](https://ai.google.dev/gemma) **Multimodal models:** diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 114a9a9743081..8f9139d1b7eca 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -111,6 +111,7 @@ class MODEL_ARCH(IntEnum): ORION = auto() INTERNLM2 = auto() MINICPM = auto() + GEMMA = auto() class MODEL_TENSOR(IntEnum): @@ -167,6 +168,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.ORION: "orion", MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", + MODEL_ARCH.GEMMA: "gemma", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -511,6 +513,19 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.GEMMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM, + ], # TODO } diff --git a/llama.cpp b/llama.cpp index 3748d5eac8a50..3a226c4260c0b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -208,6 +208,7 @@ enum llm_arch { LLM_ARCH_ORION, LLM_ARCH_INTERNLM2, LLM_ARCH_MINICPM, + LLM_ARCH_GEMMA, LLM_ARCH_UNKNOWN, }; @@ -234,6 +235,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_ORION, "orion" }, { LLM_ARCH_INTERNLM2, "internlm2" }, { LLM_ARCH_MINICPM, "minicpm" }, + { LLM_ARCH_GEMMA, "gemma" }, }; enum llm_kv { @@ -760,6 +762,22 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, }, }, + { + LLM_ARCH_GEMMA, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -3243,6 +3261,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GEMMA: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 18: model.type = e_model::MODEL_2B; break; + case 28: model.type = e_model::MODEL_7B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4360,6 +4388,37 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_GEMMA: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + + const int64_t n_ff = hparams.n_ff; + const int64_t n_embd_head_k = hparams.n_embd_head_k; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + + for (uint32_t i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -7366,6 +7425,113 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gemma() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head_k = hparams.n_embd_head_k; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); + cb(inpL, "inp_scaled", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, + n_embd_head_k, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(Qcur, "Qcur", il); + Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); + cb(Qcur, "Qcur_scaled", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, + n_embd_head_k, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + cb(cur, "kqv_out", il); + } + struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL); + cb(sa_out, "sa_out", il); + + cur = llm_build_norm(ctx0, sa_out, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + // feed-forward network + { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_GELU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, sa_out); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( @@ -7474,6 +7640,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_minicpm(); } break; + case LLM_ARCH_GEMMA: + { + result = llm.build_gemma(); + } break; default: GGML_ASSERT(false); } From cc6cac08e38e32bf40bbe07e9e8f8f0130b5fd94 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 21 Feb 2024 14:36:57 +0100 Subject: [PATCH 058/401] llava : add --skip-unknown to 1.6 convert.py (#5632) This commit adds the `--skip-unknown` option to the convert.py script and removes the saving of the updated checkpoints to avoid updating possibly checked out files. The motivation for this change is that this was done for 1.5 in Commit fc0c8d286a533363a9a663510b62af85ffad58b3 ("llava : update surgery script to not remove tensors") and makes the examples more consistent. Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 13 ++++++------- examples/llava/llava-surgery-v2.py | 12 ------------ 2 files changed, 6 insertions(+), 19 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 25ea967153510..35e6d9e5d00f7 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -63,13 +63,12 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director ```console git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b ``` -2) Backup your pth/safetensor model files as llava-surgery modifies them -3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: +2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory -4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: +3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: ```console mkdir vit cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin @@ -77,18 +76,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json ``` -5) Create the visual gguf model: +4) Create the visual gguf model: ```console python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP -6) Then convert the model to gguf format: +5) Then convert the model to gguf format: ```console -python ./convert.py ../llava-v1.6-vicuna-7b/ +python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` -7) And finally we can run the llava-cli using the 1.6 model version: +6) And finally we can run the llava-cli using the 1.6 model version: ```console ./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 ``` diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava-surgery-v2.py index 5bc5bc5137fe0..eb56d6988ac26 100644 --- a/examples/llava/llava-surgery-v2.py +++ b/examples/llava/llava-surgery-v2.py @@ -65,9 +65,7 @@ def clean_vision_tower_from_checkpoint(checkpoint_path): for name in clip_tensors: del checkpoint[name] - # Save the updated checkpoint checkpoint_path = checkpoint_path - save_model(checkpoint, checkpoint_path, file_type) return True return False @@ -152,16 +150,6 @@ def proj_criteria(checkpoint): if len(projector) > 0: save_model(projector, f"{args.model}/llava.projector", 'pytorch') -for name in mm_tensors: - del last_checkpoint[name] -for name in first_mm_tensors: - del first_checkpoint[name] - -if len(mm_tensors) > 0: - save_model(last_checkpoint, projector_checkpoint_path, file_type) -if len(first_mm_tensors) > 0: - save_model(first_checkpoint, newline_checkpoint_path, file_type) - print("Done!") print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.") print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.") From c14f72db9c62d71d35eb1c141745c0bd0cb27b49 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 21 Feb 2024 15:39:54 +0200 Subject: [PATCH 059/401] readme : update hot topics --- README.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 225db8e49ce39..ce5dec7caeac9 100644 --- a/README.md +++ b/README.md @@ -10,13 +10,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240 -- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) -- New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow -- Collecting Apple Silicon performance stats: - - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 - - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508 +- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 +- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 - Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 ---- From eccd7a26ddbff19e4b8805648f5f14c501957859 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 21 Feb 2024 16:17:10 +0200 Subject: [PATCH 060/401] sync : ggml (#5633) * ggml : fix conv_2d batch mode (ggml/737) Co-authored-by: bssrdf * ggml : compute forward no longer pass src tensors (ggml/729) * sync : ggml ggml-ci --------- Co-authored-by: bssrdf Co-authored-by: bssrdf --- ggml.c | 1150 +++++++++++++++++++++++++--------------- scripts/sync-ggml.last | 2 +- 2 files changed, 711 insertions(+), 441 deletions(-) diff --git a/ggml.c b/ggml.c index 91adbb0aee6bc..5b9fa741a6479 100644 --- a/ggml.c +++ b/ggml.c @@ -5644,7 +5644,9 @@ struct ggml_tensor * ggml_conv_2d( ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW] ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]), a->ne[3])); // [OC，IC, KH, KW] => [OC, IC * KH * KW] - result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], a->ne[3], im2col->ne[3]); // [N, OC, OH, OW] + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], im2col->ne[3], a->ne[3]); // [OC, N, OH, OW] + result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OH, OW] + return result; } @@ -6650,8 +6652,10 @@ void ggml_set_param( static void ggml_compute_forward_dup_same_cont( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == dst->type); @@ -6682,8 +6686,10 @@ static void ggml_compute_forward_dup_same_cont( } static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -6696,7 +6702,7 @@ static void ggml_compute_forward_dup_f16( const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { - ggml_compute_forward_dup_same_cont(params, src0, dst); + ggml_compute_forward_dup_same_cont(params, dst); return; } @@ -6953,8 +6959,10 @@ static void ggml_compute_forward_dup_f16( static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -6967,7 +6975,7 @@ static void ggml_compute_forward_dup_f32( const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { - ggml_compute_forward_dup_same_cont(params, src0, dst); + ggml_compute_forward_dup_same_cont(params, dst); return; } @@ -7203,8 +7211,10 @@ static void ggml_compute_forward_dup_f32( // A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. static void ggml_compute_forward_dup_bytes( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(src0->type == dst->type); @@ -7213,7 +7223,7 @@ static void ggml_compute_forward_dup_bytes( } if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { - ggml_compute_forward_dup_same_cont(params, src0, dst); + ggml_compute_forward_dup_same_cont(params, dst); return; } @@ -7352,21 +7362,23 @@ static void ggml_compute_forward_dup_bytes( static void ggml_compute_forward_dup( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + if (src0->type == dst->type) { - ggml_compute_forward_dup_bytes(params, src0, dst); + ggml_compute_forward_dup_bytes(params, dst); return; } switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_dup_f16(params, src0, dst); + ggml_compute_forward_dup_f16(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_dup_f32(params, src0, dst); + ggml_compute_forward_dup_f32(params, dst); } break; default: { @@ -7379,9 +7391,11 @@ static void ggml_compute_forward_dup( static void ggml_compute_forward_add_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -7467,9 +7481,11 @@ static void ggml_compute_forward_add_f32( static void ggml_compute_forward_add_f16_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -7544,9 +7560,11 @@ static void ggml_compute_forward_add_f16_f32( static void ggml_compute_forward_add_f16_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -7598,9 +7616,11 @@ static void ggml_compute_forward_add_f16_f16( static void ggml_compute_forward_add_q_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -7676,14 +7696,16 @@ static void ggml_compute_forward_add_q_f32( static void ggml_compute_forward_add( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + switch (src0->type) { case GGML_TYPE_F32: { if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f32(params, src0, src1, dst); + ggml_compute_forward_add_f32(params, dst); } else { GGML_ASSERT(false); @@ -7692,10 +7714,10 @@ static void ggml_compute_forward_add( case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add_f16_f16(params, src0, src1, dst); + ggml_compute_forward_add_f16_f16(params, dst); } else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f16_f32(params, src0, src1, dst); + ggml_compute_forward_add_f16_f32(params, dst); } else { GGML_ASSERT(false); @@ -7717,7 +7739,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: { - ggml_compute_forward_add_q_f32(params, src0, src1, dst); + ggml_compute_forward_add_q_f32(params, dst); } break; default: { @@ -7730,9 +7752,11 @@ static void ggml_compute_forward_add( static void ggml_compute_forward_add1_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); @@ -7782,9 +7806,11 @@ static void ggml_compute_forward_add1_f32( static void ggml_compute_forward_add1_f16_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); @@ -7832,9 +7858,11 @@ static void ggml_compute_forward_add1_f16_f32( static void ggml_compute_forward_add1_f16_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); @@ -7882,9 +7910,11 @@ static void ggml_compute_forward_add1_f16_f16( static void ggml_compute_forward_add1_q_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); @@ -7949,21 +7979,23 @@ static void ggml_compute_forward_add1_q_f32( static void ggml_compute_forward_add1( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_add1_f32(params, src0, src1, dst); + ggml_compute_forward_add1_f32(params, dst); } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add1_f16_f16(params, src0, src1, dst); + ggml_compute_forward_add1_f16_f16(params, dst); } else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_f16_f32(params, src0, src1, dst); + ggml_compute_forward_add1_f16_f32(params, dst); } else { GGML_ASSERT(false); @@ -7986,7 +8018,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: { - ggml_compute_forward_add1_q_f32(params, src0, src1, dst); + ggml_compute_forward_add1_q_f32(params, dst); } break; default: { @@ -7999,9 +8031,11 @@ static void ggml_compute_forward_add1( static void ggml_compute_forward_acc_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); @@ -8081,14 +8115,14 @@ static void ggml_compute_forward_acc_f32( static void ggml_compute_forward_acc( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_acc_f32(params, src0, src1, dst); + ggml_compute_forward_acc_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -8118,9 +8152,11 @@ static void ggml_compute_forward_acc( static void ggml_compute_forward_sub_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); @@ -8178,13 +8214,14 @@ static void ggml_compute_forward_sub_f32( static void ggml_compute_forward_sub( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sub_f32(params, src0, src1, dst); + ggml_compute_forward_sub_f32(params, dst); } break; default: { @@ -8197,9 +8234,11 @@ static void ggml_compute_forward_sub( static void ggml_compute_forward_mul_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -8280,15 +8319,17 @@ static void ggml_compute_forward_mul_f32( static void ggml_compute_forward_mul( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_mul_f32(params, src0, src1, dst); + ggml_compute_forward_mul_f32(params, dst); } break; default: { @@ -8301,9 +8342,11 @@ static void ggml_compute_forward_mul( static void ggml_compute_forward_div_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -8374,13 +8417,14 @@ static void ggml_compute_forward_div_f32( static void ggml_compute_forward_div( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_div_f32(params, src0, src1, dst); + ggml_compute_forward_div_f32(params, dst); } break; default: { @@ -8393,8 +8437,10 @@ static void ggml_compute_forward_div( static void ggml_compute_forward_sqr_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -8417,12 +8463,14 @@ static void ggml_compute_forward_sqr_f32( static void ggml_compute_forward_sqr( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sqr_f32(params, src0, dst); + ggml_compute_forward_sqr_f32(params, dst); } break; default: { @@ -8435,8 +8483,10 @@ static void ggml_compute_forward_sqr( static void ggml_compute_forward_sqrt_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -8459,12 +8509,14 @@ static void ggml_compute_forward_sqrt_f32( static void ggml_compute_forward_sqrt( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sqrt_f32(params, src0, dst); + ggml_compute_forward_sqrt_f32(params, dst); } break; default: { @@ -8477,8 +8529,10 @@ static void ggml_compute_forward_sqrt( static void ggml_compute_forward_log_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(src0, dst)); @@ -8501,12 +8555,14 @@ static void ggml_compute_forward_log_f32( static void ggml_compute_forward_log( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_log_f32(params, src0, dst); + ggml_compute_forward_log_f32(params, dst); } break; default: { @@ -8519,8 +8575,10 @@ static void ggml_compute_forward_log( static void ggml_compute_forward_sum_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_is_scalar(dst)); @@ -8552,8 +8610,10 @@ static void ggml_compute_forward_sum_f32( static void ggml_compute_forward_sum_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_is_scalar(dst)); @@ -8584,16 +8644,18 @@ static void ggml_compute_forward_sum_f16( static void ggml_compute_forward_sum( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sum_f32(params, src0, dst); + ggml_compute_forward_sum_f32(params, dst); } break; case GGML_TYPE_F16: { - ggml_compute_forward_sum_f16(params, src0, dst); + ggml_compute_forward_sum_f16(params, dst); } break; default: { @@ -8606,8 +8668,10 @@ static void ggml_compute_forward_sum( static void ggml_compute_forward_sum_rows_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -8639,12 +8703,14 @@ static void ggml_compute_forward_sum_rows_f32( static void ggml_compute_forward_sum_rows( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sum_rows_f32(params, src0, dst); + ggml_compute_forward_sum_rows_f32(params, dst); } break; default: { @@ -8657,8 +8723,10 @@ static void ggml_compute_forward_sum_rows( static void ggml_compute_forward_mean_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -8694,12 +8762,14 @@ static void ggml_compute_forward_mean_f32( static void ggml_compute_forward_mean( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_mean_f32(params, src0, dst); + ggml_compute_forward_mean_f32(params, dst); } break; default: { @@ -8712,8 +8782,10 @@ static void ggml_compute_forward_mean( static void ggml_compute_forward_argmax_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -8740,12 +8812,14 @@ static void ggml_compute_forward_argmax_f32( static void ggml_compute_forward_argmax( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_argmax_f32(params, src0, dst); + ggml_compute_forward_argmax_f32(params, dst); } break; default: { @@ -8758,8 +8832,10 @@ static void ggml_compute_forward_argmax( static void ggml_compute_forward_repeat_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); @@ -8801,8 +8877,10 @@ static void ggml_compute_forward_repeat_f32( static void ggml_compute_forward_repeat_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); @@ -8847,18 +8925,20 @@ static void ggml_compute_forward_repeat_f16( static void ggml_compute_forward_repeat( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: case GGML_TYPE_I16: { - ggml_compute_forward_repeat_f16(params, src0, dst); + ggml_compute_forward_repeat_f16(params, dst); } break; case GGML_TYPE_F32: case GGML_TYPE_I32: { - ggml_compute_forward_repeat_f32(params, src0, dst); + ggml_compute_forward_repeat_f32(params, dst); } break; default: { @@ -8871,8 +8951,10 @@ static void ggml_compute_forward_repeat( static void ggml_compute_forward_repeat_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(dst, src0)); @@ -8928,12 +9010,14 @@ static void ggml_compute_forward_repeat_back_f32( static void ggml_compute_forward_repeat_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_repeat_back_f32(params, src0, dst); + ggml_compute_forward_repeat_back_f32(params, dst); } break; default: { @@ -8946,10 +9030,11 @@ static void ggml_compute_forward_repeat_back( static void ggml_compute_forward_concat_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -8994,14 +9079,15 @@ static void ggml_compute_forward_concat_f32( static void ggml_compute_forward_concat( const struct ggml_compute_params* params, - const struct ggml_tensor* src0, - const struct ggml_tensor* src1, struct ggml_tensor* dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: case GGML_TYPE_I32: { - ggml_compute_forward_concat_f32(params, src0, src1, dst); + ggml_compute_forward_concat_f32(params, dst); } break; default: { @@ -9014,8 +9100,10 @@ static void ggml_compute_forward_concat( static void ggml_compute_forward_abs_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9038,12 +9126,14 @@ static void ggml_compute_forward_abs_f32( static void ggml_compute_forward_abs( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_abs_f32(params, src0, dst); + ggml_compute_forward_abs_f32(params, dst); } break; default: { @@ -9056,8 +9146,10 @@ static void ggml_compute_forward_abs( static void ggml_compute_forward_sgn_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9080,12 +9172,14 @@ static void ggml_compute_forward_sgn_f32( static void ggml_compute_forward_sgn( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_sgn_f32(params, src0, dst); + ggml_compute_forward_sgn_f32(params, dst); } break; default: { @@ -9098,8 +9192,10 @@ static void ggml_compute_forward_sgn( static void ggml_compute_forward_neg_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9122,12 +9218,14 @@ static void ggml_compute_forward_neg_f32( static void ggml_compute_forward_neg( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_neg_f32(params, src0, dst); + ggml_compute_forward_neg_f32(params, dst); } break; default: { @@ -9140,8 +9238,10 @@ static void ggml_compute_forward_neg( static void ggml_compute_forward_step_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9164,12 +9264,14 @@ static void ggml_compute_forward_step_f32( static void ggml_compute_forward_step( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_step_f32(params, src0, dst); + ggml_compute_forward_step_f32(params, dst); } break; default: { @@ -9182,8 +9284,10 @@ static void ggml_compute_forward_step( static void ggml_compute_forward_tanh_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9206,12 +9310,14 @@ static void ggml_compute_forward_tanh_f32( static void ggml_compute_forward_tanh( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_tanh_f32(params, src0, dst); + ggml_compute_forward_tanh_f32(params, dst); } break; default: { @@ -9224,8 +9330,10 @@ static void ggml_compute_forward_tanh( static void ggml_compute_forward_elu_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9248,12 +9356,14 @@ static void ggml_compute_forward_elu_f32( static void ggml_compute_forward_elu( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_elu_f32(params, src0, dst); + ggml_compute_forward_elu_f32(params, dst); } break; default: { @@ -9266,8 +9376,10 @@ static void ggml_compute_forward_elu( static void ggml_compute_forward_relu_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9290,12 +9402,14 @@ static void ggml_compute_forward_relu_f32( static void ggml_compute_forward_relu( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_relu_f32(params, src0, dst); + ggml_compute_forward_relu_f32(params, dst); } break; default: { @@ -9308,8 +9422,10 @@ static void ggml_compute_forward_relu( static void ggml_compute_forward_gelu_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); @@ -9349,12 +9465,14 @@ static void ggml_compute_forward_gelu_f32( static void ggml_compute_forward_gelu( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_gelu_f32(params, src0, dst); + ggml_compute_forward_gelu_f32(params, dst); } break; default: { @@ -9367,8 +9485,10 @@ static void ggml_compute_forward_gelu( static void ggml_compute_forward_gelu_quick_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); @@ -9408,12 +9528,14 @@ static void ggml_compute_forward_gelu_quick_f32( static void ggml_compute_forward_gelu_quick( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_gelu_quick_f32(params, src0, dst); + ggml_compute_forward_gelu_quick_f32(params, dst); } break; default: { @@ -9426,8 +9548,10 @@ static void ggml_compute_forward_gelu_quick( static void ggml_compute_forward_silu_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); @@ -9467,12 +9591,14 @@ static void ggml_compute_forward_silu_f32( static void ggml_compute_forward_silu( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_silu_f32(params, src0, dst); + ggml_compute_forward_silu_f32(params, dst); } break; default: { @@ -9484,8 +9610,10 @@ static void ggml_compute_forward_silu( static void ggml_compute_forward_leaky_relu_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9511,12 +9639,14 @@ static void ggml_compute_forward_leaky_relu_f32( static void ggml_compute_forward_leaky_relu( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_leaky_relu_f32(params, src0, dst); + ggml_compute_forward_leaky_relu_f32(params, dst); } break; default: { @@ -9529,9 +9659,11 @@ static void ggml_compute_forward_leaky_relu( static void ggml_compute_forward_silu_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * grad, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * grad = dst->src[1]; + GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); @@ -9574,13 +9706,14 @@ static void ggml_compute_forward_silu_back_f32( static void ggml_compute_forward_silu_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * grad, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_silu_back_f32(params, src0, grad, dst); + ggml_compute_forward_silu_back_f32(params, dst); } break; default: { @@ -9592,8 +9725,10 @@ static void ggml_compute_forward_silu_back( static void ggml_compute_forward_hardswish_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9615,12 +9750,14 @@ static void ggml_compute_forward_hardswish_f32( } static void ggml_compute_forward_hardswish( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_hardswish_f32(params, src0, dst); + ggml_compute_forward_hardswish_f32(params, dst); } break; default: { @@ -9631,8 +9768,10 @@ static void ggml_compute_forward_hardswish( static void ggml_compute_forward_hardsigmoid_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); @@ -9655,12 +9794,14 @@ static void ggml_compute_forward_hardsigmoid_f32( static void ggml_compute_forward_hardsigmoid( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_hardsigmoid_f32(params, src0, dst); + ggml_compute_forward_hardsigmoid_f32(params, dst); } break; default: { @@ -9674,8 +9815,10 @@ static void ggml_compute_forward_hardsigmoid( static void ggml_compute_forward_norm_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -9727,12 +9870,14 @@ static void ggml_compute_forward_norm_f32( static void ggml_compute_forward_norm( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_norm_f32(params, src0, dst); + ggml_compute_forward_norm_f32(params, dst); } break; default: { @@ -9745,8 +9890,10 @@ static void ggml_compute_forward_norm( static void ggml_compute_forward_rms_norm_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -9795,12 +9942,14 @@ static void ggml_compute_forward_rms_norm_f32( static void ggml_compute_forward_rms_norm( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_rms_norm_f32(params, src0, dst); + ggml_compute_forward_rms_norm_f32(params, dst); } break; default: { @@ -9811,9 +9960,11 @@ static void ggml_compute_forward_rms_norm( static void ggml_compute_forward_rms_norm_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -9968,13 +10119,14 @@ static void ggml_compute_forward_rms_norm_back_f32( static void ggml_compute_forward_rms_norm_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst); + ggml_compute_forward_rms_norm_back_f32(params, dst); } break; default: { @@ -9987,8 +10139,10 @@ static void ggml_compute_forward_rms_norm_back( static void ggml_compute_forward_group_norm_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -10059,12 +10213,14 @@ static void ggml_compute_forward_group_norm_f32( static void ggml_compute_forward_group_norm( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_group_norm_f32(params, src0, dst); + ggml_compute_forward_group_norm_f32(params, dst); } break; default: { @@ -10110,9 +10266,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -10357,10 +10515,11 @@ static void ggml_compute_forward_mul_mat( static void ggml_compute_forward_mul_mat_id( const struct ggml_compute_params * params, - const struct ggml_tensor * ids, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + const struct ggml_tensor * ids = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS GGML_TENSOR_BINARY_OP_LOCALS @@ -10551,9 +10710,11 @@ static void ggml_compute_forward_mul_mat_id( static void ggml_compute_forward_out_prod_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + // int64_t t0 = ggml_perf_time_us(); // UNUSED(t0); @@ -10743,9 +10904,11 @@ static void ggml_compute_forward_out_prod_f32( static void ggml_compute_forward_out_prod_q_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + // int64_t t0 = ggml_perf_time_us(); // UNUSED(t0); @@ -10856,9 +11019,10 @@ static void ggml_compute_forward_out_prod_q_f32( static void ggml_compute_forward_out_prod( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -10876,16 +11040,16 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: { - ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); + ggml_compute_forward_out_prod_q_f32(params, dst); } break; case GGML_TYPE_F16: { GGML_ASSERT(false); // todo - // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst); + // ggml_compute_forward_out_prod_f16_f32(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_out_prod_f32(params, src0, src1, dst); + ggml_compute_forward_out_prod_f32(params, dst); } break; default: { @@ -10898,8 +11062,10 @@ static void ggml_compute_forward_out_prod( static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); @@ -10940,12 +11106,14 @@ static void ggml_compute_forward_scale_f32( static void ggml_compute_forward_scale( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_scale_f32(params, src0, dst); + ggml_compute_forward_scale_f32(params, dst); } break; default: { @@ -10958,9 +11126,11 @@ static void ggml_compute_forward_scale( static void ggml_compute_forward_set_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); @@ -11031,14 +11201,14 @@ static void ggml_compute_forward_set_f32( static void ggml_compute_forward_set( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_set_f32(params, src0, src1, dst); + ggml_compute_forward_set_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -11068,29 +11238,25 @@ static void ggml_compute_forward_set( static void ggml_compute_forward_cpy( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, src0, dst); + ggml_compute_forward_dup(params, dst); } // ggml_compute_forward_cont static void ggml_compute_forward_cont( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, src0, dst); + ggml_compute_forward_dup(params, dst); } // ggml_compute_forward_reshape static void ggml_compute_forward_reshape( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { // NOP UNUSED(params); - UNUSED(src0); UNUSED(dst); } @@ -11098,39 +11264,41 @@ static void ggml_compute_forward_reshape( static void ggml_compute_forward_view( const struct ggml_compute_params * params, - const struct ggml_tensor * src0) { + const struct ggml_tensor * dst) { // NOP UNUSED(params); - UNUSED(src0); + UNUSED(dst); } // ggml_compute_forward_permute static void ggml_compute_forward_permute( const struct ggml_compute_params * params, - const struct ggml_tensor * src0) { + const struct ggml_tensor * dst) { // NOP UNUSED(params); - UNUSED(src0); + UNUSED(dst); } // ggml_compute_forward_transpose static void ggml_compute_forward_transpose( const struct ggml_compute_params * params, - const struct ggml_tensor * src0) { + const struct ggml_tensor * dst) { // NOP UNUSED(params); - UNUSED(src0); + UNUSED(dst); } // ggml_compute_forward_get_rows static void ggml_compute_forward_get_rows_q( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11166,9 +11334,11 @@ static void ggml_compute_forward_get_rows_q( static void ggml_compute_forward_get_rows_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11201,9 +11371,11 @@ static void ggml_compute_forward_get_rows_f16( static void ggml_compute_forward_get_rows_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11236,9 +11408,10 @@ static void ggml_compute_forward_get_rows_f32( static void ggml_compute_forward_get_rows( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -11257,16 +11430,16 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: { - ggml_compute_forward_get_rows_q(params, src0, src1, dst); + ggml_compute_forward_get_rows_q(params, dst); } break; case GGML_TYPE_F16: { - ggml_compute_forward_get_rows_f16(params, src0, src1, dst); + ggml_compute_forward_get_rows_f16(params, dst); } break; case GGML_TYPE_F32: case GGML_TYPE_I32: { - ggml_compute_forward_get_rows_f32(params, src0, src1, dst); + ggml_compute_forward_get_rows_f32(params, dst); } break; default: { @@ -11297,9 +11470,11 @@ static void ggml_compute_forward_get_rows( static void ggml_compute_forward_get_rows_back_f32_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_is_contiguous(dst)); @@ -11334,9 +11509,11 @@ static void ggml_compute_forward_get_rows_back_f32_f16( static void ggml_compute_forward_get_rows_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_is_contiguous(dst)); @@ -11371,17 +11548,18 @@ static void ggml_compute_forward_get_rows_back_f32( static void ggml_compute_forward_get_rows_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, dst); + ggml_compute_forward_get_rows_back_f32_f16(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_get_rows_back_f32(params, src0, src1, dst); + ggml_compute_forward_get_rows_back_f32(params, dst); } break; default: { @@ -11412,8 +11590,10 @@ static void ggml_compute_forward_get_rows_back( static void ggml_compute_forward_diag_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11452,12 +11632,14 @@ static void ggml_compute_forward_diag_f32( static void ggml_compute_forward_diag( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_diag_f32(params, src0, dst); + ggml_compute_forward_diag_f32(params, dst); } break; default: { @@ -11470,10 +11652,11 @@ static void ggml_compute_forward_diag( static void ggml_compute_forward_diag_mask_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst, const float value) { + const struct ggml_tensor * src0 = dst->src[0]; + const int ith = params->ith; const int nth = params->nth; @@ -11523,12 +11706,14 @@ static void ggml_compute_forward_diag_mask_f32( static void ggml_compute_forward_diag_mask_inf( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY); + ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); } break; default: { @@ -11539,12 +11724,14 @@ static void ggml_compute_forward_diag_mask_inf( static void ggml_compute_forward_diag_mask_zero( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_diag_mask_f32(params, src0, dst, 0); + ggml_compute_forward_diag_mask_f32(params, dst, 0); } break; default: { @@ -11557,10 +11744,12 @@ static void ggml_compute_forward_diag_mask_zero( static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * src2, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); @@ -11671,14 +11860,14 @@ static void ggml_compute_forward_soft_max_f32( static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * src2, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_soft_max_f32(params, src0, src1, src2, dst); + ggml_compute_forward_soft_max_f32(params, dst); } break; default: { @@ -11691,9 +11880,11 @@ static void ggml_compute_forward_soft_max( static void ggml_compute_forward_soft_max_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_contiguous(dst)); @@ -11768,13 +11959,14 @@ static void ggml_compute_forward_soft_max_back_f32( static void ggml_compute_forward_soft_max_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst); + ggml_compute_forward_soft_max_back_f32(params, dst); } break; default: { @@ -11787,8 +11979,10 @@ static void ggml_compute_forward_soft_max_back( static void ggml_compute_forward_alibi_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11844,8 +12038,10 @@ static void ggml_compute_forward_alibi_f32( static void ggml_compute_forward_alibi_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11904,16 +12100,18 @@ static void ggml_compute_forward_alibi_f16( static void ggml_compute_forward_alibi( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_alibi_f16(params, src0, dst); + ggml_compute_forward_alibi_f16(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_alibi_f32(params, src0, dst); + ggml_compute_forward_alibi_f32(params, dst); } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -11946,8 +12144,10 @@ static void ggml_compute_forward_alibi( static void ggml_compute_forward_clamp_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -11986,12 +12186,14 @@ static void ggml_compute_forward_clamp_f32( static void ggml_compute_forward_clamp( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_clamp_f32(params, src0, dst); + ggml_compute_forward_clamp_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -12081,10 +12283,12 @@ GGML_CALL void ggml_rope_yarn_corr_dims( static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst, const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -12257,10 +12461,12 @@ static void ggml_compute_forward_rope_f32( static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst, const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -12422,17 +12628,18 @@ static void ggml_compute_forward_rope_f16( static void ggml_compute_forward_rope( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_f16(params, src0, src1, dst, true); + ggml_compute_forward_rope_f16(params, dst, true); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, src1, dst, true); + ggml_compute_forward_rope_f32(params, dst, true); } break; default: { @@ -12445,17 +12652,18 @@ static void ggml_compute_forward_rope( static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_f16(params, src0, src1, dst, false); + ggml_compute_forward_rope_f16(params, dst, false); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, src1, dst, false); + ggml_compute_forward_rope_f32(params, dst, false); } break; default: { @@ -12468,9 +12676,11 @@ static void ggml_compute_forward_rope_back( static void ggml_compute_forward_conv_transpose_1d_f16_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -12565,9 +12775,11 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32( static void ggml_compute_forward_conv_transpose_1d_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -12662,17 +12874,18 @@ static void ggml_compute_forward_conv_transpose_1d_f32( static void ggml_compute_forward_conv_transpose_1d( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst); + ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst); + ggml_compute_forward_conv_transpose_1d_f32(params, dst); } break; default: { @@ -12686,9 +12899,11 @@ static void ggml_compute_forward_conv_transpose_1d( // dst: result [N, OH, OW, IC*KH*KW] static void ggml_compute_forward_im2col_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -12772,9 +12987,11 @@ static void ggml_compute_forward_im2col_f32( // dst: result [N, OH, OW, IC*KH*KW] static void ggml_compute_forward_im2col_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16); @@ -12854,17 +13071,15 @@ static void ggml_compute_forward_im2col_f16( static void ggml_compute_forward_im2col( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (dst->type) { case GGML_TYPE_F16: { - ggml_compute_forward_im2col_f16(params, src0, src1, dst); + ggml_compute_forward_im2col_f16(params, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_im2col_f32(params, src0, src1, dst); + ggml_compute_forward_im2col_f32(params, dst); } break; default: { @@ -12878,9 +13093,11 @@ static void ggml_compute_forward_im2col( static void ggml_compute_forward_conv_transpose_2d( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -12984,9 +13201,11 @@ static void ggml_compute_forward_conv_transpose_2d( static void ggml_compute_forward_pool_1d_sk_p0( const struct ggml_compute_params * params, const enum ggml_op_pool op, - const struct ggml_tensor * src, const int k, struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); @@ -13035,7 +13254,6 @@ static void ggml_compute_forward_pool_1d_sk_p0( static void ggml_compute_forward_pool_1d( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { const int32_t * opts = (const int32_t *)dst->op_params; @@ -13046,15 +13264,17 @@ static void ggml_compute_forward_pool_1d( GGML_ASSERT(p0 == 0); // padding not supported GGML_ASSERT(k0 == s0); // only s = k supported - ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst); + ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); } // ggml_compute_forward_pool_2d static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, - const struct ggml_tensor * src, struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(params->ith == 0); @@ -13127,9 +13347,10 @@ static void ggml_compute_forward_pool_2d( static void ggml_compute_forward_upscale_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -13166,12 +13387,14 @@ static void ggml_compute_forward_upscale_f32( static void ggml_compute_forward_upscale( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_upscale_f32(params, src0, dst); + ggml_compute_forward_upscale_f32(params, dst); } break; default: { @@ -13184,9 +13407,10 @@ static void ggml_compute_forward_upscale( static void ggml_compute_forward_pad_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -13224,12 +13448,14 @@ static void ggml_compute_forward_pad_f32( static void ggml_compute_forward_pad( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_pad_f32(params, src0, dst); + ggml_compute_forward_pad_f32(params, dst); } break; default: { @@ -13242,9 +13468,10 @@ static void ggml_compute_forward_pad( static void ggml_compute_forward_argsort_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -13284,13 +13511,14 @@ static void ggml_compute_forward_argsort_f32( static void ggml_compute_forward_argsort( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_argsort_f32(params, src0, dst); + ggml_compute_forward_argsort_f32(params, dst); } break; default: { @@ -13303,11 +13531,13 @@ static void ggml_compute_forward_argsort( static void ggml_compute_forward_flash_attn_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * k = dst->src[1]; + const struct ggml_tensor * v = dst->src[2]; + int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -13493,11 +13723,13 @@ static void ggml_compute_forward_flash_attn_f32( static void ggml_compute_forward_flash_attn_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * k = dst->src[1]; + const struct ggml_tensor * v = dst->src[2]; + int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -13719,19 +13951,19 @@ static void ggml_compute_forward_flash_attn_f16( static void ggml_compute_forward_flash_attn( const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + switch (q->type) { case GGML_TYPE_F16: { - ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst); + ggml_compute_forward_flash_attn_f16(params, masked, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst); + ggml_compute_forward_flash_attn_f32(params, masked, dst); } break; default: { @@ -13744,12 +13976,14 @@ static void ggml_compute_forward_flash_attn( static void ggml_compute_forward_flash_ff_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * a, // F16 - const struct ggml_tensor * b0, // F16 fc_w - const struct ggml_tensor * b1, // F32 fc_b - const struct ggml_tensor * c0, // F16 proj_w - const struct ggml_tensor * c1, // F32 proj_b struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; // F16 + const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w + const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b + const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w + const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b + int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -13877,16 +14111,14 @@ static void ggml_compute_forward_flash_ff_f16( static void ggml_compute_forward_flash_ff( const struct ggml_compute_params * params, - const struct ggml_tensor * a, - const struct ggml_tensor * b0, - const struct ggml_tensor * b1, - const struct ggml_tensor * c0, - const struct ggml_tensor * c1, struct ggml_tensor * dst) { + + const struct ggml_tensor * b0 = dst->src[1]; + switch (b0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst); + ggml_compute_forward_flash_ff_f16(params, dst); } break; case GGML_TYPE_F32: { @@ -13903,12 +14135,14 @@ static void ggml_compute_forward_flash_ff( static void ggml_compute_forward_flash_attn_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * d, const bool masked, struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * k = dst->src[1]; + const struct ggml_tensor * v = dst->src[2]; + const struct ggml_tensor * d = dst->src[3]; + int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -14256,16 +14490,15 @@ static void ggml_compute_forward_flash_attn_back_f32( static void ggml_compute_forward_flash_attn_back( const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * d, const bool masked, struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + switch (q->type) { case GGML_TYPE_F32: { - ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst); + ggml_compute_forward_flash_attn_back_f32(params, masked, dst); } break; default: { @@ -14278,8 +14511,10 @@ static void ggml_compute_forward_flash_attn_back( static void ggml_compute_forward_win_part_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14322,12 +14557,14 @@ static void ggml_compute_forward_win_part_f32( static void ggml_compute_forward_win_part( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_win_part_f32(params, src0, dst); + ggml_compute_forward_win_part_f32(params, dst); } break; default: { @@ -14340,8 +14577,10 @@ static void ggml_compute_forward_win_part( static void ggml_compute_forward_win_unpart_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14382,12 +14621,14 @@ static void ggml_compute_forward_win_unpart_f32( static void ggml_compute_forward_win_unpart( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_win_unpart_f32(params, src0, dst); + ggml_compute_forward_win_unpart_f32(params, dst); } break; default: { @@ -14400,58 +14641,58 @@ static void ggml_compute_forward_win_unpart( static void ggml_compute_forward_unary( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + const enum ggml_unary_op op = ggml_get_unary_op(dst); switch (op) { case GGML_UNARY_OP_ABS: { - ggml_compute_forward_abs(params, src0, dst); + ggml_compute_forward_abs(params, dst); } break; case GGML_UNARY_OP_SGN: { - ggml_compute_forward_sgn(params, src0, dst); + ggml_compute_forward_sgn(params, dst); } break; case GGML_UNARY_OP_NEG: { - ggml_compute_forward_neg(params, src0, dst); + ggml_compute_forward_neg(params, dst); } break; case GGML_UNARY_OP_STEP: { - ggml_compute_forward_step(params, src0, dst); + ggml_compute_forward_step(params, dst); } break; case GGML_UNARY_OP_TANH: { - ggml_compute_forward_tanh(params, src0, dst); + ggml_compute_forward_tanh(params, dst); } break; case GGML_UNARY_OP_ELU: { - ggml_compute_forward_elu(params, src0, dst); + ggml_compute_forward_elu(params, dst); } break; case GGML_UNARY_OP_RELU: { - ggml_compute_forward_relu(params, src0, dst); + ggml_compute_forward_relu(params, dst); } break; case GGML_UNARY_OP_GELU: { - ggml_compute_forward_gelu(params, src0, dst); + ggml_compute_forward_gelu(params, dst); } break; case GGML_UNARY_OP_GELU_QUICK: { - ggml_compute_forward_gelu_quick(params, src0, dst); + ggml_compute_forward_gelu_quick(params, dst); } break; case GGML_UNARY_OP_SILU: { - ggml_compute_forward_silu(params, src0, dst); + ggml_compute_forward_silu(params, dst); } break; case GGML_UNARY_OP_HARDSWISH: { - ggml_compute_forward_hardswish(params, src0, dst); + ggml_compute_forward_hardswish(params, dst); } break; case GGML_UNARY_OP_HARDSIGMOID: { - ggml_compute_forward_hardsigmoid(params, src0, dst); + ggml_compute_forward_hardsigmoid(params, dst); } break; default: { @@ -14464,8 +14705,10 @@ static void ggml_compute_forward_unary( static void ggml_compute_forward_get_rel_pos_f16( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14491,12 +14734,14 @@ static void ggml_compute_forward_get_rel_pos_f16( static void ggml_compute_forward_get_rel_pos( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_get_rel_pos_f16(params, src0, dst); + ggml_compute_forward_get_rel_pos_f16(params, dst); } break; default: { @@ -14509,11 +14754,12 @@ static void ggml_compute_forward_get_rel_pos( static void ggml_compute_forward_add_rel_pos_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * src2, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; if (!inplace && params->type == GGML_TASK_INIT) { if (params->ith != 0) { @@ -14577,14 +14823,14 @@ static void ggml_compute_forward_add_rel_pos_f32( static void ggml_compute_forward_add_rel_pos( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * src2, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_add_rel_pos_f32(params, src0, src1, src2, dst); + ggml_compute_forward_add_rel_pos_f32(params, dst); } break; default: { @@ -14597,9 +14843,11 @@ static void ggml_compute_forward_add_rel_pos( static void ggml_compute_forward_map_unary_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -14621,13 +14869,15 @@ static void ggml_compute_forward_map_unary_f32( static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_map_unary_f32(params, src0, dst, fun); + ggml_compute_forward_map_unary_f32(params, dst, fun); } break; default: { @@ -14640,10 +14890,12 @@ static void ggml_compute_forward_map_unary( static void ggml_compute_forward_map_binary_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); @@ -14668,14 +14920,15 @@ static void ggml_compute_forward_map_binary_f32( static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun); + ggml_compute_forward_map_binary_f32(params, dst, fun); } break; default: { @@ -14688,9 +14941,11 @@ static void ggml_compute_forward_map_binary( static void ggml_compute_forward_map_custom1_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * a, struct ggml_tensor * dst, const ggml_custom1_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -14704,10 +14959,12 @@ static void ggml_compute_forward_map_custom1_f32( static void ggml_compute_forward_map_custom2_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * a, - const struct ggml_tensor * b, struct ggml_tensor * dst, const ggml_custom2_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -14721,11 +14978,13 @@ static void ggml_compute_forward_map_custom2_f32( static void ggml_compute_forward_map_custom3_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * a, - const struct ggml_tensor * b, - const struct ggml_tensor * c, struct ggml_tensor * dst, const ggml_custom3_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[1]; + assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -14739,8 +14998,10 @@ static void ggml_compute_forward_map_custom3_f32( static void ggml_compute_forward_map_custom1( const struct ggml_compute_params * params, - const struct ggml_tensor * a, struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14754,9 +15015,11 @@ static void ggml_compute_forward_map_custom1( static void ggml_compute_forward_map_custom2( const struct ggml_compute_params * params, - const struct ggml_tensor * a, - const struct ggml_tensor * b, struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14770,10 +15033,12 @@ static void ggml_compute_forward_map_custom2( static void ggml_compute_forward_map_custom3( const struct ggml_compute_params * params, - const struct ggml_tensor * a, - const struct ggml_tensor * b, - const struct ggml_tensor * c, struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[2]; + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -14787,9 +15052,11 @@ static void ggml_compute_forward_map_custom3( static void ggml_compute_forward_cross_entropy_loss_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_scalar(dst)); @@ -14893,13 +15160,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32( static void ggml_compute_forward_cross_entropy_loss( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst); + ggml_compute_forward_cross_entropy_loss_f32(params, dst); } break; default: { @@ -14912,10 +15180,12 @@ static void ggml_compute_forward_cross_entropy_loss( static void ggml_compute_forward_cross_entropy_loss_back_f32( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * opt0 = dst->src[2]; + GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); @@ -15002,14 +15272,14 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( static void ggml_compute_forward_cross_entropy_loss_back( const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst); + ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); } break; default: { @@ -15057,312 +15327,312 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm switch (tensor->op) { case GGML_OP_DUP: { - ggml_compute_forward_dup(params, tensor->src[0], tensor); + ggml_compute_forward_dup(params, tensor); } break; case GGML_OP_ADD: { - ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_add(params, tensor); } break; case GGML_OP_ADD1: { - ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_add1(params, tensor); } break; case GGML_OP_ACC: { - ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_acc(params, tensor); } break; case GGML_OP_SUB: { - ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_sub(params, tensor); } break; case GGML_OP_MUL: { - ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_mul(params, tensor); } break; case GGML_OP_DIV: { - ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_div(params, tensor); } break; case GGML_OP_SQR: { - ggml_compute_forward_sqr(params, tensor->src[0], tensor); + ggml_compute_forward_sqr(params, tensor); } break; case GGML_OP_SQRT: { - ggml_compute_forward_sqrt(params, tensor->src[0], tensor); + ggml_compute_forward_sqrt(params, tensor); } break; case GGML_OP_LOG: { - ggml_compute_forward_log(params, tensor->src[0], tensor); + ggml_compute_forward_log(params, tensor); } break; case GGML_OP_SUM: { - ggml_compute_forward_sum(params, tensor->src[0], tensor); + ggml_compute_forward_sum(params, tensor); } break; case GGML_OP_SUM_ROWS: { - ggml_compute_forward_sum_rows(params, tensor->src[0], tensor); + ggml_compute_forward_sum_rows(params, tensor); } break; case GGML_OP_MEAN: { - ggml_compute_forward_mean(params, tensor->src[0], tensor); + ggml_compute_forward_mean(params, tensor); } break; case GGML_OP_ARGMAX: { - ggml_compute_forward_argmax(params, tensor->src[0], tensor); + ggml_compute_forward_argmax(params, tensor); } break; case GGML_OP_REPEAT: { - ggml_compute_forward_repeat(params, tensor->src[0], tensor); + ggml_compute_forward_repeat(params, tensor); } break; case GGML_OP_REPEAT_BACK: { - ggml_compute_forward_repeat_back(params, tensor->src[0], tensor); + ggml_compute_forward_repeat_back(params, tensor); } break; case GGML_OP_CONCAT: { - ggml_compute_forward_concat(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_concat(params, tensor); } break; case GGML_OP_SILU_BACK: { - ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_silu_back(params, tensor); } break; case GGML_OP_NORM: { - ggml_compute_forward_norm(params, tensor->src[0], tensor); + ggml_compute_forward_norm(params, tensor); } break; case GGML_OP_RMS_NORM: { - ggml_compute_forward_rms_norm(params, tensor->src[0], tensor); + ggml_compute_forward_rms_norm(params, tensor); } break; case GGML_OP_RMS_NORM_BACK: { - ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_rms_norm_back(params, tensor); } break; case GGML_OP_GROUP_NORM: { - ggml_compute_forward_group_norm(params, tensor->src[0], tensor); + ggml_compute_forward_group_norm(params, tensor); } break; case GGML_OP_MUL_MAT: { - ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_mul_mat(params, tensor); } break; case GGML_OP_MUL_MAT_ID: { - ggml_compute_forward_mul_mat_id(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_mul_mat_id(params, tensor); } break; case GGML_OP_OUT_PROD: { - ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_out_prod(params, tensor); } break; case GGML_OP_SCALE: { - ggml_compute_forward_scale(params, tensor->src[0], tensor); + ggml_compute_forward_scale(params, tensor); } break; case GGML_OP_SET: { - ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_set(params, tensor); } break; case GGML_OP_CPY: { - ggml_compute_forward_cpy(params, tensor->src[0], tensor); + ggml_compute_forward_cpy(params, tensor); } break; case GGML_OP_CONT: { - ggml_compute_forward_cont(params, tensor->src[0], tensor); + ggml_compute_forward_cont(params, tensor); } break; case GGML_OP_RESHAPE: { - ggml_compute_forward_reshape(params, tensor->src[0], tensor); + ggml_compute_forward_reshape(params, tensor); } break; case GGML_OP_VIEW: { - ggml_compute_forward_view(params, tensor->src[0]); + ggml_compute_forward_view(params, tensor); } break; case GGML_OP_PERMUTE: { - ggml_compute_forward_permute(params, tensor->src[0]); + ggml_compute_forward_permute(params, tensor); } break; case GGML_OP_TRANSPOSE: { - ggml_compute_forward_transpose(params, tensor->src[0]); + ggml_compute_forward_transpose(params, tensor); } break; case GGML_OP_GET_ROWS: { - ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_get_rows(params, tensor); } break; case GGML_OP_GET_ROWS_BACK: { - ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_get_rows_back(params, tensor); } break; case GGML_OP_DIAG: { - ggml_compute_forward_diag(params, tensor->src[0], tensor); + ggml_compute_forward_diag(params, tensor); } break; case GGML_OP_DIAG_MASK_INF: { - ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor); + ggml_compute_forward_diag_mask_inf(params, tensor); } break; case GGML_OP_DIAG_MASK_ZERO: { - ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor); + ggml_compute_forward_diag_mask_zero(params, tensor); } break; case GGML_OP_SOFT_MAX: { - ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_soft_max(params, tensor); } break; case GGML_OP_SOFT_MAX_BACK: { - ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_soft_max_back(params, tensor); } break; case GGML_OP_ROPE: { - ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_rope(params, tensor); } break; case GGML_OP_ROPE_BACK: { - ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_rope_back(params, tensor); } break; case GGML_OP_ALIBI: { - ggml_compute_forward_alibi(params, tensor->src[0], tensor); + ggml_compute_forward_alibi(params, tensor); } break; case GGML_OP_CLAMP: { - ggml_compute_forward_clamp(params, tensor->src[0], tensor); + ggml_compute_forward_clamp(params, tensor); } break; case GGML_OP_CONV_TRANSPOSE_1D: { - ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_conv_transpose_1d(params, tensor); } break; case GGML_OP_IM2COL: { - ggml_compute_forward_im2col(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_im2col(params, tensor); } break; case GGML_OP_CONV_TRANSPOSE_2D: { - ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_conv_transpose_2d(params, tensor); } break; case GGML_OP_POOL_1D: { - ggml_compute_forward_pool_1d(params, tensor->src[0], tensor); + ggml_compute_forward_pool_1d(params, tensor); } break; case GGML_OP_POOL_2D: { - ggml_compute_forward_pool_2d(params, tensor->src[0], tensor); + ggml_compute_forward_pool_2d(params, tensor); } break; case GGML_OP_UPSCALE: { - ggml_compute_forward_upscale(params, tensor->src[0], tensor); + ggml_compute_forward_upscale(params, tensor); } break; case GGML_OP_PAD: { - ggml_compute_forward_pad(params, tensor->src[0], tensor); + ggml_compute_forward_pad(params, tensor); } break; case GGML_OP_ARGSORT: { - ggml_compute_forward_argsort(params, tensor->src[0], tensor); + ggml_compute_forward_argsort(params, tensor); } break; case GGML_OP_LEAKY_RELU: { - ggml_compute_forward_leaky_relu(params, tensor->src[0], tensor); + ggml_compute_forward_leaky_relu(params, tensor); } break; case GGML_OP_FLASH_ATTN: { const int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); const bool masked = t != 0; - ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor); + ggml_compute_forward_flash_attn(params, masked, tensor); } break; case GGML_OP_FLASH_FF: { - ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4], tensor); + ggml_compute_forward_flash_ff(params, tensor); } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; - ggml_compute_forward_flash_attn_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked, tensor); + ggml_compute_forward_flash_attn_back(params, masked, tensor); } break; case GGML_OP_WIN_PART: { - ggml_compute_forward_win_part(params, tensor->src[0], tensor); + ggml_compute_forward_win_part(params, tensor); } break; case GGML_OP_WIN_UNPART: { - ggml_compute_forward_win_unpart(params, tensor->src[0], tensor); + ggml_compute_forward_win_unpart(params, tensor); } break; case GGML_OP_UNARY: { - ggml_compute_forward_unary(params, tensor->src[0], tensor); + ggml_compute_forward_unary(params, tensor); } break; case GGML_OP_GET_REL_POS: { - ggml_compute_forward_get_rel_pos(params, tensor->src[0], tensor); + ggml_compute_forward_get_rel_pos(params, tensor); } break; case GGML_OP_ADD_REL_POS: { - ggml_compute_forward_add_rel_pos(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_add_rel_pos(params, tensor); } break; case GGML_OP_MAP_UNARY: { ggml_unary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun); + ggml_compute_forward_map_unary(params, tensor, fun); } break; case GGML_OP_MAP_BINARY: { ggml_binary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun); + ggml_compute_forward_map_binary(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM1_F32: { ggml_custom1_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun); + ggml_compute_forward_map_custom1_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM2_F32: { ggml_custom2_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun); + ggml_compute_forward_map_custom2_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM3_F32: { ggml_custom3_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun); + ggml_compute_forward_map_custom3_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM1: { - ggml_compute_forward_map_custom1(params, tensor->src[0], tensor); + ggml_compute_forward_map_custom1(params, tensor); } break; case GGML_OP_MAP_CUSTOM2: { - ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_map_custom2(params, tensor); } break; case GGML_OP_MAP_CUSTOM3: { - ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_map_custom3(params, tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS: { - ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_cross_entropy_loss(params, tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { - ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_cross_entropy_loss_back(params, tensor); } break; case GGML_OP_NONE: diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 733d8f95b93df..97f34ac851722 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -818eeb8a3be99125746a90ec63af8f51516a2ec6 +4712fd12d7acb9971f850b1b98588f934cb39444 From a00a35cef93e057eace8351a667d14d152a91ebc Mon Sep 17 00:00:00 2001 From: Ettore Di Giacinto Date: Wed, 21 Feb 2024 15:39:10 +0100 Subject: [PATCH 061/401] readme : add LocalAI to the availables UI (#5629) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ce5dec7caeac9..c1624b9f9a348 100644 --- a/README.md +++ b/README.md @@ -141,6 +141,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [nat/openplayground](https://github.com/nat/openplayground) - [Faraday](https://faraday.dev/) (proprietary) - [LMStudio](https://lmstudio.ai/) (proprietary) +- [LocalAI](https://github.com/mudler/LocalAI) (MIT) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) - [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) From 1ecea255ebb70750b52688393f37a63606b90e3f Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Wed, 21 Feb 2024 15:47:48 +0100 Subject: [PATCH 062/401] server: health: fix race condition on slots data using tasks queue (#5634) * server: health: fix race condition on slots data using tasks queue * server: health: * include_slots only if slots_endpoint * fix compile warning task.target_id not initialized. --- examples/server/README.md | 2 + examples/server/server.cpp | 122 ++++++++++++++++++++++++------------- examples/server/utils.hpp | 3 +- 3 files changed, 84 insertions(+), 43 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index f6b9c74021f30..6d9f96cd4ba64 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -140,6 +140,8 @@ node index.js - 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available. - 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available. + If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set. + - **POST** `/completion`: Given a `prompt`, it returns the predicted completion. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eb01729fa7a8a..1c44795128fc1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1394,6 +1394,46 @@ struct llama_server_context case TASK_TYPE_NEXT_RESPONSE: { // do nothing } break; + case TASK_TYPE_SLOTS_DATA: { + json slots_data = json::array(); + int n_idle_slots = 0; + int n_processing_slots = 0; + + for (llama_client_slot &slot: slots) { + if (slot.available()) { + n_idle_slots++; + } else { + n_processing_slots++; + } + json slot_data = get_formated_generation(slot); + slot_data["id"] = slot.id; + slot_data["task_id"] = slot.task_id; + slot_data["state"] = slot.state; + slot_data["prompt"] = slot.prompt; + slot_data["next_token"] = { + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"num_tokens_predicted", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }; + slots_data.push_back(slot_data); + } + LOG_TEE("task %i - slots data: idle=%i processing=%i\n", task.id, n_idle_slots, n_processing_slots); + task_result res; + res.id = task.id; + res.multitask_id = task.multitask_id; + res.stop = true; + res.error = false; + res.result_json = { + { "idle", n_idle_slots }, + { "processing", n_processing_slots }, + { "slots", slots_data } + }; + queue_results.send(res); + } break; } } @@ -2557,34 +2597,38 @@ int main(int argc, char **argv) server_state current_state = state.load(); switch(current_state) { case SERVER_STATE_READY: { - int available_slots = 0; - int processing_slots = 0; - for (llama_client_slot &slot: llama.slots) { - if (slot.available()) { - available_slots++; - } else { - processing_slots++; - } + // request slots data using task queue + task_server task; + task.id = llama.queue_tasks.get_new_id(); + task.type = TASK_TYPE_SLOTS_DATA; + task.target_id = -1; + + llama.queue_results.add_waiting_task_id(task.id); + llama.queue_tasks.post(task); + + // get the result + task_result result = llama.queue_results.recv(task.id); + llama.queue_results.remove_waiting_task_id(task.id); + + int n_idle_slots = result.result_json["idle"]; + int n_processing_slots = result.result_json["processing"]; + + json health = { + {"status", "ok"}, + {"slots_idle", n_idle_slots}, + {"slots_processing", n_processing_slots}}; + res.status = 200; // HTTP OK + if (sparams.slots_endpoint && req.has_param("include_slots")) { + health["slots"] = result.result_json["slots"]; } - if (available_slots > 0) { - json health = { - {"status", "ok"}, - {"slots_idle", available_slots}, - {"slots_processing", processing_slots}}; - res.set_content(health.dump(), "application/json"); - res.status = 200; // HTTP OK - } else { - json health = { - {"status", "no slot available"}, - {"slots_idle", available_slots}, - {"slots_processing", processing_slots}}; - res.set_content(health.dump(), "application/json"); + + if (n_idle_slots == 0) { + health["status"] = "no slot available"; if (req.has_param("fail_on_no_slot")) { res.status = 503; // HTTP Service Unavailable - } else { - res.status = 200; // HTTP OK } } + res.set_content(health.dump(), "application/json"); break; } case SERVER_STATE_LOADING_MODEL: @@ -2600,26 +2644,20 @@ int main(int argc, char **argv) if (sparams.slots_endpoint) { svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) { - json slots; - for (llama_client_slot & slot : llama.slots) { - json slot_data = llama.get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["task_id"] = slot.task_id; - slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; - slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }; + // request slots data using task queue + task_server task; + task.id = llama.queue_tasks.get_new_id(); + task.type = TASK_TYPE_SLOTS_DATA; + task.target_id = -1; - slots.push_back(slot_data); - } - res.set_content(slots.dump(), "application/json"); + llama.queue_results.add_waiting_task_id(task.id); + llama.queue_tasks.post(task); + + // get the result + task_result result = llama.queue_results.recv(task.id); + llama.queue_results.remove_waiting_task_id(task.id); + + res.set_content(result.result_json["slots"].dump(), "application/json"); res.status = 200; // HTTP OK }); } diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index e954fb0ef0413..88545eb6931d0 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -49,7 +49,8 @@ enum server_state { enum task_type { TASK_TYPE_COMPLETION, TASK_TYPE_CANCEL, - TASK_TYPE_NEXT_RESPONSE + TASK_TYPE_NEXT_RESPONSE, + TASK_TYPE_SLOTS_DATA }; struct task_server { From 5022cf242d689e15defd133f96c4345ad30c5d19 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 21 Feb 2024 16:52:39 +0200 Subject: [PATCH 063/401] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 97f34ac851722..bbbf88d9d6ff7 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -4712fd12d7acb9971f850b1b98588f934cb39444 +30805514e1bf389a59d30a54a0525cbdc30d5bd1 From 89febfed9322c8849520dc63c93ee4f5fd72556e Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Wed, 21 Feb 2024 10:33:54 -0500 Subject: [PATCH 064/401] examples : do not assume BOS when shifting context (#5622) --- examples/main/main.cpp | 12 +++++++----- examples/server/server.cpp | 13 +++++++------ 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index f5d2f48935eb6..7555dffe441f0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -334,6 +334,8 @@ int main(int argc, char ** argv) { // number of tokens to keep when resetting context if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) { params.n_keep = (int)embd_inp.size(); + } else { + params.n_keep += add_bos; // always keep the BOS token } // prefix & suffix for instruct mode @@ -383,8 +385,8 @@ int main(int argc, char ** argv) { } } - if (params.n_keep > 0) { - LOG_TEE("%s: static prompt based on n_keep: '", __func__); + if (params.n_keep > add_bos) { + LOG_TEE("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); } @@ -540,14 +542,14 @@ int main(int argc, char ** argv) { break; } - const int n_left = n_past - params.n_keep - 1; + const int n_left = n_past - params.n_keep; const int n_discard = n_left/2; LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_shift(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); n_past -= n_discard; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1c44795128fc1..c84719a0d15d0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1487,14 +1487,15 @@ struct llama_server_context if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context - const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1; + const int n_keep = slot.params.n_keep + add_bos_token; + const int n_left = system_tokens.size() + slot.n_past - n_keep; const int n_discard = n_left / 2; - LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); - llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard); + LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, n_keep, n_left, n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); + llama_kv_cache_seq_shift(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); - for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; } @@ -1507,7 +1508,7 @@ struct llama_server_context LOG_VERBOSE("context shift", { { "n_ctx", n_ctx }, - { "n_keep", params.n_keep }, + { "n_keep", n_keep }, { "n_left", n_left }, }); } From ba2135ccae7462470b3865c6e41d2e1d734eac05 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 21 Feb 2024 22:18:23 +0100 Subject: [PATCH 065/401] gemma : allow offloading the output tensor (#5646) --- llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 3a226c4260c0b..4054d5da63fc4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4394,6 +4394,8 @@ static bool llm_load_tensors( // output model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading + ml.n_created--; // artificial tensor const int64_t n_ff = hparams.n_ff; const int64_t n_embd_head_k = hparams.n_embd_head_k; @@ -7525,7 +7527,7 @@ struct llm_build_context { cb(cur, "result_norm", -1); // lm_head - cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cur = ggml_mul_mat(ctx0, model.output, cur); cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); From 7fe4678b0244ba7b03eae66ebeaa947e2770bb1a Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 21 Feb 2024 22:52:39 +0100 Subject: [PATCH 066/401] llama : fix session save/load with quantized KV (#5649) --- llama.cpp | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4054d5da63fc4..d763cc80cb4c2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12176,18 +12176,19 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat data_ctx->write(&kv_used, sizeof(kv_used)); if (kv_buf_size) { - const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - std::vector tmp_buf; for (int il = 0; il < (int) n_layer; ++il) { - tmp_buf.resize(elt_size*n_embd_k_gqa*kv_head); + size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); + tmp_buf.resize(k_size); ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); // v is not contiguous, copy row by row - tmp_buf.resize(elt_size*kv_head); + size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); + size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + tmp_buf.resize(v_row_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { - ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*elt_size*n_ctx, tmp_buf.size()); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); } } @@ -12289,17 +12290,16 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { if (kv_buf_size) { GGML_ASSERT(kv_self.total_size() == kv_buf_size); - const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - for (int il = 0; il < (int) n_layer; ++il) { - size_t k_size = elt_size*n_embd_k_gqa*kv_head; + size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); inp += k_size; // v is not contiguous, copy row by row - size_t v_row_size = elt_size*kv_head; + size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); + size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { - ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*elt_size*n_ctx, v_row_size); + ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size); inp += v_row_size; } } From 7c8bcc11dc61cf5930b70cd0168b84afcebe12a9 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 22 Feb 2024 00:31:00 +0100 Subject: [PATCH 067/401] Add docs for llama_chat_apply_template (#5645) * add docs for llama_chat_apply_template * fix typo --- examples/server/README.md | 1 + llama.h | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 6d9f96cd4ba64..4b24ee5dc3f28 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -41,6 +41,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` - `-n, --n-predict`: Set the maximum tokens to predict (default: -1) - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. +- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) ## Build diff --git a/llama.h b/llama.h index 8ba20696f8af9..84f196b3bb625 100644 --- a/llama.h +++ b/llama.h @@ -708,7 +708,7 @@ extern "C" { /// Apply chat template. Inspired by hf apply_chat_template() on python. /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" - /// NOTE: This function only support some known jinja templates. It is not a jinja parser. + /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead. /// @param chat Pointer to a list of multiple llama_chat_message /// @param n_msg Number of llama_chat_message in this chat From 973053d8b0d04809836b3339a50f68d9c842de90 Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 22 Feb 2024 00:42:09 +0100 Subject: [PATCH 068/401] llama : fix loading models with shared tok_embd and output (#5651) ggml-ci --- llama.cpp | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index d763cc80cb4c2..259f2a3a3ea00 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2791,13 +2791,7 @@ struct llama_model_loader { std::vector> read_buf; - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - if (!cur) { - // some tensors may be allocated in a different context - continue; - } - + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { if (progress_callback) { if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) { return false; @@ -3722,7 +3716,7 @@ static bool llm_load_tensors( } // create one context per buffer type - size_t ctx_size = ggml_tensor_overhead()*ml.n_tensors; + size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output std::map ctx_map; for (auto & it : buft_layer_count) { struct ggml_init_params params = { @@ -3860,6 +3854,7 @@ static bool llm_load_tensors( } else { model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); } } @@ -4396,6 +4391,7 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); const int64_t n_ff = hparams.n_ff; const int64_t n_embd_head_k = hparams.n_embd_head_k; From 4ef245a92a968ba0f18a5adfd41e51980ce4fdf5 Mon Sep 17 00:00:00 2001 From: Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com> Date: Thu, 22 Feb 2024 18:15:13 +1000 Subject: [PATCH 069/401] mpt : add optional bias tensors (#5638) Update for MPT with optional bias parameters: to work with PhoGPT and SEA-LION models that were pre-trained with 'bias'. --- llama.cpp | 36 ++++++++++++++++++++++++++---------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/llama.cpp b/llama.cpp index 259f2a3a3ea00..9cae8c761f3ac 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4054,6 +4054,8 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } @@ -4063,14 +4065,23 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, false); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, false); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); // AWQ ScaleActivation layer layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); @@ -6171,7 +6182,7 @@ struct llm_build_context { attn_norm = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, - NULL, + model.layers[il].attn_norm_b, LLM_NORM, cb, il); cb(attn_norm, "attn_norm", il); @@ -6181,6 +6192,11 @@ struct llm_build_context { cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); cb(cur, "wqkv", il); + + if (model.layers[il].bqkv){ + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + } if (hparams.f_clamp_kqv > 0.0f) { cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); @@ -6198,7 +6214,7 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, + model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -6211,13 +6227,13 @@ struct llm_build_context { { cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, - NULL, + model.layers[il].ffn_norm_b, LLM_NORM, cb, il); cb(cur, "ffn_norm", il); cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, - model.layers[il].ffn_down, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); @@ -6234,7 +6250,7 @@ struct llm_build_context { cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, - NULL, + model.output_norm_b, LLM_NORM, cb, -1); cb(cur, "result_norm", -1); From c5688c6250430d2b8e0259efcf26c16dfa4c1f46 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Thu, 22 Feb 2024 08:27:32 +0000 Subject: [PATCH 070/401] server : clarify some params in the docs (#5640) --- examples/server/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 4b24ee5dc3f28..4b6cd8326efa8 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -151,7 +151,7 @@ node index.js `temperature`: Adjust the randomness of the generated text (default: 0.8). - `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled). + `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` (default: 0.0, 0.0 = disabled). `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0). @@ -209,7 +209,7 @@ node index.js `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) - `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) + `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false) `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) @@ -242,7 +242,7 @@ Notice that each `probs` is an array of length `n_probs`. - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) -- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model` +- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). - `model`: The path to the model loaded with `-m` - `prompt`: The provided `prompt` - `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token From a46f50747b2028f7f9c9883b26bfba12bf92556e Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 22 Feb 2024 09:33:24 +0100 Subject: [PATCH 071/401] server : fallback to chatml, add AlphaMonarch chat template (#5628) * server: fallback to chatml * add new chat template * server: add AlphaMonarch to test chat template * server: only check model template if there is no custom tmpl * remove TODO --- examples/server/server.cpp | 15 +++++++++++++++ llama.cpp | 9 +++++++++ tests/test-chat-template.cpp | 23 +++++++++++++++-------- 3 files changed, 39 insertions(+), 8 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c84719a0d15d0..369121e885b27 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -400,6 +400,16 @@ struct llama_server_context return true; } + void validate_model_chat_template(server_params & sparams) { + llama_chat_message chat[] = {{"user", "test"}}; + std::vector buf(1); + int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); + if (res < 0) { + LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); + sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template + } + } + void initialize() { // create slots all_slots_are_idle = true; @@ -2752,6 +2762,11 @@ int main(int argc, char **argv) LOG_INFO("model loaded", {}); } + if (sparams.chat_template.empty()) { // custom chat template is not supplied + // check if the template comes with the model is supported by us + llama.validate_model_chat_template(sparams); + } + // Middleware for API key validation auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { // If API key is not set, skip validation diff --git a/llama.cpp b/llama.cpp index 9cae8c761f3ac..055b57e3187f2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12773,6 +12773,15 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } + } else if (tmpl.find("bos_token + message['role']") != std::string::npos) { + // mlabonne/AlphaMonarch-7B template (the is included inside history) + for (auto message : chat) { + std::string bos = (message == chat.front()) ? "" : ""; // skip BOS for first message + ss << bos << message->role << "\n" << message->content << "\n"; + } + if (add_ass) { + ss << "assistant\n"; + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 9830650d4f8dd..d02b39e144947 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -27,12 +27,20 @@ int main(void) { "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <>\\\\n' + messages[idx]['content'] + '\\\\n<>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}", // bofenghuang/vigogne-2-70b-chat "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\\\n' + system_message + '\\\\n<>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\\\\n' + content.strip() + '\\\\n<>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}", + // mlabonne/AlphaMonarch-7B + "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}", }; - std::vector expected_substr = { - "<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant", - "[/INST]Hi there[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", - "[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", - "[/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + std::vector expected_output = { + // teknium/OpenHermes-2.5-Mistral-7B + "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n", + // mistralai/Mistral-7B-Instruct-v0.2 + "[INST] You are a helpful assistant\nHello [/INST]Hi there[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + // TheBloke/FusionNet_34Bx2_MoE-AWQ + "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + // bofenghuang/vigogne-2-70b-chat + "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + // mlabonne/AlphaMonarch-7B + "system\nYou are a helpful assistant\nuser\nHello\nassistant\nHi there\nuser\nWho are you\nassistant\n I am an assistant \nuser\nAnother question\nassistant\n", }; std::vector formatted_chat(1024); int32_t res; @@ -43,7 +51,7 @@ int main(void) { for (size_t i = 0; i < templates.size(); i++) { std::string custom_template = templates[i]; - std::string substr = expected_substr[i]; + std::string expected = expected_output[i]; formatted_chat.resize(1024); res = llama_chat_apply_template( nullptr, @@ -57,8 +65,7 @@ int main(void) { formatted_chat.resize(res); std::string output(formatted_chat.data(), formatted_chat.size()); std::cout << output << "\n-------------------------\n"; - // expect the "formatted_chat" to contain pre-defined strings - assert(output.find(substr) != std::string::npos); + assert(output == expected); } return 0; } From 56d03d92be57f5880b9ed94542d87bb6effae31f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 10:35:54 +0200 Subject: [PATCH 072/401] readme : update hot topics --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c1624b9f9a348..3bc512af0602b 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 - Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 - Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 From 3a03541cedea474fa9d41214484cc3fbcf468a9e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 13:54:03 +0200 Subject: [PATCH 073/401] minor : fix trailing whitespace (#5638) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 055b57e3187f2..6ab5e1bf4f409 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6192,7 +6192,7 @@ struct llm_build_context { cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); cb(cur, "wqkv", il); - + if (model.layers[il].bqkv){ cur = ggml_add(ctx0, cur, model.layers[il].bqkv); cb(cur, "bqkv", il); From 4cb4d8b22d4fda971621a68c570ce84d66897c37 Mon Sep 17 00:00:00 2001 From: Someone Date: Thu, 22 Feb 2024 16:32:09 +0000 Subject: [PATCH 074/401] workflows: nix: hardcode cachix ids, build unconditionally (#5663) GitHub does not expose environment and repository variables to PRs coming from forks implies that we've been disabling the Nix CI actions for most PRs. The `if:` also didn't make much sense, because we can always pull from cachix, and there's no point (albeit no risk either) in pushing cache for the untrusted code. --- .github/workflows/nix-ci-aarch64.yml | 7 +++---- .github/workflows/nix-ci.yml | 11 +++++------ 2 files changed, 8 insertions(+), 10 deletions(-) diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml index 0c6cf5f091528..8d0a3fd7fd313 100644 --- a/.github/workflows/nix-ci-aarch64.yml +++ b/.github/workflows/nix-ci-aarch64.yml @@ -19,7 +19,6 @@ on: jobs: nix-build-aarch64: - if: ${{ vars.CACHIX_NAME != '' }} runs-on: ubuntu-latest steps: - name: Checkout repository @@ -37,8 +36,8 @@ jobs: extra-conf: | extra-platforms = aarch64-linux extra-system-features = nixos-test kvm - extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - uses: DeterminateSystems/magic-nix-cache-action@v2 with: upstream-cache: https://${{ matrix.cachixName }}.cachix.org @@ -46,7 +45,7 @@ jobs: uses: cachix/cachix-action@v13 with: authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: ${{ vars.CACHIX_NAME }} + name: llama-cpp - name: Show all output paths run: > nix run github:nix-community/nix-eval-jobs diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index d19c7a576cdf6..01c5a9d5aaca2 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -23,8 +23,8 @@ jobs: with: github-token: ${{ secrets.GITHUB_TOKEN }} extra-conf: | - extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - uses: DeterminateSystems/magic-nix-cache-action@v2 with: upstream-cache: https://${{ matrix.cachixName }}.cachix.org @@ -37,7 +37,6 @@ jobs: --flake ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)" nix-build: - if: ${{ vars.CACHIX_NAME != '' }} strategy: fail-fast: false matrix: @@ -51,8 +50,8 @@ jobs: with: github-token: ${{ secrets.GITHUB_TOKEN }} extra-conf: | - extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - uses: DeterminateSystems/magic-nix-cache-action@v2 with: upstream-cache: https://${{ matrix.cachixName }}.cachix.org @@ -60,7 +59,7 @@ jobs: uses: cachix/cachix-action@v13 with: authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: ${{ vars.CACHIX_NAME }} + name: llama-cpp - name: Build run: > nix run github:Mic92/nix-fast-build From 373ee3fbbabc4c1508eed4f5c3795b23a20939a3 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 22 Feb 2024 19:10:21 +0100 Subject: [PATCH 075/401] Add Gemma chat template (#5665) * add gemma chat template * gemma: only apply system_prompt on non-model message --- llama.cpp | 22 ++++++++++++++++++++++ tests/test-chat-template.cpp | 4 ++++ 2 files changed, 26 insertions(+) diff --git a/llama.cpp b/llama.cpp index 6ab5e1bf4f409..40dda265ccc93 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12782,6 +12782,28 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "assistant\n"; } + } else if (tmpl.find("") != std::string::npos) { + // google/gemma-7b-it + std::string system_prompt = ""; + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken + system_prompt = trim(message->content); + continue; + } + // in gemma, "assistant" is "model" + role = role == "assistant" ? "model" : message->role; + ss << "" << role << "\n"; + if (!system_prompt.empty() && role != "model") { + ss << system_prompt << "\n\n"; + system_prompt = ""; + } + ss << trim(message->content) << "\n"; + } + if (add_ass) { + ss << "model\n"; + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index d02b39e144947..fa2eb577b6e42 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -29,6 +29,8 @@ int main(void) { "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\\\n' + system_message + '\\\\n<>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\\\\n' + content.strip() + '\\\\n<>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}", // mlabonne/AlphaMonarch-7B "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}", + // google/gemma-7b-it + "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\\n' + message['content'] | trim + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\\n'}}{% endif %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -41,6 +43,8 @@ int main(void) { "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", // mlabonne/AlphaMonarch-7B "system\nYou are a helpful assistant\nuser\nHello\nassistant\nHi there\nuser\nWho are you\nassistant\n I am an assistant \nuser\nAnother question\nassistant\n", + // google/gemma-7b-it + "user\nYou are a helpful assistant\n\nHello\nmodel\nHi there\nuser\nWho are you\nmodel\nI am an assistant\nuser\nAnother question\nmodel\n", }; std::vector formatted_chat(1024); int32_t res; From 5a9e2f60ba3d8362ba17c77ac3092906d49b813f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 20:13:25 +0200 Subject: [PATCH 076/401] py : minor fixes (#5668) --- convert-hf-to-gguf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 9771fccf9ffc1..8630bbf2980c1 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -655,6 +655,8 @@ def set_gguf_parameters(self): self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) + # note: config provides rms norm but it is actually layer norm + # ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571 self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) def write_tensors(self): @@ -1031,7 +1033,6 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) def set_vocab(self): self._set_vocab_sentencepiece() From 201294ae177b308fb3a99dc504dd6d27e8afa907 Mon Sep 17 00:00:00 2001 From: Someone Date: Thu, 22 Feb 2024 19:44:10 +0000 Subject: [PATCH 077/401] nix: init singularity and docker images (#5056) Exposes a few attributes demonstrating how to build [singularity](https://docs.sylabs.io/guides/latest/user-guide/)/[apptainer](https://apptainer.org/) and Docker images re-using llama.cpp's Nix expression. Built locally on `x86_64-linux` with `nix build github:someoneserge/llama.cpp/feat/nix/images#llamaPackages.{docker,docker-min,sif,llama-cpp}` and it's fast and effective. --- .devops/nix/docker.nix | 37 +++++++++++++++++++++++++++++++++++++ .devops/nix/scope.nix | 3 +++ .devops/nix/sif.nix | 27 +++++++++++++++++++++++++++ 3 files changed, 67 insertions(+) create mode 100644 .devops/nix/docker.nix create mode 100644 .devops/nix/sif.nix diff --git a/.devops/nix/docker.nix b/.devops/nix/docker.nix new file mode 100644 index 0000000000000..d607b4575772c --- /dev/null +++ b/.devops/nix/docker.nix @@ -0,0 +1,37 @@ +{ + lib, + dockerTools, + buildEnv, + llama-cpp, + interactive ? true, + coreutils, +}: + +# A tar that can be fed into `docker load`: +# +# $ nix build .#llamaPackages.docker +# $ docker load < result + +# For details and variations cf. +# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage +# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922 +# - https://nixery.dev/ + +# Approximate (compressed) sizes, at the time of writing, are: +# +# .#llamaPackages.docker: 125M; +# .#llamaPackagesCuda.docker: 537M; +# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M. + +dockerTools.buildLayeredImage { + name = llama-cpp.pname; + tag = "latest"; + + contents = + [ llama-cpp ] + ++ lib.optionals interactive [ + coreutils + dockerTools.binSh + dockerTools.caCertificates + ]; +} diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix index d295995a4b96b..78530c9e8a230 100644 --- a/.devops/nix/scope.nix +++ b/.devops/nix/scope.nix @@ -12,5 +12,8 @@ lib.makeScope newScope ( self: { inherit llamaVersion; llama-cpp = self.callPackage ./package.nix { }; + docker = self.callPackage ./docker.nix { }; + docker-min = self.callPackage ./docker.nix { interactive = false; }; + sif = self.callPackage ./sif.nix { }; } ) diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix new file mode 100644 index 0000000000000..7535ca0f3088e --- /dev/null +++ b/.devops/nix/sif.nix @@ -0,0 +1,27 @@ +{ + lib, + singularity-tools, + llama-cpp, + bashInteractive, + interactive ? false, +}: + +let + optionalInt = cond: x: if cond then x else 0; +in +singularity-tools.buildImage rec { + inherit (llama-cpp) name; + contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ]; + + # These are excessive (but safe) for most variants. Building singularity + # images requires superuser privileges, so we build them inside a VM in a + # writable image of pre-determined size. + # + # ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846 + # + # Expected image sizes: + # - cpu/blas: 150M, + # - cuda, all gencodes: 560M, + diskSize = 4096 + optionalInt llama-cpp.useRocm 16384; + memSize = diskSize; +} From efd56b1c2139d50b9b4381a212feb75d69598fda Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 18:31:40 +0200 Subject: [PATCH 078/401] ggml : 32-bit arm compat (whisper/1891) * ggml : 32-bit arm compat * ggml : add ggml_vqtbl1q_s8 impl * ggml : cont --- ggml-quants.c | 35 ++++++++++++++++++++++++++++++----- 1 file changed, 30 insertions(+), 5 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 6336538f0e99e..8917c8af14255 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -438,6 +438,30 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { return res; } +// NOTE: not tested +inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { + int8x16_t res; + + res[ 0] = a[b[ 0]]; + res[ 1] = a[b[ 1]]; + res[ 2] = a[b[ 2]]; + res[ 3] = a[b[ 3]]; + res[ 4] = a[b[ 4]]; + res[ 5] = a[b[ 5]]; + res[ 6] = a[b[ 6]]; + res[ 7] = a[b[ 7]]; + res[ 8] = a[b[ 8]]; + res[ 9] = a[b[ 9]]; + res[10] = a[b[10]]; + res[11] = a[b[11]]; + res[12] = a[b[12]]; + res[13] = a[b[13]]; + res[14] = a[b[14]]; + res[15] = a[b[15]]; + + return res; +} + #else #define ggml_int16x8x2_t int16x8x2_t @@ -451,6 +475,7 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { #define ggml_vld1q_u8_x4 vld1q_u8_x4 #define ggml_vld1q_s8_x2 vld1q_s8_x2 #define ggml_vld1q_s8_x4 vld1q_s8_x4 +#define ggml_vqtbl1q_s8 vqtbl1q_s8 #endif @@ -9333,7 +9358,7 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const uint16_t gindex[8]; uint16x8x2_t vindex; int8x16x4_t q1b; - int8x16x4_t q8b; + ggml_int8x16x4_t q8b; uint16x8x4_t scales; int32x4x2_t sumi; int32x4x2_t dotq; @@ -9506,10 +9531,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * q8b.val[2] = vld1q_s8(y[ib+1].qs); q8b.val[3] = vld1q_s8(y[ib+1].qs + 16); - q4b.val[0] = vqtbl1q_s8(values, vandq_u8(q4bits.val[0], m4b)); - q4b.val[1] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); - q4b.val[2] = vqtbl1q_s8(values, vandq_u8(q4bits.val[1], m4b)); - q4b.val[3] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); From 334f76fa385ed81095165e5ae068756214893901 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 23:21:05 +0200 Subject: [PATCH 079/401] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index bbbf88d9d6ff7..59de343706f2a 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -30805514e1bf389a59d30a54a0525cbdc30d5bd1 +8cdf783f288a98eddf521b0ab1b4d405be9e18ba From 7e4f339c404dbe029d4a117c03b37a9bf646cf0e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 23:21:39 +0200 Subject: [PATCH 080/401] ggml : always define ggml_fp16_t as uint16_t (#5666) * ggml : always define ggml_fp16_t as uint16_t ggml-ci * ggml : cont ggml-ci * ggml : cont * ggml : cont ggml-ci * ggml : cont ggml-ci * cuda : no longer ggml headers last ggml-ci * ggml : fix q6_K FP16 -> FP32 conversion ggml-ci * ggml : more FP16 -> FP32 conversion fixes ggml-ci --- ggml-cuda.cu | 9 ++++----- ggml-impl.h | 27 ++++++++++++++++++++------- ggml-quants.c | 30 +++++++++++++++--------------- ggml.c | 6 +++--- ggml.h | 6 ------ 5 files changed, 42 insertions(+), 36 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e7c211d7d6087..b0e454e025ec4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1,3 +1,7 @@ +#include "ggml-cuda.h" +#include "ggml.h" +#include "ggml-backend-impl.h" + #include #include #include @@ -121,11 +125,6 @@ #endif // defined(GGML_USE_HIPBLAS) -// ggml-cuda need half type so keep ggml headers include at last -#include "ggml-cuda.h" -#include "ggml.h" -#include "ggml-backend-impl.h" - #define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) #define CC_PASCAL 600 diff --git a/ggml-impl.h b/ggml-impl.h index 19df66bceee4a..c5637e4d45d8c 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -53,11 +53,23 @@ extern "C" { // #include -#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) -#define GGML_COMPUTE_FP32_TO_FP16(x) (x) +#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) +#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + +#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + +static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + __fp16 tmp; + memcpy(&tmp, &h, sizeof(ggml_fp16_t)); + return (float)tmp; +} -#define GGML_FP16_TO_FP32(x) ((float) (x)) -#define GGML_FP32_TO_FP16(x) (x) +static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + ggml_fp16_t res; + __fp16 tmp = f; + memcpy(&res, &tmp, sizeof(ggml_fp16_t)); + return res; +} #else @@ -214,8 +226,7 @@ extern float ggml_table_f32_f16[1 << 16]; // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // This is also true for POWER9. -#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) - +#if !defined(GGML_FP16_TO_FP32) inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { uint16_t s; memcpy(&s, &f, sizeof(uint16_t)); @@ -223,8 +234,10 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { } #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) +#endif +#if !defined(GGML_FP32_TO_FP16) +#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) #endif #define GGML_HASHTABLE_FULL ((size_t)-1) diff --git a/ggml-quants.c b/ggml-quants.c index 8917c8af14255..b15977f53e2f3 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -5654,8 +5654,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d = y[i].d * (float)x[i].d; - const float dmin = -y[i].d * (float)x[i].dmin; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -5804,8 +5804,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d = y[i].d * (float)x[i].d; - const float dmin = -y[i].d * (float)x[i].dmin; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -6458,7 +6458,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - const float d = y[i].d * (float)x[i].d; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1)); q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2)); @@ -6660,7 +6660,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - const float d = y[i].d * (float)x[i].d; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); @@ -7163,9 +7163,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r aux16[1] = (a[0] >> 4) & 0x0f0f; const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]); - sum_mins += y[i].d * (float)x[i].d[1] * summi; + sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi; - const float d = y[i].d * (float)x[i].d[0]; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); @@ -7823,7 +7823,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d = y[i].d * (float)x[i].d; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const int8_t * sc = x[i].scales; const uint8_t * restrict q5 = x[i].qs; @@ -7965,7 +7965,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d = y[i].d * (float)x[i].d; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const int8_t * sc = x[i].scales; const uint8_t * restrict q5 = x[i].qs; @@ -8533,7 +8533,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d_all = (float)x[i].d; + const float d_all = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -8704,7 +8704,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { - const float d_all = (float)x[i].d; + const float d_all = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -9523,7 +9523,6 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * float sumf = 0; for (int ib = 0; ib < nb; ib += 2) { - q4bits.val[0] = vld1q_u8(x[ib+0].qs); q4bits.val[1] = vld1q_u8(x[ib+1].qs); q8b.val[0] = vld1q_s8(y[ib+0].qs); @@ -9539,8 +9538,9 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); - sumf += (float)x[ib+0].d * (float)y[ib+0].d * vaddvq_s32(prod_1) + (float)x[ib+1].d * (float)y[ib+1].d * vaddvq_s32(prod_2); - + sumf += + GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) + + GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2); } *s = sumf; diff --git a/ggml.c b/ggml.c index 5b9fa741a6479..d710fe702ddbd 100644 --- a/ggml.c +++ b/ggml.c @@ -323,7 +323,7 @@ float ggml_table_f32_f16[1 << 16]; // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { - return (float) GGML_FP16_TO_FP32(x); + return GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { @@ -798,7 +798,7 @@ inline static float vaddvq_f32(float32x4_t v) { #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD vld1q_f16 + #define GGML_F16x8_LOAD(x) vld1q_f16((const __fp16 *)(x)) #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 @@ -841,7 +841,7 @@ inline static float vaddvq_f32(float32x4_t v) { #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x)) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const __fp16 *)(x))) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 diff --git a/ggml.h b/ggml.h index bed7a36a0ee6a..37eff627928e8 100644 --- a/ggml.h +++ b/ggml.h @@ -315,13 +315,7 @@ extern "C" { #endif -#if defined(__ARM_NEON) && defined(__CUDACC__) - typedef half ggml_fp16_t; -#elif defined(__ARM_NEON) && !defined(_MSC_VER) - typedef __fp16 ggml_fp16_t; -#else typedef uint16_t ggml_fp16_t; -#endif // convert FP16 <-> FP32 GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); From 847eedbdb2d1ebf14ef56eb507d4b4b975510908 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 23:22:48 +0200 Subject: [PATCH 081/401] py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel --------- Co-authored-by: Aarni Koskela Co-authored-by: Jared Van Bortel --- convert-hf-to-gguf.py | 60 +++++++++++++++++++++++++++++++++++++++++++ llama.cpp | 3 +++ 2 files changed, 63 insertions(+) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 8630bbf2980c1..481198dad042c 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -218,6 +218,8 @@ def from_model_architecture(model_architecture): return BertModel if model_architecture == "NomicBertModel": return NomicBertModel + if model_architecture == "GemmaForCausalLM": + return GemmaModel return Model def _is_model_safetensors(self) -> bool: @@ -277,6 +279,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.BERT if arch == "NomicBertModel": return gguf.MODEL_ARCH.NOMIC_BERT + if arch == "GemmaForCausalLM": + return gguf.MODEL_ARCH.GEMMA raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1786,6 +1790,62 @@ def get_tensors(self): yield name, data +class GemmaModel(Model): + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(hparams["head_dim"]) + self.gguf_writer.add_value_length(hparams["head_dim"]) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### diff --git a/llama.cpp b/llama.cpp index 40dda265ccc93..7770fa0e8f6fa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7450,6 +7450,7 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); + inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -7491,6 +7492,7 @@ struct llm_build_context { n_embd_head_k, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); + Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); cb(Qcur, "Qcur_scaled", il); @@ -7505,6 +7507,7 @@ struct llm_build_context { Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); cb(cur, "kqv_out", il); } + struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL); cb(sa_out, "sa_out", il); From 96633eeca1265ed03e57230de54032041c58f9cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 22 Feb 2024 23:23:46 +0200 Subject: [PATCH 082/401] gemma : use more bits for the token_embd.weight tensor (#5650) * gemma : use Q8_0 for the token_embd.weight tensor * llama : quantize token_embd.weight using output type --- llama.cpp | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 7770fa0e8f6fa..2ebd40df234f0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10498,7 +10498,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty return std::make_pair(i_layer, n_layer); }; - if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { + // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings + // with the quantization of the output tensor + if (name == tn(LLM_TENSOR_OUTPUT, "weight") || + (LLM_TENSOR_NAMES.at(arch).find(LLM_TENSOR_OUTPUT) == LLM_TENSOR_NAMES.at(arch).end() && name == "token_embd.weight")) { int nx = tensor->ne[0]; if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; From 15499eb94227401bdc8875da6eb85c15d37068f7 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Thu, 22 Feb 2024 17:05:23 -0500 Subject: [PATCH 083/401] mpt : do not duplicate token_embd.weight on disk (#5670) --- convert-hf-to-gguf.py | 5 ----- llama.cpp | 6 ++++-- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 481198dad042c..9bdfce07ab7db 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -622,11 +622,6 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) - # note: MPT output is tied to (same as) wte in original model; - # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ - if new_name == "token_embd.weight": - self.gguf_writer.add_tensor("output.weight", data) - class OrionModel(Model): def set_vocab(self): diff --git a/llama.cpp b/llama.cpp index 2ebd40df234f0..37477e6ef3c44 100644 --- a/llama.cpp +++ b/llama.cpp @@ -509,7 +509,6 @@ static std::map> LLM_TENSOR_NAMES = { { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, - { LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, @@ -4056,7 +4055,10 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + // same as tok_embd, duplicated to allow offloading + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); } for (int i = 0; i < n_layer; ++i) { From 54fbcd2ce6c48c9e22eca6fbf9e53fb68c3e72ea Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Fri, 23 Feb 2024 13:39:14 -0500 Subject: [PATCH 084/401] convert : fix missing ftype for gemma (#5690) --- convert-hf-to-gguf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 9bdfce07ab7db..32d54b45f3325 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1803,6 +1803,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_key_length(hparams["head_dim"]) self.gguf_writer.add_value_length(hparams["head_dim"]) + self.gguf_writer.add_file_type(self.ftype) def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) From fd43d66f46ee3b5345fb8a74a252d86ccd34a409 Mon Sep 17 00:00:00 2001 From: AlpinDale <52078762+AlpinDale@users.noreply.github.com> Date: Fri, 23 Feb 2024 19:31:54 +0000 Subject: [PATCH 085/401] server : add KV cache quantization options (#5684) --- examples/server/server.cpp | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 369121e885b27..524d0ada33ab0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1948,6 +1948,10 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); + printf(" -ctk TYPE, --cache-type-k TYPE\n"); + printf(" KV cache data type for K (default: f16)\n"); + printf(" -ctv TYPE, --cache-type-v TYPE\n"); + printf(" KV cache data type for V (default: f16)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); @@ -2386,6 +2390,12 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, ); llama.process_system_prompt_data(json::parse(systm_content)); } + else if (arg == "-ctk" || arg == "--cache-type-k") { + params.cache_type_k = argv[++i]; + } + else if (arg == "-ctv" || arg == "--cache-type-v") { + params.cache_type_v = argv[++i]; + } else if(arg == "--mmproj") { if (++i >= argc) From 525213d2f5da1eaf4b922b6b792cb52b2c613368 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 24 Feb 2024 12:28:55 +0100 Subject: [PATCH 086/401] server: init functional tests (#5566) * server: tests: init scenarios - health and slots endpoints - completion endpoint - OAI compatible chat completion requests w/ and without streaming - completion multi users scenario - multi users scenario on OAI compatible endpoint with streaming - multi users with total number of tokens to predict exceeds the KV Cache size - server wrong usage scenario, like in Infinite loop of "context shift" #3969 - slots shifting - continuous batching - embeddings endpoint - multi users embedding endpoint: Segmentation fault #5655 - OpenAI-compatible embeddings API - tokenize endpoint - CORS and api key scenario * server: CI GitHub workflow --------- Co-authored-by: Georgi Gerganov --- .github/ISSUE_TEMPLATE/bug.md | 2 + .github/workflows/server.yml | 127 ++++ examples/server/README.md | 6 + examples/server/server.cpp | 36 +- examples/server/tests/README.md | 46 ++ examples/server/tests/features/environment.py | 67 ++ examples/server/tests/features/issues.feature | 36 + .../server/tests/features/parallel.feature | 77 ++ .../server/tests/features/security.feature | 50 ++ examples/server/tests/features/server.feature | 69 ++ examples/server/tests/features/steps/steps.py | 709 ++++++++++++++++++ .../tests/features/wrong_usages.feature | 21 + examples/server/tests/requirements.txt | 3 + examples/server/tests/tests.sh | 12 + 14 files changed, 1243 insertions(+), 18 deletions(-) create mode 100644 .github/workflows/server.yml create mode 100644 examples/server/tests/README.md create mode 100644 examples/server/tests/features/environment.py create mode 100644 examples/server/tests/features/issues.feature create mode 100644 examples/server/tests/features/parallel.feature create mode 100644 examples/server/tests/features/security.feature create mode 100644 examples/server/tests/features/server.feature create mode 100644 examples/server/tests/features/steps/steps.py create mode 100644 examples/server/tests/features/wrong_usages.feature create mode 100644 examples/server/tests/requirements.txt create mode 100755 examples/server/tests/tests.sh diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md index ce69e6395daae..49812832ca542 100644 --- a/.github/ISSUE_TEMPLATE/bug.md +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -7,3 +7,5 @@ assignees: '' --- Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug. + +If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests). diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml new file mode 100644 index 0000000000000..ed27dc528fb61 --- /dev/null +++ b/.github/workflows/server.yml @@ -0,0 +1,127 @@ +# Server build and tests +name: Server + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + - test/server-add-ci-test # FIXME remove + paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + +jobs: + server: + runs-on: ubuntu-latest + + strategy: + matrix: + build: [noavx, avx2, avx, avx512, cublas, clblast, openblas, kompute, vulkan] + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug, Release] + include: + - build: 'noavx' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF' + image: ubuntu:latest + - build: 'avx2' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' + image: ubuntu:latest + - build: 'avx' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF' + image: ubuntu:latest + - build: 'avx512' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON' + image: ubuntu:latest + experimental: true + - build: 'cublas' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON' + image: nvidia/cuda:12.3.1-devel-ubuntu22.04 + arch_not_available: true # require nvidia docker engine + - build: 'clblast' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON' + image: ubuntu:latest + arch_not_available: true + - build: 'openblas' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS' + image: ubuntu:latest + - build: 'kompute' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' + image: ubuntu:latest + arch_not_available: true + - build: 'vulkan' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON' + image: ubuntu:latest + arch_not_available: true + + container: + image: ${{ matrix.image }} + ports: + - 8888 + options: --cpus 4 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + apt-get update + apt-get -y install \ + build-essential \ + pkg-config \ + git \ + cmake \ + python3-pip \ + wget \ + psmisc + + - name: Download CLBlast + id: get_clblast + if: ${{ matrix.build == 'clblast' }} + run: | + apt install -y libclblast-dev + + - name: Download OpenBLAS + id: get_openblas + if: ${{ matrix.build == 'openblas' }} + run: | + apt-get -y install libopenblas-dev + + - name: Install Vulkan SDK + id: get_vulkan + if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} + run: | + wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | tee /etc/apt/trusted.gpg.d/lunarg.asc + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list http://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + apt-get update + apt-get -y install vulkan-sdk + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ${{ matrix.defines }} + cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r examples/server/tests/requirements.txt + + - name: Download models + id: download_models + run: | + cd examples/server/tests + ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf + + - name: Tests + id: server_integration_test + continue-on-error: ${{ matrix.experimental || matrix.arch_not_available }} + run: | + cd examples/server/tests + PORT=8888 ./tests.sh diff --git a/examples/server/README.md b/examples/server/README.md index 4b6cd8326efa8..0c43ac4c97cba 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -98,6 +98,12 @@ curl --request POST \ --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}' ``` +## Advanced testing + +We implemented a [server test framework](./tests/README.md) using human-readable scenario. + +*Before submitting an issue, please try to reproduce it with this format.* + ## Node JS Test You need to have [Node.js](https://nodejs.org/en) installed. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 524d0ada33ab0..9fb436c2a18ec 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1410,11 +1410,6 @@ struct llama_server_context int n_processing_slots = 0; for (llama_client_slot &slot: slots) { - if (slot.available()) { - n_idle_slots++; - } else { - n_processing_slots++; - } json slot_data = get_formated_generation(slot); slot_data["id"] = slot.id; slot_data["task_id"] = slot.task_id; @@ -1429,6 +1424,11 @@ struct llama_server_context {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, }; + if (slot_data["state"] == IDLE) { + n_idle_slots++; + } else { + n_processing_slots++; + } slots_data.push_back(slot_data); } LOG_TEE("task %i - slots data: idle=%i processing=%i\n", task.id, n_idle_slots, n_processing_slots); @@ -2748,19 +2748,6 @@ int main(int argc, char **argv) log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; } - LOG_INFO("HTTP server listening", log_data); - // run the HTTP server in a thread - see comment below - std::thread t([&]() - { - if (!svr.listen_after_bind()) - { - state.store(SERVER_STATE_ERROR); - return 1; - } - - return 0; - }); - // load the model if (!llama.load_model(params)) { @@ -3228,6 +3215,19 @@ int main(int argc, char **argv) }*/ //); + LOG_INFO("HTTP server listening", log_data); + // run the HTTP server in a thread - see comment below + std::thread t([&]() + { + if (!svr.listen_after_bind()) + { + state.store(SERVER_STATE_ERROR); + return 1; + } + + return 0; + }); + llama.queue_tasks.on_new_task(std::bind( &llama_server_context::process_single_task, &llama, std::placeholders::_1)); llama.queue_tasks.on_finish_multitask(std::bind( diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md new file mode 100644 index 0000000000000..e44c5c286601f --- /dev/null +++ b/examples/server/tests/README.md @@ -0,0 +1,46 @@ +# Server tests + +Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/): + * [issues.feature](./features/issues.feature) Pending issues scenario + * [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests + * [security.feature](./features/security.feature) Security, CORS and API Key + * [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... + +Tests target GitHub workflows job runners with 4 vCPU. + +Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client. + +Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. + +### Install dependencies +`pip install -r requirements.txt` + +### Run tests +1. Build the server +```shell +cd ../../.. +mkdir build +cd build +cmake ../ +cmake --build . --target server +``` +2. download required models: + 1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf` +3. Start the test: `./tests.sh` + +It's possible to override some scenario steps values with environment variables: + - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080` + - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server` + - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` + +### Run @bug, @wip or @wrong_usage annotated scenario + +Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. +- `@bug` annotation aims to link a scenario with a GitHub issue. +- `@wrong_usage` are meant to show user issue that are actually an expected behavior +- `@wip` to focus on a scenario working in progress + +To run a scenario annotated with `@bug`, start: +`DEBUG=ON ./tests.sh --no-skipped --tags bug` + +After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py new file mode 100644 index 0000000000000..13cc841017f62 --- /dev/null +++ b/examples/server/tests/features/environment.py @@ -0,0 +1,67 @@ +import os +import socket +import subprocess +import time +from contextlib import closing +from signal import SIGKILL + + +def before_scenario(context, scenario): + print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") + port = 8080 + if 'PORT' in os.environ: + port = int(os.environ['PORT']) + if is_server_listening("localhost", port): + assert False, "Server already started" + + +def after_scenario(context, scenario): + if scenario.status == "failed": + if 'GITHUB_ACTIONS' in os.environ: + print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n") + if os.path.isfile('llama.log'): + with closing(open('llama.log', 'r')) as f: + for line in f: + print(line) + if not is_server_listening(context.server_fqdn, context.server_port): + print("\x1b[33;101mERROR: Server stopped listening\x1b[0m") + + if not pid_exists(context.server_process.pid): + assert False, f"Server not running pid={context.server_process.pid} ..." + + print(f"stopping server pid={context.server_process.pid} ...") + context.server_process.kill() + # Wait few for socket to free up + time.sleep(0.05) + + attempts = 0 + while is_server_listening(context.server_fqdn, context.server_port): + print(f"stopping server pid={context.server_process.pid} ...") + os.kill(context.server_process.pid, SIGKILL) + time.sleep(0.1) + attempts += 1 + if attempts > 5: + print(f"Server dangling exits, killing all {context.server_path} ...") + process = subprocess.run(['killall', '-9', context.server_path], + stderr=subprocess.PIPE, + universal_newlines=True) + print(process) + + +def is_server_listening(server_fqdn, server_port): + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: + result = sock.connect_ex((server_fqdn, server_port)) + return result == 0 + + +def pid_exists(pid): + """Check whether pid exists in the current process table.""" + import errno + if pid < 0: + return False + try: + os.kill(pid, 0) + except OSError as e: + return e.errno == errno.EPERM + else: + return True diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature new file mode 100644 index 0000000000000..542006d9a8df2 --- /dev/null +++ b/examples/server/tests/features/issues.feature @@ -0,0 +1,36 @@ +# List of ongoing issues +@bug +Feature: Issues + # Issue #5655 + Scenario: Multi users embeddings + Given a server listening on localhost:8080 + And a model file stories260K.gguf + And a model alias tinyllama-2 + And 42 as server seed + And 64 KV cache size + And 2 slots + And continuous batching + And embeddings extraction + Then the server is starting + Then the server is healthy + + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And a prompt: + """ + Write a very long poem. + """ + And a prompt: + """ + Write a very long joke. + """ + Given concurrent embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature new file mode 100644 index 0000000000000..802d624ffc9a3 --- /dev/null +++ b/examples/server/tests/features/parallel.feature @@ -0,0 +1,77 @@ +@llama.cpp +Feature: Parallel + + Background: Server startup + Given a server listening on localhost:8080 + And a model file stories260K.gguf + And a model alias tinyllama-2 + And 42 as server seed + And 64 KV cache size + And 2 slots + And continuous batching + Then the server is starting + Then the server is healthy + + Scenario Outline: Multi users completion + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And max tokens to predict + Given concurrent completion requests + Then the server is busy + Then the server is idle + And all slots are idle + Then all prompts are predicted with tokens + Examples: + | n_predict | + | 128 | + + Scenario Outline: Multi users OAI completions compatibility + Given a system prompt You are a writer. + And a model tinyllama-2 + Given a prompt: + """ + Write a very long book. + """ + And a prompt: + """ + Write another a poem. + """ + And max tokens to predict + And streaming is + Given concurrent OAI completions requests + Then the server is busy + Then the server is idle + Then all prompts are predicted with tokens + Examples: + | streaming | n_predict | + | disabled | 128 | + | enabled | 64 | + + Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And a prompt: + """ + Write a very long poem. + """ + And a prompt: + """ + Write a very long joke. + """ + And 128 max tokens to predict + Given concurrent completion requests + Then the server is busy + Then the server is idle + Then all prompts are predicted diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature new file mode 100644 index 0000000000000..db06d39775c05 --- /dev/null +++ b/examples/server/tests/features/security.feature @@ -0,0 +1,50 @@ +@llama.cpp +Feature: Security + + Background: Server startup with an api key defined + Given a server listening on localhost:8080 + And a model file stories260K.gguf + And a server api key llama.cpp + Then the server is starting + Then the server is healthy + + Scenario Outline: Completion with some user api key + Given a prompt test + And a user api key + And 4 max tokens to predict + And a completion request with api error + + Examples: Prompts + | api_key | api_error | + | llama.cpp | no | + | llama.cpp | no | + | hackeme | raised | + | | raised | + + Scenario Outline: OAI Compatibility + Given a system prompt test + And a user prompt test + And a model test + And 2 max tokens to predict + And streaming is disabled + And a user api key + Given an OAI compatible chat completions request with api error + + Examples: Prompts + | api_key | api_error | + | llama.cpp | no | + | llama.cpp | no | + | hackme | raised | + + + Scenario Outline: CORS Options + When an OPTIONS request is sent from + Then CORS header is set to + + Examples: Headers + | origin | cors_header | cors_header_value | + | localhost | Access-Control-Allow-Origin | localhost | + | web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr | + | origin | Access-Control-Allow-Credentials | true | + | web.mydomain.fr | Access-Control-Allow-Methods | POST | + | web.mydomain.fr | Access-Control-Allow-Headers | * | diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature new file mode 100644 index 0000000000000..fedcfe5aef1b3 --- /dev/null +++ b/examples/server/tests/features/server.feature @@ -0,0 +1,69 @@ +@llama.cpp +Feature: llama.cpp server + + Background: Server startup + Given a server listening on localhost:8080 + And a model file stories260K.gguf + And a model alias tinyllama-2 + And 42 as server seed + # KV Cache corresponds to the total amount of tokens + # that can be stored across all independent sequences: #4130 + # see --ctx-size and #5568 + And 32 KV cache size + And 1 slots + And embeddings extraction + And 32 server max tokens to predict + Then the server is starting + Then the server is healthy + + Scenario: Health + Then the server is ready + And all slots are idle + + Scenario Outline: Completion + Given a prompt + And max tokens to predict + And a completion request with no api error + Then tokens are predicted matching + + Examples: Prompts + | prompt | n_predict | re_content | n_predicted | + | I believe the meaning of life is | 8 | read | 8 | + | Write a joke about AI | 64 | (parkfriendsscared)+ | 32 | + + Scenario Outline: OAI Compatibility + Given a model + And a system prompt + And a user prompt + And max tokens to predict + And streaming is + Given an OAI compatible chat completions request with no api error + Then tokens are predicted matching + + Examples: Prompts + | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | + | llama-2 | Book | What is the best book | 8 | (Momwhat)+ | 8 | disabled | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thankshappybird)+ | 32 | enabled | + + Scenario: Embedding + When embeddings are computed for: + """ + What is the capital of Bulgaria ? + """ + Then embeddings are generated + + Scenario: OAI Embeddings compatibility + Given a model tinyllama-2 + When an OAI compatible embeddings computation request for: + """ + What is the capital of Spain ? + """ + Then embeddings are generated + + + Scenario: Tokenize / Detokenize + When tokenizing: + """ + What is the capital of France ? + """ + Then tokens can be detokenize diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py new file mode 100644 index 0000000000000..50f2b641e764e --- /dev/null +++ b/examples/server/tests/features/steps/steps.py @@ -0,0 +1,709 @@ +import asyncio +import json +import os +import re +import socket +import subprocess +import time +from contextlib import closing +from re import RegexFlag + +import aiohttp +import openai +from behave import step +from behave.api.async_step import async_run_until_complete + + +@step(u"a server listening on {server_fqdn}:{server_port}") +def step_server_config(context, server_fqdn, server_port): + context.server_fqdn = server_fqdn + context.server_port = int(server_port) + if 'PORT' in os.environ: + context.server_port = int(os.environ['PORT']) + print(f"$PORT set, overriding server port with to {context.server_port}") + + context.base_url = f'http://{context.server_fqdn}:{context.server_port}' + + context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' + context.model_alias = None + context.n_ctx = None + context.n_predict = None + context.n_server_predict = None + context.n_slots = None + context.server_api_key = None + context.server_continuous_batching = False + context.server_embeddings = False + context.server_seed = None + context.user_api_key = None + + context.tasks_result = [] + context.concurrent_tasks = [] + context.prompts = [] + + +@step(u'a model file {model_file}') +def step_model_file(context, model_file): + context.model_file = model_file + + +@step(u'a model alias {model_alias}') +def step_model_alias(context, model_alias): + context.model_alias = model_alias + + +@step(u'{seed} as server seed') +def step_seed(context, seed): + context.server_seed = int(seed) + + +@step(u'{n_ctx} KV cache size') +def step_n_ctx(context, n_ctx): + context.n_ctx = int(n_ctx) + + +@step(u'{n_slots} slots') +def step_n_slots(context, n_slots): + context.n_slots = int(n_slots) + + +@step(u'{n_predict} server max tokens to predict') +def step_server_n_predict(context, n_predict): + context.n_server_predict = int(n_predict) + + +@step(u'continuous batching') +def step_server_continuous_batching(context): + context.server_continuous_batching = True + + +@step(u'embeddings extraction') +def step_server_embeddings(context): + context.server_embeddings = True + + +@step(u"the server is starting") +def step_start_server(context): + start_server_background(context) + attempts = 0 + while True: + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: + result = sock.connect_ex((context.server_fqdn, context.server_port)) + if result == 0: + print("\x1b[33;46mserver started!\x1b[0m") + return + attempts += 1 + if attempts > 20: + assert False, "server not started" + print(f"waiting for server to start, connect error code = {result}...") + time.sleep(0.1) + + +@step(u"the server is {expecting_status}") +@async_run_until_complete +async def step_wait_for_the_server_to_be_started(context, expecting_status): + match expecting_status: + case 'healthy': + await wait_for_health_status(context, context.base_url, 200, 'ok') + + case 'ready' | 'idle': + await wait_for_health_status(context, context.base_url, 200, 'ok', + params={'fail_on_no_slot': 0, 'include_slots': 0}, + slots_idle=context.n_slots, + slots_processing=0, + expected_slots=[{'id': slot_id, 'state': 0} + for slot_id in range(context.n_slots)]) + case 'busy': + await wait_for_health_status(context, context.base_url, 503, + 'no slot available', + params={'fail_on_no_slot': 0, 'include_slots': 0}, + slots_idle=0, + slots_processing=context.n_slots, + expected_slots=[{'id': slot_id, 'state': 1} + for slot_id in range(context.n_slots)]) + case _: + assert False, "unknown status" + + +@step(u'all slots are {expected_slot_status_string}') +@async_run_until_complete +async def step_all_slots_status(context, expected_slot_status_string): + match expected_slot_status_string: + case 'idle': + expected_slot_status = 0 + case 'busy': + expected_slot_status = 1 + case _: + assert False, "unknown status" + + expected_slots = [{'id': slot_id, 'state': expected_slot_status} + for slot_id in range(context.n_slots)] + await request_slots_status(context, expected_slots) + + +@step(u'a completion request with {api_error} api error') +@async_run_until_complete +async def step_request_completion(context, api_error): + expect_api_error = api_error == 'raised' + completion = await request_completion(context.prompts.pop(), + context.base_url, + debug=context.debug, + n_predict=context.n_predict, + server_seed=context.server_seed, + expect_api_error=expect_api_error, + user_api_key=context.user_api_key) + context.tasks_result.append(completion) + if context.debug: + print(f"Completion response: {completion}") + if expect_api_error: + assert completion == 401, f"completion must be an 401 status code: {completion}" + + +@step(u'{predicted_n} tokens are predicted matching {re_content}') +def step_n_tokens_predicted_with_content(context, predicted_n, re_content): + assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content) + + +@step(u'{predicted_n} tokens are predicted') +def step_n_tokens_predicted(context, predicted_n): + assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n)) + + +@step(u'a user prompt {user_prompt}') +def step_user_prompt(context, user_prompt): + context.prompts.append(user_prompt) + + +@step(u'a system prompt {system_prompt}') +def step_system_prompt(context, system_prompt): + context.system_prompt = system_prompt + + +@step(u'a model {model}') +def step_model(context, model): + context.model = model + + +@step(u'{max_tokens} max tokens to predict') +def step_max_tokens(context, max_tokens): + context.n_predict = int(max_tokens) + + +@step(u'streaming is {enable_streaming}') +def step_streaming(context, enable_streaming): + context.enable_streaming = enable_streaming == 'enabled' + + +@step(u'a user api key {user_api_key}') +def step_user_api_key(context, user_api_key): + context.user_api_key = user_api_key + + +@step(u'no user api key') +def step_no_user_api_key(context): + context.user_api_key = None + + +@step(u'a user api key ') +def step_no_user_api_key_space(context): + context.user_api_key = None + + +@step(u'a server api key {server_api_key}') +def step_server_api_key(context, server_api_key): + context.server_api_key = server_api_key + + +@step(u'an OAI compatible chat completions request with {api_error} api error') +@async_run_until_complete +async def step_oai_chat_completions(context, api_error): + if context.debug: + print(f"Submitting OAI compatible completions request...") + expect_api_error = api_error == 'raised' + completion = await oai_chat_completions(context.prompts.pop(), + context.system_prompt, + context.base_url, + False, + model=context.model if hasattr(context, 'model') else None, + + n_predict=context.n_predict + if hasattr(context, 'n_predict') else None, + + enable_streaming=context.enable_streaming + if hasattr(context, 'enable_streaming') else None, + + server_seed=context.server_seed + if hasattr(context, 'server_seed') else None, + + user_api_key=context.user_api_key + if hasattr(context, 'user_api_key') else None, + + expect_api_error=expect_api_error) + context.tasks_result.append(completion) + if context.debug: + print(f"Completion response: {completion}") + if expect_api_error: + assert completion == 401, f"completion must be an 401 status code: {completion}" + + if context.debug: + print(f"Completion response: {completion}") + + +@step(u'a prompt') +def step_a_prompt(context): + context.prompts.append(context.text) + + +@step(u'a prompt {prompt}') +def step_a_prompt_prompt(context, prompt): + context.prompts.append(prompt) + + +@step(u'concurrent completion requests') +@async_run_until_complete() +async def step_concurrent_completion_requests(context): + await concurrent_completion_requests(context, + request_completion, + # prompt is inserted automatically + context.base_url, + debug=context.debug, + n_predict=context.n_predict if hasattr(context, 'n_predict') else None, + server_seed=context.server_seed if hasattr(context, 'server_seed') else None, + user_api_key=context.user_api_key if hasattr(context, + 'user_api_key') else None) + + +@step(u'concurrent OAI completions requests') +@async_run_until_complete +async def step_oai_chat_completions(context): + await concurrent_completion_requests(context, oai_chat_completions, + # user_prompt is inserted automatically + context.system_prompt, + context.base_url, + True, # async_client + model=context.model + if hasattr(context, 'model') else None, + n_predict=context.n_predict + if hasattr(context, 'n_predict') else None, + enable_streaming=context.enable_streaming + if hasattr(context, 'enable_streaming') else None, + server_seed=context.server_seed + if hasattr(context, 'server_seed') else None, + user_api_key=context.user_api_key + if hasattr(context, 'user_api_key') else None) + + +@step(u'all prompts are predicted') +@async_run_until_complete +async def step_all_prompts_are_predicted(context): + await all_prompts_are_predicted(context) + + +@step(u'all prompts are predicted with {n_predict} tokens') +@async_run_until_complete +async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict): + expected_predicted_n = int(n_predict) + await all_prompts_are_predicted(context, expected_predicted_n) + + +async def all_prompts_are_predicted(context, expected_predicted_n=None): + n_completions = await gather_tasks_results(context) + assert n_completions > 0 + for i in range(n_completions): + assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n) + assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" + + +@step(u'embeddings are computed for') +@async_run_until_complete +async def step_compute_embedding(context): + content = context.text + base_url = context.base_url + context.embeddings = await request_embedding(content, base_url) + + +@step(u'embeddings are generated') +def step_assert_embeddings(context): + assert_embeddings(context.embeddings) + + +@step(u'an OAI compatible embeddings computation request for') +def step_oai_compute_embedding(context): + openai.api_key = 'nope' # openai client always expects an api_keu + if context.user_api_key is not None: + openai.api_key = context.user_api_key + openai.api_base = f'{context.base_url}/v1' + embeddings = openai.Embedding.create( + model=context.model, + input=context.text, + ) + context.embeddings = embeddings + + +@step(u'concurrent embedding requests') +@async_run_until_complete() +async def step_concurrent_embedding_requests(context): + await concurrent_completion_requests(context, + request_embedding, + # prompt is inserted automatically + context.base_url) + + +@step(u'all embeddings are generated') +@async_run_until_complete() +async def all_embeddings_are_generated(context): + n_embedding_requests = await gather_tasks_results(context) + assert n_embedding_requests > 0 + for i in range(n_embedding_requests): + assert_embeddings(context.tasks_result.pop()) + + +@step(u'tokenizing') +@async_run_until_complete +async def step_tokenize(context): + context.tokenized_text = context.text + async with aiohttp.ClientSession() as session: + async with session.post(f'{context.base_url}/tokenize', + json={ + "content": context.tokenized_text, + }) as response: + assert response.status == 200 + tokenize_json = await response.json() + context.tokens = tokenize_json['tokens'] + + +@step(u'tokens can be detokenize') +@async_run_until_complete +async def step_detokenize(context): + assert len(context.tokens) > 0 + async with aiohttp.ClientSession() as session: + async with session.post(f'{context.base_url}/detokenize', + json={ + "tokens": context.tokens, + }) as response: + assert response.status == 200 + detokenize_json = await response.json() + # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15 + assert context.tokenized_text == detokenize_json['content'].strip() + + +@step(u'an OPTIONS request is sent from {origin}') +@async_run_until_complete +async def step_options_request(context, origin): + async with aiohttp.ClientSession() as session: + async with session.options(f'{context.base_url}/v1/chat/completions', + headers={"Origin": origin}) as response: + assert response.status == 200 + context.options_response = response + + +@step(u'CORS header {cors_header} is set to {cors_header_value}') +def step_check_options_header_value(context, cors_header, cors_header_value): + assert context.options_response.headers[cors_header] == cors_header_value + + +async def concurrent_completion_requests(context, f_completion, *args, **kwargs): + n_prompts = len(context.prompts) + if context.debug: + print(f"starting {n_prompts} concurrent completion requests...") + assert n_prompts > 0 + for prompt_no in range(n_prompts): + shifted_args = [context.prompts.pop(), *args] + context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) + await asyncio.sleep(0.1) + + +async def request_completion(prompt, + base_url, + debug=False, + n_predict=None, + server_seed=None, + expect_api_error=None, + user_api_key=None): + if debug: + print(f"Sending completion request: {prompt}") + origin = "my.super.domain" + headers = { + 'Origin': origin + } + if user_api_key is not None: + if debug: + print(f"Set user_api_key: {user_api_key}") + headers['Authorization'] = f'Bearer {user_api_key}' + + async with aiohttp.ClientSession() as session: + async with session.post(f'{base_url}/completion', + json={ + "prompt": prompt, + "n_predict": int(n_predict) if n_predict is not None else -1, + "seed": server_seed if server_seed is not None else 42 + }, + headers=headers) as response: + if expect_api_error is None or not expect_api_error: + assert response.status == 200 + assert response.headers['Access-Control-Allow-Origin'] == origin + return await response.json() + else: + return response.status + + +async def oai_chat_completions(user_prompt, + system_prompt, + base_url, + async_client, + debug=False, + model=None, + n_predict=None, + enable_streaming=None, + server_seed=None, + user_api_key=None, + expect_api_error=None): + if debug: + print(f"Sending OAI Chat completions request: {user_prompt}") + # openai client always expects an api key + user_api_key = user_api_key if user_api_key is not None else 'nope' + seed = server_seed if server_seed is not None else 42 + enable_streaming = enable_streaming if enable_streaming is not None else False + payload = { + "messages": [ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": user_prompt, + } + ], + "model": model, + "max_tokens": n_predict, + "stream": enable_streaming, + "seed": seed + } + completion_response = { + 'content': '', + 'timings': { + 'predicted_n': 0 + } + } + if async_client: + origin = 'llama.cpp' + headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} + async with aiohttp.ClientSession() as session: + async with session.post(f'{base_url}/v1/chat/completions', + json=payload, + headers=headers) as response: + if enable_streaming: + assert response.status == 200 + assert response.headers['Access-Control-Allow-Origin'] == origin + assert response.headers['Content-Type'] == "text/event-stream" + event_received = True + while event_received: + event_received = False + async for line_in_bytes in response.content: + line = line_in_bytes.decode('utf8') + line = line.rstrip('\n').rstrip('\r') + if line == '': + continue + event_data = line.split(': ', 1) + assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```' + chunk_raw = event_data[1] + + chunk = json.loads(chunk_raw) + assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```" + delta = chunk['choices'][0]['delta'] + if 'content' in delta: + completion_response['content'] += delta['content'] + completion_response['timings']['predicted_n'] += 1 + else: + if expect_api_error is None or not expect_api_error: + assert response.status == 200 + assert response.headers['Access-Control-Allow-Origin'] == origin + assert response.headers['Content-Type'] == "application/json; charset=utf-8" + chat_completion_raw = await response.json() + completion_response = { + 'content': chat_completion_raw['choices'][0]['message'], + 'timings': { + 'predicted_n': chat_completion_raw['usage']['completion_tokens'] + } + } + else: + return response.status + else: + try: + openai.api_key = user_api_key + openai.api_base = f'{base_url}/v1/chat' + chat_completion = openai.Completion.create( + messages=payload['messages'], + model=model, + max_tokens=n_predict, + stream=enable_streaming, + seed=seed + ) + except openai.error.APIError as e: + if expect_api_error is not None and expect_api_error: + return 401 + else: + assert False, f'error raised: {e}' + + if enable_streaming: + for chunk in chat_completion: + assert len(chunk.choices) == 1 + delta = chunk.choices[0].delta + if 'content' in delta: + completion_response['content'] += delta['content'] + completion_response['timings']['predicted_n'] += 1 + else: + assert len(chat_completion.choices) == 1 + completion_response = { + 'content': chat_completion.choices[0].message.content, + 'timings': { + 'predicted_n': chat_completion.usage.completion_tokens + } + } + if debug: + print("OAI response formatted to llama.cpp:", completion_response) + return completion_response + + +async def request_embedding(content, base_url): + async with aiohttp.ClientSession() as session: + async with session.post(f'{base_url}/embedding', + json={ + "content": content, + }) as response: + assert response.status == 200 + response_json = await response.json() + return response_json['embedding'] + + +def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None): + content = completion_response['content'] + n_predicted = completion_response['timings']['predicted_n'] + assert len(content) > 0, "no token predicted" + if expected_predicted_n is not None: + assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' + f' {n_predicted} <> {expected_predicted_n}') + if re_content is not None: + re_content = '^.*' + re_content.replace('', '|') + '.*$' + assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), ( + f'invalid tokens predicted:' + f' ```\n{content}\n``` do not match /{re_content}/') + + +async def gather_tasks_results(context): + n_tasks = len(context.concurrent_tasks) + if context.debug: + print(f"Waiting for all {n_tasks} tasks results...") + for task_no in range(n_tasks): + context.tasks_result.append(await context.concurrent_tasks.pop()) + n_completions = len(context.tasks_result) + return n_completions + + +async def wait_for_health_status(context, + base_url, + expected_http_status_code, + expected_health_status, + params=None, + slots_idle=None, + slots_processing=None, + expected_slots=None): + if context.debug: + print(f"Starting checking for health for expected_health_status={expected_health_status}") + timeout = 3 # seconds + interval = 0.5 + counter = 0 + async with aiohttp.ClientSession() as session: + while True: + async with await session.get(f'{base_url}/health', params=params) as health_response: + status_code = health_response.status + health = await health_response.json() + if context.debug: + print(f"HEALTH - response for expected health status='{expected_health_status}' on " + f"'{base_url}/health'?{params} is {health}") + if (status_code == expected_http_status_code + and health['status'] == expected_health_status + and (slots_idle is None or health['slots_idle'] == slots_idle) + and (slots_processing is None or health['slots_processing'] == slots_processing)): + if expected_slots is not None: + assert_slots_status(health['slots'], expected_slots) + return + if (status_code == expected_http_status_code + and health['status'] == expected_health_status + and (slots_idle is None or health['slots_idle'] == slots_idle) + and (slots_processing is None or health['slots_processing'] == slots_processing)): + if expected_slots is not None: + assert_slots_status(health['slots'], expected_slots) + return + await asyncio.sleep(interval) + + counter += interval + if counter >= timeout: + # Sometimes health requests are triggered after completions are predicted + if expected_http_status_code == 503: + if len(context.tasks_result) == 0: + print("\x1b[5;37;43mWARNING: forcing concurrent tasks," + " busy health check missed, probably too fast inference\x1b[0m") + n_completions = await gather_tasks_results(context) + if n_completions > 0: + return + + assert False, 'timeout exceeded' + + +def assert_embeddings(embeddings): + assert len(embeddings) > 0 + embeddings_computed = False + for emb in embeddings: + if emb != 0: + embeddings_computed = True + assert embeddings_computed, f"Embeddings: {embeddings}" + + +async def request_slots_status(context, expected_slots): + async with aiohttp.ClientSession() as session: + async with await session.get(f'{context.base_url}/slots') as slots_response: + assert slots_response.status == 200 + slots = await slots_response.json() + assert_slots_status(slots, expected_slots) + + +def assert_slots_status(slots, expected_slots): + assert len(slots) == len(expected_slots) + for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)): + for key in expected: + assert expected[key] == slot[key], (f"invalid slot {slot_id}" + f" expected[{key}] != slot[{key}]" + f" = {expected[key]} != {slot[key]}") + + +def start_server_background(context): + context.server_path = '../../../build/bin/server' + if 'LLAMA_SERVER_BIN_PATH' in os.environ: + context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] + server_args = [ + '--host', context.server_fqdn, + '--port', context.server_port, + '--model', context.model_file + ] + if context.server_continuous_batching: + server_args.append('--cont-batching') + if context.server_embeddings: + server_args.append('--embedding') + if context.model_alias is not None: + server_args.extend(['--alias', context.model_alias]) + if context.n_ctx is not None: + server_args.extend(['--ctx-size', context.n_ctx]) + if context.n_slots is not None: + server_args.extend(['--parallel', context.n_slots]) + if context.n_server_predict is not None: + server_args.extend(['--n-predict', context.n_server_predict]) + if context.server_api_key is not None: + server_args.extend(['--api-key', context.server_api_key]) + if context.debug: + server_args.append('--verbose') + print(f"starting server with: {context.server_path}", *server_args) + context.server_process = subprocess.Popen( + [str(arg) for arg in [context.server_path, *server_args]], + close_fds=True) + print(f"server pid={context.server_process.pid}") diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature new file mode 100644 index 0000000000000..e228b2371ccce --- /dev/null +++ b/examples/server/tests/features/wrong_usages.feature @@ -0,0 +1,21 @@ +# run with ./test.sh --tags wrong_usage +@wrong_usage +Feature: Wrong usage of llama.cpp server + + #3969 The user must always set --n-predict option + # to cap the number of tokens any completion request can generate + # or pass n_predict/max_tokens in the request. + Scenario: Infinite loop + Given a server listening on localhost:8080 + And a model file stories260K.gguf + # Uncomment below to fix the issue + #And 64 server max tokens to predict + Then the server is starting + Given a prompt: + """ + Go to: infinite loop + """ + # Uncomment below to fix the issue + #And 128 max tokens to predict + Given concurrent completion requests + Then all prompts are predicted diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt new file mode 100644 index 0000000000000..3e51b12dc8207 --- /dev/null +++ b/examples/server/tests/requirements.txt @@ -0,0 +1,3 @@ +aiohttp~=3.9.3 +behave~=1.2.6 +openai~=0.25.0 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh new file mode 100755 index 0000000000000..17a4e6fc64307 --- /dev/null +++ b/examples/server/tests/tests.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + # Start @llama.cpp scenario + behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp +else + behave "$@" +fi + From 4c4cb30736582cacb1a164a9d4bc8e17b1014be7 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 24 Feb 2024 16:23:52 +0200 Subject: [PATCH 087/401] IQ3_S: a much better alternative to Q3_K (#5676) * iq4_nl: squash commits for easier rebase * Basics (quantize, dequantize) * CUDA dequantize and dot product * Slightly faster CUDA dot product (120 t/s) * Switch to 6-bit scales * Scalar dot product * AVX2 dot product * ARM_NEON dot product * Works on metal, but still slow * Slightly better Metal dot product * Another small Metal improvement * Metal dot product is getting there * Faster CUDA dot product * Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided * Report the actual bpw * Add _xs mix that is 4.05 bpw for non-MoE models * Remove IQ4_XS for now, slightly adjust kvalues_iq4nl * AVX2 dot product uses Q8_0 instead of Q8_K * Add to test-backend-ops * Minor fix * Also use use Q5_K for attn_output in MoE models * Fixes after merging latest master * Switching to blocks of 32 * AVX2 for blocks of 32 * Scaler dot product for blocks of 32 * ARM_NEON dot product for blocks of 32 * Metal kernels for blocks of 32 * Slightly faster Metal kernels * Resurrecting iq3_xs After all the experimentation, nothing was better than this. * Minor PPL improvement via a block scale fudge factor * Minor improvement via 3 neighbours * iq3_xs: working scalar and AVX2 dot products * iq3_xs: ARM_NEON dot product - works but extremely slow (10 t/s) * iq3_xs: working Metal implementation * Adding IQ3_M - IQ3_XS mix with mostly Q4_K * iiq3_xs: a 3.4375 bpw variant * iq3_xs: make CUDA work for new version * iq3_xs: make scalar and AVX2 work for new version * iq3_s: make ARM_NEON work with new version * iq3_xs: make new version work on metal Performance is very similar to Q3_K_S * iq3_xs: tiny Metal speed improvement * iq3_xs: tiny Metal speed improvement * Fix stupid warning * Q3_K_XS now uses a mix of IQ3_XS and IQ3_XXS * iq3_xs: rename to iq3_s * iq3_s: make tests pass * Move Q3_K_XS mix to 3.25 bpw * Attempt to fix failing tests * Another attempt to fix the Windows builds * Attempt to fix ROCm * ROCm again * iq3_s: partial fix for QK_K = 64 * iq3_s: make it work on metal for QK_K = 64 Pleasent surprise: the coding was super-block size independent, so all it took was to delete some QK_K == 256 guards. * Will this fix ROCm? --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 2 + ggml-cuda.cu | 171 ++++++++- ggml-metal.m | 33 +- ggml-metal.metal | 304 +++++++++++++++ ggml-quants.c | 674 +++++++++++++++++++++++++++++---- ggml-quants.h | 20 + ggml.c | 31 ++ ggml.h | 2 + llama.cpp | 50 ++- llama.h | 2 + tests/test-backend-ops.cpp | 2 +- tests/test-quantize-fns.cpp | 4 +- 12 files changed, 1211 insertions(+), 84 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 37520857f99f7..ab7e72aaf8254 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -27,6 +27,8 @@ static const std::vector QUANT_OPTIONS = { { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, + { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, + { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , }, { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b0e454e025ec4..21c612cb71b48 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -172,6 +172,7 @@ #endif typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); +typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4))); static __device__ __forceinline__ int __vsubss4(const int a, const int b) { const int8x4_t va = reinterpret_cast(a); const int8x4_t vb = reinterpret_cast(b); @@ -196,6 +197,18 @@ static __device__ __forceinline__ int __vsub4(const int a, const int b) { return __vsubss4(a, b); } +static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) { + const uint8x4_t& va = reinterpret_cast(a); + const uint8x4_t& vb = reinterpret_cast(b); + unsigned int c; + uint8x4_t& vc = reinterpret_cast(c); +#pragma unroll + for (int i = 0; i < 4; ++i) { + vc[i] = va[i] == vb[i] ? 0xff : 0x00; + } + return c; +} + static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) c = __builtin_amdgcn_sdot4(a, b, c, false); @@ -518,6 +531,17 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#define QR3_XS 8 +#define QI3_XS (QK_K / (4*QR3_XS)) +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t signs[QK_K/8]; + uint8_t scales[QK_K/64]; +} block_iq3_s; +static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 27*(QK_K/64), "wrong iq3_s block size/padding"); + #define QR1_S 8 #define QI1_S (QK_K / (4*QR1_S)) typedef struct { @@ -1700,6 +1724,74 @@ static const __device__ uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +static const __device__ uint32_t iq3xs_grid[512] = { + 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, + 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, + 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, + 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, + 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, + 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, + 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, + 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, + 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, + 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, + 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, + 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, + 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, + 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, + 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, + 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, + 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, + 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, + 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, + 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, + 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, + 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, + 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, + 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, + 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, + 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, + 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, + 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, + 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, + 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, + 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, + 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, + 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, + 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, + 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, + 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, + 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, + 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, + 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, + 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, + 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, + 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, + 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, + 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, + 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, + 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, + 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, + 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, + 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, + 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, + 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, + 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, + 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, + 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, + 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, + 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, + 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, + 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, + 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, + 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, + 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, + 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, + 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, + 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +}; + + static const __device__ uint64_t iq1s_grid[512] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, @@ -1973,6 +2065,32 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds } +template +static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq3_s * x = (const block_iq3_s *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * qs = x[i].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f; + const uint8_t signs = x[i].signs[4*ib + il]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } +#else + assert(false); +#endif + +} + template static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -4717,6 +4835,41 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( #endif } +// TODO: don't use lookup table for signs +static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_s * bq2 = (const block_iq3_s *) vbq; + + const int ib32 = iqs; + const uint8_t * qs = bq2->qs + 8*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid2[0] ^ signs1, signs1); + sumi = __dp4a(grid_l, *((int *)q8+0), sumi); + sumi = __dp4a(grid_h, *((int *)q8+1), sumi); + q8 += 8; + } + const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + + static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if QK_K == 256 @@ -6849,6 +7002,12 @@ static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq3_xxs<<>>(vx, y); } +template +static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq3_s<<>>(vx, y); +} + template static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -6904,6 +7063,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -6943,6 +7104,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -8688,6 +8851,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -8713,6 +8877,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -8818,6 +8983,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ3_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; default: GGML_ASSERT(false); break; @@ -11541,7 +11710,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons } ggml_type a_type = a->type; if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL) { + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index 0d4aa43093739..ee584cfa71ce7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -61,6 +61,7 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, @@ -85,6 +86,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, @@ -105,6 +107,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, @@ -122,6 +125,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, @@ -139,6 +143,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, @@ -452,6 +457,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); @@ -476,6 +482,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); @@ -496,6 +503,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); @@ -513,6 +521,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); @@ -530,6 +539,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); @@ -1347,6 +1357,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); @@ -1483,6 +1494,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; } break; + case GGML_TYPE_IQ3_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1537,8 +1554,8 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src0t == GGML_TYPE_IQ3_XXS) { - const int mem_size = 256*4+128; + else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { + const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1640,6 +1657,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); @@ -1779,6 +1797,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; } break; + case GGML_TYPE_IQ3_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1849,8 +1873,8 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src2t == GGML_TYPE_IQ3_XXS) { - const int mem_size = 256*4+128; + else if (src2t == GGML_TYPE_IQ3_XXS || src2t == GGML_TYPE_IQ3_S) { + const int mem_size = src2t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1900,6 +1924,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; diff --git a/ggml-metal.metal b/ggml-metal.metal index c223a981c246a..b3bf405391d3e 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2525,6 +2525,20 @@ typedef struct { } block_iq3_xxs; // 98 bytes / block for QK_K = 256, so 3.0625 bpw +// 3.4375 bpw +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t signs[QK_K/8]; + uint8_t scales[IQ3S_N_SCALE]; +} block_iq3_s; + typedef struct { half d; uint8_t qs[QK_K/8]; @@ -3795,6 +3809,73 @@ constexpr constant static uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +constexpr constant static uint32_t iq3xs_grid[512] = { + 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, + 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, + 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, + 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, + 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, + 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, + 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, + 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, + 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, + 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, + 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, + 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, + 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, + 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, + 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, + 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, + 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, + 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, + 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, + 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, + 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, + 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, + 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, + 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, + 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, + 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, + 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, + 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, + 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, + 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, + 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, + 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, + 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, + 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, + 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, + 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, + 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, + 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, + 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, + 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, + 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, + 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, + 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, + 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, + 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, + 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, + 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, + 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, + 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, + 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, + 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, + 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, + 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, + 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, + 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, + 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, + 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, + 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, + 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, + 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, + 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, + 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, + 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, + 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +}; + #define NGRID_IQ1S 512 constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, @@ -4361,6 +4442,136 @@ kernel void kernel_mul_mv_iq3_xxs_f32( kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq3_s_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq3_s * x = (device const block_iq3_s *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; + { + int nval = 8; + int pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i]; + threadgroup_barrier(mem_flags::mem_threadgroup); + } + + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq3_s * xr = x + ibl; + device const uint8_t * qs = xr->qs + 8 * ib; + device const uint8_t * qh = xr->qh + ib; + device const uint8_t * sc = xr->scales + (ib/2); + device const uint8_t * signs = xr->signs + 4 * ib; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf)); + + float2 sum = {0}; + for (int l = 0; l < 4; ++l) { + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]); + sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]); + } + } + sumf[row] += d * (sum[0] + sum[1]); + + dh += nb*sizeof(block_iq3_s)/2; + qs += nb*sizeof(block_iq3_s); + qh += nb*sizeof(block_iq3_s); + sc += nb*sizeof(block_iq3_s); + signs += nb*sizeof(block_iq3_s); + } + + y4 += 32 * 32; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + } + } +} + +[[host_name("kernel_mul_mv_iq3_s_f32")]] +kernel void kernel_mul_mv_iq3_s_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + void kernel_mul_mv_iq1_s_f32_impl( device const void * src0, device const float * src1, @@ -4952,6 +5163,31 @@ void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x } } +template +void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 8*ib32; + device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f; + constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); + reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); + } + grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256))); + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); + reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); + } +} + template void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 @@ -5525,6 +5761,7 @@ template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; @@ -5566,6 +5803,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -5619,6 +5857,7 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; @@ -6589,6 +6828,71 @@ kernel void kernel_mul_mv_id_iq3_xxs_f32( sgitg); } +[[host_name("kernel_mul_mv_id_iq3_s_f32")]] +kernel void kernel_mul_mv_id_iq3_s_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq3_s_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} + [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel void kernel_mul_mv_id_iq1_s_f32( device const char * ids, diff --git a/ggml-quants.c b/ggml-quants.c index b15977f53e2f3..5c5f2ce1b9b87 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3505,6 +3505,73 @@ static const uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; +static const uint32_t iq3xs_grid[512] = { + 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, + 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, + 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, + 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, + 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, + 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, + 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, + 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, + 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, + 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, + 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, + 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, + 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, + 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, + 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, + 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, + 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, + 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, + 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, + 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, + 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, + 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, + 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, + 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, + 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, + 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, + 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, + 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, + 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, + 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, + 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, + 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, + 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, + 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, + 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, + 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, + 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, + 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, + 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, + 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, + 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, + 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, + 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, + 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, + 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, + 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, + 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, + 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, + 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, + 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, + 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, + 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, + 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, + 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, + 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, + 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, + 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, + 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, + 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, + 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, + 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, + 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, + 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, + 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +}; + #define NGRID_IQ2XXS 512 static const uint64_t iq1s_grid[NGRID_IQ2XXS] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, @@ -3736,6 +3803,49 @@ void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y } } +// ====================== 3.3125 bpw (de)-quantization + +void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = x[i].signs; + + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f; + const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); + } + y += 8; + } + qs += 8; + signs += 4; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); + } + y += 8; + } + qh += 2; + qs += 8; + signs += 4; + } + } +} + // ====================== 1.5625 bpw (de)-quantization void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) { @@ -8806,6 +8916,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif +#if defined (__AVX2__) || defined (__ARM_NEON) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, @@ -8840,6 +8951,7 @@ static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, }; +#endif void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -9327,6 +9439,202 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void #endif } +void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + + uint8x16x2_t vs; + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)], + iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]}; + const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)], + iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]}; + const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)], + iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]}; + const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)], + iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]}; + qs += 16; + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); + vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0])); + q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1])); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); + vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + signs += 4; + + q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0])); + q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1])); + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); + sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); + } + sumf += d*(sumi1 + sumi2); + } + *s = 0.25f * sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)], + iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)], + iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)], + iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)], + iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)], + iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)], + iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)], + iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]); + qs += 8; + const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)], + iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)], + iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)], + iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)], + iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)], + iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)], + iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)], + iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]); + qs += 8; + + __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint8_t * restrict signs = x[i].signs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1; + const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls1; + sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls2; + } + sumf += d * bsum; + } + *s = 0.25f * sumf; +#endif +} + + #ifdef __AVX2__ static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { const __m256i ax = _mm256_sign_epi8(x, x); @@ -9523,6 +9831,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * float sumf = 0; for (int ib = 0; ib < nb; ib += 2) { + q4bits.val[0] = vld1q_u8(x[ib+0].qs); q4bits.val[1] = vld1q_u8(x[ib+1].qs); q8b.val[0] = vld1q_s8(y[ib+0].qs); @@ -10239,14 +10548,15 @@ typedef struct { uint16_t * neighbours; } iq3_entry_t; -static iq3_entry_t iq3_data[1] = { +static iq3_entry_t iq3_data[2] = { + {NULL, NULL, NULL}, {NULL, NULL, NULL}, }; static inline int iq3_data_index(int grid_size) { (void)grid_size; - GGML_ASSERT(grid_size == 256); - return 0; + GGML_ASSERT(grid_size == 256 || grid_size == 512); + return grid_size == 256 ? 0 : 1; } static int iq3_compare_func(const void * left, const void * right) { @@ -10278,9 +10588,44 @@ void iq3xs_init_impl(int grid_size) { 3185, 3215, 3252, 3288, 3294, 3364, 3397, 3434, 3483, 3523, 3537, 3587, 3589, 3591, 3592, 3610, 3626, 3670, 3680, 3722, 3749, 3754, 3776, 3789, 3803, 3824, 3857, 3873, 3904, 3906, 3924, 3992, }; + static const uint16_t kgrid_512[512] = { + 0, 1, 2, 5, 7, 8, 9, 10, 12, 14, 16, 17, 21, 27, 32, 34, + 37, 39, 41, 43, 48, 50, 57, 60, 63, 64, 65, 66, 68, 72, 73, 77, + 80, 83, 87, 89, 93, 100, 113, 117, 122, 128, 129, 133, 135, 136, 139, 142, + 145, 149, 152, 156, 162, 165, 167, 169, 171, 184, 187, 195, 201, 205, 208, 210, + 217, 219, 222, 228, 232, 234, 247, 249, 253, 256, 267, 271, 273, 276, 282, 288, + 291, 297, 312, 322, 324, 336, 338, 342, 347, 353, 357, 359, 374, 379, 390, 393, + 395, 409, 426, 441, 448, 450, 452, 464, 466, 470, 475, 488, 492, 512, 513, 514, + 516, 520, 521, 523, 525, 527, 528, 530, 537, 540, 542, 556, 558, 561, 570, 576, + 577, 579, 582, 584, 588, 593, 600, 603, 609, 616, 618, 632, 638, 640, 650, 653, + 655, 656, 660, 666, 672, 675, 685, 688, 698, 705, 708, 711, 712, 715, 721, 727, + 728, 732, 737, 754, 760, 771, 773, 778, 780, 793, 795, 802, 806, 808, 812, 833, + 840, 843, 849, 856, 858, 873, 912, 916, 919, 932, 934, 961, 963, 968, 970, 977, + 989, 993, 1010, 1016, 1024, 1025, 1027, 1029, 1031, 1032, 1034, 1036, 1038, 1041, 1043, 1047, + 1048, 1050, 1057, 1059, 1061, 1064, 1066, 1079, 1080, 1083, 1085, 1088, 1090, 1096, 1099, 1103, + 1106, 1109, 1113, 1116, 1122, 1129, 1153, 1156, 1159, 1169, 1171, 1176, 1183, 1185, 1195, 1199, + 1209, 1212, 1216, 1218, 1221, 1225, 1234, 1236, 1241, 1243, 1250, 1256, 1270, 1281, 1287, 1296, + 1299, 1306, 1309, 1313, 1338, 1341, 1348, 1353, 1362, 1375, 1376, 1387, 1400, 1408, 1410, 1415, + 1425, 1453, 1457, 1477, 1481, 1494, 1496, 1507, 1512, 1538, 1545, 1547, 1549, 1551, 1554, 1561, + 1563, 1565, 1570, 1572, 1575, 1577, 1587, 1593, 1601, 1603, 1605, 1612, 1617, 1619, 1632, 1648, + 1658, 1662, 1664, 1674, 1680, 1690, 1692, 1704, 1729, 1736, 1740, 1745, 1747, 1751, 1752, 1761, + 1763, 1767, 1773, 1787, 1795, 1801, 1806, 1810, 1817, 1834, 1840, 1844, 1857, 1864, 1866, 1877, + 1882, 1892, 1902, 1915, 1934, 1953, 1985, 1987, 2000, 2002, 2013, 2048, 2052, 2058, 2064, 2068, + 2071, 2074, 2081, 2088, 2104, 2114, 2119, 2121, 2123, 2130, 2136, 2141, 2147, 2153, 2157, 2177, + 2179, 2184, 2189, 2193, 2203, 2208, 2223, 2226, 2232, 2244, 2249, 2251, 2256, 2258, 2265, 2269, + 2304, 2306, 2324, 2335, 2336, 2361, 2373, 2375, 2385, 2418, 2443, 2460, 2480, 2504, 2509, 2520, + 2531, 2537, 2562, 2568, 2572, 2578, 2592, 2596, 2599, 2602, 2614, 2620, 2625, 2627, 2629, 2634, + 2641, 2650, 2682, 2688, 2697, 2707, 2712, 2718, 2731, 2754, 2759, 2760, 2775, 2788, 2793, 2805, + 2811, 2817, 2820, 2832, 2842, 2854, 2890, 2902, 2921, 2923, 2978, 3010, 3012, 3026, 3081, 3083, + 3085, 3097, 3099, 3120, 3136, 3152, 3159, 3188, 3210, 3228, 3234, 3245, 3250, 3256, 3264, 3276, + 3281, 3296, 3349, 3363, 3378, 3392, 3395, 3420, 3440, 3461, 3488, 3529, 3531, 3584, 3588, 3591, + 3600, 3602, 3614, 3616, 3628, 3634, 3650, 3657, 3668, 3683, 3685, 3713, 3716, 3720, 3726, 3729, + 3736, 3753, 3778, 3802, 3805, 3819, 3841, 3845, 3851, 3856, 3880, 3922, 3938, 3970, 3993, 4032, + }; + const int kmap_size = 4096; - const int nwant = 2; - const uint16_t * kgrid = kgrid_256; + const int nwant = grid_size == 256 ? 2 : 3; + const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512; uint32_t * kgrid_q3xs; int * kmap_q3xs; uint16_t * kneighbors_q3xs; @@ -10377,7 +10722,7 @@ void iq3xs_init_impl(int grid_size) { } void iq3xs_free_impl(int grid_size) { - GGML_ASSERT(grid_size == 256); + GGML_ASSERT(grid_size == 256 || grid_size == 512); const int gindex = iq3_data_index(grid_size); if (iq3_data[gindex].grid) { free(iq3_data[gindex].grid); iq3_data[gindex].grid = NULL; @@ -10410,9 +10755,10 @@ static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const u return grid_index; } -static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { +static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int n, + const float * restrict quant_weights) { - const int gindex = iq3_data_index(256); + const int gindex = iq3_data_index(grid_size); const uint32_t * kgrid_q3xs = iq3_data[gindex].grid; const int * kmap_q3xs = iq3_data[gindex].map; @@ -10426,9 +10772,23 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict const int kMaxQ = 8; - const int nbl = n/256; + const int nbl = n/QK_K; - block_iq3_xxs * y = vy; + ggml_fp16_t * dh; + uint8_t * qs; + int block_size; + if (grid_size == 256) { + block_iq3_xxs * y = vy; + dh = &y->d; + qs = y->qs; + block_size = sizeof(block_iq3_xxs); + } else { + block_iq3_s * y = vy; + dh = &y->d; + qs = y->qs; + block_size = sizeof(block_iq3_s); + } + int quant_size = block_size - sizeof(ggml_fp16_t); float scales[QK_K/32]; float weight[32]; @@ -10439,20 +10799,21 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict bool is_on_grid[8]; bool is_on_grid_aux[8]; uint8_t block_signs[8]; - uint8_t q3[3*(QK_K/8)]; + uint8_t q3[3*(QK_K/8)+QK_K/32]; uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4); + uint8_t * qh = q3 + 3*(QK_K/8); for (int ibl = 0; ibl < nbl; ++ibl) { - y[ibl].d = GGML_FP32_TO_FP16(0.f); - memset(q3, 0, 3*QK_K/8); + dh[0] = GGML_FP32_TO_FP16(0.f); + memset(q3, 0, 3*QK_K/8+QK_K/32); float max_scale = 0; const float * xbl = x + QK_K*ibl; float sumx2 = 0; for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; - float sigma2 = sumx2/QK_K; + float sigma2 = 2*sumx2/QK_K; for (int ib = 0; ib < QK_K/32; ++ib) { const float * xb = xbl + 32*ib; @@ -10570,7 +10931,13 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict printf("\n"); GGML_ASSERT(false); } - q3[8*ib+k] = grid_index; + if (grid_size == 256) { + q3[8*ib+k] = grid_index; + } else { + q3[8*ib+k] = grid_index & 255; + qh[ib] |= ((grid_index >> 8) << k); + } + } scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21); GGML_ASSERT(scale >= 0); @@ -10579,63 +10946,25 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict } if (!max_scale) { - memset(y[ibl].qs, 0, 3*QK_K/8); + memset(qs, 0, quant_size); + dh += block_size/sizeof(ggml_fp16_t); + qs += block_size; continue; } float d = max_scale/31; - y[ibl].d = GGML_FP32_TO_FP16(d); + dh[0] = GGML_FP32_TO_FP16(d * 1.0125f); // small improvement via this fudge factor float id = 1/d; - float sumqx = 0, sumq2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); l = MAX(0, MIN(15, l)); scales_and_signs[ib] |= ((uint32_t)l << 28); - if (false) { - const float * xb = xbl + 32*ib; - if (quant_weights) { - const float * qw = quant_weights + QK_K*ibl + 32*ib; - for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); - } else { - for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i]; - } - const float db = 0.25f * d * (1 + 2*l); - for (int k = 0; k < 8; ++k) { - const int8_t * signs = keven_signs_q2xs + 8*((scales_and_signs[ib] >> 7*(k/2)) & 127) + 4*(k%2); - const float * xk = xb + 4*k; - const float * wk = weight + 4*k; - //const uint8_t * grid = (const uint8_t *)(kgrid_q3xs + q3[8*ib+k]); - const uint8_t * grid = (const uint8_t *)(iq3xxs_grid + q3[8*ib+k]); - float best_mse = 0; int best_index = q3[8*ib+k]; - for (int j = 0; j < 4; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - best_mse += wk[j] * diff * diff; - } - for (int idx = 0; idx < 256; ++idx) { - //grid = (const uint8_t *)(kgrid_q3xs + idx); - grid = (const uint8_t *)(iq3xxs_grid + idx); - float mse = 0; - for (int j = 0; j < 4; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - mse += wk[j] * diff * diff; - } - if (mse < best_mse) { - best_mse = mse; best_index = idx; - } - } - q3[8*ib+k] = best_index; - //grid = (const uint8_t *)(kgrid_q3xs + best_index); - grid = (const uint8_t *)(iq3xxs_grid + best_index); - for (int j = 0; j < 4; ++j) { - float q = db * grid[j] * signs[j]; - sumqx += wk[j] * q * xk[j]; - sumq2 += wk[j] * q * q; - } - } - if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); - } } - memcpy(y[ibl].qs, q3, 3*QK_K/8); + memcpy(qs, q3, quant_size); + + dh += block_size/sizeof(ggml_fp16_t); + qs += block_size; + } } @@ -10645,7 +10974,7 @@ size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int nblock = n_per_row/QK_K; char * qrow = (char *)dst; for (int row = 0; row < nrow; ++row) { - quantize_row_iq3_xxs_impl(src, qrow, n_per_row, quant_weights); + quantize_row_iq3_xxs_impl(256, src, qrow, n_per_row, quant_weights); src += n_per_row; qrow += nblock*sizeof(block_iq3_xxs); } @@ -10660,9 +10989,226 @@ void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) { assert(k % QK_K == 0); - quantize_row_iq3_xxs_impl(x, y, k, NULL); + quantize_row_iq3_xxs_impl(256, x, y, k, NULL); +} + +static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, void * restrict vy, int n, + const float * restrict quant_weights, + float * scales, + float * weight, + float * xval, + int8_t * L, + int8_t * Laux, + float * waux, + bool * is_on_grid, + bool * is_on_grid_aux, + uint8_t * block_signs) { + + const int gindex = iq3_data_index(512); + + const uint32_t * kgrid_q3xs = iq3_data[gindex].grid; + const int * kmap_q3xs = iq3_data[gindex].map; + const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours; + + //GGML_ASSERT(quant_weights && "missing quantization weights"); + GGML_ASSERT(kgrid_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kmap_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 8; + + const int nbl = n/QK_K; + + block_iq3_s * y = vy; + + const int bs4 = block_size/4; + const int bs8 = block_size/8; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + memset(&y[ibl], 0, sizeof(block_iq3_s)); + y[ibl].d = GGML_FP32_TO_FP16(0.f); + + uint8_t * qs = y[ibl].qs; + uint8_t * qh = y[ibl].qh; + uint8_t * signs = y[ibl].signs; + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = 2*sumx2/QK_K; + + for (int ib = 0; ib < QK_K/block_size; ++ib) { + const float * xb = xbl + block_size*ib; + if (quant_weights) { + const float * qw = quant_weights + QK_K*ibl + block_size*ib; + for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + } else { + for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i]; + } + for (int i = 0; i < block_size; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < bs8; ++k) { + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; s |= (1 << i); + } + } + block_signs[k] = s; + } + float max = xval[0]; + for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + for (int is = -15; is <= 15; ++is) { + float id = (2*kMaxQ-1+is*0.2f)/max; + float this_scale = 1/id; + for (int k = 0; k < bs4; ++k) { + for (int i = 0; i < 4; ++i) { + int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); + Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i); + int grid_index = kmap_q3xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1; + grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < block_size; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < block_size; ++i) L[i] = Laux[i]; + for (int k = 0; k < bs4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < bs4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < bs4; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 4; ++i) { + int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 3*i); + } + int grid_index = kmap_q3xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1; + grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k); + } + const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index); + for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2; + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < block_size; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale) + // and correspondingly flip quant signs. + scale = -scale; + for (int k = 0; k < bs8; ++k) block_signs[k] = ~block_signs[k]; + } + for (int k = 0; k < bs4; ++k) { + uint16_t u = 0; + for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i); + int grid_index = kmap_q3xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + qs[k] = grid_index & 255; + qh[(ib*bs4+k)/8] |= ((grid_index >> 8) << ((ib*bs4+k)%8)); + } + qs += bs4; + for (int k = 0; k < bs8; ++k) signs[k] = block_signs[k]; + signs += bs8; + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + for (int ib = 0; ib < QK_K/block_size; ib += 2) { + int l1 = nearest_int(0.5f*(id*scales[ib+0]-1)); + l1 = MAX(0, MIN(15, l1)); + int l2 = nearest_int(0.5f*(id*scales[ib+1]-1)); + l2 = MAX(0, MIN(15, l2)); + y[ibl].scales[ib/2] = l1 | (l2 << 4); + } + + } +} + +#define IQ3S_BLOCK_SIZE 32 +size_t quantize_iq3_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + float scales[QK_K/IQ3S_BLOCK_SIZE]; + float weight[IQ3S_BLOCK_SIZE]; + float xval[IQ3S_BLOCK_SIZE]; + int8_t L[IQ3S_BLOCK_SIZE]; + int8_t Laux[IQ3S_BLOCK_SIZE]; + float waux[IQ3S_BLOCK_SIZE]; + bool is_on_grid[IQ3S_BLOCK_SIZE/4]; + bool is_on_grid_aux[IQ3S_BLOCK_SIZE/4]; + uint8_t block_signs[IQ3S_BLOCK_SIZE/8]; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq3_s_impl(IQ3S_BLOCK_SIZE, src, qrow, n_per_row, quant_weights, + scales, weight, xval, L, Laux, waux, is_on_grid, is_on_grid_aux, block_signs); + src += n_per_row; + qrow += nblock*sizeof(block_iq3_s); + } + return nrow * nblock * sizeof(block_iq3_s); +} + +void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq3_s * restrict y = vy; + quantize_row_iq3_s_reference(x, y, k); } +void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) { + assert(k % QK_K == 0); + quantize_iq3_s(x, y, 1, k, NULL, NULL); +} + + // =================================== 1.5 bpw =================================================== static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid, diff --git a/ggml-quants.h b/ggml-quants.h index 113623b62938a..303b0b6f9552e 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -191,6 +191,21 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +// 3.4375 bpw +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif +typedef struct { + ggml_fp16_t d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t signs[QK_K/8]; + uint8_t scales[IQ3S_N_SCALE]; +} block_iq3_s; +static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); + typedef struct { ggml_fp16_t d; uint8_t qs[QK_K/8]; @@ -226,6 +241,7 @@ void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGM void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); +void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k); void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -242,6 +258,7 @@ void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -262,6 +279,7 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_ void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -280,6 +298,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -289,6 +308,7 @@ size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index d710fe702ddbd..c09a3cad657f2 100644 --- a/ggml.c +++ b/ggml.c @@ -678,6 +678,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ3_S] = { + .type_name = "iq3_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq3_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq3_s, + .from_float = quantize_row_iq3_s, + .from_float_reference = (ggml_from_float_t)quantize_row_iq3_s_reference, + .vec_dot = ggml_vec_dot_iq3_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", .blck_size = QK_K, @@ -2304,6 +2316,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; + case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7738,6 +7751,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: { ggml_compute_forward_add_q_f32(params, dst); } break; @@ -8017,6 +8031,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: { ggml_compute_forward_add1_q_f32(params, dst); } break; @@ -8141,6 +8156,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: default: { GGML_ASSERT(false); @@ -11039,6 +11055,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: { ggml_compute_forward_out_prod_q_f32(params, dst); } break; @@ -11227,6 +11244,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: default: { GGML_ASSERT(false); @@ -11429,6 +11447,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: { ggml_compute_forward_get_rows_q(params, dst); } break; @@ -12129,6 +12148,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -12212,6 +12232,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ3_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -19463,6 +19484,7 @@ void ggml_quantize_init(enum ggml_type type) { case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; + case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; default: // nothing break; } @@ -19737,6 +19759,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ3_S: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_IQ1_S: { GGML_ASSERT(start % QK_K == 0); diff --git a/ggml.h b/ggml.h index 37eff627928e8..a4166e1f7afd0 100644 --- a/ggml.h +++ b/ggml.h @@ -350,6 +350,7 @@ extern "C" { GGML_TYPE_IQ3_XXS = 18, GGML_TYPE_IQ1_S = 19, GGML_TYPE_IQ4_NL = 20, + GGML_TYPE_IQ3_S = 21, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -389,6 +390,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index 37477e6ef3c44..1f6b6cff48987 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2545,6 +2545,7 @@ struct llama_model_loader { case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; + case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2890,6 +2891,8 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw"; default: return "unknown, may not work"; } @@ -10544,6 +10547,12 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) { + new_type = GGML_TYPE_Q4_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { + new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } @@ -10575,13 +10584,17 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = GGML_TYPE_Q8_0; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { - new_type = GGML_TYPE_Q2_K; + new_type = GGML_TYPE_IQ3_XXS; + } + } else if (name.find("attn_q.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { + new_type = GGML_TYPE_IQ3_XXS; } } else if (name.find("ffn_down") != std::string::npos) { auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str()); int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { + else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) { @@ -10592,6 +10605,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 || + (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) { + new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K; } @@ -10623,37 +10640,41 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (qs.model.hparams.n_expert == 8) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || - ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { + ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || + ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { new_type = GGML_TYPE_Q5_K; } } else { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K; } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; } } else if (name.find("attn_qkv.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { + new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K; } else if (name.find("ffn_gate") != std::string::npos) { auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str()); int i_layer = info.first, n_layer = info.second; - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) { - new_type = GGML_TYPE_Q2_K; + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { + new_type = GGML_TYPE_IQ3_XXS; } ++qs.i_ffn_gate; } else if (name.find("ffn_up") != std::string::npos) { auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str()); int i_layer = info.first, n_layer = info.second; - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) { - new_type = GGML_TYPE_Q2_K; + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { + new_type = GGML_TYPE_IQ3_XXS; } ++qs.i_ffn_up; } @@ -10673,7 +10694,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || - new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -10688,6 +10709,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: new_type = GGML_TYPE_IQ4_NL; break; @@ -10719,7 +10741,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K_S: case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; - case LLAMA_FTYPE_MOSTLY_Q3_K_XS: + case LLAMA_FTYPE_MOSTLY_Q3_K_XS: quantized_type = GGML_TYPE_IQ3_S; break; case LLAMA_FTYPE_MOSTLY_Q3_K_S: case LLAMA_FTYPE_MOSTLY_Q3_K_M: case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; @@ -10733,6 +10755,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break; + case LLAMA_FTYPE_MOSTLY_IQ3_S: quantized_type = GGML_TYPE_IQ3_S; break; + case LLAMA_FTYPE_MOSTLY_IQ3_M: quantized_type = GGML_TYPE_IQ3_S; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } diff --git a/llama.h b/llama.h index 84f196b3bb625..889edf4d97b96 100644 --- a/llama.h +++ b/llama.h @@ -102,6 +102,8 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 55db42bf6e851..f8574588bcf2f 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1918,7 +1918,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, - GGML_TYPE_IQ4_NL, + GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, }; // unary ops diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 5e92d5742a3cc..04656bb9e8e83 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -151,6 +151,7 @@ int main(int argc, char * argv[]) { const float max_quantization_error = type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : + type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR; failed = !(total_error < max_quantization_error); num_failed += failed; @@ -167,7 +168,8 @@ int main(int argc, char * argv[]) { const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data()); const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS || - type == GGML_TYPE_IQ3_XXS ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR; + type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S ? MAX_DOT_PRODUCT_ERROR_LOWBIT + : MAX_DOT_PRODUCT_ERROR; failed = !(vec_dot_error < max_allowed_error); num_failed += failed; if (failed || verbose) { From 9e359a4f47c1b2dceb99e29706c9f7403d32ab5e Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 24 Feb 2024 19:16:04 +0100 Subject: [PATCH 088/401] server: continue to update other slots on embedding concurrent request (#5699) * server: #5655 - continue to update other slots on embedding concurrent request. * server: tests: add multi users embeddings as fixed * server: tests: adding OAI compatible embedding concurrent endpoint * server: tests: adding OAI compatible embedding with multiple inputs --- examples/server/server.cpp | 2 +- examples/server/tests/features/issues.feature | 34 +--- .../server/tests/features/parallel.feature | 46 ++++++ examples/server/tests/features/server.feature | 13 ++ examples/server/tests/features/steps/steps.py | 151 +++++++++++++----- 5 files changed, 168 insertions(+), 78 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9fb436c2a18ec..19a8c1067e72a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1836,7 +1836,7 @@ struct llama_server_context send_embedding(slot); slot.release(); slot.i_batch = -1; - return true; + continue; } completion_token_output result; diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature index 542006d9a8df2..bf5a175a357ca 100644 --- a/examples/server/tests/features/issues.feature +++ b/examples/server/tests/features/issues.feature @@ -1,36 +1,4 @@ # List of ongoing issues @bug Feature: Issues - # Issue #5655 - Scenario: Multi users embeddings - Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 - And 42 as server seed - And 64 KV cache size - And 2 slots - And continuous batching - And embeddings extraction - Then the server is starting - Then the server is healthy - - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - Given concurrent embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated + # No confirmed issue at the moment diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 802d624ffc9a3..c85f9de1d9a52 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -8,6 +8,7 @@ Feature: Parallel And 42 as server seed And 64 KV cache size And 2 slots + And embeddings extraction And continuous batching Then the server is starting Then the server is healthy @@ -75,3 +76,48 @@ Feature: Parallel Then the server is busy Then the server is idle Then all prompts are predicted + + Scenario: Multi users embeddings + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And a prompt: + """ + Write a very long poem. + """ + And a prompt: + """ + Write a very long joke. + """ + Given concurrent embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated + + Scenario: Multi users OAI compatibility embeddings + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + And a prompt: + """ + What is the biggest US city ? + """ + And a prompt: + """ + What is the capital of Bulgaria ? + """ + And a model tinyllama-2 + Given concurrent OAI embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index fedcfe5aef1b3..5f81d256a548c 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -60,6 +60,19 @@ Feature: llama.cpp server """ Then embeddings are generated + Scenario: OAI Embeddings compatibility with multiple inputs + Given a model tinyllama-2 + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + When an OAI compatible embeddings computation request for multiple inputs + Then embeddings are generated + Scenario: Tokenize / Detokenize When tokenizing: diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 50f2b641e764e..9c825fdbcd7f5 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -1,4 +1,5 @@ import asyncio +import collections import json import os import re @@ -261,35 +262,35 @@ def step_a_prompt_prompt(context, prompt): @step(u'concurrent completion requests') @async_run_until_complete() async def step_concurrent_completion_requests(context): - await concurrent_completion_requests(context, - request_completion, - # prompt is inserted automatically - context.base_url, - debug=context.debug, - n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - server_seed=context.server_seed if hasattr(context, 'server_seed') else None, - user_api_key=context.user_api_key if hasattr(context, - 'user_api_key') else None) + await concurrent_requests(context, + request_completion, + # prompt is inserted automatically + context.base_url, + debug=context.debug, + n_predict=context.n_predict if hasattr(context, 'n_predict') else None, + server_seed=context.server_seed if hasattr(context, 'server_seed') else None, + user_api_key=context.user_api_key if hasattr(context, + 'user_api_key') else None) @step(u'concurrent OAI completions requests') @async_run_until_complete async def step_oai_chat_completions(context): - await concurrent_completion_requests(context, oai_chat_completions, - # user_prompt is inserted automatically - context.system_prompt, - context.base_url, - True, # async_client - model=context.model - if hasattr(context, 'model') else None, - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) + await concurrent_requests(context, oai_chat_completions, + # user_prompt is inserted automatically + context.system_prompt, + context.base_url, + True, # async_client + model=context.model + if hasattr(context, 'model') else None, + n_predict=context.n_predict + if hasattr(context, 'n_predict') else None, + enable_streaming=context.enable_streaming + if hasattr(context, 'enable_streaming') else None, + server_seed=context.server_seed + if hasattr(context, 'server_seed') else None, + user_api_key=context.user_api_key + if hasattr(context, 'user_api_key') else None) @step(u'all prompts are predicted') @@ -316,36 +317,58 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None): @step(u'embeddings are computed for') @async_run_until_complete async def step_compute_embedding(context): - content = context.text - base_url = context.base_url - context.embeddings = await request_embedding(content, base_url) + context.embeddings = await request_embedding(context.text, base_url=context.base_url) @step(u'embeddings are generated') def step_assert_embeddings(context): - assert_embeddings(context.embeddings) + if len(context.prompts) == 0: + assert_embeddings(context.embeddings) + else: + assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n" + f"context.prompts={context.prompts}\n" + f"context.embeddings={context.embeddings}") + for embedding in context.embeddings: + context.prompts.pop() + assert_embeddings(embedding) @step(u'an OAI compatible embeddings computation request for') -def step_oai_compute_embedding(context): - openai.api_key = 'nope' # openai client always expects an api_keu - if context.user_api_key is not None: - openai.api_key = context.user_api_key - openai.api_base = f'{context.base_url}/v1' - embeddings = openai.Embedding.create( - model=context.model, - input=context.text, - ) - context.embeddings = embeddings +@async_run_until_complete +async def step_oai_compute_embeddings(context): + context.embeddings = await request_oai_embeddings(context.text, + base_url=context.base_url, + user_api_key=context.user_api_key, + model=context.model) + + +@step(u'an OAI compatible embeddings computation request for multiple inputs') +@async_run_until_complete +async def step_oai_compute_embeddings_multiple_inputs(context): + context.embeddings = await request_oai_embeddings(context.prompts, + base_url=context.base_url, + user_api_key=context.user_api_key, + model=context.model) @step(u'concurrent embedding requests') @async_run_until_complete() async def step_concurrent_embedding_requests(context): - await concurrent_completion_requests(context, - request_embedding, - # prompt is inserted automatically - context.base_url) + await concurrent_requests(context, + request_embedding, + # prompt is inserted automatically + base_url=context.base_url) + + +@step(u'concurrent OAI embedding requests') +@async_run_until_complete() +async def step_concurrent_oai_embedding_requests(context): + await concurrent_requests(context, + request_oai_embeddings, + # prompt is inserted automatically + base_url=context.base_url, + async_client=True, + model=context.model) @step(u'all embeddings are generated') @@ -401,7 +424,7 @@ def step_check_options_header_value(context, cors_header, cors_header_value): assert context.options_response.headers[cors_header] == cors_header_value -async def concurrent_completion_requests(context, f_completion, *args, **kwargs): +async def concurrent_requests(context, f_completion, *args, **kwargs): n_prompts = len(context.prompts) if context.debug: print(f"starting {n_prompts} concurrent completion requests...") @@ -565,7 +588,7 @@ async def oai_chat_completions(user_prompt, return completion_response -async def request_embedding(content, base_url): +async def request_embedding(content, base_url=None): async with aiohttp.ClientSession() as session: async with session.post(f'{base_url}/embedding', json={ @@ -576,6 +599,46 @@ async def request_embedding(content, base_url): return response_json['embedding'] +async def request_oai_embeddings(input, + base_url=None, user_api_key=None, + model=None, async_client=False): + # openai client always expects an api_key + user_api_key = user_api_key if user_api_key is not None else 'nope' + if async_client: + origin = 'llama.cpp' + if user_api_key is not None: + headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} + async with aiohttp.ClientSession() as session: + async with session.post(f'{base_url}/v1/embeddings', + json={ + "input": input, + "model": model, + }, + headers=headers) as response: + assert response.status == 200, f"received status code not expected: {response.status}" + assert response.headers['Access-Control-Allow-Origin'] == origin + assert response.headers['Content-Type'] == "application/json; charset=utf-8" + response_json = await response.json() + assert response_json['model'] == model, f"invalid model received: {response_json['model']}" + assert response_json['object'] == 'list' + return response_json['data'] + else: + openai.api_key = user_api_key + openai.api_base = f'{base_url}/v1' + oai_embeddings = openai.Embedding.create( + model=model, + input=input, + ) + + if isinstance(input, collections.abc.Sequence): + embeddings = [] + for an_oai_embeddings in oai_embeddings.data: + embeddings.append(an_oai_embeddings.embedding) + else: + embeddings = oai_embeddings.data.embedding + return embeddings + + def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None): content = completion_response['content'] n_predicted = completion_response['timings']['predicted_n'] From 69917dfa55674c608360638bb4d6a12a315e2810 Mon Sep 17 00:00:00 2001 From: Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> Date: Sun, 25 Feb 2024 10:54:04 +0100 Subject: [PATCH 089/401] py : fix StableLM conversion after config.json changes (#5703) * Fix issues during StableLM models conversion * Fix hard coded layer_norm_eps * Support layer_norm_eps for LlavaStableLM Co-authored-by: Jared Van Bortel * Add missing parenthesis Co-authored-by: Jared Van Bortel * Support rotary_factor for LlavaStableLM Co-authored-by: Jared Van Bortel * fix typo * Add StableLMEpochForCausalLM for safety Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> * Add StableLMEpochForCausalLM for safety 2 Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> --------- Co-authored-by: Jared Van Bortel Co-authored-by: Jared Van Bortel Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> --- convert-hf-to-gguf.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 32d54b45f3325..ae30b2a76971a 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -192,7 +192,7 @@ def from_model_architecture(model_architecture): return RefactModel if model_architecture == "PersimmonForCausalLM": return PersimmonModel - if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): return StableLMModel if model_architecture == "QWenLMHeadModel": return QwenModel @@ -253,7 +253,7 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.REFACT if arch == "PersimmonForCausalLM": return gguf.MODEL_ARCH.PERSIMMON - if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): return gguf.MODEL_ARCH.STABLELM if arch == "QWenLMHeadModel": return gguf.MODEL_ARCH.QWEN @@ -1074,10 +1074,11 @@ def set_gguf_parameters(self): self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) - self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"]))) + rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) + self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) - self.gguf_writer.add_layer_norm_eps(1e-5) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) class MixtralModel(Model): From ab336a9d5e5352ecdcdf4c12d2d54cf4ef82ce31 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Feb 2024 12:09:09 +0200 Subject: [PATCH 090/401] code : normalize enum names (#5697) * coda : normalize enum names ggml-ci * code : cont * code : cont --- common/common.cpp | 18 +- common/common.h | 4 +- common/train.cpp | 10 +- examples/baby-llama/baby-llama.cpp | 2 +- examples/finetune/finetune.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 14 +- examples/llava/llava.cpp | 2 +- examples/server/server.cpp | 18 +- .../train-text-from-scratch.cpp | 2 +- ggml-cuda.cu | 138 +++---- ggml-metal.m | 4 +- ggml-opencl.cpp | 50 +-- ggml-sycl.cpp | 152 ++++---- ggml-vulkan.cpp | 102 ++--- ggml.c | 350 +++++++++--------- ggml.h | 38 +- llama.cpp | 64 ++-- llama.h | 28 +- tests/test-backend-ops.cpp | 4 +- tests/test-opt.cpp | 2 +- 20 files changed, 502 insertions(+), 502 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 10ef11829cc50..ec596f5a075de 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -295,9 +295,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } } else if (arg == "--rope-scale") { if (++i >= argc) { @@ -630,11 +630,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } std::string arg_next = argv[i]; if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_NONE; + params.split_mode = LLAMA_SPLIT_MODE_NONE; } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_LAYER; + params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { - params.split_mode = LLAMA_SPLIT_ROW; + params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; break; @@ -837,15 +837,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { sep++; if (strncmp(sep, "int:", 4) == 0) { sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_INT; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; kvo.int_value = std::atol(sep); } else if (strncmp(sep, "float:", 6) == 0) { sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_FLOAT; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; kvo.float_value = std::atof(sep); } else if (strncmp(sep, "bool:", 5) == 0) { sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_BOOL; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; if (std::strcmp(sep, "true") == 0) { kvo.bool_value = true; } else if (std::strcmp(sep, "false") == 0) { diff --git a/common/common.h b/common/common.h index 935771d44ca9c..3e21579b00545 100644 --- a/common/common.h +++ b/common/common.h @@ -61,7 +61,7 @@ struct gpt_params { float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs + llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs int32_t n_beams = 0; // if non-zero then use beam search of given width. @@ -75,7 +75,7 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; + int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; // // sampling parameters diff --git a/common/train.cpp b/common/train.cpp index e4c3d5df61818..0dbfd24df2314 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -31,7 +31,7 @@ struct train_state * init_train_state() { state->opt = new struct ggml_opt_context; state->opt->ctx = NULL; - state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM); + state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; state->opt->loss_after = 0.0f; @@ -556,7 +556,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g std::string opt_type; GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE); if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) { - opt->params.type = GGML_OPT_ADAM; + opt->params.type = GGML_OPT_TYPE_ADAM; GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS); GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS); @@ -568,7 +568,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) { - opt->params.type = GGML_OPT_LBFGS; + opt->params.type = GGML_OPT_TYPE_LBFGS; GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT); GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS); @@ -603,7 +603,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized); switch (opt->params.type) { - case GGML_OPT_ADAM: + case GGML_OPT_TYPE_ADAM: { gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM); gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best); @@ -622,7 +622,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * gguf_add_tensor(fctx, opt->adam.pf); } } break; - case GGML_OPT_LBFGS: + case GGML_OPT_TYPE_LBFGS: { gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS); gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m); diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 65bb238a0d565..bf0125e753746 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -1547,7 +1547,7 @@ int main(int argc, char ** argv) { float error_before_opt = ggml_get_f32_1d(e, 0); - struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS); + struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS); opt_params_lbfgs.print_forward_graph = false; opt_params_lbfgs.print_backward_graph = false; opt_params_lbfgs.lbfgs.n_iter = 16; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 98bf5a07a7ed1..3da5317b3d910 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1531,7 +1531,7 @@ int main(int argc, char ** argv) { lora.hparams.n_rank_output = n_rank_output; // set opt params from command line - opt->params = ggml_opt_default_params(GGML_OPT_ADAM); + opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 11410f8ae7625..8fec3d43ddfdd 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -157,9 +157,9 @@ static const char * output_format_str(output_formats format) { static const char * split_mode_str(llama_split_mode mode) { switch (mode) { - case LLAMA_SPLIT_NONE: return "none"; - case LLAMA_SPLIT_LAYER: return "layer"; - case LLAMA_SPLIT_ROW: return "row"; + case LLAMA_SPLIT_MODE_NONE: return "none"; + case LLAMA_SPLIT_MODE_LAYER: return "layer"; + case LLAMA_SPLIT_MODE_ROW: return "row"; default: GGML_ASSERT(!"invalid split mode"); } } @@ -193,7 +193,7 @@ static const cmd_params cmd_params_defaults = { /* type_v */ {GGML_TYPE_F16}, /* n_threads */ {get_num_physical_cores()}, /* n_gpu_layers */ {99}, - /* split_mode */ {LLAMA_SPLIT_LAYER}, + /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, @@ -358,11 +358,11 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { for (const auto & m : p) { llama_split_mode mode; if (m == "none") { - mode = LLAMA_SPLIT_NONE; + mode = LLAMA_SPLIT_MODE_NONE; } else if (m == "layer") { - mode = LLAMA_SPLIT_LAYER; + mode = LLAMA_SPLIT_MODE_LAYER; } else if (m == "row") { - mode = LLAMA_SPLIT_ROW; + mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; break; diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 1a1cf7c78bf34..9801281661b25 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -152,7 +152,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip); model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); - if (newline_tmp->backend != GGML_BACKEND_CPU) { + if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { if (newline_tmp->buffer == NULL) { printf("newline_tmp tensor buffer is NULL\n"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 19a8c1067e72a..780862ef67810 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2086,9 +2086,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } } else if (arg == "--rope-freq-base") @@ -2212,15 +2212,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::string arg_next = argv[i]; if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_NONE; + params.split_mode = LLAMA_SPLIT_MODE_NONE; } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_LAYER; + params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { - params.split_mode = LLAMA_SPLIT_ROW; + params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; @@ -2447,15 +2447,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, sep++; if (strncmp(sep, "int:", 4) == 0) { sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_INT; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; kvo.int_value = std::atol(sep); } else if (strncmp(sep, "float:", 6) == 0) { sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_FLOAT; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; kvo.float_value = std::atof(sep); } else if (strncmp(sep, "bool:", 5) == 0) { sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_BOOL; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; if (std::strcmp(sep, "true") == 0) { kvo.bool_value = true; } else if (std::strcmp(sep, "false") == 0) { diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index e78ab185d89f3..7eafe8515a943 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -960,7 +960,7 @@ int main(int argc, char ** argv) { struct ggml_opt_context * opt = train->opt; // set opt params from command line - opt->params = ggml_opt_default_params(GGML_OPT_ADAM); + opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 21c612cb71b48..fb6d4f7d215b6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6369,11 +6369,11 @@ static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int n int ixj = col ^ j; if (ixj > col) { if ((col & k) == 0) { - if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { swap(dst_row[col], dst_row[ixj]); } } else { - if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { swap(dst_row[col], dst_row[ixj]); } } @@ -7927,10 +7927,10 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co const dim3 block_dims(ncols, 1, 1); const dim3 block_nums(1, nrows, 1); - if (order == GGML_SORT_ASC) { - k_argsort_f32_i32<<>>(x, dst, ncols); - } else if (order == GGML_SORT_DESC) { - k_argsort_f32_i32<<>>(x, dst, ncols); + if (order == GGML_SORT_ORDER_ASC) { + k_argsort_f32_i32<<>>(x, dst, ncols); + } else if (order == GGML_SORT_ORDER_DESC) { + k_argsort_f32_i32<<>>(x, dst, ncols); } else { GGML_ASSERT(false); } @@ -8362,11 +8362,11 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( cudaMemcpyKind kind; char * src_ptr; - if (src->backend == GGML_BACKEND_CPU) { + if (src->backend == GGML_BACKEND_TYPE_CPU) { kind = cudaMemcpyHostToDevice; src_ptr = (char *) src->data; - } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); + } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { + GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); kind = cudaMemcpyDeviceToDevice; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; @@ -8771,7 +8771,7 @@ static void ggml_cuda_op_mul_mat_q( // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; switch (src0->type) { case GGML_TYPE_Q4_0: @@ -8920,7 +8920,7 @@ static void ggml_cuda_op_mul_mat_vec_q( // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; switch (src0->type) { case GGML_TYPE_Q4_0: @@ -9096,7 +9096,7 @@ static void ggml_cuda_op_mul_mat_cublas( // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; const int compute_capability = g_device_caps[id].cc; @@ -9444,7 +9444,7 @@ static void ggml_cuda_op_soft_max( const bool use_src2 = src2 != nullptr; if (use_src2) { - const bool src2_on_device = src2->backend == GGML_BACKEND_GPU; + const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; if (src2_on_device) { ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; @@ -9502,16 +9502,16 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s const bool use_src1 = src1 != nullptr; const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; - const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU; + const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; + const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; // dd = data device float * src0_ddf = nullptr; @@ -9555,7 +9555,7 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { CUDA_CHECK(cudaDeviceSynchronize()); } } @@ -9636,8 +9636,8 @@ static void ggml_cuda_op_mul_mat( const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; - GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -9653,20 +9653,20 @@ static void ggml_cuda_op_mul_mat( ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; const bool src0_is_contiguous = ggml_is_contiguous(src0); const bool src1_is_contiguous = ggml_is_contiguous(src1); const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); std::array tensor_split; if (split) { - // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; tensor_split = buft_ctx->tensor_split; @@ -9724,8 +9724,8 @@ static void ggml_cuda_op_mul_mat( used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; ggml_cuda_set_device(id); cudaStream_t stream = g_cudaStreams[id][0]; @@ -9776,8 +9776,8 @@ static void ggml_cuda_op_mul_mat( continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; const int64_t row_diff = dev[id].row_high - dev[id].row_low; ggml_cuda_set_device(id); @@ -9802,12 +9802,12 @@ static void ggml_cuda_op_mul_mat( // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) { + if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device) { dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary - if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) { + if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { if (id != g_main_device) { if (convert_src1_to_q8_1) { char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; @@ -9820,14 +9820,14 @@ static void ggml_cuda_op_mul_mat( src1_ncols*ne10*sizeof(float), stream)); } } - } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) { + } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) { CUDA_CHECK(ggml_cuda_cpy_tensor_2d( src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); } else { GGML_ASSERT(false); } - if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) { + if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) { quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); CUDA_CHECK(cudaGetLastError()); } @@ -9845,10 +9845,10 @@ static void ggml_cuda_op_mul_mat( if (!dst_on_device) { void * dst_off_device; cudaMemcpyKind kind; - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { dst_off_device = dst->data; kind = cudaMemcpyDeviceToHost; - } else if (dst->backend == GGML_BACKEND_GPU) { + } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { dst_off_device = dst_extra->data_device[g_main_device]; kind = cudaMemcpyDeviceToDevice; } else { @@ -9913,7 +9913,7 @@ static void ggml_cuda_op_mul_mat( } } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaDeviceSynchronize()); } @@ -10019,7 +10019,7 @@ GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const stru static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -10050,7 +10050,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -10109,7 +10109,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS @@ -10255,11 +10255,11 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = - (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && - (src1->backend == GGML_BACKEND_GPU) && - ( dst->backend == GGML_BACKEND_GPU); + (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && + (src1->backend == GGML_BACKEND_TYPE_GPU) && + ( dst->backend == GGML_BACKEND_TYPE_GPU); - const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; @@ -10409,7 +10409,7 @@ static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src00)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->type == GGML_TYPE_F32); const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00); @@ -10553,7 +10553,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s cudaStream_t stream = g_cudaStreams[g_main_device][0]; - if (ids->backend == GGML_BACKEND_GPU) { + if (ids->backend == GGML_BACKEND_TYPE_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); CUDA_CHECK(cudaStreamSynchronize(stream)); @@ -10570,20 +10570,20 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; - src1_row.backend = GGML_BACKEND_GPU; - dst_row.backend = GGML_BACKEND_GPU; + src1_row.backend = GGML_BACKEND_TYPE_GPU; + dst_row.backend = GGML_BACKEND_TYPE_GPU; src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; - char * src1_original = src1->backend == GGML_BACKEND_CPU ? + char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; - char * dst_original = dst->backend == GGML_BACKEND_CPU ? + char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { //int32_t row_id; @@ -10611,9 +10611,9 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); - const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ? + const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_TYPE_CPU ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; - const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_CPU ? + const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_TYPE_CPU ? cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice; for (int32_t row_id = 0; row_id < n_as; ++row_id) { @@ -10668,7 +10668,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s } } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { CUDA_CHECK(cudaStreamSynchronize(stream)); } } @@ -10685,8 +10685,8 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne == ggml_nelements(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); @@ -10817,9 +10817,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st if (!g_cublas_loaded) return false; ggml_cuda_func_t func; - const bool any_on_device = tensor->backend == GGML_BACKEND_GPU - || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) - || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); + const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU + || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) + || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { return false; @@ -10966,14 +10966,14 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st return false; } - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) { + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { ggml_cuda_set_peer_access(tensor->src[1]->ne[1]); } if (params->ith != 0) { return true; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return true; } func(tensor->src[0], tensor->src[1], tensor); @@ -11072,7 +11072,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_GPU; + tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { @@ -11087,7 +11087,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t } GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -11098,7 +11098,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t } GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -11333,7 +11333,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); } } - tensor->backend = GGML_BACKEND_GPU_SPLIT; + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; tensor->extra = extra; } @@ -11605,7 +11605,7 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); } @@ -11614,7 +11614,7 @@ GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } @@ -11644,7 +11644,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg ggml_cuda_set_main_device(cuda_ctx->device); ggml_compute_params params = {}; - params.type = GGML_TASK_COMPUTE; + params.type = GGML_TASK_TYPE_COMPUTE; params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -11654,13 +11654,13 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg } #ifndef NDEBUG - assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT); + assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT); + assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); assert(node->src[j]->extra != nullptr); } diff --git a/ggml-metal.m b/ggml-metal.m index ee584cfa71ce7..3d6b01263acb5 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2262,8 +2262,8 @@ static bool ggml_metal_graph_compute( id pipeline = nil; switch (order) { - case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; - case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; + case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; default: GGML_ASSERT(false); }; diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 797bee66799b5..df619a884842c 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1354,7 +1354,7 @@ static void ggml_cl_pool_free(cl_mem mem, size_t size) { } void ggml_cl_free_data(const struct ggml_tensor* tensor) { - if (tensor->backend != GGML_BACKEND_GPU) { + if (tensor->backend != GGML_BACKEND_TYPE_GPU) { return; } @@ -1412,7 +1412,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o } static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; @@ -1476,7 +1476,7 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src } static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; @@ -1566,13 +1566,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr size_t y_size; size_t d_size; cl_mem d_X; - if (src0->backend == GGML_BACKEND_GPU) { // NOLINT + if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT d_X = (cl_mem) src0->extra; } else { d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } - cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = dst->backend == GGML_BACKEND_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); + cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); + cl_mem d_D = dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); size_t x_offset = 0; @@ -1580,7 +1580,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr // TODO: copy src0 here when r3>1 for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_GPU) { + if (src0->backend == GGML_BACKEND_TYPE_GPU) { x_offset = (i03 * ne02 + i02) * x_ne; } else { // copy src0 to device @@ -1589,7 +1589,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { // copy src1 to device - if (src1->backend == GGML_BACKEND_CPU) { + if (src1->backend == GGML_BACKEND_TYPE_CPU) { CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); } @@ -1612,7 +1612,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); } @@ -1621,13 +1621,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } } - if (src0->backend != GGML_BACKEND_GPU) { + if (src0->backend != GGML_BACKEND_TYPE_GPU) { ggml_cl_pool_free(d_X, x_size); } - if (src1->backend != GGML_BACKEND_GPU) { + if (src1->backend != GGML_BACKEND_TYPE_GPU) { ggml_cl_pool_free(d_Y, y_size); } - if (dst->backend != GGML_BACKEND_GPU) { + if (dst->backend != GGML_BACKEND_TYPE_GPU) { ggml_cl_pool_free(d_D, d_size); } } @@ -1670,7 +1670,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr size_t y_size; size_t d_size; cl_mem d_X; - if (src0->backend == GGML_BACKEND_GPU) { // NOLINT + if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT d_X = (cl_mem) src0->extra; } else { d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); @@ -1687,7 +1687,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr // TODO: copy src0 here when r3>1 for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_GPU) { + if (src0->backend == GGML_BACKEND_TYPE_GPU) { x_offset = (i03 * ne02 + i02) * x_ne; } else { // copy src0 to device @@ -1741,7 +1741,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host, then convert to float - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); ggml_fp16_to_fp32_row(tmp, d, d_ne); @@ -1753,7 +1753,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr } } - if (src0->backend != GGML_BACKEND_GPU) { + if (src0->backend != GGML_BACKEND_TYPE_GPU) { ggml_cl_pool_free(d_X, x_size); } ggml_cl_pool_free(d_Y, y_size); @@ -1798,7 +1798,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); cl_mem d_Q; - if (src0->backend == GGML_BACKEND_CPU) { + if (src0->backend == GGML_BACKEND_TYPE_CPU) { d_Q = ggml_cl_pool_malloc(q_sz, &q_size); } @@ -1817,10 +1817,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { for (int64_t i02 = 0; i02 < ne02; i02++) { // copy src0 to device if necessary - if (src0->backend == GGML_BACKEND_CPU) { + if (src0->backend == GGML_BACKEND_TYPE_CPU) { events.emplace_back(); CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++)); - } else if (src0->backend == GGML_BACKEND_GPU) { + } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { d_Q = (cl_mem) src0->extra; } else { GGML_ASSERT(false); @@ -1829,7 +1829,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * if (!mul_mat_vec) { // convert src0 to fp32 on device const size_t global = x_ne / global_denom; - const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; + const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q)); CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X)); CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); @@ -1843,7 +1843,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * // compute const size_t global = ne01 * local; - const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0; + const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; const cl_int ncols = ne00; events.emplace_back(); CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q)); @@ -1895,7 +1895,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * } ggml_cl_pool_free(d_Y, y_size); ggml_cl_pool_free(d_D, d_size); - if (src0->backend == GGML_BACKEND_CPU) { + if (src0->backend == GGML_BACKEND_TYPE_CPU) { ggml_cl_pool_free(d_Q, q_size); } } @@ -1911,7 +1911,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) { + ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) { return true; } @@ -1993,7 +1993,7 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) { CL_CHECK(clFinish(queue)); tensor->extra = dst; - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); } // ggml-backend @@ -2045,7 +2045,7 @@ static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ctx->sub_buffers.push_back(sub_buffer); tensor->extra = sub_buffer; } - tensor->backend = GGML_BACKEND_GPU; + tensor->backend = GGML_BACKEND_TYPE_GPU; } static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index b897828f967b1..c6c3c6e6fef07 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3338,7 +3338,7 @@ void print_ggml_tensor(const char*name, struct ggml_tensor *src){ size_t total_elements = ggml_nelements(src); - const bool src_on_device = src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT; + const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT; float *src_data =NULL; if(src_on_device) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; @@ -8086,11 +8086,11 @@ static void k_argsort_f32_i32(const float * x, int * dst, const int ncols, int ixj = col ^ j; if (ixj > col) { if ((col & k) == 0) { - if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { swap(dst_row[col], dst_row[ixj]); } } else { - if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { swap(dst_row[col], dst_row[ixj]); } } @@ -10825,7 +10825,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const sycl::range<3> block_dims(1, 1, ncols); const sycl::range<3> block_nums(1, nrows, 1); - if (order == GGML_SORT_ASC) { + if (order == GGML_SORT_ORDER_ASC) { /* DPCT1049:44: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query @@ -10834,9 +10834,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - k_argsort_f32_i32(x, dst, ncols, item_ct1); + k_argsort_f32_i32(x, dst, ncols, item_ct1); }); - } else if (order == GGML_SORT_DESC) { + } else if (order == GGML_SORT_ORDER_DESC) { /* DPCT1049:45: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query @@ -10845,7 +10845,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - k_argsort_f32_i32(x, dst, ncols, item_ct1); + k_argsort_f32_i32(x, dst, ncols, item_ct1); }); } else { GGML_ASSERT(false); @@ -11407,12 +11407,12 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, dpct::memcpy_direction kind; char * src_ptr; - if (src->backend == GGML_BACKEND_CPU) { + if (src->backend == GGML_BACKEND_TYPE_CPU) { kind = dpct::host_to_device; src_ptr = (char *) src->data; - // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_CPU src_ptr %p\n", src_ptr); - } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); + // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr); + } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { + GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); kind = dpct::device_to_device; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; @@ -11846,7 +11846,7 @@ inline void ggml_sycl_op_mul_mat_q( // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; + const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; switch (src0->type) { case GGML_TYPE_Q4_0: @@ -12119,7 +12119,7 @@ inline void ggml_sycl_op_mul_mat_sycl( // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; + int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check @@ -12501,16 +12501,16 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, const bool use_src1 = src1 != nullptr; const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; - const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU; + const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; + const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; // dd = data device float * src0_ddf = nullptr; @@ -12565,7 +12565,7 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)))); } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { SYCL_CHECK(CHECK_TRY_ERROR( dpct::get_current_device().queues_wait_and_throw())); } @@ -12640,8 +12640,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; - GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -12656,13 +12656,13 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; const bool src0_is_contiguous = ggml_is_contiguous(src0); const bool src1_is_contiguous = ggml_is_contiguous(src1); int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); @@ -12717,8 +12717,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; ggml_sycl_set_device(get_device_id_by_index(id)); const dpct::queue_ptr stream = g_syclStreams[id][0]; @@ -12782,8 +12782,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; const int64_t row_diff = row_high[id] - row_low[id]; ggml_sycl_set_device(get_device_id_by_index(id)); @@ -12809,12 +12809,12 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_GPU && id == g_main_device_index) { + if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) { dst_dd_i += row_low[id]; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary - if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) { + if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { if (id != g_main_device_index) { if (convert_src1_to_q8_1) { char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset; @@ -12830,14 +12830,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, src1_ncols * ne10 * sizeof(float)))); } } - } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) { + } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) { SYCL_CHECK(ggml_sycl_cpy_tensor_2d( src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); } else { GGML_ASSERT(false); } - if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) { + if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) { quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); /* DPCT1010:92: SYCL uses exceptions to report errors and does @@ -12867,10 +12867,10 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, if (!dst_on_device) { void * dst_off_device; dpct::memcpy_direction kind; - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { dst_off_device = dst->data; kind = dpct::device_to_host; - } else if (dst->backend == GGML_BACKEND_GPU) { + } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { dst_off_device = dst_extra->data_device[g_main_device_index]; kind = dpct::device_to_device; } else { @@ -12954,7 +12954,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { SYCL_CHECK(ggml_sycl_set_device(g_main_device)); SYCL_CHECK(CHECK_TRY_ERROR( dpct::get_current_device().queues_wait_and_throw())); @@ -13091,7 +13091,7 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -13129,7 +13129,7 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13196,7 +13196,7 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13372,11 +13372,11 @@ catch (sycl::exception const &exc) { static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = - (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && - (src1->backend == GGML_BACKEND_GPU) && - ( dst->backend == GGML_BACKEND_GPU); + (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && + (src1->backend == GGML_BACKEND_TYPE_GPU) && + ( dst->backend == GGML_BACKEND_TYPE_GPU); - const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; for (int64_t id = 0; id < g_device_count; ++id) { @@ -13505,7 +13505,7 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src00)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne); @@ -13643,7 +13643,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; - if (ids->backend == GGML_BACKEND_GPU) { + if (ids->backend == GGML_BACKEND_TYPE_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index]; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); @@ -13661,20 +13661,20 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; - src1_row.backend = GGML_BACKEND_GPU; - dst_row.backend = GGML_BACKEND_GPU; + src1_row.backend = GGML_BACKEND_TYPE_GPU; + dst_row.backend = GGML_BACKEND_TYPE_GPU; src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; - char * src1_original = src1->backend == GGML_BACKEND_CPU ? + char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index]; - char * dst_original = dst->backend == GGML_BACKEND_CPU ? + char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? (char *) dst->data : (char *) dst_extra->data_device[g_main_device_index]; if (src1->ne[1] == 1) { - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { //int32_t row_id; @@ -13756,7 +13756,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, } } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); } } @@ -13779,8 +13779,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne == ggml_nelements(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); - GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); @@ -13887,17 +13887,17 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { memset(extra, 0, sizeof(*extra)); for (int64_t id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_GPU && id != g_main_device_index) { + if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) { continue; } ggml_sycl_set_device(get_device_id_by_index(id)); const dpct::queue_ptr stream = g_syclStreams[id][0]; int64_t row_low, row_high; - if (backend == GGML_BACKEND_GPU) { + if (backend == GGML_BACKEND_TYPE_GPU) { row_low = 0; row_high = nrows; - } else if (backend == GGML_BACKEND_GPU_SPLIT) { + } else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { const int64_t rounding = get_row_rounding(tensor->type); row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; @@ -13946,7 +13946,7 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { extra->data_device[id] = buf; - if (backend == GGML_BACKEND_GPU_SPLIT) { + if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { for (int64_t is = 0; is < MAX_STREAMS; ++is) { SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] = new sycl::event())); @@ -13963,7 +13963,7 @@ catch (sycl::exception const &exc) { } void ggml_sycl_free_data(struct ggml_tensor *tensor) try { - if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { + if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) { return; } @@ -14016,15 +14016,15 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, return; } - tensor->backend = GGML_BACKEND_GPU; + tensor->backend = GGML_BACKEND_TYPE_GPU; - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) { + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) { const ggml_op src0_op = tensor->src[0]->op; if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) { ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc); } } - if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) { + if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) { ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc); } @@ -14042,7 +14042,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, SYCL_CHECK(ggml_sycl_set_device(g_main_device)); const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; - if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { + if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; size_t offset = 0; @@ -14111,7 +14111,7 @@ void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor, const bool inplace = tensor->view_src != nullptr; - if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) { + if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; size_t view_offset = 0; @@ -14132,7 +14132,7 @@ catch (sycl::exception const &exc) { } void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(ggml_is_contiguous(tensor)); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -14219,9 +14219,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ if (!g_sycl_loaded) return false; ggml_sycl_func_t func; - const bool any_on_device = tensor->backend == GGML_BACKEND_GPU - || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) - || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); + const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU + || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) + || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { return false; @@ -14359,14 +14359,14 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ return false; } - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) { + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { ggml_sycl_set_peer_access(tensor->src[1]->ne[1]); } if (params->ith != 0) { return true; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return true; } func(tensor->src[0], tensor->src[1], tensor); @@ -14517,7 +14517,7 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_GPU; + tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { @@ -14548,7 +14548,7 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; @@ -14573,7 +14573,7 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; @@ -14809,7 +14809,7 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( (char *)tensor->data + offset, data, size))); @@ -14827,7 +14827,7 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( data, (const char *)tensor->data + offset, size))); @@ -14880,7 +14880,7 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph ggml_sycl_set_main_device(sycl_ctx->device); ggml_compute_params params = {}; - params.type = GGML_TASK_COMPUTE; + params.type = GGML_TASK_TYPE_COMPUTE; params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -14888,13 +14888,13 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) continue; - assert(node->backend == GGML_BACKEND_GPU); + assert(node->backend == GGML_BACKEND_TYPE_GPU); assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_GPU); + assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU); assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->src[j]->extra != nullptr); } diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 4e5eaff15110b..6caafb82279ae 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -2320,8 +2320,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su src1_uma = d_Qy != nullptr; } - const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma; - const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; + const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma; + const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); @@ -2453,7 +2453,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su // compute ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data); ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); @@ -2506,8 +2506,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context src1_uma = d_Qy != nullptr; } - const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma; - const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; + const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma; + const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); @@ -2630,7 +2630,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); ggml_vk_sync_buffers(subctx); @@ -2647,7 +2647,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -2679,7 +2679,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c src1_uma = d_Qy != nullptr; } - const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; + const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; const uint64_t x_ne = ne00 * ne01 * ne02; const uint64_t y_ne = ne10 * ne11 * ne12; @@ -2721,7 +2721,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host float * d = (float *) dst->data; ggml_vk_sync_buffers(subctx); @@ -2738,7 +2738,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -2771,7 +2771,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con src1_uma = d_Qy != nullptr; } - const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; + const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; const uint64_t d_ne = ne01 * ne11 * ne12; @@ -2814,7 +2814,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host float * d = (float *) dst->data; ggml_vk_sync_buffers(subctx); @@ -2832,7 +2832,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) && dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU); + ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU); } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { @@ -2880,8 +2880,8 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; @@ -3110,8 +3110,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } } - const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma; - const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma; + const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma; + const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; @@ -3120,7 +3120,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c vk_buffer d_D = extra->buffer_gpu.lock(); // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) { + if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } @@ -3209,9 +3209,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) { + if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) { ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); - } else if(dst->backend == GGML_BACKEND_CPU) { + } else if(dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host float * d = (float *) dst->data; ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); @@ -3253,7 +3253,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); } @@ -3359,7 +3359,7 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device - if (dst->backend == GGML_BACKEND_CPU) { + if (dst->backend == GGML_BACKEND_TYPE_CPU) { ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; vk_buffer d_D = extra_src0->buffer_gpu.lock(); ggml_vk_sync_buffers(subctx); @@ -3994,9 +3994,9 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif - const bool any_on_device = node->backend == GGML_BACKEND_GPU - || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) - || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU)); + const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU + || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) + || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU)); if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { return; @@ -4215,9 +4215,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ - const bool any_on_device = node->backend == GGML_BACKEND_GPU - || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) - || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU); + const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU + || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) + || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU); if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; @@ -4371,7 +4371,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod last_node = true; #endif - if (node->backend == GGML_BACKEND_CPU || last_node) { + if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) { ggml_vk_ctx_end(ctx->compute_ctx); ctx->compute_ctx->exit_tensor = node; ctx->compute_ctx = nullptr; @@ -4379,9 +4379,9 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ - const bool any_on_device = tensor->backend == GGML_BACKEND_GPU - || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) - || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); + const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU + || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) + || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { return false; @@ -4442,7 +4442,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ if (params->ith != 0) { return true; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return true; } @@ -4745,7 +4745,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; } - tensor->backend = GGML_BACKEND_GPU; + tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; } @@ -4753,7 +4753,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; @@ -4768,7 +4768,7 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; @@ -4999,7 +4999,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -5020,7 +5020,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -5097,7 +5097,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml int last_node = cgraph->n_nodes - 1; // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly - while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) { + while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) { last_node -= 1; } @@ -5106,7 +5106,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } ggml_compute_params params = {}; - params.type = GGML_TASK_COMPUTE; + params.type = GGML_TASK_TYPE_COMPUTE; params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -5410,7 +5410,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_GPU) { + if (tensor->backend == GGML_BACKEND_TYPE_GPU) { const size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -5436,14 +5436,14 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso std::vector done; ggml_vk_print_graph_origin(tensor, done); - if (tensor->backend == GGML_BACKEND_GPU) { + if (tensor->backend == GGML_BACKEND_TYPE_GPU) { free(tensor_data); } } static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { return; - GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU); + GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU); if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { return; } @@ -5481,7 +5481,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (params->ith != 0) { return; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { return; } @@ -5518,10 +5518,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_buffer = malloc(src0_size); src0_clone->data = src0_buffer; - if (src0->backend == GGML_BACKEND_CPU) { + if (src0->backend == GGML_BACKEND_TYPE_CPU) { memcpy(src0_clone->data, src0->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src0->backend == GGML_BACKEND_GPU) { + } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; uint64_t offset = extra->offset; if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { @@ -5561,10 +5561,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_buffer = malloc(src1_size); src1_clone->data = src1_buffer; - if (src1->backend == GGML_BACKEND_CPU) { + if (src1->backend == GGML_BACKEND_TYPE_CPU) { memcpy(src1_clone->data, src1->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src1->backend == GGML_BACKEND_GPU) { + } else if (src1->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; uint64_t offset = extra->offset; if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { @@ -5723,7 +5723,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (params->ith != 0) { return; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { return; } if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { @@ -5735,7 +5735,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_GPU) { + if (tensor->backend == GGML_BACKEND_TYPE_GPU) { size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -5868,7 +5868,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ comp_result = nullptr; comp_size = 0; - if (tensor->backend == GGML_BACKEND_GPU) { + if (tensor->backend == GGML_BACKEND_TYPE_GPU) { free(tensor_data); } } diff --git a/ggml.c b/ggml.c index c09a3cad657f2..1d81553f47106 100644 --- a/ggml.c +++ b/ggml.c @@ -2721,7 +2721,7 @@ static struct ggml_tensor * ggml_new_tensor_impl( } } - struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); + struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here @@ -2729,7 +2729,7 @@ static struct ggml_tensor * ggml_new_tensor_impl( *result = (struct ggml_tensor) { /*.type =*/ type, - /*.backend =*/ GGML_BACKEND_CPU, + /*.backend =*/ GGML_BACKEND_TYPE_CPU, /*.buffer =*/ NULL, /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, @@ -3302,7 +3302,7 @@ struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) { char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { - if (obj->type == GGML_OBJECT_TENSOR) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { return (struct ggml_tensor *)(mem_buffer + obj->offs); } @@ -3319,7 +3319,7 @@ struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struc char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { - if (obj->type == GGML_OBJECT_TENSOR) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { return (struct ggml_tensor *)(mem_buffer + obj->offs); } @@ -3335,7 +3335,7 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { - if (obj->type == GGML_OBJECT_TENSOR) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs); if (strcmp(cur->name, name) == 0) { return cur; @@ -5879,7 +5879,7 @@ struct ggml_tensor * ggml_top_k( int k) { GGML_ASSERT(a->ne[0] >= k); - struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_DESC); + struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC); result = ggml_view_4d(ctx, result, k, result->ne[1], result->ne[2], result->ne[3], @@ -6673,7 +6673,7 @@ static void ggml_compute_forward_dup_same_cont( GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == dst->type); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -6705,7 +6705,7 @@ static void ggml_compute_forward_dup_f16( GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -6978,7 +6978,7 @@ static void ggml_compute_forward_dup_f32( GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7231,7 +7231,7 @@ static void ggml_compute_forward_dup_bytes( GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(src0->type == dst->type); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7411,7 +7411,7 @@ static void ggml_compute_forward_add_f32( GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7419,7 +7419,7 @@ static void ggml_compute_forward_add_f32( const int nth = params->nth; #ifdef GGML_USE_CLBLAST - if (src1->backend == GGML_BACKEND_GPU) { + if (src1->backend == GGML_BACKEND_TYPE_GPU) { // TODO: OpenCL kernel support full broadcast GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); if (ith == 0) { @@ -7501,7 +7501,7 @@ static void ggml_compute_forward_add_f16_f32( GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7580,7 +7580,7 @@ static void ggml_compute_forward_add_f16_f16( GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7636,7 +7636,7 @@ static void ggml_compute_forward_add_q_f32( GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7774,7 +7774,7 @@ static void ggml_compute_forward_add1_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7828,7 +7828,7 @@ static void ggml_compute_forward_add1_f16_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7880,7 +7880,7 @@ static void ggml_compute_forward_add1_f16_f16( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -7932,7 +7932,7 @@ static void ggml_compute_forward_add1_q_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8062,7 +8062,7 @@ static void ggml_compute_forward_acc_f32( size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - if (!inplace && (params->type == GGML_TASK_INIT)) { + if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) { if (params->ith != 0) { return; } @@ -8074,7 +8074,7 @@ static void ggml_compute_forward_acc_f32( ggml_nbytes(dst)); } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8176,7 +8176,7 @@ static void ggml_compute_forward_sub_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8257,14 +8257,14 @@ static void ggml_compute_forward_mul_f32( GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; #if defined(GGML_USE_CLBLAST) - if (src1->backend == GGML_BACKEND_GPU) { + if (src1->backend == GGML_BACKEND_TYPE_GPU) { // TODO: OpenCL kernel support full broadcast GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); if (ith == 0) { @@ -8365,7 +8365,7 @@ static void ggml_compute_forward_div_f32( GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8460,7 +8460,7 @@ static void ggml_compute_forward_sqr_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8506,7 +8506,7 @@ static void ggml_compute_forward_sqrt_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8552,7 +8552,7 @@ static void ggml_compute_forward_log_f32( GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8598,7 +8598,7 @@ static void ggml_compute_forward_sum_f32( assert(params->ith == 0); assert(ggml_is_scalar(dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8633,7 +8633,7 @@ static void ggml_compute_forward_sum_f16( assert(params->ith == 0); assert(ggml_is_scalar(dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8690,7 +8690,7 @@ static void ggml_compute_forward_sum_rows_f32( GGML_ASSERT(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8745,7 +8745,7 @@ static void ggml_compute_forward_mean_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8804,7 +8804,7 @@ static void ggml_compute_forward_argmax_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8855,7 +8855,7 @@ static void ggml_compute_forward_repeat_f32( GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8900,7 +8900,7 @@ static void ggml_compute_forward_repeat_f16( GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -8974,7 +8974,7 @@ static void ggml_compute_forward_repeat_back_f32( GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(dst, src0)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9051,7 +9051,7 @@ static void ggml_compute_forward_concat_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9123,7 +9123,7 @@ static void ggml_compute_forward_abs_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9169,7 +9169,7 @@ static void ggml_compute_forward_sgn_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9215,7 +9215,7 @@ static void ggml_compute_forward_neg_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9261,7 +9261,7 @@ static void ggml_compute_forward_step_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9307,7 +9307,7 @@ static void ggml_compute_forward_tanh_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9353,7 +9353,7 @@ static void ggml_compute_forward_elu_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9399,7 +9399,7 @@ static void ggml_compute_forward_relu_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9446,7 +9446,7 @@ static void ggml_compute_forward_gelu_f32( GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9509,7 +9509,7 @@ static void ggml_compute_forward_gelu_quick_f32( GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9572,7 +9572,7 @@ static void ggml_compute_forward_silu_f32( GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9633,7 +9633,7 @@ static void ggml_compute_forward_leaky_relu_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9686,7 +9686,7 @@ static void ggml_compute_forward_silu_back_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src0, grad)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9748,7 +9748,7 @@ static void ggml_compute_forward_hardswish_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9791,7 +9791,7 @@ static void ggml_compute_forward_hardsigmoid_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9837,7 +9837,7 @@ static void ggml_compute_forward_norm_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9912,7 +9912,7 @@ static void ggml_compute_forward_rms_norm_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -9983,7 +9983,7 @@ static void ggml_compute_forward_rms_norm_back_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10161,7 +10161,7 @@ static void ggml_compute_forward_group_norm_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10328,7 +10328,7 @@ static void ggml_compute_forward_mul_mat( #if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { + if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); } return; @@ -10341,7 +10341,7 @@ static void ggml_compute_forward_mul_mat( const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); UNUSED(desired_wsize); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (type != GGML_TYPE_F32) { assert(params->wsize >= desired_wsize); // parallelize by src0 rows @@ -10364,7 +10364,7 @@ static void ggml_compute_forward_mul_mat( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10402,7 +10402,7 @@ static void ggml_compute_forward_mul_mat( } #endif - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -10426,7 +10426,7 @@ static void ggml_compute_forward_mul_mat( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10583,7 +10583,7 @@ static void ggml_compute_forward_mul_mat_id( #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)] - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -10620,7 +10620,7 @@ static void ggml_compute_forward_mul_mat_id( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10768,7 +10768,7 @@ static void ggml_compute_forward_out_prod_f32( (ggml_is_contiguous(src1) || ggml_is_transposed(src1)); #endif - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst if (use_blas) { return; @@ -10781,7 +10781,7 @@ static void ggml_compute_forward_out_prod_f32( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -10961,7 +10961,7 @@ static void ggml_compute_forward_out_prod_q_f32( // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -10969,7 +10969,7 @@ static void ggml_compute_forward_out_prod_q_f32( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11087,7 +11087,7 @@ static void ggml_compute_forward_scale_f32( GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11159,7 +11159,7 @@ static void ggml_compute_forward_set_f32( size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - if (!inplace && (params->type == GGML_TASK_INIT)) { + if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) { if (params->ith != 0) { return; } @@ -11171,7 +11171,7 @@ static void ggml_compute_forward_set_f32( ggml_nbytes(dst)); } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11319,7 +11319,7 @@ static void ggml_compute_forward_get_rows_q( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11359,7 +11359,7 @@ static void ggml_compute_forward_get_rows_f16( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11396,7 +11396,7 @@ static void ggml_compute_forward_get_rows_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11499,14 +11499,14 @@ static void ggml_compute_forward_get_rows_back_f32_f16( // ggml_compute_forward_dup_same_cont(params, opt0, dst); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memset(dst->data, 0, ggml_nbytes(dst)); } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11538,14 +11538,14 @@ static void ggml_compute_forward_get_rows_back_f32( // ggml_compute_forward_dup_same_cont(params, opt0, dst); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memset(dst->data, 0, ggml_nbytes(dst)); } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11615,7 +11615,7 @@ static void ggml_compute_forward_diag_f32( GGML_ASSERT(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11684,7 +11684,7 @@ static void ggml_compute_forward_diag_mask_f32( GGML_ASSERT(n_past >= 0); - if (!inplace && (params->type == GGML_TASK_INIT)) { + if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) { if (ith != 0) { return; } @@ -11698,7 +11698,7 @@ static void ggml_compute_forward_diag_mask_f32( ggml_nbytes(dst)); } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11772,7 +11772,7 @@ static void ggml_compute_forward_soft_max_f32( assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11910,7 +11910,7 @@ static void ggml_compute_forward_soft_max_back_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src1, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12004,7 +12004,7 @@ static void ggml_compute_forward_alibi_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12063,7 +12063,7 @@ static void ggml_compute_forward_alibi_f16( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12170,7 +12170,7 @@ static void ggml_compute_forward_clamp_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12310,7 +12310,7 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12488,7 +12488,7 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12719,7 +12719,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32( GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -12759,7 +12759,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12818,7 +12818,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32( GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -12858,7 +12858,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -12962,11 +12962,11 @@ static void ggml_compute_forward_im2col_f32( GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13050,11 +13050,11 @@ static void ggml_compute_forward_im2col_f16( GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13136,7 +13136,7 @@ static void ggml_compute_forward_conv_transpose_2d( GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } @@ -13178,7 +13178,7 @@ static void ggml_compute_forward_conv_transpose_2d( return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13230,7 +13230,7 @@ static void ggml_compute_forward_pool_1d_sk_p0( assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13299,7 +13299,7 @@ static void ggml_compute_forward_pool_2d( GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13372,7 +13372,7 @@ static void ggml_compute_forward_upscale_f32( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13432,7 +13432,7 @@ static void ggml_compute_forward_pad_f32( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13493,7 +13493,7 @@ static void ggml_compute_forward_argsort_f32( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13519,8 +13519,8 @@ static void ggml_compute_forward_argsort_f32( // C doesn't have a functional sort, so we do a bubble sort instead for (int64_t j = 0; j < ne0; j++) { for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { + if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || + (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { int32_t tmp = dst_data[j]; dst_data[j] = dst_data[k]; dst_data[k] = tmp; @@ -13603,11 +13603,11 @@ static void ggml_compute_forward_flash_attn_f32( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -13795,11 +13795,11 @@ static void ggml_compute_forward_flash_attn_f16( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14054,11 +14054,11 @@ static void ggml_compute_forward_flash_ff_f16( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14213,14 +14213,14 @@ static void ggml_compute_forward_flash_attn_back_f32( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith == 0) { memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); } return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14536,7 +14536,7 @@ static void ggml_compute_forward_win_part_f32( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14602,7 +14602,7 @@ static void ggml_compute_forward_win_unpart_f32( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14730,7 +14730,7 @@ static void ggml_compute_forward_get_rel_pos_f16( const struct ggml_tensor * src0 = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14782,14 +14782,14 @@ static void ggml_compute_forward_add_rel_pos_f32( const struct ggml_tensor * src2 = dst->src[2]; const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; - if (!inplace && params->type == GGML_TASK_INIT) { + if (!inplace && params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); return; } - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14871,7 +14871,7 @@ static void ggml_compute_forward_map_unary_f32( GGML_ASSERT(ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14920,7 +14920,7 @@ static void ggml_compute_forward_map_binary_f32( assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14969,7 +14969,7 @@ static void ggml_compute_forward_map_custom1_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -14988,7 +14988,7 @@ static void ggml_compute_forward_map_custom2_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15008,7 +15008,7 @@ static void ggml_compute_forward_map_custom3_f32( assert(params->ith == 0); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15023,7 +15023,7 @@ static void ggml_compute_forward_map_custom1( const struct ggml_tensor * a = dst->src[0]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15041,7 +15041,7 @@ static void ggml_compute_forward_map_custom2( const struct ggml_tensor * a = dst->src[0]; const struct ggml_tensor * b = dst->src[1]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15060,7 +15060,7 @@ static void ggml_compute_forward_map_custom3( const struct ggml_tensor * b = dst->src[1]; const struct ggml_tensor * c = dst->src[2]; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15094,14 +15094,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32( GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); - if (params->type == GGML_TASK_INIT) { + if (params->type == GGML_TASK_TYPE_INIT) { if (ith == 0) { memset(sums, 0, sizeof(float) * (nth + nth * nc)); } return; } - if (params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_FINALIZE) { if (ith == 0) { float * dp = (float *) dst->data; ggml_vec_sum_f32(nth, dp, sums); @@ -15216,7 +15216,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( const int64_t ith = params->ith; const int64_t nth = params->nth; - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -15323,8 +15323,8 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm if (skip_cpu) { return; } - GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); - GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); + GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU); + GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU); #elif defined(GGML_USE_VULKAN) const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor); #ifdef GGML_VULKAN_CHECK_RESULTS @@ -15335,8 +15335,8 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm if (skip_cpu) { return; } - GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); - GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); + GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU); + GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU); #endif // GGML_USE_CUBLAS #ifdef GGML_USE_SYCL @@ -16882,7 +16882,7 @@ size_t ggml_graph_overhead(void) { struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) { const size_t obj_size = ggml_graph_nbytes(size, grads); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size); struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1); @@ -17429,7 +17429,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { set_numa_thread_affinity(state->ith); int node_n = -1; - int task_phase = GGML_TASK_FINALIZE; + int task_phase = GGML_TASK_TYPE_FINALIZE; while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { @@ -17441,7 +17441,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { // all other threads are finished and spinning // do finalize and init here so we don't have synchronize again struct ggml_compute_params params = { - /*.type =*/ GGML_TASK_FINALIZE, + /*.type =*/ GGML_TASK_TYPE_FINALIZE, /*.ith =*/ 0, /*.nth =*/ 0, /*.wsize =*/ cplan->work_size, @@ -17472,17 +17472,17 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (n_tasks == 1) { /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { - params.type = GGML_TASK_INIT; + params.type = GGML_TASK_TYPE_INIT; ggml_compute_forward(¶ms, node); } // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) - params.type = GGML_TASK_COMPUTE; + params.type = GGML_TASK_TYPE_COMPUTE; ggml_compute_forward(¶ms, node); if (GGML_OP_HAS_FINALIZE[node->op]) { - params.type = GGML_TASK_FINALIZE; + params.type = GGML_TASK_TYPE_FINALIZE; ggml_compute_forward(¶ms, node); } @@ -17496,7 +17496,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } } - task_phase = GGML_TASK_INIT; + task_phase = GGML_TASK_TYPE_INIT; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_n, node_n); atomic_store(&state->shared->node_task, task_phase); @@ -17513,7 +17513,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { const int n_tasks = ggml_get_n_tasks(node, n_threads); struct ggml_compute_params params = { - /*.type =*/ GGML_TASK_INIT, + /*.type =*/ GGML_TASK_TYPE_INIT, /*.ith =*/ state->ith, /*.nth =*/ n_tasks, /*.wsize =*/ cplan->work_size, @@ -17527,7 +17527,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { - task_phase = GGML_TASK_COMPUTE; + task_phase = GGML_TASK_TYPE_COMPUTE; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_task, task_phase); } @@ -17542,12 +17542,12 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } if (state->ith < n_tasks) { - params.type = GGML_TASK_COMPUTE; + params.type = GGML_TASK_TYPE_COMPUTE; ggml_compute_forward(¶ms, node); } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { - task_phase = GGML_TASK_FINALIZE; + task_phase = GGML_TASK_TYPE_FINALIZE; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_task, task_phase); } @@ -17783,7 +17783,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { /*.n_threads =*/ n_threads, /*.n_active =*/ n_threads, /*.node_n =*/ -1, - /*.node_task =*/ GGML_TASK_FINALIZE, + /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, }; @@ -17851,7 +17851,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; @@ -18659,7 +18659,7 @@ static enum ggml_opt_result ggml_opt_adam( float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; bool cancel = false; @@ -18671,7 +18671,7 @@ static enum ggml_opt_result ggml_opt_adam( if (callback) { callback(callback_data, accum_step, &sched, &cancel); if (cancel) { - return GGML_OPT_CANCEL; + return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); @@ -18762,7 +18762,7 @@ static enum ggml_opt_result ggml_opt_adam( if (callback) { callback(callback_data, accum_step, &sched, &cancel); if (cancel) { - return GGML_OPT_CANCEL;; + return GGML_OPT_RESULT_CANCEL;; } } // ggml_graph_reset (gf); @@ -18779,7 +18779,7 @@ static enum ggml_opt_result ggml_opt_adam( if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } // delta-based convergence test @@ -18789,7 +18789,7 @@ static enum ggml_opt_result ggml_opt_adam( const float rate = (pf[(iter0 + t)%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } } @@ -18805,7 +18805,7 @@ static enum ggml_opt_result ggml_opt_adam( ++n_no_improvement[0]; if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } } } @@ -18823,7 +18823,7 @@ static enum ggml_opt_result ggml_opt_adam( } } - return GGML_OPT_DID_NOT_CONVERGE; + return GGML_OPT_RESULT_DID_NOT_CONVERGE; } // @@ -18904,7 +18904,7 @@ static enum ggml_opt_result linesearch_backtracking( float sched = 0; callback(callback_data, accum_step, &sched, cancel); if (*cancel) { - return GGML_OPT_CANCEL; + return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); @@ -18977,7 +18977,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { - return GGML_OPT_INVALID_WOLFE; + return GGML_OPT_RESULT_INVALID_WOLFE; } } @@ -19006,7 +19006,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( } struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; float * x = opt->lbfgs.x->data; // current parameters @@ -19047,7 +19047,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( float sched = 0; callback(callback_data, accum_step, &sched, &cancel); if (cancel) { - return GGML_OPT_CANCEL; + return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); @@ -19075,7 +19075,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( // already optimized if (gnorm/xnorm <= params.lbfgs.eps) { - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } if (opt->just_initialized) { @@ -19120,7 +19120,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( // way to test and don't want to break something with so many changes lined up ls = linesearch_backtracking(¶ms, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data); if (cancel) { - return GGML_OPT_CANCEL; + return GGML_OPT_RESULT_CANCEL; } if (ls < 0) { @@ -19143,7 +19143,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( } if (gnorm/xnorm <= params.lbfgs.eps) { // converged - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } // delta-based convergence test @@ -19153,7 +19153,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( const float rate = (pf[k[0]%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } } @@ -19169,14 +19169,14 @@ static enum ggml_opt_result ggml_opt_lbfgs( n_no_improvement[0]++; if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_OK; + return GGML_OPT_RESULT_OK; } } } if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) { // reached the maximum number of iterations - return GGML_OPT_DID_NOT_CONVERGE; + return GGML_OPT_RESULT_DID_NOT_CONVERGE; } // update vectors s and y: @@ -19232,17 +19232,17 @@ static enum ggml_opt_result ggml_opt_lbfgs( GGML_ASSERT(false && "lbfgs failed"); - return GGML_OPT_DID_NOT_CONVERGE; + return GGML_OPT_RESULT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { struct ggml_opt_params result; switch (type) { - case GGML_OPT_ADAM: + case GGML_OPT_TYPE_ADAM: { result = (struct ggml_opt_params) { - .type = GGML_OPT_ADAM, + .type = GGML_OPT_TYPE_ADAM, .graph_size = GGML_DEFAULT_GRAPH_SIZE, .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? .past = 0, @@ -19270,10 +19270,10 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { }, }; } break; - case GGML_OPT_LBFGS: + case GGML_OPT_TYPE_LBFGS: { result = (struct ggml_opt_params) { - .type = GGML_OPT_LBFGS, + .type = GGML_OPT_TYPE_LBFGS, .graph_size = GGML_DEFAULT_GRAPH_SIZE, .n_threads = 1, .past = 0, @@ -19318,12 +19318,12 @@ GGML_API void ggml_opt_init( opt->just_initialized = true; if (opt->ctx == NULL) { struct ggml_init_params ctx_opt_params; - if (opt->params.type == GGML_OPT_ADAM) { + if (opt->params.type == GGML_OPT_TYPE_ADAM) { ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3; if (opt->params.past > 0) { ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; } - } else if (opt->params.type == GGML_OPT_LBFGS) { + } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) { ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2); if (opt->params.past > 0) { ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; @@ -19335,7 +19335,7 @@ GGML_API void ggml_opt_init( opt->ctx = ggml_init(ctx_opt_params); } switch (opt->params.type) { - case GGML_OPT_ADAM: + case GGML_OPT_TYPE_ADAM: { opt->adam.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->adam.m = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); @@ -19349,7 +19349,7 @@ GGML_API void ggml_opt_init( ggml_set_zero(opt->adam.pf); } } break; - case GGML_OPT_LBFGS: + case GGML_OPT_TYPE_LBFGS: { opt->lbfgs.x = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); @@ -19393,13 +19393,13 @@ enum ggml_opt_result ggml_opt( ctx = ggml_init(params_ctx); if (ctx == NULL) { - return GGML_OPT_NO_CONTEXT; + return GGML_OPT_RESULT_NO_CONTEXT; } free_ctx = true; } - enum ggml_opt_result result = GGML_OPT_OK; + enum ggml_opt_result result = GGML_OPT_RESULT_OK; struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context)); @@ -19438,14 +19438,14 @@ enum ggml_opt_result ggml_opt_resume_g( void * callback_data) { // build forward + backward compute graphs - enum ggml_opt_result result = GGML_OPT_OK; + enum ggml_opt_result result = GGML_OPT_RESULT_OK; switch (opt->params.type) { - case GGML_OPT_ADAM: + case GGML_OPT_TYPE_ADAM: { result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; - case GGML_OPT_LBFGS: + case GGML_OPT_TYPE_LBFGS: { result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; diff --git a/ggml.h b/ggml.h index a4166e1f7afd0..75fd035a4698f 100644 --- a/ggml.h +++ b/ggml.h @@ -364,9 +364,9 @@ extern "C" { }; enum ggml_backend_type { - GGML_BACKEND_CPU = 0, - GGML_BACKEND_GPU = 10, - GGML_BACKEND_GPU_SPLIT = 20, + GGML_BACKEND_TYPE_CPU = 0, + GGML_BACKEND_TYPE_GPU = 10, + GGML_BACKEND_TYPE_GPU_SPLIT = 20, }; // model file types @@ -498,9 +498,9 @@ extern "C" { }; enum ggml_object_type { - GGML_OBJECT_TENSOR, - GGML_OBJECT_GRAPH, - GGML_OBJECT_WORK_BUFFER + GGML_OBJECT_TYPE_TENSOR, + GGML_OBJECT_TYPE_GRAPH, + GGML_OBJECT_TYPE_WORK_BUFFER }; enum ggml_log_level { @@ -642,9 +642,9 @@ extern "C" { // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled. // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995. enum ggml_task_type { - GGML_TASK_INIT = 0, - GGML_TASK_COMPUTE, - GGML_TASK_FINALIZE, + GGML_TASK_TYPE_INIT = 0, + GGML_TASK_TYPE_COMPUTE, + GGML_TASK_TYPE_FINALIZE, }; struct ggml_compute_params { @@ -1649,8 +1649,8 @@ extern "C" { // sort rows enum ggml_sort_order { - GGML_SORT_ASC, - GGML_SORT_DESC, + GGML_SORT_ORDER_ASC, + GGML_SORT_ORDER_DESC, }; GGML_API struct ggml_tensor * ggml_argsort( @@ -1943,8 +1943,8 @@ extern "C" { // optimization methods enum ggml_opt_type { - GGML_OPT_ADAM, - GGML_OPT_LBFGS, + GGML_OPT_TYPE_ADAM, + GGML_OPT_TYPE_LBFGS, }; // linesearch methods @@ -1958,12 +1958,12 @@ extern "C" { // optimization return values enum ggml_opt_result { - GGML_OPT_OK = 0, - GGML_OPT_DID_NOT_CONVERGE, - GGML_OPT_NO_CONTEXT, - GGML_OPT_INVALID_WOLFE, - GGML_OPT_FAIL, - GGML_OPT_CANCEL, + GGML_OPT_RESULT_OK = 0, + GGML_OPT_RESULT_DID_NOT_CONVERGE, + GGML_OPT_RESULT_NO_CONTEXT, + GGML_OPT_RESULT_INVALID_WOLFE, + GGML_OPT_RESULT_FAIL, + GGML_OPT_RESULT_CANCEL, GGML_LINESEARCH_FAIL = -128, GGML_LINESEARCH_MINIMUM_STEP, diff --git a/llama.cpp b/llama.cpp index 1f6b6cff48987..acd9be08a6e5e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -850,9 +850,9 @@ struct LLM_TN { // static std::map LLAMA_ROPE_SCALING_TYPES = { - { LLAMA_ROPE_SCALING_NONE, "none" }, - { LLAMA_ROPE_SCALING_LINEAR, "linear" }, - { LLAMA_ROPE_SCALING_YARN, "yarn" }, + { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, + { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, + { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, }; static int32_t llama_rope_scaling_type_from_string(const std::string & name) { @@ -862,7 +862,7 @@ static int32_t llama_rope_scaling_type_from_string(const std::string & name) { } } - return LLAMA_ROPE_SCALING_UNSPECIFIED; + return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; } static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { @@ -1580,7 +1580,7 @@ struct llama_hparams { bool causal_attn = true; bool need_kq_pos = false; - uint32_t pooling_type = LLAMA_POOLING_NONE; + uint32_t pooling_type = LLAMA_POOLING_TYPE_NONE; bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -2345,9 +2345,9 @@ namespace GGUFMeta { static const char * override_type_to_str(const llama_model_kv_override_type ty) { switch (ty) { - case LLAMA_KV_OVERRIDE_BOOL: return "bool"; - case LLAMA_KV_OVERRIDE_INT: return "int"; - case LLAMA_KV_OVERRIDE_FLOAT: return "float"; + case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool"; + case LLAMA_KV_OVERRIDE_TYPE_INT: return "int"; + case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float"; } return "unknown"; } @@ -2358,13 +2358,13 @@ namespace GGUFMeta { LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ", __func__, override_type_to_str(override->tag), override->key); switch (override->tag) { - case LLAMA_KV_OVERRIDE_BOOL: { + case LLAMA_KV_OVERRIDE_TYPE_BOOL: { LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false"); } break; - case LLAMA_KV_OVERRIDE_INT: { + case LLAMA_KV_OVERRIDE_TYPE_INT: { LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value); } break; - case LLAMA_KV_OVERRIDE_FLOAT: { + case LLAMA_KV_OVERRIDE_TYPE_FLOAT: { LLAMA_LOG_INFO("%.6f\n", override->float_value); } break; default: @@ -2383,7 +2383,7 @@ namespace GGUFMeta { template static typename std::enable_if::value, bool>::type try_override(OT & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, override)) { target = override->bool_value; return true; } @@ -2393,7 +2393,7 @@ namespace GGUFMeta { template static typename std::enable_if::value && std::is_integral::value, bool>::type try_override(OT & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, override)) { target = override->int_value; return true; } @@ -2403,7 +2403,7 @@ namespace GGUFMeta { template static typename std::enable_if::value, bool>::type try_override(T & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, override)) { target = override->float_value; return true; } @@ -2999,7 +2999,7 @@ static void llm_load_hparams( std::string rope_scaling("linear"); ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false); hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling); - GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED); + GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED); // rope_freq_scale (inverse of the kv) is optional float ropescale = 0.0f; @@ -3643,7 +3643,7 @@ static bool llm_load_tensors( model.buft_layer[i] = llama_default_buffer_type_cpu(true); } - if (split_mode == LLAMA_SPLIT_LAYER) { + if (split_mode == LLAMA_SPLIT_MODE_LAYER) { // calculate the split points int device_count = llama_get_device_count(); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); @@ -3682,10 +3682,10 @@ static bool llm_load_tensors( } } else { ggml_backend_buffer_type_t split_buft; - if (split_mode == LLAMA_SPLIT_ROW) { + if (split_mode == LLAMA_SPLIT_MODE_ROW) { split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); } else { - // LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported + // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported split_buft = llama_default_buffer_type_offload(main_gpu); } // assign the repeating layers @@ -5070,7 +5070,7 @@ struct llm_build_context { kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), - pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE), + pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_TYPE_NONE), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { // all initializations should be done in init() @@ -6050,12 +6050,12 @@ struct llm_build_context { cur = inpL; // pooling layer - if (pooling_type == LLAMA_POOLING_MEAN) { + if (pooling_type == LLAMA_POOLING_TYPE_MEAN) { cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); - } else if (pooling_type == LLAMA_POOLING_CLS) { + } else if (pooling_type == LLAMA_POOLING_TYPE_CLS) { cur = ggml_get_rows(ctx0, cur, inp_cls); } else { - GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type"); + GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type"); } cb(cur, "result_embd", -1); @@ -7754,7 +7754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) { + if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -7782,7 +7782,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) { + if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -11351,7 +11351,7 @@ static int llama_apply_lora_from_file_internal( struct llama_model_params llama_model_default_params() { struct llama_model_params result = { /*.n_gpu_layers =*/ 0, - /*.split_mode =*/ LLAMA_SPLIT_LAYER, + /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, /*.progress_callback =*/ nullptr, @@ -11377,7 +11377,7 @@ struct llama_context_params llama_context_default_params() { /*.n_batch =*/ 512, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, - /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED, + /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, /*.yarn_ext_factor =*/ -1.0f, @@ -11565,16 +11565,16 @@ struct llama_context * llama_new_context_with_model( cparams.cb_eval_user_data = params.cb_eval_user_data; auto rope_scaling_type = params.rope_scaling_type; - if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) { + if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) { rope_scaling_type = hparams.rope_scaling_type_train; } - if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) { + if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) { cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none } if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set' - cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f; + cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } if (params.seed == LLAMA_DEFAULT_SEED) { @@ -11608,8 +11608,8 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_CUBLAS) if (model->n_gpu_layers > 0) { - // with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used - if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) { + // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); if (backend == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu); @@ -11618,7 +11618,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(backend); } else { - // LLAMA_SPLIT_LAYER requires a backend for each GPU + // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) { ggml_backend_t backend = ggml_backend_cuda_init(device); if (backend == nullptr) { diff --git a/llama.h b/llama.h index 889edf4d97b96..947284ea2f535 100644 --- a/llama.h +++ b/llama.h @@ -109,23 +109,23 @@ extern "C" { }; enum llama_rope_scaling_type { - LLAMA_ROPE_SCALING_UNSPECIFIED = -1, - LLAMA_ROPE_SCALING_NONE = 0, - LLAMA_ROPE_SCALING_LINEAR = 1, - LLAMA_ROPE_SCALING_YARN = 2, - LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, + LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1, + LLAMA_ROPE_SCALING_TYPE_NONE = 0, + LLAMA_ROPE_SCALING_TYPE_LINEAR = 1, + LLAMA_ROPE_SCALING_TYPE_YARN = 2, + LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN, }; enum llama_pooling_type { - LLAMA_POOLING_NONE = 0, - LLAMA_POOLING_MEAN = 1, - LLAMA_POOLING_CLS = 2, + LLAMA_POOLING_TYPE_NONE = 0, + LLAMA_POOLING_TYPE_MEAN = 1, + LLAMA_POOLING_TYPE_CLS = 2, }; enum llama_split_mode { - LLAMA_SPLIT_NONE = 0, // single GPU - LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs - LLAMA_SPLIT_ROW = 2, // split rows across GPUs + LLAMA_SPLIT_MODE_NONE = 0, // single GPU + LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs + LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs }; typedef struct llama_token_data { @@ -173,9 +173,9 @@ extern "C" { } llama_batch; enum llama_model_kv_override_type { - LLAMA_KV_OVERRIDE_INT, - LLAMA_KV_OVERRIDE_FLOAT, - LLAMA_KV_OVERRIDE_BOOL, + LLAMA_KV_OVERRIDE_TYPE_INT, + LLAMA_KV_OVERRIDE_TYPE_FLOAT, + LLAMA_KV_OVERRIDE_TYPE_BOOL, }; struct llama_model_kv_override { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f8574588bcf2f..24d12ef141efd 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1264,7 +1264,7 @@ struct test_argsort : public test_case { test_argsort(ggml_type type = GGML_TYPE_F32, std::array ne = {16, 10, 10, 10}, - ggml_sort_order order = GGML_SORT_ASC) + ggml_sort_order order = GGML_SORT_ORDER_ASC) : type(type), ne(ne), order(order) {} ggml_tensor * build_graph(ggml_context * ctx) override { @@ -2116,7 +2116,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); - for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) { + for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) { test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order)); test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order)); } diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index 2c9997fca7705..546ca230ba417 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -118,7 +118,7 @@ int main(void) { const float fe = ggml_get_f32_1d(e, 0); printf("%s: e = %.4f\n", __func__, fe); - struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM); + struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); ggml_opt(ctx, opt_params, e); From 12894088170f62e4cad4f8d6a3043c185b414bab Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rados=C5=82aw=20Gryta?= Date: Sun, 25 Feb 2024 11:53:11 +0100 Subject: [PATCH 091/401] cmake : fix compilation for Android armeabi-v7a (#5702) --- CMakeLists.txt | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3c4629001a38d..48880f7204bf5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -936,10 +936,16 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") - # Raspberry Pi 2 - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") + # Android armeabi-v7a + list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) + else() + # Raspberry Pi 2 + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + endif() endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") + # Android arm64-v8a # Raspberry Pi 3, 4, Zero 2 (32-bit) list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() From d52d7819b8ced70c642a88a59da8c78208dc58ec Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 25 Feb 2024 13:49:43 +0100 Subject: [PATCH 092/401] server: concurrency fix + monitoring - add /metrics prometheus compatible endpoint (#5708) * server: monitoring - add /metrics prometheus compatible endpoint * server: concurrency issue, when 2 task are waiting for results, only one call thread is notified * server: metrics - move to a dedicated struct --- examples/server/README.md | 13 ++ examples/server/server.cpp | 150 +++++++++++++++++- examples/server/tests/features/environment.py | 2 + examples/server/tests/features/server.feature | 2 + examples/server/tests/features/steps/steps.py | 27 ++++ examples/server/tests/requirements.txt | 1 + examples/server/utils.hpp | 4 +- 7 files changed, 191 insertions(+), 8 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 0c43ac4c97cba..2129f7fb2b463 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -41,6 +41,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` - `-n, --n-predict`: Set the maximum tokens to predict (default: -1) - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. +- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled) - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) ## Build @@ -457,6 +458,18 @@ Notice that each `probs` is an array of length `n_probs`. ] ``` +- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled: + +Available metrics: +- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed. +- `llamacpp:tokens_predicted_total`: Number of generation tokens processed. +- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s. +- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s. +- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. 1 means 100 percent usage. +- `llamacpp:kv_cache_tokens`: KV-cache tokens. +- `llamacpp:requests_processing`: Number of request processing. +- `llamacpp:requests_deferred`: Number of request deferred. + ## More examples ### Change system prompt on runtime diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 780862ef67810..811495915f6dd 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -43,6 +43,7 @@ struct server_params int32_t read_timeout = 600; int32_t write_timeout = 600; bool slots_endpoint = true; + bool metrics_endpoint = false; }; bool server_verbose = false; @@ -310,6 +311,39 @@ struct llama_client_slot } }; +struct llama_metrics { + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t n_tokens_predicted_total = 0; + + uint64_t n_prompt_tokens_processed = 0; + uint64_t t_prompt_processing = 0; + + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; + + + void on_prompt_eval(const llama_client_slot &slot) { + n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed; + + n_prompt_tokens_processed += slot.num_prompt_tokens_processed; + t_prompt_processing += slot.t_prompt_processing; + } + + void on_prediction(const llama_client_slot &slot) { + n_tokens_predicted_total += slot.n_decoded; + + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; + } + + void reset_bucket() { + n_prompt_tokens_processed = 0; + t_prompt_processing = 0; + n_tokens_predicted = 0; + t_tokens_generation = 0; + } +}; + struct llama_server_context { llama_model *model = nullptr; @@ -344,6 +378,8 @@ struct llama_server_context llama_server_queue queue_tasks; llama_server_response queue_results; + llama_metrics metrics; + ~llama_server_context() { if (ctx) @@ -1404,7 +1440,7 @@ struct llama_server_context case TASK_TYPE_NEXT_RESPONSE: { // do nothing } break; - case TASK_TYPE_SLOTS_DATA: { + case TASK_TYPE_METRICS: { json slots_data = json::array(); int n_idle_slots = 0; int n_processing_slots = 0; @@ -1438,10 +1474,24 @@ struct llama_server_context res.stop = true; res.error = false; res.result_json = { - { "idle", n_idle_slots }, - { "processing", n_processing_slots }, - { "slots", slots_data } + { "idle", n_idle_slots }, + { "processing", n_processing_slots }, + { "deferred", queue_tasks.queue_tasks_deferred.size() }, + + { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, + { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, + + { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, + { "t_prompt_processing", metrics.t_prompt_processing}, + { "n_tokens_predicted", metrics.n_tokens_predicted}, + { "t_tokens_generation", metrics.t_tokens_generation}, + + { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, + { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, + + { "slots", slots_data }, }; + metrics.reset_bucket(); queue_results.send(res); } break; } @@ -1849,6 +1899,7 @@ struct llama_server_context { slot.t_start_genereration = ggml_time_us(); slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + metrics.on_prompt_eval(slot); } llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; @@ -1871,6 +1922,7 @@ struct llama_server_context slot.release(); slot.print_timings(); send_final_response(slot); + metrics.on_prediction(slot); } slot.i_batch = -1; @@ -1955,6 +2007,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); + printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); printf("\n"); printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); @@ -2414,6 +2467,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, { sparams.slots_endpoint = false; } + else if (arg == "--metrics") + { + sparams.metrics_endpoint = true; + } else if (arg == "--chat-template") { if (++i >= argc) @@ -2621,7 +2678,7 @@ int main(int argc, char **argv) // request slots data using task queue task_server task; task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_SLOTS_DATA; + task.type = TASK_TYPE_METRICS; task.target_id = -1; llama.queue_results.add_waiting_task_id(task.id); @@ -2668,7 +2725,7 @@ int main(int argc, char **argv) // request slots data using task queue task_server task; task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_SLOTS_DATA; + task.type = TASK_TYPE_METRICS; task.target_id = -1; llama.queue_results.add_waiting_task_id(task.id); @@ -2683,6 +2740,87 @@ int main(int argc, char **argv) }); } + if (sparams.metrics_endpoint) { + svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) { + // request slots data using task queue + task_server task; + task.id = llama.queue_tasks.get_new_id(); + task.type = TASK_TYPE_METRICS; + task.target_id = -1; + + llama.queue_results.add_waiting_task_id(task.id); + llama.queue_tasks.post(task); + + // get the result + task_result result = llama.queue_results.recv(task.id); + llama.queue_results.remove_waiting_task_id(task.id); + + json data = result.result_json; + + uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; + uint64_t t_prompt_processing = data["t_prompt_processing"]; + + uint64_t n_tokens_predicted = data["n_tokens_predicted"]; + uint64_t t_tokens_generation = data["t_tokens_generation"]; + + int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + + // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names + json all_metrics_def = json { + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", data["n_prompt_tokens_processed_total"]} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", data["n_tokens_predicted_total"]} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", data["kv_cache_tokens_count"]} + },{ + {"name", "requests_processing"}, + {"help", "Number of request processing."}, + {"value", data["processing"]} + },{ + {"name", "requests_deferred"}, + {"help", "Number of request deferred."}, + {"value", data["deferred"]} + }}} + }; + + std::stringstream prometheus; + for (const auto& el : all_metrics_def.items()) { + const auto& type = el.key(); + const auto& metrics_def = el.value(); + for (const auto& metric_def : metrics_def) { + std::string name = metric_def["name"]; + std::string help = metric_def["help"]; + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << metric_def["value"] << "\n"; + } + } + + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); + res.status = 200; // HTTP OK + }); + } + svr.set_logger(log_server_request); svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 13cc841017f62..09e8267476135 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -16,6 +16,8 @@ def before_scenario(context, scenario): def after_scenario(context, scenario): + if context.server_process is None: + return if scenario.status == "failed": if 'GITHUB_ACTIONS' in os.environ: print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n") diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 5f81d256a548c..0139f89d831a2 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -13,6 +13,7 @@ Feature: llama.cpp server And 1 slots And embeddings extraction And 32 server max tokens to predict + And prometheus compatible metrics exposed Then the server is starting Then the server is healthy @@ -25,6 +26,7 @@ Feature: llama.cpp server And max tokens to predict And a completion request with no api error Then tokens are predicted matching + And prometheus metrics are exposed Examples: Prompts | prompt | n_predict | re_content | n_predicted | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 9c825fdbcd7f5..051fd440c3391 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -13,6 +13,7 @@ import openai from behave import step from behave.api.async_step import async_run_until_complete +from prometheus_client import parser @step(u"a server listening on {server_fqdn}:{server_port}") @@ -34,6 +35,8 @@ def step_server_config(context, server_fqdn, server_port): context.server_api_key = None context.server_continuous_batching = False context.server_embeddings = False + context.server_metrics = False + context.server_process = None context.server_seed = None context.user_api_key = None @@ -82,6 +85,11 @@ def step_server_embeddings(context): context.server_embeddings = True +@step(u'prometheus compatible metrics exposed') +def step_server_metrics(context): + context.server_metrics = True + + @step(u"the server is starting") def step_start_server(context): start_server_background(context) @@ -424,6 +432,23 @@ def step_check_options_header_value(context, cors_header, cors_header_value): assert context.options_response.headers[cors_header] == cors_header_value +@step(u'prometheus metrics are exposed') +@async_run_until_complete +async def step_prometheus_metrics_exported(context): + async with aiohttp.ClientSession() as session: + async with await session.get(f'{context.base_url}/metrics') as metrics_response: + assert metrics_response.status == 200 + assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" + metrics_raw = await metrics_response.text() + metric_exported = False + for metric in parser.text_string_to_metric_families(metrics_raw): + match metric.name: + case "llamacpp:kv_cache_usage_ratio": + assert len(metric.samples) > 0 + metric_exported = True + assert metric_exported, "No metrics exported" + + async def concurrent_requests(context, f_completion, *args, **kwargs): n_prompts = len(context.prompts) if context.debug: @@ -753,6 +778,8 @@ def start_server_background(context): server_args.append('--cont-batching') if context.server_embeddings: server_args.append('--embedding') + if context.server_metrics: + server_args.append('--metrics') if context.model_alias is not None: server_args.extend(['--alias', context.model_alias]) if context.n_ctx is not None: diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 3e51b12dc8207..334fa4a70ea72 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,3 +1,4 @@ aiohttp~=3.9.3 behave~=1.2.6 openai~=0.25.0 +prometheus-client~=0.20.0 diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 88545eb6931d0..71cc5b0b8b6de 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -50,7 +50,7 @@ enum task_type { TASK_TYPE_COMPLETION, TASK_TYPE_CANCEL, TASK_TYPE_NEXT_RESPONSE, - TASK_TYPE_SLOTS_DATA + TASK_TYPE_METRICS }; struct task_server { @@ -441,7 +441,7 @@ struct llama_server_response { { LOG_VERBOSE("queue_results.push_back", {}); queue_results.push_back(result); - condition_results.notify_one(); + condition_results.notify_all(); return; } } From 930b1780269a69948d106e2d1b838ab7661f679a Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 25 Feb 2024 13:50:32 +0100 Subject: [PATCH 093/401] server: logs - unified format and --log-format option (#5700) * server: logs - always use JSON logger, add add thread_id in message, log task_id and slot_id * server : skip GH copilot requests from logging * server : change message format of server_log() * server : no need to repeat log in comment * server : log style consistency * server : fix compile warning * server : fix tests regex patterns on M2 Ultra * server: logs: PR feedback on log level * server: logs: allow to choose log format in json or plain text * server: tests: output server logs in text * server: logs switch init logs to server logs macro * server: logs ensure value json value does not raised error * server: logs reduce level VERBOSE to VERB to max 4 chars * server: logs lower case as other log messages * server: logs avoid static in general Co-authored-by: Georgi Gerganov * server: logs PR feedback: change text log format to: LEVEL [function_name] message | additional=data --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 4 +- examples/server/server.cpp | 218 ++++++++++++++---- examples/server/tests/README.md | 1 + examples/server/tests/features/server.feature | 6 +- examples/server/tests/features/steps/steps.py | 2 + examples/server/utils.hpp | 80 ++++--- 6 files changed, 231 insertions(+), 80 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 2129f7fb2b463..cb3fd6054095b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -39,10 +39,12 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. - `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` -- `-n, --n-predict`: Set the maximum tokens to predict (default: -1) +- `-n N, --n-predict N`: Set the maximum tokens to predict (default: -1) - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. - `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled) - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) +- `--log-disable`: Output logs to stdout only, default: enabled. +- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json) ## Build diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 811495915f6dd..d970202d2b5d3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -47,6 +47,7 @@ struct server_params }; bool server_verbose = false; +bool server_log_json = true; static size_t common_part(const std::vector &a, const std::vector &b) { @@ -302,12 +303,43 @@ struct llama_client_slot } void print_timings() const { - LOG_TEE("\n"); - LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", - __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed); - LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", - __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded); - LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation); + char buffer[512]; + double t_token = t_prompt_processing / num_prompt_tokens_processed; + double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed; + sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", + t_prompt_processing, num_prompt_tokens_processed, + t_token, n_tokens_second); + LOG_INFO(buffer, { + {"slot_id", id}, + {"task_id", task_id}, + {"t_prompt_processing", t_prompt_processing}, + {"num_prompt_tokens_processed", num_prompt_tokens_processed}, + {"t_token", t_token}, + {"n_tokens_second", n_tokens_second}, + }); + + t_token = t_token_generation / n_decoded; + n_tokens_second = 1e3 / t_token_generation * n_decoded; + sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", + t_token_generation, n_decoded, + t_token, n_tokens_second); + LOG_INFO(buffer, { + {"slot_id", id}, + {"task_id", task_id}, + {"t_token_generation", t_token_generation}, + {"n_decoded", n_decoded}, + {"t_token", t_token}, + {"n_tokens_second", n_tokens_second}, + }); + + sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation); + LOG_INFO(buffer, { + {"slot_id", id}, + {"task_id", task_id}, + {"t_prompt_processing", t_prompt_processing}, + {"t_token_generation", t_token_generation}, + {"t_total", t_prompt_processing + t_token_generation}, + }); } }; @@ -399,7 +431,7 @@ struct llama_server_context params = params_; if (!params.mmproj.empty()) { multimodal = true; - LOG_TEE("Multi Modal Mode Enabled"); + LOG_INFO("Multi Modal Mode Enabled", {}); clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); if(clp_ctx == nullptr) { LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); @@ -452,7 +484,7 @@ struct llama_server_context const int32_t n_ctx_slot = n_ctx / params.n_parallel; - LOG_TEE("Available slots:\n"); + LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); for (int i = 0; i < params.n_parallel; i++) { llama_client_slot slot; @@ -461,7 +493,10 @@ struct llama_server_context slot.n_ctx = n_ctx_slot; slot.n_predict = params.n_predict; - LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); + LOG_INFO("new slot", { + {"slot_id", slot.id}, + {"n_ctx_slot", slot.n_ctx} + }); const int ga_n = params.grp_attn_n; const int ga_w = params.grp_attn_w; @@ -471,7 +506,12 @@ struct llama_server_context GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT - LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w); + + LOG_INFO("slot self-extend", { + {"slot_id", slot.id}, + {"ga_n", ga_n}, + {"ga_w", ga_w} + }); } slot.ga_i = 0; @@ -765,10 +805,16 @@ struct llama_server_context img_sl.img_data = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) { - LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); + LOG_ERROR("failed to load image", { + {"slot_id", slot->id}, + {"img_sl_id", img_sl.id} + }); return false; } - LOG_TEE("slot %i - loaded image\n", slot->id); + LOG_VERBOSE("image loaded", { + {"slot_id", slot->id}, + {"img_sl_id", img_sl.id} + }); img_sl.request_encode_image = true; slot->images.push_back(img_sl); } @@ -828,7 +874,10 @@ struct llama_server_context all_slots_are_idle = false; - LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); + LOG_INFO("slot is processing task", { + {"slot_id", slot->id}, + {"task_id", slot->task_id}, + }); return true; } @@ -1391,7 +1440,7 @@ struct llama_server_context if (slot == nullptr) { // if no slot is available, we defer this task for processing later - LOG_VERBOSE("no slot is available", {}); + LOG_VERBOSE("no slot is available", {{"task_id", task.id}}); queue_tasks.defer(task); break; } @@ -1467,7 +1516,17 @@ struct llama_server_context } slots_data.push_back(slot_data); } - LOG_TEE("task %i - slots data: idle=%i processing=%i\n", task.id, n_idle_slots, n_processing_slots); + LOG_INFO("slot data", { + {"task_id", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots} + }); + LOG_VERBOSE("slot data", { + {"task_id", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots}, + {"slots", slots_data} + }); task_result res; res.id = task.id; res.multitask_id = task.multitask_id; @@ -1519,7 +1578,7 @@ struct llama_server_context bool update_slots() { if (system_need_update) { - LOG_TEE("updating system prompt\n"); + LOG_INFO("updating system prompt", {}); update_system_prompt(); } @@ -1529,12 +1588,13 @@ struct llama_server_context { if (system_prompt.empty() && clean_kv_cache) { - LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); + LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {}); kv_cache_clear(); } return true; } + LOG_VERBOSE("posting NEXT_RESPONSE", {}); task_server task; task.type = TASK_TYPE_NEXT_RESPONSE; task.target_id = -1; @@ -1548,10 +1608,20 @@ struct llama_server_context { // Shift context const int n_keep = slot.params.n_keep + add_bos_token; - const int n_left = system_tokens.size() + slot.n_past - n_keep; + const int n_left = (int) system_tokens.size() + slot.n_past - n_keep; const int n_discard = n_left / 2; - LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, n_keep, n_left, n_discard); + LOG_INFO("slot context shift", { + {"slot_id", slot.id}, + {"task_id", slot.task_id}, + {"n_keep", n_keep}, + {"n_left", n_left}, + {"n_discard", n_discard}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()} + }); llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); llama_kv_cache_seq_shift(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); @@ -1565,17 +1635,12 @@ struct llama_server_context slot.n_past -= n_discard; slot.truncated = true; - - LOG_VERBOSE("context shift", { - { "n_ctx", n_ctx }, - { "n_keep", n_keep }, - { "n_left", n_left }, - }); } } } // decode any currently ongoing sequences + LOG_VERBOSE("decoding ongoing sequences", {}); for (auto & slot : slots) { // release the slot @@ -1585,7 +1650,15 @@ struct llama_server_context slot.command = NONE; slot.t_last_used = ggml_time_us(); - LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); + LOG_INFO("slot released", { + {"slot_id", slot.id}, + {"task_id", slot.task_id}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()}, + {"truncated", slot.truncated} + }); queue_tasks.notify_slot_changed(); continue; @@ -1733,7 +1806,12 @@ struct llama_server_context slot.ga_i = ga_i; } - LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); + LOG_INFO("slot progression", { + { "slot_id", slot.id }, + { "task_id", slot.task_id }, + { "n_past", slot.n_past }, + { "num_prompt_tokens_processed", slot.num_prompt_tokens_processed } + }); } slot.cache_tokens = prompt_tokens; @@ -1741,7 +1819,10 @@ struct llama_server_context if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. - LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); + LOG_INFO("we have to evaluate at least 1 token to generate logits", { + { "slot_id", slot.id }, + { "task_id", slot.task_id } + }); slot.n_past--; if (slot.ga_i > 0) { @@ -1749,9 +1830,13 @@ struct llama_server_context } } - LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); - - llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); + int p0 = (int) system_tokens.size() + slot.n_past; + LOG_INFO("kv cache rm [p0, end)", { + { "slot_id", slot.id }, + { "task_id", slot.task_id }, + { "p0", p0 } + }); + llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, @@ -1786,7 +1871,13 @@ struct llama_server_context if (has_images && !ingest_images(slot, n_batch)) { - LOG_TEE("failed processing images\n"); + LOG_ERROR("failed processing images", { + "slot_id", slot.id, + "task_id", slot.task_id, + }); + // FIXME @phymbert: to be properly tested + // early returning without changing the slot state will block the slot for ever + // no one at the moment is checking the return value return false; } @@ -1928,6 +2019,8 @@ struct llama_server_context slot.i_batch = -1; } } + + LOG_VERBOSE("slots updated", {}); return true; } @@ -2005,6 +2098,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -ctv TYPE, --cache-type-v TYPE\n"); printf(" KV cache data type for V (default: f16)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); + printf(" --log-format log output format: json or text (default: json)\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); @@ -2458,6 +2552,27 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.mmproj = argv[i]; } + else if (arg == "--log-format") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + if (std::strcmp(argv[i], "json") == 0) + { + server_log_json = true; + } + else if (std::strcmp(argv[i], "text") == 0) + { + server_log_json = false; + } + else + { + invalid_param = true; + break; + } + } else if (arg == "--log-disable") { log_set_target(stdout); @@ -2571,32 +2686,40 @@ static json format_partial_response( static json format_tokenizer_response(const std::vector &tokens) { - return json{ - {"tokens", tokens}}; + return json { + {"tokens", tokens} + }; } static json format_detokenized_response(std::string content) { - return json{ - {"content", content}}; + return json { + {"content", content} + }; } static void log_server_request(const httplib::Request &req, const httplib::Response &res) { + // skip GH copilot requests when using default port + if (req.path == "/v1/health" || req.path == "/v1/completions") + { + return; + } + LOG_INFO("request", { - {"remote_addr", req.remote_addr}, - {"remote_port", req.remote_port}, - {"status", res.status}, - {"method", req.method}, - {"path", req.path}, - {"params", req.params}, - }); + {"remote_addr", req.remote_addr}, + {"remote_port", req.remote_port}, + {"status", res.status}, + {"method", req.method}, + {"path", req.path}, + {"params", req.params}, + }); LOG_VERBOSE("request", { - {"request", req.body}, - {"response", res.body}, - }); + {"request", req.body}, + {"response", res.body}, + }); } struct token_translator @@ -2873,9 +2996,6 @@ int main(int argc, char **argv) // Set the base directory for serving static files svr.set_base_dir(sparams.public_path); - // to make it ctrl+clickable: - LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); - std::unordered_map log_data; log_data["hostname"] = sparams.hostname; log_data["port"] = std::to_string(sparams.port); diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index e44c5c286601f..0b9fdc4e72678 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -32,6 +32,7 @@ It's possible to override some scenario steps values with environment variables: - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080` - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server` - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` + - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format ### Run @bug, @wip or @wrong_usage annotated scenario diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 0139f89d831a2..b571582a7857e 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -29,9 +29,9 @@ Feature: llama.cpp server And prometheus metrics are exposed Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | read | 8 | - | Write a joke about AI | 64 | (parkfriendsscared)+ | 32 | + | prompt | n_predict | re_content | n_predicted | + | I believe the meaning of life is | 8 | (readgoing)+ | 8 | + | Write a joke about AI | 64 | (parkfriendsscaredalways)+ | 32 | Scenario Outline: OAI Compatibility Given a model diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 051fd440c3391..8e4babf204f8a 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -792,6 +792,8 @@ def start_server_background(context): server_args.extend(['--api-key', context.server_api_key]) if context.debug: server_args.append('--verbose') + if 'SERVER_LOG_FORMAT_JSON' not in os.environ: + server_args.extend(['--log-format', "text"]) print(f"starting server with: {context.server_path}", *server_args) context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 71cc5b0b8b6de..d7abd7cbba71c 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -14,6 +14,7 @@ using json = nlohmann::json; extern bool server_verbose; +extern bool server_log_json; #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 @@ -27,14 +28,14 @@ extern bool server_verbose; { \ if (server_verbose) \ { \ - server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ + server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \ } \ } while (0) #endif -#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_ERROR( MSG, ...) server_log("ERR", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) // // parallel @@ -133,26 +134,48 @@ struct completion_token_output std::string text_to_send; }; -static inline void server_log(const char *level, const char *function, int line, - const char *message, const nlohmann::ordered_json &extra) +static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { - nlohmann::ordered_json log - { + std::stringstream ss_tid; + ss_tid << std::this_thread::get_id(); + json log = nlohmann::ordered_json{ + {"tid", ss_tid.str()}, {"timestamp", time(nullptr)}, - {"level", level}, - {"function", function}, - {"line", line}, - {"message", message}, }; - if (!extra.empty()) - { - log.merge_patch(extra); - } + if (server_log_json) { + log.merge_patch( + { + {"level", level}, + {"function", function}, + {"line", line}, + {"msg", message}, + }); + if (!extra.empty()) { + log.merge_patch(extra); + } + + std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush; + } else { + char buf[1024]; + snprintf(buf, 1024, "%4s [%24s] %s", level, function, message); - const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); - printf("%.*s\n", (int)str.size(), str.data()); - fflush(stdout); + if (!extra.empty()) { + log.merge_patch(extra); + } + std::stringstream ss; + ss << buf << " |"; + for (const auto& el : log.items()) + { + const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); + snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str()); + ss << buf; + } + + const std::string str = ss.str(); + printf("%.*s\n", (int)str.size(), str.data()); + fflush(stdout); + } } // @@ -234,6 +257,7 @@ struct llama_server_queue { std::unique_lock lock(mutex_tasks); if (task.id == -1) { task.id = id++; + LOG_VERBOSE("new task id", {{"new_id", task.id}}); } queue_tasks.push_back(std::move(task)); condition_tasks.notify_one(); @@ -249,7 +273,9 @@ struct llama_server_queue { // Get the next id for creating anew task int get_new_id() { std::unique_lock lock(mutex_tasks); - return id++; + int new_id = id++; + LOG_VERBOSE("new task id", {{"new_id", new_id}}); + return new_id; } // Register function to process a new task @@ -290,8 +316,7 @@ struct llama_server_queue { void start_loop() { running = true; while (true) { - // new task arrived - LOG_VERBOSE("have new task", {}); + LOG_VERBOSE("new task may arrive", {}); { while (true) { @@ -303,7 +328,7 @@ struct llama_server_queue { task_server task = queue_tasks.front(); queue_tasks.erase(queue_tasks.begin()); lock.unlock(); - LOG_VERBOSE("callback_new_task", {}); + LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); callback_new_task(task); } LOG_VERBOSE("callback_all_task_finished", {}); @@ -384,11 +409,13 @@ struct llama_server_response { std::condition_variable condition_results; void add_waiting_task_id(int task_id) { + LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); waiting_task_ids.insert(task_id); } void remove_waiting_task_id(int task_id) { + LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); waiting_task_ids.erase(task_id); } @@ -401,7 +428,6 @@ struct llama_server_response { condition_results.wait(lock, [&]{ return !queue_results.empty(); }); - LOG_VERBOSE("condition_results unblock", {}); for (int i = 0; i < (int) queue_results.size(); i++) { @@ -426,20 +452,20 @@ struct llama_server_response { // Send a new result to a waiting task_id void send(task_result result) { std::unique_lock lock(mutex_results); - LOG_VERBOSE("send new result", {}); + LOG_VERBOSE("send new result", {{"task_id", result.id}}); for (auto& task_id : waiting_task_ids) { // LOG_TEE("waiting task id %i \n", task_id); // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result if (result.multitask_id == task_id) { - LOG_VERBOSE("callback_update_multitask", {}); + LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}}); callback_update_multitask(task_id, result.id, result); continue; } if (result.id == task_id) { - LOG_VERBOSE("queue_results.push_back", {}); + LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}}); queue_results.push_back(result); condition_results.notify_all(); return; From 7d548a1827f6fc6aece6db74c9d112da42c40d68 Mon Sep 17 00:00:00 2001 From: Ashok Gelal <401055+ashokgelal@users.noreply.github.com> Date: Sun, 25 Feb 2024 10:57:34 -0500 Subject: [PATCH 094/401] readme : add Msty to UI list (#5618) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 3bc512af0602b..d61f9171b1b62 100644 --- a/README.md +++ b/README.md @@ -155,6 +155,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [semperai/amica](https://github.com/semperai/amica) - [withcatai/catai](https://github.com/withcatai/catai) - [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) +- [Msty](https://msty.app) (proprietary) --- From f1a98c52546d009f742bdec2154c2a314ea950a6 Mon Sep 17 00:00:00 2001 From: kwin1412 <42286931+kwin1412@users.noreply.github.com> Date: Mon, 26 Feb 2024 00:46:49 +0800 Subject: [PATCH 095/401] make : fix nvcc version is empty (#5713) fix nvcc version is empty --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index f03faf6eda0fb..068f6ed028460 100644 --- a/Makefile +++ b/Makefile @@ -597,7 +597,7 @@ $(info I CC: $(shell $(CC) --version | head -n 1)) $(info I CXX: $(shell $(CXX) --version | head -n 1)) ifdef LLAMA_CUBLAS $(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) -CUDA_VERSION := $(shell nvcc --version | grep -oP 'release (\K[0-9]+\.[0-9])') +CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])') ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) ifndef CUDA_DOCKER_ARCH ifndef CUDA_POWER_ARCH From abbabc5e51d0d4656b438aec10b7fae9479ef37d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rados=C5=82aw=20Gryta?= Date: Sun, 25 Feb 2024 19:43:00 +0100 Subject: [PATCH 096/401] ggml-quants : provide ggml_vqtbl1q_u8 for 64bit compatibility (#5711) * [ggml-quants] Provide ggml_vqtbl1q_u8 for 64bit compatibility vqtbl1q_u8 is not part of arm v7 neon library * [android-example] Remove abi filter after arm v7a fix * [github-workflows] Do not skip Android armeabi-v7a build --- .github/workflows/build.yml | 3 +- examples/llama.android/app/build.gradle.kts | 8 ++--- ggml-quants.c | 33 ++++++++++++++++++--- 3 files changed, 32 insertions(+), 12 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 03d76d45560cf..66ad85938ca16 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -669,8 +669,7 @@ jobs: run: | cd examples/llama.android - # Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820). - ./gradlew build --no-daemon -Pskip-armeabi-v7a + ./gradlew build --no-daemon # freeBSD-latest: # runs-on: macos-12 diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index aadbe22c91835..d42140efe8168 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -21,12 +21,8 @@ android { useSupportLibrary = true } ndk { - // Workaround for https://github.com/llvm/llvm-project/issues/65820 - // affecting armeabi-v7a. Skip armeabi-v7a when invoked with - // -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a). - if (project.hasProperty("skip-armeabi-v7a")) { - abiFilters += listOf("arm64-v8a", "x86_64", "x86") - } + // Add NDK properties if wanted, e.g. + // abiFilters += listOf("arm64-v8a") } externalNativeBuild { cmake { diff --git a/ggml-quants.c b/ggml-quants.c index 5c5f2ce1b9b87..3d94d166d1b6d 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -462,6 +462,30 @@ inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { return res; } +// NOTE: not tested +inline static int8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { + int8x16_t res; + + res[ 0] = a[b[ 0]]; + res[ 1] = a[b[ 1]]; + res[ 2] = a[b[ 2]]; + res[ 3] = a[b[ 3]]; + res[ 4] = a[b[ 4]]; + res[ 5] = a[b[ 5]]; + res[ 6] = a[b[ 6]]; + res[ 7] = a[b[ 7]]; + res[ 8] = a[b[ 8]]; + res[ 9] = a[b[ 9]]; + res[10] = a[b[10]]; + res[11] = a[b[11]]; + res[12] = a[b[12]]; + res[13] = a[b[13]]; + res[14] = a[b[14]]; + res[15] = a[b[15]]; + + return res; +} + #else #define ggml_int16x8x2_t int16x8x2_t @@ -476,6 +500,7 @@ inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { #define ggml_vld1q_s8_x2 vld1q_s8_x2 #define ggml_vld1q_s8_x4 vld1q_s8_x4 #define ggml_vqtbl1q_s8 vqtbl1q_s8 +#define ggml_vqtbl1q_u8 vqtbl1q_u8 #endif @@ -9488,8 +9513,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v qs += 16; vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); - vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); vs.val[1] = vceqq_u8(vs.val[1], mask2); @@ -9497,8 +9522,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1])); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); - vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); vs.val[1] = vceqq_u8(vs.val[1], mask2); From f7625019c51ca437a5840576d92362cfa710e4a2 Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Sun, 25 Feb 2024 13:43:50 -0500 Subject: [PATCH 097/401] server : fix crash when system prompt is bigger than batch size (#5714) The system prompt is now decoded in batches. * server : fix off-by-one n_past when start of prompt matches whole cache The tokens right after the matching part would otherwise skip a pos value. --- examples/server/server.cpp | 28 +++++++++++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d970202d2b5d3..c1eb61678c38a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -902,10 +902,24 @@ struct llama_server_context llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - if (llama_decode(ctx, batch) != 0) + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) { - LOG_TEE("%s: llama_decode() failed\n", __func__); - return; + const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view) != 0) + { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; + } } // assign the system KV cache to all parallel sequences @@ -1785,6 +1799,14 @@ struct llama_server_context } slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + + // the last token of the cache is not in the KV cache until the next call to llama_decode + // (it was sampled, pushed into the "cache_tokens", but not yet put in the context) + if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size()) + { + slot.n_past -= 1; + } + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; if (slot.ga_n != 1) From bf08e00643fd529f748f0a858fd79f3061e3fa18 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Feb 2024 22:12:24 +0200 Subject: [PATCH 098/401] llama : refactor k-shift implementation + KV defragmentation (#5691) * llama : refactor k-shift implementation ggml-ci * llama : rename llama_kv_cache_seq_shift to llama_kv_cache_seq_add * llama : cont k-shift refactoring + normalize type names ggml-ci * minor : fix MPI builds * llama : reuse n_rot from the build context ggml-ci * llama : revert enum name changes from this PR ggml-ci * llama : update llama_rope_type * llama : add comment about rope values * llama : fix build * passkey : apply kv cache updates explicitly ggml-ci * llama : change name to llama_kv_cache_update() * llama : add llama_kv_cache_seq_pos_max() * passkey : fix llama_kv_cache_seq_pos_max() usage * llama : some llama_kv_cell simplifications * llama : add llama_kv_cache_compress (EXPERIMENTAL) * llama : add alternative KV cache merging (EXPERIMENTAL) * llama : add llama_kv_cache_defrag * llama : comments * llama : remove llama_kv_cache_compress will add in a separate PR ggml-ci * llama : defragment via non-overlapping moves * llama : ggml_graph based defrag implementation ggml-ci * llama : switch the loop order in build_defrag * llama : add comments --- examples/infill/infill.cpp | 4 +- examples/main/main.cpp | 10 +- examples/passkey/passkey.cpp | 25 +- examples/server/server.cpp | 8 +- llama.cpp | 869 ++++++++++++++++++++++++----------- llama.h | 34 +- 6 files changed, 646 insertions(+), 304 deletions(-) diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 92c67b7cff5c8..d4b8729dd0283 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -447,8 +447,8 @@ int main(int argc, char ** argv) { LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); n_past -= n_discard; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7555dffe441f0..34e84d0d42f87 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -548,8 +548,8 @@ int main(int argc, char ** argv) { LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); n_past -= n_discard; @@ -576,9 +576,9 @@ int main(int argc, char ** argv) { LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); - llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd); - llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); - llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); + llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd); + llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); + llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); n_past -= bd; diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index e12a1cdf19a79..47de67a93047f 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -126,7 +126,7 @@ int main(int argc, char ** argv) { const int n_batch = ctx_params.n_batch; const int n_batch_grp = ctx_params.n_batch/n_grp; - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch); + LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos); // print the prompt token-by-token @@ -146,10 +146,11 @@ int main(int argc, char ** argv) { const int ib = i/n_batch - 1; const int bd = n_batch_grp*(n_grp - 1); - llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch, n_past, ib*bd); - llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + llama_kv_cache_seq_add (ctx, 0, n_past - n_batch, n_past, ib*bd); + llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + llama_kv_cache_update (ctx); - n_past -= bd; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; } llama_batch_clear(batch); @@ -179,10 +180,12 @@ int main(int argc, char ** argv) { LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard); - llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_defrag (ctx); + llama_kv_cache_update (ctx); - n_past -= n_discard; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; llama_batch_clear(batch); @@ -208,10 +211,12 @@ int main(int argc, char ** argv) { if (n_discard > 0) { LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard); - llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_defrag (ctx); + llama_kv_cache_update (ctx); - n_past -= n_discard; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; } } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c1eb61678c38a..8aadc95a9728f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1636,8 +1636,8 @@ struct llama_server_context {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()} }); - llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1941,9 +1941,9 @@ struct llama_server_context LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); - llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); - llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; diff --git a/llama.cpp b/llama.cpp index acd9be08a6e5e..3424b1999ebdd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1550,8 +1550,9 @@ static const size_t MiB = 1024*kiB; static const size_t GiB = 1024*MiB; struct llama_hparams { - bool vocab_only; - bool rope_finetuned; + bool vocab_only; + bool rope_finetuned; + uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; @@ -1580,7 +1581,8 @@ struct llama_hparams { bool causal_attn = true; bool need_kq_pos = false; - uint32_t pooling_type = LLAMA_POOLING_TYPE_NONE; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; + enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -1707,11 +1709,20 @@ struct llama_kv_cell { bool has_seq_id(const llama_seq_id & id) const { return seq_id.find(id) != seq_id.end(); } + + bool is_empty() const { + return seq_id.empty(); + } + + bool is_same_seq(const llama_kv_cell & other) const { + return seq_id == other.seq_id; + } }; // ring-buffer of cached KV data struct llama_kv_cache { bool has_shift = false; + bool do_defrag = false; // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_internal also uses it, so it @@ -1723,6 +1734,9 @@ struct llama_kv_cache { // computed before each graph build uint32_t n = 0; + ggml_type type_k = GGML_TYPE_F16; + ggml_type type_v = GGML_TYPE_F16; + std::vector cells; std::vector k_l; // per layer @@ -1958,8 +1972,8 @@ struct llama_context { static bool llama_kv_cache_init( struct llama_kv_cache & cache, const llama_model & model, - ggml_type ktype, - ggml_type vtype, + ggml_type type_k, + ggml_type type_v, uint32_t n_ctx, bool offload) { const struct llama_hparams & hparams = model.hparams; @@ -1974,6 +1988,9 @@ static bool llama_kv_cache_init( cache.size = n_ctx; cache.used = 0; + cache.type_k = type_k; + cache.type_v = type_v; + cache.cells.clear(); cache.cells.resize(n_ctx); @@ -2014,8 +2031,8 @@ static bool llama_kv_cache_init( for (int i = 0; i < (int) n_layer; i++) { struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); - ggml_tensor * k = ggml_new_tensor_1d(ctx, ktype, n_embd_k_gqa*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(ctx, vtype, n_embd_v_gqa*n_ctx); + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*n_ctx); + ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*n_ctx); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); @@ -2099,7 +2116,7 @@ static bool llama_kv_cache_find_slot( // find how many cells are currently in use static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { for (uint32_t i = cache.size - 1; i > 0; --i) { - if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) { + if (cache.cells[i].pos >= 0 && !cache.cells[i].is_empty()) { return i + 1; } } @@ -2135,7 +2152,7 @@ static void llama_kv_cache_seq_rm( } else { continue; } - if (cache.cells[i].seq_id.empty()) { + if (cache.cells[i].is_empty()) { // keep count of the number of used cells if (cache.cells[i].pos >= 0) cache.used--; @@ -2186,7 +2203,7 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id if (new_head != cache.size && new_head < cache.head) cache.head = new_head; } -static void llama_kv_cache_seq_shift( +static void llama_kv_cache_seq_add( struct llama_kv_cache & cache, llama_seq_id seq_id, llama_pos p0, @@ -2204,10 +2221,14 @@ static void llama_kv_cache_seq_shift( cache.cells[i].delta += delta; if (cache.cells[i].pos < 0) { - if (!cache.cells[i].seq_id.empty()) cache.used--; + if (!cache.cells[i].is_empty()) { + cache.used--; + } cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); - if (new_head == cache.size) new_head = i; + if (new_head == cache.size) { + new_head = i; + } } } } @@ -2239,6 +2260,22 @@ static void llama_kv_cache_seq_div( } } +static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) { + llama_pos result = 0; + + for (uint32_t i = 0; i < cache.size; ++i) { + if (cache.cells[i].has_seq_id(seq_id)) { + result = std::max(result, cache.cells[i].pos); + } + } + + return result; +} + +static void llama_kv_cache_defrag(struct llama_kv_cache & cache) { + cache.do_defrag = true; +} + // // model loading and saving // @@ -2310,7 +2347,7 @@ namespace GGUFMeta { } }; - struct ArrayInfo{ + struct ArrayInfo { const gguf_type gt; const size_t length; const void * data; @@ -2329,7 +2366,7 @@ namespace GGUFMeta { }; template - class GKV: public GKV_Base { + class GKV : public GKV_Base { GKV() = delete; public: @@ -2352,39 +2389,39 @@ namespace GGUFMeta { return "unknown"; } - static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override *override) { - if (!override) { return false; } - if (override->tag == expected_type) { + static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) { + if (!ovrd) { return false; } + if (ovrd->tag == expected_type) { LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ", - __func__, override_type_to_str(override->tag), override->key); - switch (override->tag) { + __func__, override_type_to_str(ovrd->tag), ovrd->key); + switch (ovrd->tag) { case LLAMA_KV_OVERRIDE_TYPE_BOOL: { - LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false"); + LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false"); } break; case LLAMA_KV_OVERRIDE_TYPE_INT: { - LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value); + LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value); } break; case LLAMA_KV_OVERRIDE_TYPE_FLOAT: { - LLAMA_LOG_INFO("%.6f\n", override->float_value); + LLAMA_LOG_INFO("%.6f\n", ovrd->float_value); } break; default: // Shouldn't be possible to end up here, but just in case... throw std::runtime_error( format("Unsupported attempt to override %s type for metadata key %s\n", - override_type_to_str(override->tag), override->key)); + override_type_to_str(ovrd->tag), ovrd->key)); } return true; } LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n", - __func__, override->key, override_type_to_str(expected_type), override_type_to_str(override->tag)); + __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag)); return false; } template static typename std::enable_if::value, bool>::type - try_override(OT & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, override)) { - target = override->bool_value; + try_override(OT & target, const struct llama_model_kv_override * ovrd) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) { + target = ovrd->bool_value; return true; } return false; @@ -2392,9 +2429,9 @@ namespace GGUFMeta { template static typename std::enable_if::value && std::is_integral::value, bool>::type - try_override(OT & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, override)) { - target = override->int_value; + try_override(OT & target, const struct llama_model_kv_override * ovrd) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) { + target = ovrd->int_value; return true; } return false; @@ -2402,9 +2439,9 @@ namespace GGUFMeta { template static typename std::enable_if::value, bool>::type - try_override(T & target, const struct llama_model_kv_override *override) { - if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, override)) { - target = override->float_value; + try_override(T & target, const struct llama_model_kv_override * ovrd) { + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) { + target = ovrd->float_value; return true; } return false; @@ -2412,17 +2449,17 @@ namespace GGUFMeta { template static typename std::enable_if::value, bool>::type - try_override(T & target, const struct llama_model_kv_override *override) { + try_override(T & target, const struct llama_model_kv_override * ovrd) { (void)target; - (void)override; - if (!override) { return false; } + (void)ovrd; + if (!ovrd) { return false; } // Currently, we should never end up here so it would be a bug if we do. throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n", - override ? override->key : "NULL")); + ovrd ? ovrd->key : "NULL")); } - static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override *override = nullptr) { - if (try_override(target, override)) { + static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) { + if (try_override(target, ovrd)) { return true; } if (k < 0) { return false; } @@ -2430,12 +2467,12 @@ namespace GGUFMeta { return true; } - static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override *override = nullptr) { - return set(ctx, gguf_find_key(ctx, key), target, override); + static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) { + return set(ctx, gguf_find_key(ctx, key), target, ovrd); } - static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override *override = nullptr) { - return set(ctx, key.c_str(), target, override); + static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) { + return set(ctx, key.c_str(), target, ovrd); } }; } @@ -2846,6 +2883,15 @@ struct llama_model_loader { } }; +template<> +bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { + uint32_t tmp; + const bool found = get_key(kid, tmp, required); + result = (enum llama_pooling_type) tmp; + return found; +} + + // // load LLaMA models // @@ -2926,16 +2972,16 @@ static const char * llama_model_type_name(e_model type) { default: return "?B"; } } + static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ switch (type) { - case LLAMA_VOCAB_TYPE_SPM: return "SPM"; - case LLAMA_VOCAB_TYPE_BPE: return "BPE"; - case LLAMA_VOCAB_TYPE_WPM: return "WPM"; - default: return "unknown"; + case LLAMA_VOCAB_TYPE_SPM: return "SPM"; + case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + case LLAMA_VOCAB_TYPE_WPM: return "WPM"; + default: return "unknown"; } } - static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); if (model.arch == LLM_ARCH_UNKNOWN) { @@ -3112,10 +3158,10 @@ static void llm_load_hparams( } break; case LLM_ARCH_BERT: { - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); switch (hparams.n_layer) { case 3: @@ -3133,10 +3179,10 @@ static void llm_load_hparams( } break; case LLM_ARCH_NOMIC_BERT: { - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); if (hparams.n_layer == 12 && hparams.n_embd == 768) { model.type = e_model::MODEL_137M; @@ -3275,6 +3321,8 @@ static void llm_load_hparams( if (hparams.f_max_alibi_bias > 0.0f) { hparams.need_kq_pos = true; } + + hparams.rope_type = llama_rope_type(&model); } // TODO: This should probably be in llama.h @@ -3577,6 +3625,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); + LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); + LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); @@ -4598,12 +4648,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam using llm_build_cb = std::function; -enum llm_rope_type { - LLM_ROPE, - LLM_ROPE_NEOX, - LLM_ROPE_GLM, -}; - enum llm_ffn_op_type { LLM_FFN_SILU, LLM_FFN_GELU, @@ -4649,55 +4693,6 @@ static struct ggml_tensor * llm_build_inp_embd( return inpL; } -// Persimmon: n_rot = n_embd_head_k/2 -// Other: n_rot = n_embd_head_k -static void llm_build_k_shift( - struct ggml_context * ctx, - const llama_hparams & hparams, - const llama_cparams & cparams, - const llama_kv_cache & kv, - struct ggml_cgraph * graph, - struct ggml_tensor * K_shift, - llm_rope_type type, - int64_t n_ctx, - float freq_base, - float freq_scale, - const llm_build_cb & cb) { - const int64_t n_layer = hparams.n_layer; - const int64_t n_head_kv = hparams.n_head_kv; - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int32_t n_rot = hparams.n_rot; - const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; - const float ext_factor = cparams.yarn_ext_factor; - const float attn_factor = cparams.yarn_attn_factor; - const float beta_fast = cparams.yarn_beta_fast; - const float beta_slow = cparams.yarn_beta_slow; - - int rope_type = 0; - - switch (type) { - case LLM_ROPE: rope_type = 0; break; - case LLM_ROPE_NEOX: rope_type = 2; break; - case LLM_ROPE_GLM: rope_type = 4; break; - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * tmp = - // we rotate only the first n_rot dimensions - ggml_rope_custom_inplace(ctx, - ggml_view_3d(ctx, kv.k_l[il], - n_embd_head_k, n_head_kv, n_ctx, - ggml_row_size(kv.k_l[il]->type, n_embd_head_k), - ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa), - 0), - K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow); - cb(tmp, "K_shifted", il); - ggml_build_forward_expand(graph, tmp); - } -} - static void llm_build_kv_store( struct ggml_context * ctx, const llama_hparams & hparams, @@ -5001,6 +4996,7 @@ struct llm_build_context { const int64_t n_embd; const int64_t n_layer; + const int64_t n_rot; const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train) const int64_t n_head; const int64_t n_head_kv; @@ -5025,8 +5021,8 @@ struct llm_build_context { const int32_t kv_head; // index of where we store new KV data in the cache const int32_t n_orig_ctx; - const bool do_rope_shift; - const uint32_t pooling_type; + const enum llama_pooling_type pooling_type; + const enum llama_rope_type rope_type; const llm_build_cb & cb; @@ -5048,6 +5044,7 @@ struct llm_build_context { kv_self (lctx.kv_self), n_embd (hparams.n_embd), n_layer (hparams.n_layer), + n_rot (hparams.n_rot), n_ctx (cparams.n_ctx), n_head (hparams.n_head), n_head_kv (hparams.n_head_kv), @@ -5069,8 +5066,8 @@ struct llm_build_context { n_kv (worst_case ? n_ctx : kv_self.n), kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), - do_rope_shift (worst_case || kv_self.has_shift), - pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_TYPE_NONE), + pooling_type (cparams.do_pooling ? hparams.pooling_type : LLAMA_POOLING_TYPE_NONE), + rope_type (hparams.rope_type), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { // all initializations should be done in init() @@ -5093,6 +5090,74 @@ struct llm_build_context { } } + struct ggml_cgraph * build_k_shift() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * tmp = + // we rotate only the first n_rot dimensions + ggml_rope_custom_inplace(ctx0, + ggml_view_3d(ctx0, kv_self.k_l[il], + n_embd_head_k, n_head_kv, n_ctx, + ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), + 0), + lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(tmp, "K_shifted", il); + ggml_build_forward_expand(gf, tmp); + } + + return gf; + } + + struct ggml_cgraph * build_defrag(const std::vector & ids) { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + for (int i = 0; i < n_kv; ++i) { + const int id = ids[i]; + + if (i == id || id == n_kv) { + continue; + } + + int nm = 1; + + while (i + nm < n_kv && (int) ids[i + nm] == id + nm) { + nm++; + } + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i)); + + ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id)); + + ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(kv_self.v_l[il]->type, kv_self.size), + ggml_row_size(kv_self.v_l[il]->type, i)); + + ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(kv_self.v_l[il]->type, kv_self.size), + ggml_row_size(kv_self.v_l[il]->type, id)); + + ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst)); + ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst)); + } + + i += nm - 1; + } + + return gf; + } + struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -5114,11 +5179,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -5154,14 +5214,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5302,11 +5362,6 @@ struct llm_build_context { struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); cb(KQ_pos, "KQ_pos", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -5330,12 +5385,12 @@ struct llm_build_context { case MODEL_7B: Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); break; @@ -5420,11 +5475,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -5463,13 +5513,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5639,10 +5689,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * residual = inpL; @@ -5700,7 +5746,7 @@ struct llm_build_context { // RoPE the first n_rot of q/k, pass the other half, and concat. struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, hparams.n_rot, n_head, n_tokens, + ctx0, tmpq, n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, 0 @@ -5708,7 +5754,7 @@ struct llm_build_context { cb(qrot, "qrot", il); struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, hparams.n_rot, n_head, n_tokens, + ctx0, tmpk, n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, 0 @@ -5717,29 +5763,29 @@ struct llm_build_context { // get the second half of tmpq, e.g tmpq[n_rot:, :, :] struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, hparams.n_rot, n_head, n_tokens, + ctx0, tmpq, n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * hparams.n_rot + ggml_element_size(tmpq) * n_rot ); cb(qpass, "qpass", il); struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, hparams.n_rot, n_head, n_tokens, + ctx0, tmpk, n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * hparams.n_rot + ggml_element_size(tmpk) * n_rot ); cb(kpass, "kpass", il); struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(qrotated, "qrotated", il); struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(krotated, "krotated", il); @@ -5991,14 +6037,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -6287,11 +6333,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6328,14 +6369,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -6410,11 +6451,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6444,13 +6480,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -6524,11 +6560,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6564,14 +6595,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -6645,11 +6676,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, @@ -6687,7 +6713,7 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -6698,7 +6724,7 @@ struct llm_build_context { cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -6767,11 +6793,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { // norm @@ -6795,14 +6816,14 @@ struct llm_build_context { cb(Vcur, "Vcur", il); Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, hparams.n_rot, n_head, n_tokens), inp_pos, - n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, + ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, + n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, hparams.n_rot, n_head_kv, n_tokens), inp_pos, - n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, + ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, + n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -6972,11 +6993,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, @@ -7002,14 +7018,14 @@ struct llm_build_context { struct ggml_tensor * Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); struct ggml_tensor * Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7080,11 +7096,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7120,14 +7131,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7199,11 +7210,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7239,14 +7245,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7331,11 +7337,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7371,14 +7372,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7467,11 +7468,6 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - for (int il = 0; il < n_layer; ++il) { // norm @@ -7494,7 +7490,7 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, - n_embd_head_k, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -7503,7 +7499,7 @@ struct llm_build_context { Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, - n_embd_head_k, 2, 0, n_orig_ctx, freq_base, freq_scale, + n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -7556,6 +7552,40 @@ struct llm_build_context { } }; +static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { + llama_batch dummy; + dummy.n_tokens = 0; + + llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; + + struct llm_build_context llm(lctx, dummy, cb, false); + + llm.init(); + + struct ggml_cgraph * result = llm.build_defrag(ids); + + llm.free(); + + return result; +} + +static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { + llama_batch dummy; + dummy.n_tokens = 0; + + llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; + + struct llm_build_context llm(lctx, dummy, cb, false); + + llm.init(); + + struct ggml_cgraph * result = llm.build_k_shift(); + + llm.free(); + + return result; +} + static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch, @@ -7675,6 +7705,20 @@ static struct ggml_cgraph * llama_build_graph( return result; } +static void llama_set_k_shift(llama_context & lctx) { + const auto & cparams = lctx.cparams; + + const int64_t n_ctx = cparams.n_ctx; + + assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); + + int32_t * data = (int32_t *) lctx.inp_K_shift->data; + + for (int i = 0; i < n_ctx; ++i) { + data[i] = lctx.kv_self.cells[i].delta; + } +} + static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // // set input data @@ -7742,18 +7786,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (kv_self.has_shift) { - const int64_t n_ctx = cparams.n_ctx; - - assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); - - int32_t * data = (int32_t *) lctx.inp_K_shift->data; - - for (int i = 0; i < n_ctx; ++i) { - data[i] = lctx.kv_self.cells[i].delta; - } - } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; @@ -7798,6 +7830,34 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } +static void llama_graph_compute( + llama_context & lctx, + ggml_cgraph * gf, + int n_threads) { +#ifdef GGML_USE_MPI + const int64_t n_layer = lctx.model.hparams.n_layer; + ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); +#endif + +#ifdef GGML_USE_METAL + if (ggml_backend_is_metal(lctx.backend_metal)) { + ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); + } +#endif + + if (lctx.backend_cpu != nullptr) { + ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); + } + + ggml_backend_sched_graph_compute(lctx.sched, gf); + + // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); + +#ifdef GGML_USE_MPI + ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); +#endif +} + // decode a batch of tokens by evaluating the transformer // // - lctx: llama context @@ -7893,14 +7953,17 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); + llama_kv_cache_update(&lctx); + ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, batch, false); // the output is always the last tensor in the graph - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; + if (strcmp(res->name, "result_output") == 0) { // the embeddings could be the second to last tensor, or the third to last tensor if (strcmp(embeddings->name, "result_norm") != 0) { @@ -7927,40 +7990,12 @@ static int llama_decode_internal( n_threads = std::min(4, n_threads); } -#ifdef GGML_USE_MPI - const int64_t n_layer = hparams.n_layer; - ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); -#endif - -#ifdef GGML_USE_METAL - if (ggml_backend_is_metal(lctx.backend_metal)) { - ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); - } -#endif - - if (lctx.backend_cpu != nullptr) { - ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); - } - llama_set_inputs(lctx, batch); - ggml_backend_sched_graph_compute(lctx.sched, gf); - - // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); - -#ifdef GGML_USE_MPI - ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); -#endif + llama_graph_compute(lctx, gf, n_threads); // update the kv ring buffer { - if (kv_self.has_shift) { - kv_self.has_shift = false; - for (uint32_t i = 0; i < kv_self.size; ++i) { - kv_self.cells[i].delta = 0; - } - } - kv_self.head += n_tokens; // Ensure kv cache head points to a valid index. @@ -8056,6 +8091,221 @@ static int llama_decode_internal( return 0; } +// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache +static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { + auto & kv_self = lctx.kv_self; + + const uint32_t n_kv = llama_kv_cache_cell_max(kv_self); + const uint32_t n_used = kv_self.used; + + assert(n_used <= n_kv); + + const int64_t t_start = ggml_time_us(); + + // number of cells moved + uint32_t n_moves = 0; + + // determine which KV cells to move where + // + // cell i moves to ids[i] + // + // if ids[i] == i || ids[i] == n_kv, then cell i is not moved + // + std::vector ids(n_kv, n_kv); + + for (uint32_t i0 = 0; i0 < n_used; ++i0) { + const auto & cell0 = kv_self.cells[i0]; + + if (!cell0.is_empty()) { + ids[i0] = i0; + + continue; + } + + // found a hole - fill it with data from the end of the cache + + // determine the size of the hole + uint32_t nh = 1; + while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) { + nh++; + } + + // starting from the end, find nh non-empty cells + uint32_t nf = 0; + uint32_t is = n_kv - 1; + for (; is > i0; --is) { + const auto & cell1 = kv_self.cells[is]; + + if (cell1.is_empty() || ids[is] != n_kv) { + continue; + } + + // non-empty cell which is not yet moved + nf++; + + if (nf == nh) { + break; + } + } + + // this can only happen if `n_used` is not accurate, which would be a bug + GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh"); + + nf = 0; + + // go back and move the nf cells to the hole + for (uint32_t i1 = is; i1 < n_kv; ++i1) { + const auto & cell1 = kv_self.cells[i1]; + + if (cell1.is_empty() || ids[i1] != n_kv) { + continue; + } + + // this cell goes to (i0 + nf) + ids[i1] = i0 + nf; + + // move the cell meta data + kv_self.cells[i0 + nf] = cell1; + + n_moves++; + nf++; + } + + LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, n_kv, i0, i0 + nh); + + i0 += nh - 1; + } + + if (n_moves == 0) { + return; + } + + LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves); + + kv_self.head = n_used; + kv_self.used = n_used; + + // zero the rest of the cells + for (uint32_t i = n_used; i < n_kv; ++i) { + kv_self.cells[i] = llama_kv_cell(); + } + +#if 0 + // CPU defrag + // + // TODO: optimizations are possible: + // - multiple threads + // - avoid copying to the host memory when already there + // + // likely not worth the effort, as we have ggml_graph based defrag + // + + const auto & hparams = lctx.model.hparams; + + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + + const uint32_t kv_size = kv_self.size; + + std::vector buf_k; + std::vector buf_v; + + for (uint32_t il = 0; il < n_layer; ++il) { + const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa); + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size); + + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + const size_t v_size = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size); + + buf_k.resize(k_size); + buf_v.resize(v_size); + + ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size()); + + // batch move [i, i+nm) to [id, id+nm) + // note: cells can move only to a lower index + for (uint32_t i = 0; i < n_kv; ++i) { + const uint32_t id = ids[i]; + + if (i == id || id == n_kv) { + continue; + } + + uint32_t nm = 1; + + while (i + nm < n_kv && ids[i + nm] == id + nm) { + nm++; + } + + // move keys + { + const int64_t os = i*k_size_row; + const int64_t od = id*k_size_row; + + memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row); + } + + // move values (note: they are transposed) + { + const int64_t os = i; + const int64_t od = id; + + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el); + } + } + + i += nm - 1; + } + + ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size()); + } +#else + // ggml_graph defrag + + ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids); + + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); +#endif + + const int64_t t_end = ggml_time_us(); + + LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0); +} + +static void llama_kv_cache_update_internal(struct llama_context & lctx) { + // apply K-shift if needed + if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) { + llama_set_k_shift(lctx); + + { + ggml_cgraph * gf = llama_build_graph_k_shift(lctx); + + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); + } + + { + auto & kv_self = lctx.kv_self; + + kv_self.has_shift = false; + + for (uint32_t i = 0; i < kv_self.size; ++i) { + kv_self.cells[i].delta = 0; + } + } + } + + // defragment the KV cache if needed + if (lctx.kv_self.do_defrag) { + llama_kv_cache_defrag_internal(lctx); + + lctx.kv_self.do_defrag = false; + } +} + // // tokenizer // @@ -11671,8 +11921,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, - cparams.n_ctx, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, cparams.n_ctx, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -11820,6 +12069,49 @@ enum llama_vocab_type llama_vocab_type(const struct llama_model * model) { return model->vocab.type; } +enum llama_rope_type llama_rope_type(const struct llama_model * model) { + switch (model->arch) { + // these models do not use RoPE + case LLM_ARCH_GPT2: + case LLM_ARCH_GPTJ: + case LLM_ARCH_GPTNEOX: + case LLM_ARCH_MPT: + case LLM_ARCH_REFACT: + case LLM_ARCH_BLOOM: + return LLAMA_ROPE_TYPE_NONE; + + // use what we call a normal RoPE, operating on pairs of consecutive head values + case LLM_ARCH_LLAMA: + case LLM_ARCH_BAICHUAN: + case LLM_ARCH_STARCODER: + case LLM_ARCH_PLAMO: + case LLM_ARCH_CODESHELL: + case LLM_ARCH_ORION: + case LLM_ARCH_INTERNLM2: + case LLM_ARCH_MINICPM: + case LLM_ARCH_GEMMA: + return LLAMA_ROPE_TYPE_NORM; + + // the pairs of head values are offset by n_rot/2 + case LLM_ARCH_FALCON: + case LLM_ARCH_PERSIMMON: + case LLM_ARCH_BERT: + case LLM_ARCH_NOMIC_BERT: + case LLM_ARCH_STABLELM: + case LLM_ARCH_QWEN: + case LLM_ARCH_QWEN2: + case LLM_ARCH_PHI2: + return LLAMA_ROPE_TYPE_NEOX; + + // all model arches should be listed explicitly here + case LLM_ARCH_UNKNOWN: + GGML_ASSERT(false && "unknown architecture"); + break; + } + + return LLAMA_ROPE_TYPE_NONE; +} + int32_t llama_n_vocab(const struct llama_model * model) { return model->vocab.id_to_token.size(); } @@ -12062,12 +12354,12 @@ void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) { llama_kv_cache_seq_keep(ctx->kv_self, seq_id); } -void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { +void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { if (delta == 0) { return; } - llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta); + llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta); } void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { @@ -12078,6 +12370,19 @@ void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, lla llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d); } +llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) { + return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id); +} + +void llama_kv_cache_defrag(struct llama_context * ctx) { + llama_kv_cache_defrag(ctx->kv_self); +} + +void llama_kv_cache_update(struct llama_context * ctx) { + llama_kv_cache_update_internal(*ctx); +} + + // Returns the *maximum* size of the state size_t llama_get_state_size(const struct llama_context * ctx) { // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state. @@ -12204,10 +12509,10 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto & hparams = ctx->model.hparams; const auto & cparams = ctx->cparams; - const auto n_layer = hparams.n_layer; - const auto n_embd_k_gqa = hparams.n_embd_k_gqa(); - const auto n_embd_v_gqa = hparams.n_embd_v_gqa(); - const auto n_ctx = cparams.n_ctx; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_ctx = cparams.n_ctx; const size_t kv_buf_size = kv_self.total_size(); const uint32_t kv_head = kv_self.head; @@ -12222,14 +12527,16 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat if (kv_buf_size) { std::vector tmp_buf; for (int il = 0; il < (int) n_layer; ++il) { - size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); + tmp_buf.resize(k_size); ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); // v is not contiguous, copy row by row - size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + tmp_buf.resize(v_row_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size()); @@ -12316,10 +12623,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const auto & hparams = ctx->model.hparams; const auto & cparams = ctx->cparams; - const int n_layer = hparams.n_layer; - const int n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int n_ctx = cparams.n_ctx; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_ctx = cparams.n_ctx; size_t kv_buf_size; uint32_t kv_head; @@ -12335,13 +12642,15 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { GGML_ASSERT(kv_self.total_size() == kv_buf_size); for (int il = 0; il < (int) n_layer; ++il) { - size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); + ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); inp += k_size; // v is not contiguous, copy row by row - size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size); inp += v_row_size; diff --git a/llama.h b/llama.h index 947284ea2f535..ff131996d9a38 100644 --- a/llama.h +++ b/llama.h @@ -64,6 +64,15 @@ extern "C" { LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece }; + // note: these values should be synchronized with ggml_rope + // TODO: maybe move this enum to ggml.h (ggml_rope_type) + enum llama_rope_type { + LLAMA_ROPE_TYPE_NONE = -1, + LLAMA_ROPE_TYPE_NORM = 0, + LLAMA_ROPE_TYPE_NEOX = 2, + LLAMA_ROPE_TYPE_GLM = 4, + }; + enum llama_token_type { LLAMA_TOKEN_TYPE_UNDEFINED = 0, LLAMA_TOKEN_TYPE_NORMAL = 1, @@ -360,6 +369,7 @@ extern "C" { LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); + LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); @@ -514,10 +524,12 @@ extern "C" { llama_seq_id seq_id); // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1) - // If the KV cache is RoPEd, the KV data is updated accordingly + // If the KV cache is RoPEd, the KV data is updated accordingly: + // - lazily on next llama_decode() + // - explicitly with llama_kv_cache_update() // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) - LLAMA_API void llama_kv_cache_seq_shift( + LLAMA_API void llama_kv_cache_seq_add( struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, @@ -525,7 +537,9 @@ extern "C" { llama_pos delta); // Integer division of the positions by factor of `d > 1` - // If the KV cache is RoPEd, the KV data is updated accordingly + // If the KV cache is RoPEd, the KV data is updated accordingly: + // - lazily on next llama_decode() + // - explicitly with llama_kv_cache_update() // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) LLAMA_API void llama_kv_cache_seq_div( @@ -535,6 +549,20 @@ extern "C" { llama_pos p1, int d); + // Returns the largest position present in the KV cache for the specified sequence + LLAMA_API llama_pos llama_kv_cache_seq_pos_max( + struct llama_context * ctx, + llama_seq_id seq_id); + + // Defragment the KV cache + // This will be applied: + // - lazily on next llama_decode() + // - explicitly with llama_kv_cache_update() + LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx); + + // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) + LLAMA_API void llama_kv_cache_update(struct llama_context * ctx); + // // State / sessions // From 8b350356b28f782deab63d8b0e9ae103ceb25fcd Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 25 Feb 2024 21:46:29 +0100 Subject: [PATCH 099/401] server: docs - refresh and tease a little bit more the http server (#5718) * server: docs - refresh and tease a little bit more the http server * Rephrase README.md server doc Co-authored-by: Georgi Gerganov * Update examples/server/README.md Co-authored-by: Georgi Gerganov * Update examples/server/README.md Co-authored-by: Georgi Gerganov * Update README.md --------- Co-authored-by: Georgi Gerganov --- README.md | 3 +++ examples/server/README.md | 18 +++++++++++++++--- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d61f9171b1b62..d0af5d0b9b077 100644 --- a/README.md +++ b/README.md @@ -114,6 +114,9 @@ Typically finetunes of the base models below are supported as well. - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) +**HTTP server** + +[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. **Bindings:** diff --git a/examples/server/README.md b/examples/server/README.md index cb3fd6054095b..0e9bd7fd404ba 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -1,8 +1,20 @@ -# llama.cpp/example/server +# LLaMA.cpp HTTP Server -This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp. +Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**. -Command line options: +Set of LLM REST APIs and a simple web front end to interact with llama.cpp. + +**Features:** + * LLM inference of F16 and quantum models on GPU and CPU + * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes + * Parallel decoding with multi-user support + * Continuous batching + * Multimodal (wip) + * Monitoring endpoints + +The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). + +**Command line options:** - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. From e3965cf35aac00d4e24998c8a3d0093ae1d98bd3 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 25 Feb 2024 22:48:33 +0100 Subject: [PATCH 100/401] server: tests - slow inference causes timeout on the CI (#5715) * server: tests - longer inference timeout for CI --- common/sampling.cpp | 2 +- examples/server/tests/features/steps/steps.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index de4331a1182d6..e67096bea6932 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -266,7 +266,7 @@ static llama_token llama_sampling_sample_impl( // } //} - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); + //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); } } diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 8e4babf204f8a..ad87fcb820aa8 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -699,6 +699,8 @@ async def wait_for_health_status(context, if context.debug: print(f"Starting checking for health for expected_health_status={expected_health_status}") timeout = 3 # seconds + if expected_health_status == 'ok': + timeout = 10 # CI slow inference interval = 0.5 counter = 0 async with aiohttp.ClientSession() as session: @@ -736,7 +738,7 @@ async def wait_for_health_status(context, if n_completions > 0: return - assert False, 'timeout exceeded' + assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}' def assert_embeddings(embeddings): From c39373398803c669056304090050fe3f44b41bf9 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 25 Feb 2024 00:17:11 +0000 Subject: [PATCH 101/401] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/5863c27340ba4de8f83e7e3c023b9599c3cb3c80' (2024-02-16) → 'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 47d6448b5ceb9..9f659ba8f4cef 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1708118438, - "narHash": "sha256-kk9/0nuVgA220FcqH/D2xaN6uGyHp/zoxPNUmPCMmEE=", + "lastModified": 1708655239, + "narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "5863c27340ba4de8f83e7e3c023b9599c3cb3c80", + "rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a", "type": "github" }, "original": { From 269de86ba073b5dc9ce687c11a3bc4d7d873b962 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Feb 2024 08:30:17 +0200 Subject: [PATCH 102/401] llama : fix Gemma rope type (#5691) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 3424b1999ebdd..28430254f698f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12089,7 +12089,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_ORION: case LLM_ARCH_INTERNLM2: case LLM_ARCH_MINICPM: - case LLM_ARCH_GEMMA: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -12101,6 +12100,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN: case LLM_ARCH_QWEN2: case LLM_ARCH_PHI2: + case LLM_ARCH_GEMMA: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here From 8a533f0d9078396ebaee9ba213038a1322976dee Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Mon, 26 Feb 2024 09:56:10 +0100 Subject: [PATCH 103/401] server: CI tests reduce build matrix (#5725) --- .github/workflows/server.yml | 78 ++++++++---------------------------- 1 file changed, 17 insertions(+), 61 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index ed27dc528fb61..1211ba128d3a0 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -6,11 +6,10 @@ on: push: branches: - master - - test/server-add-ci-test # FIXME remove - paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] pull_request: types: [opened, synchronize, reopened] - paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] jobs: server: @@ -18,45 +17,21 @@ jobs: strategy: matrix: - build: [noavx, avx2, avx, avx512, cublas, clblast, openblas, kompute, vulkan] sanitizer: [ADDRESS, THREAD, UNDEFINED] build_type: [Debug, Release] include: - - build: 'noavx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF' - image: ubuntu:latest - - build: 'avx2' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' - image: ubuntu:latest - - build: 'avx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF' - image: ubuntu:latest - - build: 'avx512' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON' - image: ubuntu:latest - experimental: true - - build: 'cublas' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON' - image: nvidia/cuda:12.3.1-devel-ubuntu22.04 - arch_not_available: true # require nvidia docker engine - - build: 'clblast' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON' - image: ubuntu:latest - arch_not_available: true - - build: 'openblas' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS' - image: ubuntu:latest - - build: 'kompute' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' - image: ubuntu:latest - arch_not_available: true - - build: 'vulkan' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON' - image: ubuntu:latest - arch_not_available: true + - build_type: Release + sanitizer: "" + exclude: + - build_type: Release + sanitizer: ADDRESS + - build_type: Release + sanitizer: THREAD + - build_type: Release + sanitizer: UNDEFINED container: - image: ${{ matrix.image }} + image: ubuntu:latest ports: - 8888 options: --cpus 4 @@ -72,40 +47,22 @@ jobs: apt-get update apt-get -y install \ build-essential \ - pkg-config \ git \ cmake \ python3-pip \ wget \ psmisc - - name: Download CLBlast - id: get_clblast - if: ${{ matrix.build == 'clblast' }} - run: | - apt install -y libclblast-dev - - - name: Download OpenBLAS - id: get_openblas - if: ${{ matrix.build == 'openblas' }} - run: | - apt-get -y install libopenblas-dev - - - name: Install Vulkan SDK - id: get_vulkan - if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} - run: | - wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | tee /etc/apt/trusted.gpg.d/lunarg.asc - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list http://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list - apt-get update - apt-get -y install vulkan-sdk - - name: Build id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ${{ matrix.defines }} + cmake .. \ + -DLLAMA_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server - name: Tests dependencies @@ -121,7 +78,6 @@ jobs: - name: Tests id: server_integration_test - continue-on-error: ${{ matrix.experimental || matrix.arch_not_available }} run: | cd examples/server/tests PORT=8888 ./tests.sh From 4804215cb833841ffb15a710a16b77ca0a29eb4b Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Mon, 26 Feb 2024 11:41:34 +0100 Subject: [PATCH 104/401] server: CI fix trailing space (#5728) --- .github/workflows/server.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 1211ba128d3a0..0b6f6669b23c7 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -62,7 +62,7 @@ jobs: -DLLAMA_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server - name: Tests dependencies From 67fd33132fab93e6c2087bd6fa656a8a57419efa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Feb 2024 14:02:12 +0200 Subject: [PATCH 105/401] unicode : reuse iterator (#5726) --- unicode.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/unicode.h b/unicode.h index 263260702e640..10a5dab0185fe 100644 --- a/unicode.h +++ b/unicode.h @@ -404,7 +404,8 @@ static std::unordered_map codepoint_type_map() { static int codepoint_type(uint32_t cp) { static std::unordered_map codepoint_types = codepoint_type_map(); - return codepoint_types.find(cp) == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : codepoint_types.at(cp); + const auto it = codepoint_types.find(cp); + return it == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; } static int codepoint_type(const std::string & utf8) { From e849078c6e09e72fdd2c95ba61f5fba9a7b2d9ef Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 26 Feb 2024 14:02:11 +0000 Subject: [PATCH 106/401] [SYCL] Add support for soft_max ALiBi (#5639) * Add support for bias * Update pre-processor * rm commented code * fix format * fix CI --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 246 +++++++++++++++++++++++++++++++++----------------- llama.cpp | 4 +- 2 files changed, 167 insertions(+), 83 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index c6c3c6e6fef07..835967fb64d9e 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8126,23 +8126,51 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } -static void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale, - const sycl::nd_item<3> &item_ct1, float *buf) { + +template +static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; + const int tid = item_ct1.get_local_id(2); const int rowx = item_ct1.get_group(2); const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension - const int block_size = item_ct1.get_local_range(2); + const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + float slope = 0.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = rowx/nrows_y; // head index + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = sycl::pow(base, float(exp)); + } + + float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols; float max_val = -INFINITY; - for (int col = tid; col < ncols; col += block_size) { + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + break; + } + const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - max_val = sycl::max(max_val, x[ix] * scale + (y ? y[iy] : 0.0f)); + + const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + + vals[col] = val; + max_val = sycl::max(max_val, val); } // find the max value in the block @@ -8151,30 +8179,12 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in if (warp_id == 0) { buf[lane_id] = -INFINITY; } - /* - DPCT1118:12: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:60: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); if (lane_id == 0) { buf[warp_id] = max_val; } - /* - DPCT1118:13: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:61: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); max_val = buf[lane_id]; max_val = warp_reduce_max(max_val, item_ct1); @@ -8182,13 +8192,16 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in float tmp = 0.f; - for (int col = tid; col < ncols; col += block_size) { - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - const float val = - sycl::native::exp((x[ix] * scale + (y ? y[iy] : 0.0f)) - max_val); +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + if (ncols_template == 0 && col >= ncols) { + break; + } + + const float val = sycl::native::exp(vals[col] - max_val); tmp += val; - dst[ix] = val; + vals[col] = val; } // find the sum of exps in the block @@ -8197,40 +8210,29 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in if (warp_id == 0) { buf[lane_id] = 0.f; } - /* - DPCT1118:14: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:62: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); if (lane_id == 0) { buf[warp_id] = tmp; } - /* - DPCT1118:15: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:63: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); tmp = buf[lane_id]; tmp = warp_reduce_sum(tmp, item_ct1); } - const float inv_tmp = 1.f / tmp; + const float inv_sum = 1.f / tmp; - for (int col = tid; col < ncols; col += block_size) { - const int i = rowx*ncols + col; - dst[i] *= inv_tmp; +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + return; + } + + const int idst = rowx*ncols + col; + dst[idst] = vals[col] * inv_sum; } } @@ -10867,37 +10869,98 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, }); } -static void soft_max_f32_sycl(const float *x, const float *y, float *dst, - const int ncols_x, const int nrows_x, - const int nrows_y, const float scale, - dpct::queue_ptr stream) { - int nth = WARP_SIZE; - while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; - const sycl::range<3> block_dims(1, 1, nth); - const sycl::range<3> block_nums(1, 1, nrows_x); - /* - DPCT1049:46: The work-group size passed to the SYCL kernel may exceed the - limit. To get the device limit, query info::device::max_work_group_size. - Adjust the work-group size if needed. - */ +template +static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, + const size_t n_local_scratch, dpct::queue_ptr stream) { stream->submit([&](sycl::handler &cgh) { - /* - DPCT1101:96: 'SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE' expression was - replaced with a value. Modify the code to use the original expression, - provided in comments, if it is correct. - */ - sycl::local_accessor buf_acc_ct1( - sycl::range<1>(32 /*SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE*/), cgh); + sycl::local_accessor local_buf_acc(n_local_scratch, cgh); cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - soft_max_f32(x, y, dst, ncols_x, nrows_y, scale, item_ct1, - buf_acc_ct1.get_pointer()); + soft_max_f32(x, mask, pos, dst, ncols_par, + nrows_y, scale, max_bias, m0, + m1, n_head_log2, item_ct1, + local_buf_acc.get_pointer()); }); }); } +static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos, + float * dst, const int ncols_x, const int nrows_x, + const int nrows_y, const float scale, const float max_bias, + dpct::queue_ptr stream) { + int nth = WARP_SIZE; + while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; + const sycl::range<3> block_dims(1, 1, nth); + const sycl::range<3> block_nums(1, 1, nrows_x); + const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE); + static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + const size_t local_mem_size = stream->get_device().get_info(); + if (n_local_scratch*sizeof(float) < local_mem_size) { + switch (ncols_x) { + case 32: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 64: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 128: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 256: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 512: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 1024: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 2048: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 4096: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + default: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + } + } else { + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, WARP_SIZE, stream); + } +} + template static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, @@ -12435,14 +12498,35 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1; + const int64_t nrows_y = src0->ne[1]; float scale = 1.0f; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias = 0.0f; - soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + memcpy(&scale, dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, dst->op_params + 1, sizeof(float)); - (void) dst; + // positions tensor + float * src2_dd = nullptr; + sycl_pool_alloc src2_f; + + ggml_tensor * src2 = dst->src[2]; + const bool use_src2 = src2 != nullptr; + + if (use_src2) { + const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; + + if (src2_on_device) { + ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; + src2_dd = (float *) src2_extra->data_device[g_main_device]; + } else { + src2_dd = src2_f.alloc(ggml_nelements(src2)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); + } + } + + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, + nrows_x, nrows_y, scale, max_bias, main_stream); } inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1, diff --git a/llama.cpp b/llama.cpp index 28430254f698f..f549e7d04b5a1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4894,8 +4894,8 @@ static struct ggml_tensor * llm_build_kqv( ggml_mul_mat_set_prec(kq, GGML_PREC_F32); } -#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_SYCL) -#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, Kompute, and SYCL") +#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) +#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, and Kompute") #pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") if (hparams.f_max_alibi_bias > 0.0f) { From c4d7f8178608440506e5489bae0109e4ca12e44a Mon Sep 17 00:00:00 2001 From: Artem Date: Mon, 26 Feb 2024 17:15:28 +0300 Subject: [PATCH 107/401] readme : update ui list (#5731) * Add LLMFarm (ui for iOS) to list --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d0af5d0b9b077..507a2888bf410 100644 --- a/README.md +++ b/README.md @@ -159,6 +159,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [withcatai/catai](https://github.com/withcatai/catai) - [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) - [Msty](https://msty.app) (proprietary) +- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) --- From 47bb7b48c7cec9d8f57d56812ce811ec130b89a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 26 Feb 2024 15:36:38 +0100 Subject: [PATCH 108/401] CUDA: fix DEBUG_CUDA_MALLOC (#5729) --- ggml-cuda.cu | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index fb6d4f7d215b6..15322fb59f466 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8079,8 +8079,8 @@ static void * ggml_cuda_pool_malloc_leg(int device, size_t size, size_t * actual *actual_size = look_ahead_size; g_cuda_pool_size[device] += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[device]/1024/1024), (uint32_t)(size/1024/1024)); #endif return ptr; } @@ -8166,7 +8166,7 @@ static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual g_cuda_pool_used[device] += size; #ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr); + printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr); #endif return ptr; @@ -8176,7 +8176,7 @@ static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); #ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); + printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr); #endif g_cuda_pool_used[device] -= size; From a33e6a0d2a66104ea9a906bdbf8a94d050189d91 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 26 Feb 2024 18:28:38 +0200 Subject: [PATCH 109/401] Adding IQ2_S and IQ2_M to complete coverage of the 2-3 bit quantization range (#5721) * Adding IQ2_S and IQ2_M as a single cumulative commit * Update examples/quantize/quantize.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- examples/quantize/quantize.cpp | 7 +- ggml-cuda.cu | 358 ++++++++++++++- ggml-metal.m | 37 +- ggml-metal.metal | 487 +++++++++++++++++++++ ggml-quants.c | 775 ++++++++++++++++++++++++++++++++- ggml-quants.h | 14 + ggml.c | 31 ++ ggml.h | 2 + llama.cpp | 69 ++- llama.h | 4 +- tests/test-backend-ops.cpp | 2 +- tests/test-quantize-fns.cpp | 4 +- 12 files changed, 1753 insertions(+), 37 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index ab7e72aaf8254..2d187823f4c3d 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -23,14 +23,16 @@ static const std::vector QUANT_OPTIONS = { { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, + { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, + { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, - { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, + { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, - { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , }, + { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization" , }, { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, @@ -292,6 +294,7 @@ int main(int argc, char ** argv) { } if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) { fprintf(stderr, "\n===============================================================================================\n"); fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 15322fb59f466..964fb7351d5d8 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -523,6 +523,17 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +// 2.5625 bpw quants +#define QR2_S 8 +#define QI2_S (QK_K / (4*QR2_S)) +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; +static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); + #define QR3_XXS 8 #define QI3_XXS (QK_K / (4*QR3_XXS)) typedef struct { @@ -1689,6 +1700,265 @@ static const __device__ uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const __device__ uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, + 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, + 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, + 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, + 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, + 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, + 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, + 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, + 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, + 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, + 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, + 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, + 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, + 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, + 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, + 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, + 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, + 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, + 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, + 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, + 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, + 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, + 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, + 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, + 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, + 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, + 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, + 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, + 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, + 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, + 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, + 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, + 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, + 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, + 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, + 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, + 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, + 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, + 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, + 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, + 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, + 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, + 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, + 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, + 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, + 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, + 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, + 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, + 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, + 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, + 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, + 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, + 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, + 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, + 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, + 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, + 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, + 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, + 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, + 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, + 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, + 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, + 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, + 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, + 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, + 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, + 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, + 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, + 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, + 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, + 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, + 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, + 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, + 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, + 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, + 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, + 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, + 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, + 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, + 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, + 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, + 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, + 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, + 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, + 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, + 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, + 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, + 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, + 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, + 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, + 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, + 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, + 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, + 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, + 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, + 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, + 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, + 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, + 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, + 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, + 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, + 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, + 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, + 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, + 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, + 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, + 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, + 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, + 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, + 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, + 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, + 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, + 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, + 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, + 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, + 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, + 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, + 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, + 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, + 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, + 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, + 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, + 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, + 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, + 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, + 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, + 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, + 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, + 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, + 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, + 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, + 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, + 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, + 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, + 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, + 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, + 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, + 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, + 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, + 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, + 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, + 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, + 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, + 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, + 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, + 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, + 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, + 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, + 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, + 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + static const __device__ uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -2037,6 +2307,27 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst } +template +static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq2_s * x = (const block_iq2_s *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + template static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -4800,6 +5091,54 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( #endif } +// TODO +static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_s * bq2 = (const block_iq2_s *) vbq; + + const int ib32 = iqs; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + (void) ksigns64; + assert(false); + return 0.f; +#endif +#else + (void) ksigns64; + assert(false); + return 0.f; +#endif +} + static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics @@ -6996,6 +7335,12 @@ static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq2_xs<<>>(vx, y); } +template +static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_s<<>>(vx, y); +} + template static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -7057,6 +7402,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: @@ -7098,6 +7445,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: @@ -8848,6 +9197,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ2_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; case GGML_TYPE_IQ3_XXS: mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); @@ -11710,7 +12065,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons } ggml_type a_type = a->type; if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S) { + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || + a_type == GGML_TYPE_IQ2_S) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index 3d6b01263acb5..251d04fb0a571 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -62,6 +62,7 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, @@ -87,6 +88,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, @@ -108,6 +110,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, @@ -126,6 +129,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, @@ -144,6 +148,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, @@ -458,6 +463,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); @@ -483,6 +489,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); @@ -504,6 +511,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); @@ -522,6 +530,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); @@ -540,6 +549,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); @@ -1358,6 +1368,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); @@ -1500,6 +1511,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1544,9 +1561,9 @@ static bool ggml_metal_graph_compute( [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || - src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S) { // || src0t == GGML_TYPE_Q4_K) { + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || + src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || + src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -1658,6 +1675,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); @@ -1803,6 +1821,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1863,9 +1887,9 @@ static bool ggml_metal_graph_compute( [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; } - if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || - src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S) { // || src2t == GGML_TYPE_Q4_K) { + if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || + src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || + src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { @@ -1925,6 +1949,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; diff --git a/ggml-metal.metal b/ggml-metal.metal index b3bf405391d3e..47354e9529440 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2519,6 +2519,14 @@ typedef struct { } block_iq2_xs; // 74 bytes / block for QK_K = 256, so 2.3125 bpw +// 2.5625 bpw quants +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; + typedef struct { half d; uint8_t qs[3*QK_K/8]; @@ -3774,6 +3782,265 @@ constexpr constant static uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +constexpr constant static uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 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0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + constexpr constant static uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -4572,6 +4839,139 @@ kernel void kernel_mul_mv_iq3_s_f32( kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq2_s_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + //{ + // int nval = 32; + // int pos = (32*sgitg + tiisg)*nval; + // for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i]; + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq2_s * xr = x + ibl; + device const uint8_t * qs = xr->qs + 4 * ib; + device const uint8_t * qh = xr->qh + ib; + device const uint8_t * sc = xr->scales + ib; + device const uint8_t * signs = qs + QK_K/8; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + const float d1 = db * (0.5f + (sc[0] & 0xf)); + const float d2 = db * (0.5f + (sc[0] >> 4)); + + float2 sum = {0}; + for (int l = 0; l < 2; ++l) { + //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + for (int j = 0; j < 8; ++j) { + sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]); + sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]); + } + } + sumf[row] += d1 * sum[0] + d2 * sum[1]; + + dh += nb*sizeof(block_iq2_s)/2; + qs += nb*sizeof(block_iq2_s); + qh += nb*sizeof(block_iq2_s); + sc += nb*sizeof(block_iq2_s); + signs += nb*sizeof(block_iq2_s); + } + + y4 += 32 * 32; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + } + } +} + +[[host_name("kernel_mul_mv_iq2_s_f32")]] +kernel void kernel_mul_mv_iq2_s_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + void kernel_mul_mv_iq1_s_f32_impl( device const void * src0, device const float * src1, @@ -5188,6 +5588,25 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & } } +template +void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * signs = qs + QK_K/8; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); + for (int i = 0; i < 8; ++i) { + reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); + reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); + } +} + template void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 @@ -5762,6 +6181,7 @@ template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; @@ -5804,6 +6224,7 @@ template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -5858,6 +6279,7 @@ template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; @@ -6893,6 +7315,71 @@ kernel void kernel_mul_mv_id_iq3_s_f32( sgitg); } +[[host_name("kernel_mul_mv_id_iq2_s_f32")]] +kernel void kernel_mul_mv_id_iq2_s_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq2_s_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} + [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel void kernel_mul_mv_id_iq1_s_f32( device const char * ids, diff --git a/ggml-quants.c b/ggml-quants.c index 3d94d166d1b6d..ce654f094da69 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3495,6 +3495,265 @@ static const uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, + 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, + 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, + 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, + 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, + 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, + 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, + 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, + 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, + 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, + 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, + 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, + 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, + 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, + 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, + 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, + 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, + 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, + 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, + 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, + 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, + 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, + 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, + 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, + 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, + 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, + 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, + 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, + 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, + 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, + 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, + 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, + 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, + 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, + 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, + 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, + 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, + 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, + 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, + 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, + 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, + 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, + 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, + 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, + 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, + 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, + 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, + 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, + 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, + 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, + 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, + 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, + 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, + 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, + 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, + 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, + 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, + 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, + 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, + 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, + 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, + 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, + 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, + 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, + 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, + 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, + 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, + 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, + 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, + 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, + 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, + 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, + 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, + 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, + 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, + 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, + 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, + 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, + 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, + 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, + 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, + 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, + 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, + 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, + 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, + 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, + 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, + 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, + 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, + 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, + 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, + 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, + 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, + 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, + 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, + 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, + 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, + 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, + 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, + 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, + 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, + 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, + 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, + 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, + 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, + 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, + 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, + 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, + 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, + 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, + 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, + 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, + 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, + 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, + 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, + 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, + 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, + 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, + 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, + 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, + 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, + 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, + 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, + 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, + 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, + 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, + 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, + 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, + 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, + 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, + 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, + 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, + 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, + 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, + 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, + 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, + 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, + 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, + 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, + 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, + 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, + 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, + 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, + 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, + 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, + 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, + 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, + 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, + 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, + 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + static const uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -3796,6 +4055,38 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, } } +// ====================== 2.5625 bpw (de)-quantization + +void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + float db[2]; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f; + db[1] = d * (0.5f + (x[i].scales[ib32] >> 4)) * 0.25f; + for (int l = 0; l < 4; ++l) { + const float dl = db[l/2]; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + y[j] = dl * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1.f : 1.f); + } + y += 8; + } + qs += 4; + signs += 4; + } + } +} + // ====================== 3.0625 bpw (de)-quantization void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) { @@ -9330,6 +9621,210 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * #endif } +void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const uint8x16_t m1 = vdupq_n_u8(1); + const int32x4_t vzero = vdupq_n_s32(0); + + uint8x16x2_t vs; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); + q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); + q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); + q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); + qs += 8; + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); + vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); + q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); + vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + signs += 4; + + q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); + q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); + + const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); + sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); + sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); + sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); + } + sumf += d*(sumi1 + sumi2); + } + + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + uint64_t aux64; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + int bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); + int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += ls1 * sumi1 + ls2 * sumi2; + qs += 4; + signs += 4; + } + + sumf += d * bsum; + } + + *s = 0.125f * sumf; + +#endif + +} + void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -9934,22 +10429,25 @@ typedef struct { uint16_t * neighbours; } iq2_entry_t; -static iq2_entry_t iq2_data[3] = { +static iq2_entry_t iq2_data[4] = { + {NULL, NULL, NULL}, {NULL, NULL, NULL}, {NULL, NULL, NULL}, {NULL, NULL, NULL}, }; static inline int iq2_data_index(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); return type == GGML_TYPE_IQ2_XXS ? 0 : - type == GGML_TYPE_IQ2_XS ? 1 : 2; + type == GGML_TYPE_IQ2_XS ? 1 : + type == GGML_TYPE_IQ1_S ? 2 : 3; } static inline int iq2_grid_size(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); return type == GGML_TYPE_IQ2_XXS ? 256 : - type == GGML_TYPE_IQ2_XS ? 512 : 512; + type == GGML_TYPE_IQ2_XS ? 512 : + type == GGML_TYPE_IQ1_S ? 512 : 1024; } static int iq2_compare_func(const void * left, const void * right) { @@ -10050,11 +10548,79 @@ void iq2xs_init_impl(enum ggml_type type) { 41557, 41633, 41989, 42021, 42056, 42068, 42074, 42113, 42242, 42265, 42274, 42325, 42340, 42402, 42501, 42512, 42533, 42624, 42632, 42666, 43040, 43093, 43106, 43168, 43176, 43264, 43286, 43345, 43429, 43590, 43618, 43680, }; + static const uint16_t kgrid_2bit_1024[1024] = { + 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, + 73, 80, 82, 85, 88, 97, 100, 102, 105, 128, 130, 133, 136, 145, 148, 160, + 165, 170, 257, 260, 262, 265, 272, 274, 277, 280, 289, 292, 320, 322, 325, 328, + 337, 340, 342, 345, 352, 357, 360, 385, 388, 400, 402, 405, 417, 420, 512, 514, + 517, 520, 529, 532, 544, 554, 577, 580, 582, 585, 592, 597, 640, 645, 650, 660, + 674, 1025, 1028, 1030, 1033, 1040, 1042, 1045, 1048, 1057, 1060, 1062, 1065, 1088, 1090, 1093, + 1096, 1098, 1105, 1108, 1110, 1113, 1120, 1122, 1125, 1153, 1156, 1158, 1161, 1168, 1173, 1176, + 1185, 1188, 1280, 1282, 1285, 1288, 1290, 1297, 1300, 1302, 1305, 1312, 1317, 1320, 1345, 1348, + 1350, 1353, 1360, 1362, 1365, 1368, 1377, 1380, 1408, 1410, 1413, 1416, 1425, 1428, 1440, 1537, + 1540, 1542, 1545, 1552, 1557, 1600, 1605, 1608, 1617, 1620, 1632, 1665, 1668, 1680, 2048, 2050, + 2053, 2056, 2065, 2068, 2070, 2073, 2080, 2085, 2090, 2113, 2116, 2118, 2121, 2128, 2130, 2133, + 2136, 2145, 2148, 2176, 2181, 2196, 2218, 2305, 2308, 2320, 2322, 2325, 2328, 2337, 2368, 2373, + 2376, 2385, 2388, 2400, 2433, 2448, 2560, 2577, 2580, 2594, 2600, 2602, 2640, 2713, 4097, 4100, + 4102, 4105, 4112, 4114, 4117, 4120, 4129, 4132, 4134, 4160, 4162, 4165, 4168, 4177, 4180, 4182, + 4185, 4192, 4194, 4197, 4200, 4225, 4228, 4230, 4240, 4245, 4248, 4257, 4260, 4352, 4354, 4357, + 4360, 4362, 4369, 4372, 4374, 4377, 4384, 4386, 4389, 4392, 4417, 4420, 4422, 4425, 4432, 4434, + 4437, 4440, 4449, 4452, 4480, 4482, 4485, 4488, 4497, 4500, 4609, 4612, 4617, 4624, 4629, 4641, + 4644, 4672, 4677, 4689, 4692, 4737, 4740, 4752, 5120, 5122, 5125, 5128, 5137, 5140, 5142, 5145, + 5152, 5157, 5160, 5185, 5188, 5190, 5193, 5200, 5202, 5205, 5208, 5217, 5220, 5248, 5250, 5253, + 5256, 5265, 5268, 5280, 5377, 5380, 5382, 5385, 5392, 5394, 5397, 5400, 5409, 5412, 5440, 5442, + 5445, 5448, 5457, 5460, 5472, 5505, 5508, 5520, 5632, 5637, 5640, 5649, 5652, 5664, 5697, 5700, + 5712, 5760, 5802, 6145, 6148, 6150, 6153, 6160, 6165, 6168, 6177, 6208, 6210, 6213, 6216, 6225, + 6228, 6240, 6273, 6276, 6400, 6402, 6405, 6408, 6417, 6420, 6432, 6465, 6468, 6480, 6505, 6562, + 6660, 6672, 6720, 6742, 8192, 8194, 8197, 8200, 8209, 8212, 8214, 8217, 8224, 8229, 8234, 8257, + 8260, 8272, 8274, 8277, 8292, 8320, 8330, 8340, 8362, 8449, 8452, 8464, 8466, 8469, 8481, 8512, + 8514, 8517, 8529, 8532, 8544, 8577, 8580, 8592, 8704, 8714, 8738, 8744, 8746, 8772, 8784, 8840, + 8842, 8872, 9217, 9220, 9222, 9225, 9232, 9237, 9240, 9249, 9252, 9280, 9282, 9285, 9288, 9297, + 9300, 9312, 9345, 9348, 9360, 9472, 9477, 9480, 9489, 9492, 9504, 9537, 9540, 9552, 9574, 9600, + 9729, 9732, 9744, 9792, 9817, 10240, 10245, 10257, 10260, 10305, 10308, 10320, 10378, 10410, 10497, 10500, + 10512, 10645, 10762, 10786, 10852, 10888, 10890, 16385, 16388, 16390, 16393, 16400, 16402, 16405, 16408, 16410, + 16417, 16420, 16422, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16470, 16473, 16480, 16482, 16485, 16513, + 16516, 16528, 16533, 16536, 16545, 16548, 16640, 16642, 16645, 16648, 16657, 16660, 16662, 16665, 16672, 16674, + 16677, 16705, 16708, 16710, 16713, 16720, 16722, 16725, 16728, 16737, 16740, 16768, 16770, 16773, 16776, 16785, + 16788, 16800, 16897, 16900, 16912, 16914, 16917, 16920, 16932, 16960, 16965, 16968, 16977, 16980, 16992, 17025, + 17028, 17408, 17410, 17413, 17416, 17418, 17425, 17428, 17430, 17433, 17440, 17442, 17445, 17448, 17473, 17476, + 17478, 17481, 17488, 17490, 17493, 17496, 17505, 17508, 17536, 17538, 17541, 17544, 17553, 17556, 17568, 17665, + 17668, 17670, 17673, 17680, 17682, 17685, 17688, 17697, 17700, 17728, 17730, 17733, 17736, 17745, 17748, 17760, + 17770, 17793, 17796, 17808, 17920, 17922, 17925, 17928, 17937, 17940, 17952, 17985, 17988, 18000, 18048, 18085, + 18433, 18436, 18441, 18448, 18450, 18453, 18456, 18465, 18468, 18496, 18498, 18501, 18504, 18513, 18516, 18528, + 18564, 18576, 18688, 18690, 18693, 18696, 18705, 18708, 18720, 18753, 18756, 18768, 18816, 18838, 18945, 18948, + 18960, 19008, 20480, 20482, 20485, 20488, 20497, 20500, 20502, 20505, 20512, 20514, 20517, 20520, 20545, 20548, + 20550, 20553, 20560, 20562, 20565, 20568, 20577, 20580, 20608, 20610, 20613, 20616, 20625, 20628, 20737, 20740, + 20742, 20745, 20752, 20754, 20757, 20760, 20769, 20772, 20800, 20802, 20805, 20808, 20817, 20820, 20832, 20865, + 20868, 20880, 20992, 20997, 21000, 21009, 21012, 21024, 21057, 21060, 21072, 21097, 21120, 21505, 21508, 21510, + 21513, 21520, 21522, 21525, 21528, 21537, 21540, 21568, 21570, 21573, 21576, 21585, 21588, 21600, 21633, 21636, + 21648, 21760, 21762, 21765, 21768, 21777, 21780, 21792, 21825, 21828, 21840, 21888, 22017, 22020, 22032, 22054, + 22080, 22528, 22530, 22533, 22536, 22545, 22548, 22560, 22593, 22596, 22608, 22618, 22656, 22785, 22788, 22800, + 22848, 23040, 23065, 23173, 23208, 24577, 24580, 24582, 24592, 24594, 24597, 24600, 24609, 24612, 24640, 24645, + 24648, 24657, 24660, 24672, 24708, 24720, 24832, 24834, 24837, 24840, 24849, 24852, 24864, 24897, 24900, 24912, + 24960, 24985, 25092, 25104, 25152, 25174, 25249, 25600, 25605, 25608, 25617, 25620, 25632, 25665, 25668, 25680, + 25728, 25857, 25860, 25872, 25920, 25930, 25960, 26002, 26112, 26260, 26625, 26628, 26640, 26725, 26776, 26880, + 26922, 27202, 27297, 32768, 32770, 32773, 32776, 32785, 32788, 32793, 32800, 32805, 32833, 32836, 32848, 32850, + 32853, 32856, 32865, 32896, 32901, 32913, 32916, 33025, 33028, 33033, 33040, 33042, 33045, 33048, 33057, 33060, + 33088, 33090, 33093, 33096, 33105, 33108, 33153, 33156, 33168, 33193, 33280, 33285, 33290, 33297, 33300, 33345, + 33348, 33360, 33793, 33796, 33798, 33801, 33808, 33810, 33813, 33816, 33825, 33856, 33858, 33861, 33864, 33873, + 33876, 33888, 33921, 33924, 33936, 34048, 34050, 34053, 34056, 34065, 34068, 34080, 34113, 34116, 34128, 34176, + 34186, 34305, 34308, 34320, 34345, 34368, 34816, 34821, 34833, 34836, 34881, 34884, 34896, 34978, 35073, 35076, + 35136, 35173, 35362, 35416, 35418, 35458, 35490, 36865, 36868, 36873, 36880, 36882, 36885, 36888, 36900, 36928, + 36930, 36933, 36936, 36945, 36948, 36960, 36993, 36996, 37008, 37120, 37125, 37137, 37140, 37185, 37188, 37200, + 37210, 37377, 37380, 37392, 37440, 37542, 37888, 37890, 37893, 37896, 37905, 37908, 37920, 37953, 37956, 37968, + 38016, 38038, 38145, 38148, 38160, 38208, 38296, 38305, 38400, 38470, 38500, 38913, 38916, 38928, 38950, 38976, + 39081, 39168, 39241, 39250, 39568, 40960, 40965, 40970, 40980, 40994, 41002, 41025, 41028, 41040, 41122, 41130, + 41280, 41317, 41474, 41482, 41506, 41512, 41514, 41602, 41608, 41610, 41640, 41985, 41988, 42000, 42048, 42121, + 42148, 42240, 42265, 42577, 43018, 43048, 43170, 43348, 43398, 43528, 43530, 43552, 43554, 43560, 43656, 43690, + }; const int kmap_size = 43692; - const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; + //const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; + const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2; const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 : - type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : kgrid_1bit_512; + type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : + type == GGML_TYPE_IQ1_S ? kgrid_1bit_512 : kgrid_2bit_1024; uint64_t * kgrid_q2xs; int * kmap_q2xs; uint16_t * kneighbors_q2xs; @@ -10151,7 +10717,7 @@ void iq2xs_init_impl(enum ggml_type type) { } void iq2xs_free_impl(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); const int gindex = iq2_data_index(type); if (iq2_data[gindex].grid) { free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; @@ -11557,3 +12123,196 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest quantize_iq4_nl(x, y, 1, k, NULL, NULL); } +// =============================== 2.5625 bpw + +static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(GGML_TYPE_IQ2_S); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/256; + + block_iq2_s * y = vy; + + float scales[QK_K/16]; + float weight[16]; + float xval[16]; + int8_t L[16]; + int8_t Laux[16]; + float waux[16]; + bool is_on_grid[2]; + bool is_on_grid_aux[2]; + uint8_t block_signs[2]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + memset(&y[ibl], 0, sizeof(block_iq2_s)); + y[ibl].d = GGML_FP32_TO_FP16(0.f); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = 2*sumx2/QK_K; + + for (int ib = 0; ib < QK_K/16; ++ib) { + const float * xb = xbl + 16*ib; + if (quant_weights) { + const float * qw = quant_weights + QK_K*ibl + 16*ib; + for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + } else { + for (int i = 0; i < 16; ++i) weight[i] = 0.25f*sigma2 + xb[i]*xb[i]; + } + for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 2; ++k) { + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; s |= (1 << i); + } + } + block_signs[k] = s; + } + float max = xval[0]; + for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + is_on_grid[0] = is_on_grid[1] = true; + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 2; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 16; ++i) L[i] = Laux[i]; + for (int k = 0; k < 2; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 2; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + L[8*k + i] = l; + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + scale = -scale; + for (int k = 0; k < 2; ++k) block_signs[k] = ~block_signs[k]; + } + for (int k = 0; k < 2; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + const int i8 = 2*ib + k; + y[ibl].qs[i8] = grid_index & 255; + y[ibl].qh[i8/4] |= ((grid_index >> 8) << 2*(i8%4)); + y[ibl].qs[QK_K/8 + i8] = block_signs[k]; + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d * 0.9875f); + float id = 1/d; + for (int ib = 0; ib < QK_K/16; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + if (ib%2 == 0) y[ibl].scales[ib/2] = l; + else y[ibl].scales[ib/2] |= (l << 4); + } + } +} + +size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_s_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_s); + } + return nrow * nblock * sizeof(block_iq2_s); +} + +void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) { + assert(k % QK_K == 0); + quantize_iq2_s(x, y, 1, k, NULL, NULL); +} + +void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq2_s * restrict y = vy; + quantize_row_iq2_s_reference(x, y, k); +} diff --git a/ggml-quants.h b/ggml-quants.h index 303b0b6f9552e..4731dde0cb5a9 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -182,6 +182,15 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +// 2.5625 bpw quants +typedef struct { + ggml_fp16_t d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; +static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); + // (Almost) "true" 3-bit quantization. // Due to the need to use blocks as per ggml design, it ends up using // 3.0625 bpw because of the 16-bit scale for each block of 256. @@ -242,6 +251,7 @@ void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGM void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k); +void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int k); void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -259,6 +269,7 @@ void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -276,6 +287,7 @@ void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRI void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -295,6 +307,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -305,6 +318,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const // size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index 1d81553f47106..6be07bb6f6db4 100644 --- a/ggml.c +++ b/ggml.c @@ -690,6 +690,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ2_S] = { + .type_name = "iq2_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_s, + .from_float = quantize_row_iq2_s, + .from_float_reference = (ggml_from_float_t)quantize_row_iq2_s_reference, + .vec_dot = ggml_vec_dot_iq2_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", .blck_size = QK_K, @@ -2317,6 +2329,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; + case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7752,6 +7765,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_add_q_f32(params, dst); } break; @@ -8032,6 +8046,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_add1_q_f32(params, dst); } break; @@ -8157,6 +8172,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: default: { GGML_ASSERT(false); @@ -11056,6 +11072,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_out_prod_q_f32(params, dst); } break; @@ -11245,6 +11262,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: default: { GGML_ASSERT(false); @@ -11448,6 +11466,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_get_rows_q(params, dst); } break; @@ -12149,6 +12168,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -12233,6 +12253,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -19482,6 +19503,7 @@ void ggml_quantize_init(enum ggml_type type) { switch (type) { case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; @@ -19768,6 +19790,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ2_S: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_IQ1_S: { GGML_ASSERT(start % QK_K == 0); diff --git a/ggml.h b/ggml.h index 75fd035a4698f..8c7ca4588a4c4 100644 --- a/ggml.h +++ b/ggml.h @@ -351,6 +351,7 @@ extern "C" { GGML_TYPE_IQ1_S = 19, GGML_TYPE_IQ4_NL = 20, GGML_TYPE_IQ3_S = 21, + GGML_TYPE_IQ2_S = 22, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -391,6 +392,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index f549e7d04b5a1..80dc4d166383e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2579,6 +2579,7 @@ struct llama_model_loader { case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; + case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break; case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; @@ -2933,7 +2934,9 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; - case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; + case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; @@ -10761,31 +10764,47 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || + ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { new_type = GGML_TYPE_Q5_K; } else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } } else if (name == "token_embd.weight") { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || + ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { new_type = GGML_TYPE_Q2_K; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { + new_type = GGML_TYPE_IQ3_S; + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - new_type = GGML_TYPE_Q4_K; + new_type = GGML_TYPE_IQ3_S; } - } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || + ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { if (name.find("attn_v.weight") != std::string::npos) { if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; - else new_type = GGML_TYPE_Q2_K; + else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K; ++qs.i_attention_wv; } + else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) { + new_type = GGML_TYPE_Q4_K; + } else if (name.find("ffn_down") != std::string::npos) { - if (qs.i_ffn_down < qs.n_ffn_down/8) new_type = GGML_TYPE_Q2_K; + if (qs.i_ffn_down < qs.n_ffn_down/8) { + new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K; + } ++qs.i_ffn_down; } else if (name.find("attn_output.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS; + if (qs.model.hparams.n_expert == 8) { + new_type = GGML_TYPE_Q5_K; + } else { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S; + } } } else if (name.find("attn_v.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { @@ -10795,7 +10814,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS; + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) { + new_type = GGML_TYPE_Q4_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { + new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; @@ -10833,13 +10858,19 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty // TODO: explore better strategies new_type = GGML_TYPE_Q8_0; } - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) { new_type = GGML_TYPE_IQ3_XXS; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = GGML_TYPE_IQ2_S; + } } else if (name.find("attn_q.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) { new_type = GGML_TYPE_IQ3_XXS; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = GGML_TYPE_IQ2_S; + } } else if (name.find("ffn_down") != std::string::npos) { auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str()); int i_layer = info.first, n_layer = info.second; @@ -10888,7 +10919,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (name.find("attn_output.weight") != std::string::npos) { if (arch != LLM_ARCH_FALCON) { if (qs.model.hparams.n_expert == 8) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { @@ -10896,7 +10927,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K; @@ -10915,7 +10946,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (name.find("ffn_gate") != std::string::npos) { auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str()); int i_layer = info.first, n_layer = info.second; - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { new_type = GGML_TYPE_IQ3_XXS; } ++qs.i_ffn_gate; @@ -10923,7 +10954,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (name.find("ffn_up") != std::string::npos) { auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str()); int i_layer = info.first, n_layer = info.second; - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { new_type = GGML_TYPE_IQ3_XXS; } ++qs.i_ffn_up; @@ -10943,7 +10974,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || - new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || + new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S || new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; @@ -10958,6 +10989,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty switch (new_type) { case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: @@ -10991,7 +11023,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K_S: case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; - case LLAMA_FTYPE_MOSTLY_Q3_K_XS: quantized_type = GGML_TYPE_IQ3_S; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XS: quantized_type = GGML_TYPE_IQ3_S; break; case LLAMA_FTYPE_MOSTLY_Q3_K_S: case LLAMA_FTYPE_MOSTLY_Q3_K_M: case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; @@ -11002,6 +11034,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ2_XS: quantized_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_S: quantized_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_M: quantized_type = GGML_TYPE_IQ2_S; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break; @@ -11180,6 +11214,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS || + new_type == GGML_TYPE_IQ2_S || new_type == GGML_TYPE_IQ1_S || (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { LLAMA_LOG_ERROR("\n\n============================================================\n"); diff --git a/llama.h b/llama.h index ff131996d9a38..3ff77d5a8997d 100644 --- a/llama.h +++ b/llama.h @@ -107,12 +107,14 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors - LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 24d12ef141efd..60a8527798833 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1916,7 +1916,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K, - GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, + GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, }; diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 04656bb9e8e83..f615b612d9189 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -150,6 +150,7 @@ int main(int argc, char * argv[]) { const float total_error = total_quantization_error(qfns, test_size, test_data.data()); const float max_quantization_error = type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : + type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR; @@ -168,7 +169,8 @@ int main(int argc, char * argv[]) { const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data()); const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS || - type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S ? MAX_DOT_PRODUCT_ERROR_LOWBIT + type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S + ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR; failed = !(vec_dot_error < max_allowed_error); num_failed += failed; From b11a93df41921846a10628a7c306d5c82a549939 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 26 Feb 2024 23:15:48 +0100 Subject: [PATCH 110/401] fix server hangs on empty prompt (#5733) --- examples/server/server.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8aadc95a9728f..846ef7e5fee4f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1336,6 +1336,10 @@ struct llama_server_context split_multiprompt_task(task_id, task); } } else { + // an empty prompt can make slot become buggy + if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get().empty()) { + task.data["prompt"] = " "; // add a space so that we have one token + } queue_tasks.post(task); } } From cbbd1efa06f8c09f9dff58ff9d9af509cc4c152b Mon Sep 17 00:00:00 2001 From: "le.chang" Date: Tue, 27 Feb 2024 10:03:06 +0800 Subject: [PATCH 111/401] Makefile: use variables for cublas (#5689) * make: use arch variable for cublas * fix UNAME_M * check opt first --------- Co-authored-by: lindeer --- Makefile | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 068f6ed028460..4f26c0463fcd8 100644 --- a/Makefile +++ b/Makefile @@ -381,8 +381,13 @@ ifdef LLAMA_BLIS endif # LLAMA_BLIS ifdef LLAMA_CUBLAS - MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include - MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib + ifneq ('', '$(wildcard /opt/cuda)') + CUDA_PATH ?= /opt/cuda + else + CUDA_PATH ?= /usr/local/cuda + endif + MK_CPPFLAGS += -DGGML_USE_CUBLAS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o MK_NVCCFLAGS += -use_fast_math ifdef LLAMA_FATAL_WARNINGS From 9d533a77d0c3850ce09d736bc1baa67fd6ad27b3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 27 Feb 2024 14:35:51 +0200 Subject: [PATCH 112/401] llama : fix defrag bugs + add parameter (#5735) * llama : fix defrag bugs + enable by default ggml-ci * llama : add defrag_thold parameter ggml-ci * llama : cont * llama : disable log message ggml-ci * llama : fix graph size check during defrag --- common/common.cpp | 9 ++++ common/common.h | 1 + examples/passkey/passkey.cpp | 4 +- llama.cpp | 97 +++++++++++++++++++++++++----------- llama.h | 1 + 5 files changed, 82 insertions(+), 30 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index ec596f5a075de..18289755c9ceb 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -335,6 +335,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--defrag-thold" || arg == "-dt") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.defrag_thold = std::stof(argv[i]); } else if (arg == "--samplers") { if (++i >= argc) { invalid_param = true; @@ -1004,6 +1010,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" -dt N, --defrag-thold N\n"); + printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); @@ -1285,6 +1293,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = !params.no_kv_offload; cparams.type_k = kv_cache_type_from_str(params.cache_type_k); diff --git a/common/common.h b/common/common.h index 3e21579b00545..25003df2600d1 100644 --- a/common/common.h +++ b/common/common.h @@ -75,6 +75,7 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length + float defrag_thold = -1.0f; // KV cache defragmentation threshold int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 47de67a93047f..2cbc9e1fa89ed 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -182,7 +182,7 @@ int main(int argc, char ** argv) { llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); - llama_kv_cache_defrag (ctx); + //llama_kv_cache_defrag (ctx); llama_kv_cache_update (ctx); n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; @@ -213,7 +213,7 @@ int main(int argc, char ** argv) { llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); - llama_kv_cache_defrag (ctx); + //llama_kv_cache_defrag (ctx); llama_kv_cache_update (ctx); n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; diff --git a/llama.cpp b/llama.cpp index 80dc4d166383e..6729bb99c91fd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1641,6 +1641,7 @@ struct llama_cparams { float yarn_attn_factor; float yarn_beta_fast; float yarn_beta_slow; + float defrag_thold; bool mul_mat_q; bool offload_kqv; @@ -5117,16 +5118,16 @@ struct llm_build_context { struct ggml_cgraph * build_defrag(const std::vector & ids) { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - for (int i = 0; i < n_kv; ++i) { - const int id = ids[i]; + for (uint32_t i = 0; i < ids.size(); ++i) { + const uint32_t id = ids[i]; - if (i == id || id == n_kv) { + if (i == id || id == ids.size()) { continue; } - int nm = 1; + uint32_t nm = 1; - while (i + nm < n_kv && (int) ids[i + nm] == id + nm) { + while (i + nm < ids.size() && ids[i + nm] == id + nm) { nm++; } @@ -5158,6 +5159,8 @@ struct llm_build_context { i += nm - 1; } + //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); + return gf; } @@ -7938,6 +7941,8 @@ static int llama_decode_internal( batch.seq_id = seq_id_arr.data(); } + llama_kv_cache_update(&lctx); + // if we have enough unused cells before the current head -> // better to start searching from the beginning of the cache, hoping to fill it if (kv_self.head > kv_self.used + 2*n_tokens) { @@ -7956,8 +7961,6 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - llama_kv_cache_update(&lctx); - ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); @@ -8007,6 +8010,18 @@ static int llama_decode_internal( } } + // decide if we need to defrag the kv cache + if (cparams.defrag_thold >= 0.0f) { + const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used + n_tokens)/float(kv_self.n) : 0.0f; + + // queue defragmentation for next llama_kv_cache_update + if (fragmentation > cparams.defrag_thold) { + //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation); + + llama_kv_cache_defrag(kv_self); + } + } + #ifdef GGML_PERF // print timing information per ggml operation (for debugging purposes) // requires GGML_PERF to be defined @@ -8098,12 +8113,16 @@ static int llama_decode_internal( static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { auto & kv_self = lctx.kv_self; + const auto & hparams = lctx.model.hparams; + + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_kv = llama_kv_cache_cell_max(kv_self); const uint32_t n_used = kv_self.used; assert(n_used <= n_kv); - const int64_t t_start = ggml_time_us(); + //const int64_t t_start = ggml_time_us(); // number of cells moved uint32_t n_moves = 0; @@ -8127,15 +8146,26 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // found a hole - fill it with data from the end of the cache - // determine the size of the hole uint32_t nh = 1; + + // determine the size of the hole while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) { nh++; } - // starting from the end, find nh non-empty cells + // each move requires 6*n_layer tensors (see build_defrag) + // - source view, destination view, copy operation + // - x2 for keys and values + // + if (6*(n_moves + nh)*n_layer >= LLAMA_MAX_NODES) { + // the graph is too big, we cannot move more cells + break; + } + uint32_t nf = 0; uint32_t is = n_kv - 1; + + // starting from the end, find nh non-empty cells for (; is > i0; --is) { const auto & cell1 = kv_self.cells[is]; @@ -8156,11 +8186,17 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { nf = 0; + uint32_t i1 = is; + + // are we moving a continuous block of memory? + bool cont = false; + // go back and move the nf cells to the hole - for (uint32_t i1 = is; i1 < n_kv; ++i1) { - const auto & cell1 = kv_self.cells[i1]; + for (; i1 < n_kv; ++i1) { + auto & cell1 = kv_self.cells[i1]; if (cell1.is_empty() || ids[i1] != n_kv) { + cont = false; continue; } @@ -8170,11 +8206,23 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // move the cell meta data kv_self.cells[i0 + nf] = cell1; - n_moves++; + // clear the old cell and move the head there + cell1 = llama_kv_cell(); + kv_self.head = n_used; + + if (!cont) { + n_moves++; + cont = true; + } + nf++; + + if (nf == nh) { + break; + } } - LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, n_kv, i0, i0 + nh); + //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh); i0 += nh - 1; } @@ -8183,15 +8231,9 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { return; } - LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves); - - kv_self.head = n_used; - kv_self.used = n_used; + //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves); - // zero the rest of the cells - for (uint32_t i = n_used; i < n_kv; ++i) { - kv_self.cells[i] = llama_kv_cell(); - } + //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer); #if 0 // CPU defrag @@ -8203,9 +8245,6 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // likely not worth the effort, as we have ggml_graph based defrag // - const auto & hparams = lctx.model.hparams; - - const uint32_t n_layer = hparams.n_layer; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); @@ -8274,9 +8313,9 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { llama_graph_compute(lctx, gf, lctx.cparams.n_threads); #endif - const int64_t t_end = ggml_time_us(); + //const int64_t t_end = ggml_time_us(); - LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0); + //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0); } static void llama_kv_cache_update_internal(struct llama_context & lctx) { @@ -11670,6 +11709,7 @@ struct llama_context_params llama_context_default_params() { /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, /*.yarn_orig_ctx =*/ 0, + /*.defrag_thold =*/ -1.0f, /*.cb_eval =*/ nullptr, /*.cb_eval_user_data =*/ nullptr, /*.type_k =*/ GGML_TYPE_F16, @@ -11834,6 +11874,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_attn_factor = params.yarn_attn_factor; cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.defrag_thold = params.defrag_thold; cparams.mul_mat_q = params.mul_mat_q; cparams.offload_kqv = params.offload_kqv; cparams.do_pooling = params.do_pooling; @@ -12035,7 +12076,7 @@ struct llama_context * llama_new_context_with_model( } // buffer used to store the computation graph and the tensor meta data - ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); + ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false)); ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES); diff --git a/llama.h b/llama.h index 3ff77d5a8997d..6041618080344 100644 --- a/llama.h +++ b/llama.h @@ -245,6 +245,7 @@ extern "C" { float yarn_beta_fast; // YaRN low correction dim float yarn_beta_slow; // YaRN high correction dim uint32_t yarn_orig_ctx; // YaRN original context size + float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default) ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; From 1f30b7a9f1b86baa455072d3182b9ebeee0cd845 Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Tue, 27 Feb 2024 06:50:18 -0600 Subject: [PATCH 113/401] ggml-quants : fix avx2 iq1_s vec_dot when compiled with gcc (#5742) --- ggml-quants.c | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index ce654f094da69..73c3bb4123da5 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -10248,8 +10248,12 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const uint64_t aux64; - __m256i v_gindex; - const uint16_t * gindex = (const uint16_t *)&v_gindex; + typedef union m256i_uint16 { + __m256i reg; + uint16_t s[16]; + } m256i_uint16_t; + + m256i_uint16_t v_gindex; __m256 accum = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { @@ -10264,13 +10268,13 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const memcpy(&aux64, sc, 8); sc += 8; const __m128i qh = _mm_shuffle_epi8(_mm_set_epi64x(aux64 >> 4, aux64), shuffle_h); const __m256i hbit = _mm256_cvtepu8_epi16(_mm_and_si128(qh, m8)); - v_gindex = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); + v_gindex.reg = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); const __m128i scales = _mm_or_si128(_mm_slli_epi16(_mm_and_si128(qh, m7), 1), m1); for (int i32 = 0; i32 < 4; ++i32) { const __m256i q8b = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q1b = _mm256_set_epi64x(iq1s_grid[gindex[4*i32+3]], iq1s_grid[gindex[4*i32+2]], - iq1s_grid[gindex[4*i32+1]], iq1s_grid[gindex[4*i32+0]]); + const __m256i q1b = _mm256_set_epi64x(iq1s_grid[v_gindex.s[4*i32+3]], iq1s_grid[v_gindex.s[4*i32+2]], + iq1s_grid[v_gindex.s[4*i32+1]], iq1s_grid[v_gindex.s[4*i32+0]]); const __m256i dot = mul_add_epi8(q1b, q8b); const __m256i s16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, shuffle_s[i32])); const __m256i p = _mm256_madd_epi16(s16, dot); From c24a2a6e6005e5d424301525a42ba45a4a362d30 Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Tue, 27 Feb 2024 07:22:45 -0600 Subject: [PATCH 114/401] cuda : replace remaining shfl_xor with calls to warp_reduce functions (#5744) --- ggml-cuda.cu | 73 +++++++++++++++++----------------------------------- 1 file changed, 24 insertions(+), 49 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 964fb7351d5d8..caef65de56c07 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -696,18 +696,20 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { return a; } -//static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -//#pragma unroll -// for (int mask = 16; mask > 0; mask >>= 1) { -// a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); -// } -// return a; -//#else -// (void) a; -// NO_DEVICE_CODE; -//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -//} +#ifdef GGML_CUDA_F16 +static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + } + return a; +#else + (void) a; + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +} +#endif // GGML_CUDA_F16 static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll @@ -2521,10 +2523,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2625,10 +2624,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2761,10 +2757,7 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { dst[row] = tmp; @@ -2877,10 +2870,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2987,10 +2977,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { dst[row] = tmp; @@ -3025,11 +3012,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest float amax = fabsf(xi); float sum = xi; -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32)); - sum += __shfl_xor_sync(0xffffffff, sum, mask, 32); - } + amax = warp_reduce_max(amax); + sum = warp_reduce_sum(sum); const float d = amax / 127; const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); @@ -6222,10 +6206,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons } // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { #ifdef GGML_CUDA_F16 @@ -6275,10 +6256,7 @@ static __global__ void mul_mat_p021_f16_f32( const int idst = channel*nrows_dst + row_dst; // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[idst] = tmp; @@ -6321,10 +6299,7 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous } // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[idst] = tmp; From 0becb22ac05b6542bd9d5f2235691aa1d3d4d307 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 27 Feb 2024 16:34:24 +0200 Subject: [PATCH 115/401] IQ4_XS: a 4.25 bpw quantization (#5747) * Try IQ4_NL with blocks of 64 - does not look good * iq4_xs: go to super-blocks of 256 and 6-bit scales for blocks of 32 * iq4_xs: CUDA works - 133.2 t/s * iq4_xs: AVX2 dot product * iq4_xs: ARM_NEON dot product * iq4_nl: Metal implementation As usual, Metal / Apple Silicon don't like my quants. * iq3_xs: minor fix * iq4_xs: shrink by using IQ3_S for attn_k and attn_q * iq4_xs: revert using IQ3_S for attn_k and attn_v PPL vs size is good, but CPU performance suffers: on M2 Max TG-128 drops to 21.7 t/s from 28.8, and on a Ryzen-7950X to 14.5 t/s from 15.8 t/s. On CUDA we have 135 t/s when using IQ3_S vs 133 t/s with pure IQ4_XS. * Fix CI * iq4_xs: Added forgotten check for 256 divisibility --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 3 +- ggml-cuda.cu | 119 ++++++++++++++- ggml-metal.m | 29 +++- ggml-metal.metal | 224 +++++++++++++++++++++++++++- ggml-quants.c | 261 ++++++++++++++++++++++++++++++--- ggml-quants.h | 13 ++ ggml.c | 30 ++++ ggml.h | 2 + llama.cpp | 22 +-- llama.h | 1 + tests/test-backend-ops.cpp | 2 +- 11 files changed, 668 insertions(+), 38 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 2d187823f4c3d..7662ec80c5e0f 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -36,7 +36,8 @@ static const std::vector QUANT_OPTIONS = { { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, - { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.25 bpw non-linear quantization", }, + { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, + { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", }, { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", }, diff --git a/ggml-cuda.cu b/ggml-cuda.cu index caef65de56c07..dfd28df628c3a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -571,6 +571,18 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +// QR4_XS = 8 is very slightly faster than QR4_XS = 4 +#define QR4_XS 8 +#define QI4_XS (QK_K / (4*QR4_XS)) +typedef struct { + half d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); + + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -2427,6 +2439,25 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst } +template +static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq4_xs * x = (const block_iq4_xs *)vx; + + const int tid = threadIdx.x; + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[i].qs + 16*ib + 4*il; + const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; + y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; + } + +} + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -5286,6 +5317,76 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( return d * (sumi1 + sumi2); } +static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + +#if QK_K == 256 +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + + const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; + const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + + //// iqs is 0...7 + //const int ib64 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il; + //const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il; + //const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il; + //const uint32_t * q4_2 = q4_1 + 4; + //const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4); + //const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4); + //const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds); + //const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4_1[j], values, v1, v2); + // sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1)); + // get_int_from_table_16(q4_2[j], values, v1, v2); + // sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2)); + //} + //return d1 * sumi1 + d2 * sumi2; + + // iqs is 0...7 + const int ib32 = iqs; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; + const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + get_int_from_table_16(q4[j], values, v1, v2); + sumi1 = __dp4a(v1, q8[j+0], sumi1); + sumi2 = __dp4a(v2, q8[j+4], sumi2); + } + return d * (sumi1 + sumi2); + + //// iqs is 0...15 + //const int ib32 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il; + //const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il; + //const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + //const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4[j], values, v1, v2); + // sumi1 = __dp4a(v1, q8[j+0], sumi1); + // sumi2 = __dp4a(v2, q8[j+4], sumi2); + //} + //return d * (sumi1 + sumi2); +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + template static __device__ __forceinline__ void mul_mat_q( @@ -7340,6 +7441,12 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq4_nl<<>>(vx, y); } +template +static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = (k + QK_K - 1) / QK_K; + dequantize_block_iq4_xs<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -7385,6 +7492,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F32: @@ -7428,6 +7537,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F16: @@ -9176,6 +9287,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: @@ -9203,6 +9315,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: @@ -9313,6 +9426,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ4_XS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; case GGML_TYPE_IQ3_S: mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); @@ -12041,7 +12158,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons ggml_type a_type = a->type; if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || - a_type == GGML_TYPE_IQ2_S) { + a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index 251d04fb0a571..9eba2f5d20375 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -65,6 +65,7 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, @@ -91,6 +92,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, @@ -113,6 +115,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, @@ -132,6 +135,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, @@ -151,6 +155,7 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, @@ -466,6 +471,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); @@ -492,6 +498,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); @@ -514,6 +521,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); @@ -533,6 +541,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); @@ -552,6 +561,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); @@ -1371,6 +1381,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } @@ -1529,6 +1540,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1576,7 +1593,7 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src0t == GGML_TYPE_IQ4_NL) { + else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { const int mem_size = 32*sizeof(float); [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1678,6 +1695,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } @@ -1839,6 +1857,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1902,7 +1926,7 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src2t == GGML_TYPE_IQ4_NL) { + else if (src2t == GGML_TYPE_IQ4_NL || src2t == GGML_TYPE_IQ4_XS) { const int mem_size = 32*sizeof(float); [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1952,6 +1976,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 47354e9529440..6894119035b9c 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2560,6 +2560,13 @@ typedef struct { uint8_t qs[QK4_NL/2]; } block_iq4_nl; +typedef struct { + half d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -5160,6 +5167,100 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } +void kernel_mul_mv_iq4_xs_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + const int first_row = (r0 * 2 + sgitg) * 2; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + const int ix = tiisg/16; // 0 or 1 + const int it = tiisg%16; // 0...15 + const int ib = it/2; + const int il = it%2; + + shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + threadgroup_barrier(mem_flags::mem_threadgroup); + + float4 yl[4]; + float sumf[2]={0.f}, all_sum; + + device const float * yb = y + ix * QK_K + ib * 32 + il * 8; + + uint32_t aux32[2]; + thread const uint8_t * q8 = (thread const uint8_t *)aux32; + + float4 qf1, qf2; + + for (int ibl = ix; ibl < nb; ibl += 2) { + + device const float4 * y4 = (device const float4 *)yb; + yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; + + for (int row = 0; row < 2; ++row) { + + device const block_iq4_xs & xb = x[row*nb + ibl]; + device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); + + float4 acc1 = {0.f}, acc2 = {0.f}; + + aux32[0] = q4[0] & 0x0f0f0f0f; + aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[0] * qf1; + acc2 += yl[1] * qf2; + + aux32[0] = q4[1] & 0x0f0f0f0f; + aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[2] * qf1; + acc2 += yl[3] * qf2; + + acc1 += acc2; + + const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32; + sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]); + + } + + yb += 2 * QK_K; + } + + for (int row = 0; row < 2; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + } + } +} + [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( device const void * src0, @@ -5217,6 +5318,35 @@ kernel void kernel_mul_mv_iq4_nl_f32( kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +[[host_name("kernel_mul_mv_iq4_xs_f32")]] +kernel void kernel_mul_mv_iq4_xs_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + //============================= templates and their specializations ============================= // NOTE: this is not dequantizing - we are simply fitting the template @@ -5638,6 +5768,26 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 } } +template +void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; + const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)xb->d * (ls - 32); + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -6183,7 +6333,8 @@ template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; -template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -6226,7 +6377,8 @@ template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -6281,7 +6433,8 @@ template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -7507,3 +7660,68 @@ kernel void kernel_mul_mv_id_iq4_nl_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq4_xs_f32")]] +kernel void kernel_mul_mv_id_iq4_xs_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq4_xs_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index 73c3bb4123da5..607d50925b6da 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -4225,6 +4225,29 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, } } +void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const uint8_t * qs = x[i].qs; + + const float d = GGML_FP16_TO_FP32(x[i].d); + + for (int ib = 0; ib < QK_K/32; ++ib) { + const int ls = ((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4); + const float dl = d * (ls - 32); + for (int j = 0; j < 16; ++j) { + y[j+ 0] = dl * kvalues_iq4nl[qs[j] & 0xf]; + y[j+16] = dl * kvalues_iq4nl[qs[j] >> 4]; + } + y += 32; + qs += 16; + } + } +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -9675,8 +9698,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * qs += 8; vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); - vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); vs.val[1] = vceqq_u8(vs.val[1], mask2); @@ -9684,8 +9707,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); - vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); vs.val[1] = vceqq_u8(vs.val[1], mask2); @@ -10425,6 +10448,134 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * #endif } +void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_K == 0); + + const block_iq4_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + uint8x16x2_t q4bits; + int8x16x4_t q4b; + int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + float sumf = 0; + + for (int ibl = 0; ibl < nb; ++ibl) { + + const int8_t * q8 = y[ibl].qs; + const uint8_t * q4 = x[ibl].qs; + uint16_t h = x[ibl].scales_h; + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/64; ++ib) { + + q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; + int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; + h >>= 4; + sumi1 += vaddvq_s32(prod_1) * ls1; + sumi2 += vaddvq_s32(prod_2) * ls2; + + } + + sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); + const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); + sumi1 = _mm256_add_epi32(p_1, sumi1); + sumi2 = _mm256_add_epi32(p_2, sumi2); + } + accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#else + float sumf = 0; + for (int ibl = 0; ibl < nb; ++ibl) { + const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; + uint16_t h = x[ibl].scales_h; + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); + const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); + h >>= 4; + const float d1 = d4d8*(ls1 - 32); + const float d2 = d4d8*(ls2 - 32); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d1 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + sumi1 = sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d2 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + } + } + *s = sumf; +#endif +} + // ================================ IQ2 quantization ============================================= typedef struct { @@ -12021,23 +12172,23 @@ static inline int best_index_int8(int n, const int8_t * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RESTRICT x, - ggml_fp16_t * dh, uint8_t * q4, - float * weight, uint8_t * L, +static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x, + ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l, + float * scales, float * weight, uint8_t * L, const int8_t * values, const float * quant_weights) { const int ntry = 7; float sigma2 = 0; - for (int j = 0; j < QK4_NL; ++j) sigma2 += x[j]*x[j]; - sigma2 *= 2.f/QK4_NL; + for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j]; + sigma2 *= 2.f/super_block_size; - const int nb = QK4_NL/block_size; + memset(q4, 0, super_block_size/2); + dh[0] = GGML_FP32_TO_FP16(0.f); - memset(q4, 0, QK4_NL/2); - for (int ib = 0; ib < nb; ++ib) { - dh[ib] = GGML_FP32_TO_FP16(0.f); + float max_scale = 0, amax_scale = 0; + for (int ib = 0; ib < super_block_size/block_size; ++ib) { const float * xb = x + ib*block_size; if (quant_weights) { const float * qw = quant_weights + ib*block_size; @@ -12053,6 +12204,7 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE } } if (!amax) { + scales[ib] = 0; continue; } float d = -max/values[0]; @@ -12066,7 +12218,6 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE sumqx += w*q*xb[j]; sumq2 += w*q*q; } - float best_id = id; d = sumqx/sumq2; float best = d*sumqx; for (int itry = -ntry; itry <= ntry; ++itry) { @@ -12082,15 +12233,47 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE } if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { d = sumqx/sumq2; best = d * sumqx; - best_id = id; } } - dh[ib] = GGML_FP32_TO_FP16(d); - for (int j = 0; j < block_size; ++j) { - L[ib*block_size + j] = best_index_int8(16, values, best_id*xb[j]); + scales[ib] = d; + float abs_d = fabsf(d); + if (abs_d > amax_scale) { + amax_scale = abs_d; max_scale = d; } } - for (int i = 0; i < QK4_NL/32; ++i) { + + if (super_block_size/block_size > 1) { + int nb = super_block_size/block_size; + memset(scales_h, 0, ((nb+7)/8)*sizeof(uint16_t)); + float d = -max_scale/32; + dh[0] = GGML_FP32_TO_FP16(d); + float id = d ? 1/d : 0.f; + for (int ib = 0; ib < super_block_size/block_size; ++ib) { + int l = nearest_int(id*scales[ib]); + l = MAX(-32, MIN(31, l)); + float dl = d * l; + float idl = dl ? 1/dl : 0.f; + uint8_t * Lb = L + ib*block_size; + const float * xb = x + ib*block_size; + for (int j = 0; j < block_size; ++j) { + Lb[j] = best_index_int8(16, values, idl*xb[j]); + } + l += 32; + uint8_t l_l = l & 0xf; + uint8_t l_h = l >> 4; + if (ib%2 == 0) scales_l[ib/2] = l_l; + else scales_l[ib/2] |= (l_l << 4); + scales_h[ib/8] |= (l_h << 2*(ib%8)); + } + } else { + dh[0] = GGML_FP32_TO_FP16(scales[0]); + float id = scales[0] ? 1/scales[0] : 0; + for (int j = 0; j < super_block_size; ++j) { + L[j] = best_index_int8(16, values, id*x[j]); + } + } + + for (int i = 0; i < super_block_size/32; ++i) { for (int j = 0; j < 16; ++j) { q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4); } @@ -12103,12 +12286,16 @@ size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, i int nblock = n_per_row/QK4_NL; char * qrow = (char *)dst; uint8_t L[QK4_NL]; - float weight[32]; + float weight[QK4_NL]; + uint16_t unused_h; + uint8_t * unused_l = NULL; + float scale; for (int row = 0; row < nrow; ++row) { block_iq4_nl * iq4 = (block_iq4_nl *)qrow; for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL; - quantize_row_iq4_nl_impl(32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, weight, L, kvalues_iq4nl, qw); + quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, + &scale, weight, L, kvalues_iq4nl, qw); } src += n_per_row; qrow += nblock*sizeof(block_iq4_nl); @@ -12127,6 +12314,38 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest quantize_iq4_nl(x, y, 1, k, NULL, NULL); } +size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + uint8_t L[QK_K]; + float weight[32]; + float scales[QK_K/32]; + for (int row = 0; row < nrow; ++row) { + block_iq4_xs * iq4 = (block_iq4_xs *)qrow; + for (int ibl = 0; ibl < nblock; ++ibl) { + const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL; + quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l, + scales, weight, L, kvalues_iq4nl, qw); + } + src += n_per_row; + qrow += nblock*sizeof(block_iq4_xs); + } + return nrow * nblock * sizeof(block_iq4_xs); +} + +void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq4_xs * restrict y = vy; + quantize_row_iq4_xs_reference(x, y, k); +} + +void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) { + assert(k % QK_K == 0); + quantize_iq4_xs(x, y, 1, k, NULL, NULL); +} + // =============================== 2.5625 bpw static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { diff --git a/ggml-quants.h b/ggml-quants.h index 4731dde0cb5a9..2c61134c49e44 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -230,6 +230,14 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +typedef struct { + ggml_fp16_t d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); + #ifdef __cplusplus extern "C" { #endif @@ -250,6 +258,7 @@ void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGM void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); +void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k); void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k); void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int k); @@ -268,6 +277,7 @@ void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -291,6 +301,7 @@ void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_ void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product @@ -311,6 +322,7 @@ void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // @@ -322,6 +334,7 @@ size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index 6be07bb6f6db4..d66db3352c1f3 100644 --- a/ggml.c +++ b/ggml.c @@ -726,6 +726,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, + [GGML_TYPE_IQ4_XS] = { + .type_name = "iq4_xs", + .blck_size = QK_K, + .type_size = sizeof(block_iq4_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, + .from_float = quantize_row_iq4_xs, + .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, + .vec_dot = ggml_vec_dot_iq4_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2328,6 +2340,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; + case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break; case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; @@ -7764,6 +7777,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: { @@ -8045,6 +8059,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: { @@ -8171,6 +8186,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: default: @@ -11071,6 +11087,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: { @@ -11261,6 +11278,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: default: @@ -11465,6 +11483,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: { @@ -12167,6 +12186,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: @@ -12252,6 +12272,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: @@ -19817,6 +19838,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ4_XS: + { + GGML_ASSERT(start % QK4_NL == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index 8c7ca4588a4c4..23b7686407895 100644 --- a/ggml.h +++ b/ggml.h @@ -352,6 +352,7 @@ extern "C" { GGML_TYPE_IQ4_NL = 20, GGML_TYPE_IQ3_S = 21, GGML_TYPE_IQ2_S = 22, + GGML_TYPE_IQ4_XS = 23, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -393,6 +394,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index 6729bb99c91fd..464e1b89b2827 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2584,6 +2584,7 @@ struct llama_model_loader { case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; + case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break; case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break; default: { @@ -2941,6 +2942,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw"; @@ -10871,7 +10873,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && qs.model.hparams.n_gqa() >= 4) { + else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q5_K; } else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && @@ -10940,8 +10942,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; } } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && !qs.has_imatrix) { - if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; + else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) { + new_type = GGML_TYPE_Q5_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) { @@ -10961,7 +10963,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || - ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { + ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) { new_type = GGML_TYPE_Q5_K; } } else { @@ -11012,7 +11014,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty //} bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || - new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || + new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS || new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S || new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) { int nx = tensor->ne[0]; @@ -11033,10 +11035,11 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: new_type = GGML_TYPE_IQ4_NL; break; - case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; - case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; - case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; + case GGML_TYPE_Q3_K: + case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break; + case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; + case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; + case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; default: throw std::runtime_error("\nUnsupported tensor size encountered\n"); } LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type)); @@ -11078,6 +11081,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break; + case LLAMA_FTYPE_MOSTLY_IQ4_XS: quantized_type = GGML_TYPE_IQ4_XS; break; case LLAMA_FTYPE_MOSTLY_IQ3_S: quantized_type = GGML_TYPE_IQ3_S; break; case LLAMA_FTYPE_MOSTLY_IQ3_M: quantized_type = GGML_TYPE_IQ3_S; break; diff --git a/llama.h b/llama.h index 6041618080344..16e28e91deb54 100644 --- a/llama.h +++ b/llama.h @@ -115,6 +115,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 60a8527798833..d4cea805f554f 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1918,7 +1918,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, - GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, + GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS, }; // unary ops From cb49e0f8c906e5da49e9f6d64a57742a9a241c6a Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 27 Feb 2024 19:16:49 +0200 Subject: [PATCH 116/401] Attempt to fix android build (#5752) Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 607d50925b6da..f73d17ce24cff 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -10464,9 +10464,9 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * #if defined __ARM_NEON const int8x16_t values = vld1q_s8(kvalues_iq4nl); const uint8x16_t m4b = vdupq_n_u8(0x0f); - uint8x16x2_t q4bits; - int8x16x4_t q4b; - int8x16x4_t q8b; + ggml_uint8x16x2_t q4bits; + ggml_int8x16x4_t q4b; + ggml_int8x16x4_t q8b; int32x4_t prod_1, prod_2; float sumf = 0; From 7c4263d4261d6ee6f0539d53eb9e1b4d120ba8af Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Wed, 28 Feb 2024 10:37:02 +0200 Subject: [PATCH 117/401] ggml : make i-quants work with super-blocks of 64 (CPU,Metal) (#5760) * WIP: make i-quants work for QK_K = 64 * iq2_xs: attempt to fix AVX dot product for QK_K = 64 Tests pass, but I get gibberish. * QK_K = 64 tests pass on ARM_NEON and Metal Sadly, that does not mean it actually works. * Make CUDA compile with QK_K = 64 Tests don't pass, plus we get misaligned access * Q2_K: fixed bug in imatrix quantization for QK_K = 64 * iq1_s: turn off SIMD implementation for QK_K = 64 (it does not work) --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 27 ++++++--- ggml-metal.metal | 58 ++++++++++--------- ggml-quants.c | 148 +++++++++++++++++++++++++++++++++++++++-------- ggml-quants.h | 5 ++ ggml.c | 15 ++++- 5 files changed, 194 insertions(+), 59 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index dfd28df628c3a..831c84efb5286 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -544,14 +544,19 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define QR3_XS 8 #define QI3_XS (QK_K / (4*QR3_XS)) +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif typedef struct { half d; uint8_t qs[QK_K/4]; uint8_t qh[QK_K/32]; uint8_t signs[QK_K/8]; - uint8_t scales[QK_K/64]; + uint8_t scales[IQ3S_N_SCALE]; } block_iq3_s; -static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 27*(QK_K/64), "wrong iq3_s block size/padding"); +static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); #define QR1_S 8 #define QI1_S (QK_K / (4*QR1_S)) @@ -571,6 +576,11 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +#define QR4_XS QR4_NL +#define QI4_XS QI4_NL +#else // QR4_XS = 8 is very slightly faster than QR4_XS = 4 #define QR4_XS 8 #define QI4_XS (QK_K / (4*QR4_XS)) @@ -581,7 +591,7 @@ typedef struct { uint8_t qs[QK_K/2]; } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); - +#endif #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -2439,9 +2449,9 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst } +#if QK_K != 64 template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; const block_iq4_xs * x = (const block_iq4_xs *)vx; @@ -2455,8 +2465,8 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } - } +#endif static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { @@ -5382,8 +5392,7 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( return 0.f; #endif #else - assert(false); - return 0.f; + return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); #endif } @@ -7444,7 +7453,11 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, template static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; +#if QK_K == 64 + dequantize_block_iq4_nl<<>>(vx, y); +#else dequantize_block_iq4_xs<<>>(vx, y); +#endif } template diff --git a/ggml-metal.metal b/ggml-metal.metal index 6894119035b9c..74a5e0b039fc7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2560,12 +2560,16 @@ typedef struct { uint8_t qs[QK4_NL/2]; } block_iq4_nl; +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +#else typedef struct { half d; uint16_t scales_h; uint8_t scales_l[QK_K/64]; uint8_t qs[QK_K/2]; } block_iq4_xs; +#endif //====================================== dot products ========================= @@ -4346,7 +4350,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4387,12 +4390,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4482,7 +4479,6 @@ void kernel_mul_mv_iq2_xs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4533,12 +4529,6 @@ void kernel_mul_mv_iq2_xs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4628,7 +4618,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4672,12 +4661,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -5016,7 +4999,6 @@ void kernel_mul_mv_iq1_s_f32_impl( const int nb32 = nb * (QK_K / 32); -#if QK_K == 256 const int ix = tiisg/2; const int il = tiisg%2; @@ -5055,12 +5037,6 @@ void kernel_mul_mv_iq1_s_f32_impl( y4 += 16 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -5167,6 +5143,7 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } +#if QK_K != 64 void kernel_mul_mv_iq4_xs_f32_impl( device const void * src0, device const float * src1, @@ -5260,6 +5237,7 @@ void kernel_mul_mv_iq4_xs_f32_impl( } } } +#endif [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( @@ -5344,7 +5322,11 @@ kernel void kernel_mul_mv_iq4_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { +#if QK_K == 64 + kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +#else kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +#endif } //============================= templates and their specializations ============================= @@ -5770,6 +5752,9 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 template void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { +#if QK_K == 64 + dequantize_iq4_nl(xb, il, reg); +#else // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 const int ib32 = il/2; il = il%2; @@ -5786,6 +5771,7 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; } +#endif } template @@ -6334,7 +6320,11 @@ template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; +#if QK_K == 64 +template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; +#else template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; +#endif // // matrix-matrix multiplication @@ -6378,7 +6368,11 @@ template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; +#if QK_K == 64 +template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; +#else template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; +#endif // // indirect matrix-matrix multiplication @@ -6434,7 +6428,11 @@ template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#if QK_K == 64 +template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#else template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#endif // // matrix-vector multiplication @@ -7707,7 +7705,11 @@ kernel void kernel_mul_mv_id_iq4_xs_f32( const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; +#if QK_K == 64 + kernel_mul_mv_iq4_nl_f32_impl( +#else kernel_mul_mv_iq4_xs_f32_impl( +#endif src0[id], (device const float *) (src1 + bid*nb11), dst + bid*ne0, diff --git a/ggml-quants.c b/ggml-quants.c index f73d17ce24cff..371826f14719d 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1877,7 +1877,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri float mins[QK_K/16]; float scales[QK_K/16]; float sw[QK_K/16]; - float weight[QK_K/16]; + float weight[16]; uint8_t Ls[QK_K/16], Lm[QK_K/16]; for (int i = 0; i < nb; i++) { @@ -1887,13 +1887,42 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri float sigma2 = sumx2/QK_K; for (int j = 0; j < QK_K/16; ++j) { const float * restrict qw = quant_weights + QK_K * i + 16*j; - for (int l = 0; l < QK_K/16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); for (int l = 0; l < QK_K/16; ++l) sw[j] += weight[l]; - scales[j] = make_qkx3_quants(QK_K/16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); } - float dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); - float mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); + float dm, mm; +#if QK_K == 64 + float max_scale = 0, max_min = 0; + for (int j = 0; j < QK_K/16; ++j) { + max_scale = MAX(max_scale, scales[j]); + max_min = MAX(max_min, mins[j]); + } + dm = max_scale/15; + mm = max_min/15; + if (max_scale) { + float id = 1/dm; + for (int j = 0; j < QK_K/16; ++j) { + int l = nearest_int(id*scales[j]); + Ls[j] = MAX(0, MIN(15, l)); + } + } else { + memset(Ls, 0, QK_K/16); + } + if (max_min) { + float id = 1/mm; + for (int j = 0; j < QK_K/16; ++j) { + int l = nearest_int(id*mins[j]); + Lm[j] = MAX(0, MIN(15, l)); + } + } else { + memset(Lm, 0, QK_K/16); + } +#else + dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); + mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); +#endif y[i].d = GGML_FP32_TO_FP16(dm); y[i].dmin = GGML_FP32_TO_FP16(mm); dm = GGML_FP16_TO_FP32(y[i].d); @@ -4227,6 +4256,9 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); +#if QK_K == 64 + dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); +#else const int nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -4246,6 +4278,7 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, qs += 16; } } +#endif } //===================================== Q8_K ============================================== @@ -6306,7 +6339,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r float sumf = 0; - int isum[4]; + int isum[QK_K/16]; for (int i = 0; i < nb; ++i) { @@ -6322,14 +6355,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - isum[0] = isum[1] = isum[2] = isum[3] = 0; + memset(isum, 0, (QK_K/16)*sizeof(int)); for (int l = 0; l < 16; ++l) { isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); } - for (int l = 0; l < 4; ++l) { + for (int l = 0; l < QK_K/16; ++l) { isum[l] *= (sc[l] & 0xF); } sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; @@ -9488,15 +9521,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * #elif defined(__AVX2__) - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - const __m256i m511 = _mm256_set1_epi16(511); const __m256i mone = _mm256_set1_epi8(1); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; static const char block_sign_shuffle_mask_1[32] = { 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, @@ -9510,11 +9535,77 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, }; - const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); +#if QK_K == 64 + static const uint8_t k_bit_helper[16] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper); + const __m128i m511 = _mm_set1_epi16(511); + typedef union { + __m128i vec_index; + uint16_t index[8]; + } index_t; + + index_t idx; + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs); + idx.vec_index = _mm_and_si128(q2_data, m511); + + const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9); + const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13); + const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper); + + const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); + const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); + const __m256i full_signs = _mm256_set_m128i(full_sign_bits, full_sign_bits); + + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); + + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], + iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], + iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); + + __m256i signs; + signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + + const __m256i sc1 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); + const __m256i sc2 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); + + const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#else + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); + const __m256i m511 = _mm256_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + uint64_t aux64; // somewhat hacky, but gives a significant boost in performance @@ -9603,6 +9694,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); +#endif #else @@ -10199,7 +10291,8 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const const int nb = n / QK_K; -#if defined __ARM_NEON + // TODO: implement for QK_K = 64 +#if defined __ARM_NEON && QK_K == 256 const uint8x16_t m8 = vdupq_n_u8(0x08); const uint8x16_t m7 = vdupq_n_u8(0x07); @@ -10256,7 +10349,8 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const *s = sumf; -#elif defined __AVX2__ + // TODO: implement for QK_K = 64 +#elif defined __AVX2__ && QK_K == 256 const __m128i m8 = _mm_set1_epi8(0x08); const __m128i m7 = _mm_set1_epi8(0x07); @@ -10455,6 +10549,9 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * UNUSED(by); UNUSED(bs); assert(n % QK_K == 0); +#if QK_K == 64 + ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc); +#else const block_iq4_xs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -10574,6 +10671,7 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = sumf; #endif +#endif } // ================================ IQ2 quantization ============================================= @@ -10921,7 +11019,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict const int kMaxQ = 3; - const int nbl = n/256; + const int nbl = n/QK_K; block_iq2_xxs * y = vy; @@ -11094,7 +11192,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v const int kMaxQ = 3; - const int nbl = n/256; + const int nbl = n/QK_K; block_iq2_xs * y = vy; @@ -12037,7 +12135,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); GGML_ASSERT(n%QK_K == 0); - const int nbl = n/256; + const int nbl = n/QK_K; block_iq1_s * y = vy; @@ -12315,6 +12413,9 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest } size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +#if QK_K == 64 + return quantize_iq4_nl(src, dst, nrow, n_per_row, hist, quant_weights); +#else (void)hist; GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; @@ -12333,6 +12434,7 @@ size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, i qrow += nblock*sizeof(block_iq4_xs); } return nrow * nblock * sizeof(block_iq4_xs); +#endif } void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { @@ -12363,7 +12465,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy const int kMaxQ = 3; - const int nbl = n/256; + const int nbl = n/QK_K; block_iq2_s * y = vy; diff --git a/ggml-quants.h b/ggml-quants.h index 2c61134c49e44..316e356876037 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -230,6 +230,10 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +//typedef struct block_iq4_nl block_iq4_xs; +#else typedef struct { ggml_fp16_t d; uint16_t scales_h; @@ -237,6 +241,7 @@ typedef struct { uint8_t qs[QK_K/2]; } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +#endif #ifdef __cplusplus extern "C" { diff --git a/ggml.c b/ggml.c index d66db3352c1f3..4591644adbdd4 100644 --- a/ggml.c +++ b/ggml.c @@ -728,14 +728,22 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", +#if QK_K == 64 + .blck_size = QK4_NL, +#else .blck_size = QK_K, +#endif .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, .from_float = quantize_row_iq4_xs, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, .vec_dot = ggml_vec_dot_iq4_xs_q8_K, +#if QK_K == 64 + .vec_dot_type = GGML_TYPE_Q8_0, +#else .vec_dot_type = GGML_TYPE_Q8_K, +#endif .nrows = 1, }, [GGML_TYPE_Q8_K] = { @@ -19830,6 +19838,9 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ4_NL: +#if QK_K == 64 + case GGML_TYPE_IQ4_XS: +#endif { GGML_ASSERT(start % QK4_NL == 0); GGML_ASSERT(start % n_per_row == 0); @@ -19838,15 +19849,17 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; +#if QK_K != 64 case GGML_TYPE_IQ4_XS: { - GGML_ASSERT(start % QK4_NL == 0); + GGML_ASSERT(start % QK_K == 0); GGML_ASSERT(start % n_per_row == 0); size_t start_row = start / n_per_row; size_t row_size = ggml_row_size(type, n_per_row); result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; +#endif case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); From efc72253f7987ed7bdc8bde9d9fa5c7cac2f6292 Mon Sep 17 00:00:00 2001 From: Jorge A <161275481+jorgealias@users.noreply.github.com> Date: Wed, 28 Feb 2024 01:39:15 -0700 Subject: [PATCH 118/401] server : add "/chat/completions" alias for "/v1/...` (#5722) * Add "/chat/completions" as alias for "/v1/chat/completions" * merge to upstream master * minor : fix trailing whitespace --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 131 +++++++++--------- .../server/tests/features/parallel.feature | 22 +++ examples/server/tests/features/steps/steps.py | 28 +++- 3 files changed, 114 insertions(+), 67 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 846ef7e5fee4f..6b3ee531cfb57 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3211,87 +3211,88 @@ int main(int argc, char **argv) res.set_content(models.dump(), "application/json; charset=utf-8"); }); + const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) + { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; + } + json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - // TODO: add mount point without "/v1" prefix -- how? - svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, data, false, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); + if (!json_value(data, "stream", false)) { + std::string completion_text; + task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) { - json oaicompat_result = format_final_response_oaicompat(data, result); + if (!result.error && result.stop) { + json oaicompat_result = format_final_response_oaicompat(data, result); - res.set_content(oaicompat_result.dump(-1, ' ', false, - json::error_handler_t::replace), - "application/json; charset=utf-8"); - } else { - res.status = 500; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { - while (true) { - task_result llama_result = llama.queue_results.recv(task_id); - if (!llama_result.error) { - std::vector result_array = format_partial_response_oaicompat( llama_result); + res.set_content(oaicompat_result.dump(-1, ' ', false, + json::error_handler_t::replace), + "application/json; charset=utf-8"); + } else { + res.status = 500; + res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); + } + llama.queue_results.remove_waiting_task_id(task_id); + } else { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { + while (true) { + task_result llama_result = llama.queue_results.recv(task_id); + if (!llama_result.error) { + std::vector result_array = format_partial_response_oaicompat( llama_result); - for (auto it = result_array.begin(); it != result_array.end(); ++it) - { - if (!it->empty()) { - const std::string str = - "data: " + - it->dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", {{"to_send", str}}); - if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - } - } - if (llama_result.stop) { - break; - } - } else { + for (auto it = result_array.begin(); it != result_array.end(); ++it) + { + if (!it->empty()) { const std::string str = - "error: " + - llama_result.result_json.dump(-1, ' ', false, - json::error_handler_t::replace) + + "data: " + + it->dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { llama.queue_results.remove_waiting_task_id(task_id); return false; } - break; } } - sink.done(); - llama.queue_results.remove_waiting_task_id(task_id); - return true; - }; + if (llama_result.stop) { + break; + } + } else { + const std::string str = + "error: " + + llama_result.result_json.dump(-1, ' ', false, + json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", {{"to_send", str}}); + if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); + return false; + } + break; + } + } + sink.done(); + llama.queue_results.remove_waiting_task_id(task_id); + return true; + }; - auto on_complete = [task_id, &llama](bool) { - // cancel request - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - }; + auto on_complete = [task_id, &llama](bool) { + // cancel request + llama.request_cancel(task_id); + llama.queue_results.remove_waiting_task_id(task_id); + }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }; + + svr.Post("/chat/completions", chat_completions); + svr.Post("/v1/chat/completions", chat_completions); svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index c85f9de1d9a52..5f895cf90b966 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -54,6 +54,28 @@ Feature: Parallel | disabled | 128 | | enabled | 64 | + Scenario Outline: Multi users OAI completions compatibility no v1 + Given a system prompt You are a writer. + And a model tinyllama-2 + Given a prompt: + """ + Write a very long book. + """ + And a prompt: + """ + Write another a poem. + """ + And max tokens to predict + And streaming is + Given concurrent OAI completions requests no v1 + Then the server is busy + Then the server is idle + Then all prompts are predicted with tokens + Examples: + | streaming | n_predict | + | disabled | 128 | + | enabled | 64 | + Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 Given a prompt: """ diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index ad87fcb820aa8..381da105e279e 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -231,6 +231,7 @@ async def step_oai_chat_completions(context, api_error): completion = await oai_chat_completions(context.prompts.pop(), context.system_prompt, context.base_url, + '/v1/chat', False, model=context.model if hasattr(context, 'model') else None, @@ -288,6 +289,28 @@ async def step_oai_chat_completions(context): # user_prompt is inserted automatically context.system_prompt, context.base_url, + '/v1/chat/completions', + True, # async_client + model=context.model + if hasattr(context, 'model') else None, + n_predict=context.n_predict + if hasattr(context, 'n_predict') else None, + enable_streaming=context.enable_streaming + if hasattr(context, 'enable_streaming') else None, + server_seed=context.server_seed + if hasattr(context, 'server_seed') else None, + user_api_key=context.user_api_key + if hasattr(context, 'user_api_key') else None) + + +@step(u'concurrent OAI completions requests no v1') +@async_run_until_complete +async def step_oai_chat_completions(context): + await concurrent_requests(context, oai_chat_completions, + # user_prompt is inserted automatically + context.system_prompt, + context.base_url, + '/chat/completions', True, # async_client model=context.model if hasattr(context, 'model') else None, @@ -497,6 +520,7 @@ async def request_completion(prompt, async def oai_chat_completions(user_prompt, system_prompt, base_url, + base_path, async_client, debug=False, model=None, @@ -537,7 +561,7 @@ async def oai_chat_completions(user_prompt, origin = 'llama.cpp' headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} async with aiohttp.ClientSession() as session: - async with session.post(f'{base_url}/v1/chat/completions', + async with session.post(f'{base_url}{base_path}', json=payload, headers=headers) as response: if enable_streaming: @@ -579,7 +603,7 @@ async def oai_chat_completions(user_prompt, else: try: openai.api_key = user_api_key - openai.api_base = f'{base_url}/v1/chat' + openai.api_base = f'{base_url}{base_path}' chat_completion = openai.Completion.create( messages=payload['messages'], model=model, From 6c4416868df2e5455da7d20547f62bcf9735ba8e Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 28 Feb 2024 09:39:39 +0100 Subject: [PATCH 119/401] readme : add link to LLaVA 1.6 models (#5758) Signed-off-by: Daniel Bevenius --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 507a2888bf410..5401e197f67d7 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,7 @@ Typically finetunes of the base models below are supported as well. **Multimodal models:** -- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) +- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) From 177628bfd85565070916ad66a5ac4071ee0527d8 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Wed, 28 Feb 2024 02:51:11 -0600 Subject: [PATCH 120/401] llama : improve BERT tokenization (#5740) * implement nfd for stripping accents in wpm tokenizer * sort nfd map; reuse iterator * use builtin tolower * add locale include * Simplify to_lower cases Co-authored-by: Jared Van Bortel --------- Co-authored-by: Jared Van Bortel --- llama.cpp | 135 +++++++++------------------- unicode.h | 262 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 304 insertions(+), 93 deletions(-) diff --git a/llama.cpp b/llama.cpp index 464e1b89b2827..356ca107670c8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -68,10 +68,12 @@ #include #include #include +#include #include #include #include #include +#include #include #include #include @@ -8941,37 +8943,46 @@ struct llm_tokenizer_wpm { } std::vector preprocess(const std::string & text) { - std::string ori_str = normalize(text); - uint64_t ori_size = ori_str.size(); + // normalalization form D + std::vector codepoints = codepoints_from_utf8(text); + std::vector nfd_codepoints; + for (uint32_t code : codepoints) { + auto it = nfd_map.find(code); + if (it != nfd_map.end()) { + for (uint32_t c : it->second) { + nfd_codepoints.push_back(c); + } + } else { + nfd_codepoints.push_back(code); + } + } - // single punct / single symbol / single digit - // baseline: add whitespace on the left and right of punct and chinese characters - std::vector words; + // strip accents, strip control, uniformize whitespace, + // to lowercase, pad chinese characters, pad punctuation std::string new_str = ""; - uint64_t i = 0; - while (i < ori_size) { - int utf_char_len = utf8_len(ori_str[i]); - if ((utf_char_len == 1) && ispunct(ori_str[i])) { - new_str += " "; - new_str += ori_str[i]; - new_str += " "; - i += 1; + for (uint32_t code : nfd_codepoints) { + int type = codepoint_type(code); + if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) { + continue; } - else if ((utf_char_len == 3) && is_chinese_char(ori_str.substr(i, 3))) { + code = to_lower(code); + if (type == CODEPOINT_TYPE_WHITESPACE) { + code = ' '; + } + std::string s = codepoint_to_utf8(code); + if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) { new_str += " "; - new_str += ori_str.substr(i, 3); + new_str += s; new_str += " "; - i += 3; - } - else { - new_str += ori_str[i]; - i += 1; + } else { + new_str += s; } } // split by whitespace uint64_t l = 0; uint64_t r = 0; + std::vector words; while (r < new_str.size()) { // if is whitespace if (isspace(new_str[r])) { @@ -8989,47 +9000,20 @@ struct llm_tokenizer_wpm { return words; } - std::string normalize(const std::string & text) { - // TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98 - std::string text2 = strip_accents(text); - for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i])) { - char c = text2[i]; - if (c >= 'A' && c <= 'Z') { - text2[i] = c - 'A' + 'a'; - } + uint32_t to_lower(uint32_t code) { +#if defined(_WIN32) + if (code > 0xFFFF) { + return code; } - return text2; +#endif + return std::tolower(wchar_t(code), std::locale("en_US.UTF-8")); } - bool is_chinese_char(const std::string & str) { - int len = str.length(); - unsigned int codepoint = 0; - int num_bytes = 0; - int i = 0; - unsigned char ch = static_cast(str[i]); - if (ch <= 0x7f) { - codepoint = ch; - num_bytes = 1; - } else if ((ch >> 5) == 0x06) { - codepoint = ch & 0x1f; - num_bytes = 2; - } else if ((ch >> 4) == 0x0e) { - codepoint = ch & 0x0f; - num_bytes = 3; - } else if ((ch >> 3) == 0x1e) { - codepoint = ch & 0x07; - num_bytes = 4; - } - for (int j = 1; j < num_bytes; ++j) { - if (i + j >= len) { - return false; // incomplete UTF-8 character - } - unsigned char next_ch = static_cast(str[i + j]); - if ((next_ch >> 6) != 0x02) { - return false; // invalid trailing byte - } - codepoint = (codepoint << 6) | (next_ch & 0x3f); - } + bool is_ascii_punct(uint32_t code) { + return code < 256 && ispunct(code); + } + + bool is_chinese_char(uint32_t codepoint) { if ((codepoint >= 0x4E00 && codepoint <= 0x9FFF) || (codepoint >= 0x3400 && codepoint <= 0x4DBF) || (codepoint >= 0x20000 && codepoint <= 0x2A6DF) || @@ -9045,41 +9029,6 @@ struct llm_tokenizer_wpm { return false; } - std::string strip_accents(const std::string & input_string) { - std::string resultString; - std::map accent_map = { - {"À", 'A'}, {"Á", 'A'}, {"Â", 'A'}, {"Ã", 'A'}, {"Ä", 'A'}, {"Å", 'A'}, - {"à", 'a'}, {"á", 'a'}, {"â", 'a'}, {"ã", 'a'}, {"ä", 'a'}, {"å", 'a'}, - {"È", 'E'}, {"É", 'E'}, {"Ê", 'E'}, {"Ë", 'E'}, {"è", 'e'}, {"é", 'e'}, - {"ê", 'e'}, {"ë", 'e'}, {"Ì", 'I'}, {"Í", 'I'}, {"Î", 'I'}, {"Ï", 'I'}, - {"ì", 'i'}, {"í", 'i'}, {"î", 'i'}, {"ï", 'i'}, {"Ò", 'O'}, {"Ó", 'O'}, - {"Ô", 'O'}, {"Õ", 'O'}, {"Ö", 'O'}, {"ò", 'o'}, {"ó", 'o'}, {"ô", 'o'}, - {"õ", 'o'}, {"ö", 'o'}, {"Ù", 'U'}, {"Ú", 'U'}, {"Û", 'U'}, {"Ü", 'U'}, - {"ù", 'u'}, {"ú", 'u'}, {"û", 'u'}, {"ü", 'u'}, {"Ý", 'Y'}, {"ý", 'y'}, - {"Ç", 'C'}, {"ç", 'c'}, {"Ñ", 'N'}, {"ñ", 'n'}, - }; - - for (size_t i = 0; i < input_string.length();) { - int len = utf8_len(input_string[i]); - std::string curChar = input_string.substr(i, len); - auto iter = accent_map.find(curChar); - if (iter != accent_map.end()) { - resultString += iter->second; - } else { - resultString += curChar; - } - i += len; - } - - return resultString; - } - - static size_t utf8_len(char src) { - const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4}; - uint8_t highbits = static_cast(src) >> 4; - return lookup[highbits]; - } - const llama_vocab & vocab; }; diff --git a/unicode.h b/unicode.h index 10a5dab0185fe..620e2b5806c24 100644 --- a/unicode.h +++ b/unicode.h @@ -223,6 +223,268 @@ static const std::vector> control_ranges = { {0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, }; +static const std::unordered_map> nfd_map = { +{0xC0, {0x41, 0x300}}, {0xC1, {0x41, 0x301}}, {0xC2, {0x41, 0x302}}, {0xC3, {0x41, 0x303}}, {0xC4, {0x41, 0x308}}, {0xC5, {0x41, 0x30A}}, {0xC7, {0x43, 0x327}}, {0xC8, {0x45, 0x300}}, +{0xC9, {0x45, 0x301}}, {0xCA, {0x45, 0x302}}, {0xCB, {0x45, 0x308}}, {0xCC, {0x49, 0x300}}, {0xCD, {0x49, 0x301}}, {0xCE, {0x49, 0x302}}, {0xCF, {0x49, 0x308}}, {0xD1, {0x4E, 0x303}}, +{0xD2, {0x4F, 0x300}}, {0xD3, {0x4F, 0x301}}, {0xD4, {0x4F, 0x302}}, {0xD5, {0x4F, 0x303}}, {0xD6, {0x4F, 0x308}}, {0xD9, {0x55, 0x300}}, {0xDA, {0x55, 0x301}}, {0xDB, {0x55, 0x302}}, +{0xDC, {0x55, 0x308}}, {0xDD, {0x59, 0x301}}, {0xE0, {0x61, 0x300}}, {0xE1, {0x61, 0x301}}, {0xE2, {0x61, 0x302}}, {0xE3, {0x61, 0x303}}, {0xE4, {0x61, 0x308}}, {0xE5, {0x61, 0x30A}}, +{0xE7, {0x63, 0x327}}, {0xE8, {0x65, 0x300}}, {0xE9, {0x65, 0x301}}, {0xEA, {0x65, 0x302}}, {0xEB, {0x65, 0x308}}, {0xEC, {0x69, 0x300}}, {0xED, {0x69, 0x301}}, {0xEE, {0x69, 0x302}}, +{0xEF, {0x69, 0x308}}, {0xF1, {0x6E, 0x303}}, {0xF2, {0x6F, 0x300}}, {0xF3, {0x6F, 0x301}}, {0xF4, {0x6F, 0x302}}, {0xF5, {0x6F, 0x303}}, {0xF6, {0x6F, 0x308}}, {0xF9, {0x75, 0x300}}, +{0xFA, {0x75, 0x301}}, {0xFB, {0x75, 0x302}}, {0xFC, {0x75, 0x308}}, {0xFD, {0x79, 0x301}}, {0xFF, {0x79, 0x308}}, {0x100, {0x41, 0x304}}, {0x101, {0x61, 0x304}}, {0x102, {0x41, 0x306}}, +{0x103, {0x61, 0x306}}, {0x104, {0x41, 0x328}}, {0x105, {0x61, 0x328}}, {0x106, {0x43, 0x301}}, {0x107, {0x63, 0x301}}, {0x108, {0x43, 0x302}}, {0x109, {0x63, 0x302}}, {0x10A, {0x43, 0x307}}, +{0x10B, {0x63, 0x307}}, {0x10C, {0x43, 0x30C}}, {0x10D, {0x63, 0x30C}}, {0x10E, {0x44, 0x30C}}, {0x10F, {0x64, 0x30C}}, {0x112, {0x45, 0x304}}, {0x113, {0x65, 0x304}}, {0x114, {0x45, 0x306}}, +{0x115, {0x65, 0x306}}, {0x116, {0x45, 0x307}}, {0x117, {0x65, 0x307}}, {0x118, {0x45, 0x328}}, {0x119, {0x65, 0x328}}, {0x11A, {0x45, 0x30C}}, {0x11B, {0x65, 0x30C}}, {0x11C, {0x47, 0x302}}, +{0x11D, {0x67, 0x302}}, {0x11E, {0x47, 0x306}}, {0x11F, {0x67, 0x306}}, {0x120, {0x47, 0x307}}, {0x121, {0x67, 0x307}}, {0x122, {0x47, 0x327}}, {0x123, {0x67, 0x327}}, {0x124, {0x48, 0x302}}, +{0x125, {0x68, 0x302}}, {0x128, {0x49, 0x303}}, {0x129, {0x69, 0x303}}, {0x12A, {0x49, 0x304}}, {0x12B, {0x69, 0x304}}, {0x12C, {0x49, 0x306}}, {0x12D, {0x69, 0x306}}, {0x12E, {0x49, 0x328}}, +{0x12F, {0x69, 0x328}}, {0x130, {0x49, 0x307}}, {0x134, {0x4A, 0x302}}, {0x135, {0x6A, 0x302}}, {0x136, {0x4B, 0x327}}, {0x137, {0x6B, 0x327}}, {0x139, {0x4C, 0x301}}, {0x13A, {0x6C, 0x301}}, +{0x13B, {0x4C, 0x327}}, {0x13C, {0x6C, 0x327}}, {0x13D, {0x4C, 0x30C}}, {0x13E, {0x6C, 0x30C}}, {0x143, {0x4E, 0x301}}, {0x144, {0x6E, 0x301}}, {0x145, {0x4E, 0x327}}, {0x146, {0x6E, 0x327}}, +{0x147, {0x4E, 0x30C}}, {0x148, {0x6E, 0x30C}}, {0x14C, {0x4F, 0x304}}, {0x14D, {0x6F, 0x304}}, {0x14E, {0x4F, 0x306}}, {0x14F, {0x6F, 0x306}}, {0x150, {0x4F, 0x30B}}, {0x151, {0x6F, 0x30B}}, +{0x154, {0x52, 0x301}}, {0x155, {0x72, 0x301}}, {0x156, {0x52, 0x327}}, {0x157, {0x72, 0x327}}, {0x158, {0x52, 0x30C}}, {0x159, {0x72, 0x30C}}, {0x15A, {0x53, 0x301}}, {0x15B, {0x73, 0x301}}, +{0x15C, {0x53, 0x302}}, {0x15D, {0x73, 0x302}}, {0x15E, {0x53, 0x327}}, {0x15F, {0x73, 0x327}}, {0x160, {0x53, 0x30C}}, {0x161, {0x73, 0x30C}}, {0x162, {0x54, 0x327}}, {0x163, {0x74, 0x327}}, +{0x164, {0x54, 0x30C}}, {0x165, {0x74, 0x30C}}, {0x168, {0x55, 0x303}}, {0x169, {0x75, 0x303}}, {0x16A, {0x55, 0x304}}, {0x16B, {0x75, 0x304}}, {0x16C, {0x55, 0x306}}, {0x16D, {0x75, 0x306}}, +{0x16E, {0x55, 0x30A}}, {0x16F, {0x75, 0x30A}}, {0x170, {0x55, 0x30B}}, {0x171, {0x75, 0x30B}}, {0x172, {0x55, 0x328}}, {0x173, {0x75, 0x328}}, {0x174, {0x57, 0x302}}, {0x175, {0x77, 0x302}}, +{0x176, {0x59, 0x302}}, {0x177, {0x79, 0x302}}, {0x178, {0x59, 0x308}}, {0x179, {0x5A, 0x301}}, {0x17A, {0x7A, 0x301}}, {0x17B, {0x5A, 0x307}}, {0x17C, {0x7A, 0x307}}, {0x17D, {0x5A, 0x30C}}, +{0x17E, {0x7A, 0x30C}}, {0x1A0, {0x4F, 0x31B}}, {0x1A1, {0x6F, 0x31B}}, {0x1AF, {0x55, 0x31B}}, {0x1B0, {0x75, 0x31B}}, {0x1CD, {0x41, 0x30C}}, {0x1CE, {0x61, 0x30C}}, {0x1CF, {0x49, 0x30C}}, +{0x1D0, {0x69, 0x30C}}, {0x1D1, {0x4F, 0x30C}}, {0x1D2, {0x6F, 0x30C}}, {0x1D3, {0x55, 0x30C}}, {0x1D4, {0x75, 0x30C}}, {0x1D5, {0x55, 0x308, 0x304}}, {0x1D6, {0x75, 0x308, 0x304}}, +{0x1D7, {0x55, 0x308, 0x301}}, {0x1D8, {0x75, 0x308, 0x301}}, {0x1D9, {0x55, 0x308, 0x30C}}, {0x1DA, {0x75, 0x308, 0x30C}}, {0x1DB, {0x55, 0x308, 0x300}}, {0x1DC, {0x75, 0x308, 0x300}}, +{0x1DE, {0x41, 0x308, 0x304}}, {0x1DF, {0x61, 0x308, 0x304}}, {0x1E0, {0x41, 0x307, 0x304}}, {0x1E1, {0x61, 0x307, 0x304}}, {0x1E2, {0xC6, 0x304}}, {0x1E3, {0xE6, 0x304}}, {0x1E6, {0x47, 0x30C}}, +{0x1E7, {0x67, 0x30C}}, {0x1E8, {0x4B, 0x30C}}, {0x1E9, {0x6B, 0x30C}}, {0x1EA, {0x4F, 0x328}}, {0x1EB, {0x6F, 0x328}}, {0x1EC, {0x4F, 0x328, 0x304}}, {0x1ED, {0x6F, 0x328, 0x304}}, +{0x1EE, {0x1B7, 0x30C}}, {0x1EF, {0x292, 0x30C}}, {0x1F0, {0x6A, 0x30C}}, {0x1F4, {0x47, 0x301}}, {0x1F5, {0x67, 0x301}}, {0x1F8, {0x4E, 0x300}}, {0x1F9, {0x6E, 0x300}}, {0x1FA, {0x41, 0x30A, 0x301}}, +{0x1FB, {0x61, 0x30A, 0x301}}, {0x1FC, {0xC6, 0x301}}, {0x1FD, {0xE6, 0x301}}, {0x1FE, {0xD8, 0x301}}, {0x1FF, {0xF8, 0x301}}, {0x200, {0x41, 0x30F}}, {0x201, {0x61, 0x30F}}, {0x202, {0x41, 0x311}}, +{0x203, {0x61, 0x311}}, {0x204, {0x45, 0x30F}}, {0x205, {0x65, 0x30F}}, {0x206, {0x45, 0x311}}, {0x207, {0x65, 0x311}}, {0x208, {0x49, 0x30F}}, {0x209, {0x69, 0x30F}}, {0x20A, {0x49, 0x311}}, +{0x20B, {0x69, 0x311}}, {0x20C, {0x4F, 0x30F}}, {0x20D, {0x6F, 0x30F}}, {0x20E, {0x4F, 0x311}}, {0x20F, {0x6F, 0x311}}, {0x210, {0x52, 0x30F}}, {0x211, {0x72, 0x30F}}, {0x212, {0x52, 0x311}}, +{0x213, {0x72, 0x311}}, {0x214, {0x55, 0x30F}}, {0x215, {0x75, 0x30F}}, {0x216, {0x55, 0x311}}, {0x217, {0x75, 0x311}}, {0x218, {0x53, 0x326}}, {0x219, {0x73, 0x326}}, {0x21A, {0x54, 0x326}}, +{0x21B, {0x74, 0x326}}, {0x21E, {0x48, 0x30C}}, {0x21F, {0x68, 0x30C}}, {0x226, {0x41, 0x307}}, {0x227, {0x61, 0x307}}, {0x228, {0x45, 0x327}}, {0x229, {0x65, 0x327}}, {0x22A, {0x4F, 0x308, 0x304}}, +{0x22B, {0x6F, 0x308, 0x304}}, {0x22C, {0x4F, 0x303, 0x304}}, {0x22D, {0x6F, 0x303, 0x304}}, {0x22E, {0x4F, 0x307}}, {0x22F, {0x6F, 0x307}}, {0x230, {0x4F, 0x307, 0x304}}, +{0x231, {0x6F, 0x307, 0x304}}, {0x232, {0x59, 0x304}}, {0x233, {0x79, 0x304}}, {0x340, {0x300}}, {0x341, {0x301}}, {0x343, {0x313}}, {0x344, {0x308, 0x301}}, {0x374, {0x2B9}}, {0x37E, {0x3B}}, +{0x385, {0xA8, 0x301}}, {0x386, {0x391, 0x301}}, {0x387, {0xB7}}, {0x388, {0x395, 0x301}}, {0x389, {0x397, 0x301}}, {0x38A, {0x399, 0x301}}, {0x38C, {0x39F, 0x301}}, {0x38E, {0x3A5, 0x301}}, +{0x38F, {0x3A9, 0x301}}, {0x390, {0x3B9, 0x308, 0x301}}, {0x3AA, {0x399, 0x308}}, {0x3AB, {0x3A5, 0x308}}, {0x3AC, {0x3B1, 0x301}}, {0x3AD, {0x3B5, 0x301}}, {0x3AE, {0x3B7, 0x301}}, +{0x3AF, {0x3B9, 0x301}}, {0x3B0, {0x3C5, 0x308, 0x301}}, {0x3CA, {0x3B9, 0x308}}, {0x3CB, {0x3C5, 0x308}}, {0x3CC, {0x3BF, 0x301}}, {0x3CD, {0x3C5, 0x301}}, {0x3CE, {0x3C9, 0x301}}, +{0x3D3, {0x3D2, 0x301}}, {0x3D4, {0x3D2, 0x308}}, {0x400, {0x415, 0x300}}, {0x401, {0x415, 0x308}}, {0x403, {0x413, 0x301}}, {0x407, {0x406, 0x308}}, {0x40C, {0x41A, 0x301}}, {0x40D, {0x418, 0x300}}, +{0x40E, {0x423, 0x306}}, {0x419, {0x418, 0x306}}, {0x439, {0x438, 0x306}}, {0x450, {0x435, 0x300}}, {0x451, {0x435, 0x308}}, {0x453, {0x433, 0x301}}, {0x457, {0x456, 0x308}}, {0x45C, {0x43A, 0x301}}, +{0x45D, {0x438, 0x300}}, {0x45E, {0x443, 0x306}}, {0x476, {0x474, 0x30F}}, {0x477, {0x475, 0x30F}}, {0x4C1, {0x416, 0x306}}, {0x4C2, {0x436, 0x306}}, {0x4D0, {0x410, 0x306}}, {0x4D1, {0x430, 0x306}}, +{0x4D2, {0x410, 0x308}}, {0x4D3, {0x430, 0x308}}, {0x4D6, {0x415, 0x306}}, {0x4D7, {0x435, 0x306}}, {0x4DA, {0x4D8, 0x308}}, {0x4DB, {0x4D9, 0x308}}, {0x4DC, {0x416, 0x308}}, {0x4DD, {0x436, 0x308}}, +{0x4DE, {0x417, 0x308}}, {0x4DF, {0x437, 0x308}}, {0x4E2, {0x418, 0x304}}, {0x4E3, {0x438, 0x304}}, {0x4E4, {0x418, 0x308}}, {0x4E5, {0x438, 0x308}}, {0x4E6, {0x41E, 0x308}}, {0x4E7, {0x43E, 0x308}}, +{0x4EA, {0x4E8, 0x308}}, {0x4EB, {0x4E9, 0x308}}, {0x4EC, {0x42D, 0x308}}, {0x4ED, {0x44D, 0x308}}, {0x4EE, {0x423, 0x304}}, {0x4EF, {0x443, 0x304}}, {0x4F0, {0x423, 0x308}}, {0x4F1, {0x443, 0x308}}, +{0x4F2, {0x423, 0x30B}}, {0x4F3, {0x443, 0x30B}}, {0x4F4, {0x427, 0x308}}, {0x4F5, {0x447, 0x308}}, {0x4F8, {0x42B, 0x308}}, {0x4F9, {0x44B, 0x308}}, {0x622, {0x627, 0x653}}, {0x623, {0x627, 0x654}}, +{0x624, {0x648, 0x654}}, {0x625, {0x627, 0x655}}, {0x626, {0x64A, 0x654}}, {0x6C0, {0x6D5, 0x654}}, {0x6C2, {0x6C1, 0x654}}, {0x6D3, {0x6D2, 0x654}}, {0x929, {0x928, 0x93C}}, {0x931, {0x930, 0x93C}}, +{0x934, {0x933, 0x93C}}, {0x958, {0x915, 0x93C}}, {0x959, {0x916, 0x93C}}, {0x95A, {0x917, 0x93C}}, {0x95B, {0x91C, 0x93C}}, {0x95C, {0x921, 0x93C}}, {0x95D, {0x922, 0x93C}}, {0x95E, {0x92B, 0x93C}}, +{0x95F, {0x92F, 0x93C}}, {0x9CB, {0x9C7, 0x9BE}}, {0x9CC, {0x9C7, 0x9D7}}, {0x9DC, {0x9A1, 0x9BC}}, {0x9DD, {0x9A2, 0x9BC}}, {0x9DF, {0x9AF, 0x9BC}}, {0xA33, {0xA32, 0xA3C}}, {0xA36, {0xA38, 0xA3C}}, +{0xA59, {0xA16, 0xA3C}}, {0xA5A, {0xA17, 0xA3C}}, {0xA5B, {0xA1C, 0xA3C}}, {0xA5E, {0xA2B, 0xA3C}}, {0xB48, {0xB47, 0xB56}}, {0xB4B, {0xB47, 0xB3E}}, {0xB4C, {0xB47, 0xB57}}, {0xB5C, {0xB21, 0xB3C}}, +{0xB5D, {0xB22, 0xB3C}}, {0xB94, {0xB92, 0xBD7}}, {0xBCA, {0xBC6, 0xBBE}}, {0xBCB, {0xBC7, 0xBBE}}, {0xBCC, {0xBC6, 0xBD7}}, {0xC48, {0xC46, 0xC56}}, {0xCC0, {0xCBF, 0xCD5}}, {0xCC7, {0xCC6, 0xCD5}}, +{0xCC8, {0xCC6, 0xCD6}}, 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{0x4AB2}}, {0x2F9FD, {0x29496}}, {0x2F9FE, {0x980B}}, {0x2F9FF, {0x980B}}, {0x2FA00, {0x9829}}, +{0x2FA01, {0x295B6}}, {0x2FA02, {0x98E2}}, {0x2FA03, {0x4B33}}, {0x2FA04, {0x9929}}, {0x2FA05, {0x99A7}}, {0x2FA06, {0x99C2}}, {0x2FA07, {0x99FE}}, {0x2FA08, {0x4BCE}}, {0x2FA09, {0x29B30}}, +{0x2FA0A, {0x9B12}}, {0x2FA0B, {0x9C40}}, {0x2FA0C, {0x9CFD}}, {0x2FA0D, {0x4CCE}}, {0x2FA0E, {0x4CED}}, {0x2FA0F, {0x9D67}}, {0x2FA10, {0x2A0CE}}, {0x2FA11, {0x4CF8}}, {0x2FA12, {0x2A105}}, +{0x2FA13, {0x2A20E}}, {0x2FA14, {0x2A291}}, {0x2FA15, {0x9EBB}}, {0x2FA16, {0x4D56}}, {0x2FA17, {0x9EF9}}, {0x2FA18, {0x9EFE}}, {0x2FA19, {0x9F05}}, {0x2FA1A, {0x9F0F}}, {0x2FA1B, {0x9F16}}, +{0x2FA1D, {0x2A600}}, +}; + static std::string codepoint_to_utf8(uint32_t cp) { std::string result; if (/* 0x00 <= cp && */ cp <= 0x7f) { From adcb12a9bad87bc96f2f158c95892b3d04aa7ffb Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Wed, 28 Feb 2024 03:52:56 -0500 Subject: [PATCH 121/401] llama : fix non-quantization of expert gating tensors (#5754) This reverts a single line from #5475 --- llama.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 356ca107670c8..893bcdbc0147d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11162,7 +11162,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s quantize &= !params->only_copy; // do not quantize expert gating tensors - quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_FFN_GATE_INP, "weight"); + // NOTE: can't use LLM_TN here because the layer number is not known + quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; // do not quantize positional embeddings and token types (BERT) quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight"); From a693bea1e6762a17b78b6ddf4611e54136941ea2 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Wed, 28 Feb 2024 09:55:37 +0100 Subject: [PATCH 122/401] server : hit Ctrl+C twice to exit (#5734) * server: twice ctrl+C to exit * std::atomic_flag * sigint: message * sigint: stderr * Update examples/server/server.cpp Co-authored-by: Jared Van Bortel --------- Co-authored-by: Jared Van Bortel --- examples/server/server.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6b3ee531cfb57..080fa9bd5702c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2772,7 +2772,16 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con } std::function shutdown_handler; -inline void signal_handler(int signal) { shutdown_handler(signal); } +std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; +inline void signal_handler(int signal) { + if (is_terminating.test_and_set()) { + // in case it hangs, we can force terminate the server by hitting Ctrl+C twice + // this is for better developer experience, we can remove when the server is stable enough + fprintf(stderr, "Received second interrupt, terminating immediately.\n"); + exit(1); + } + shutdown_handler(signal); +} int main(int argc, char **argv) { From 5f706718566e3a5147916dc381f3b99de0ffad47 Mon Sep 17 00:00:00 2001 From: "UEXTM.com" <84163508+uextm@users.noreply.github.com> Date: Sat, 24 Feb 2024 11:27:36 -0500 Subject: [PATCH 123/401] Introduce backend GUIDs (ggml/743) * Introduce backend GUIDs Initial proposed implementation of backend GUIDs (Discussed in https://github.com/ggerganov/ggml/pull/741) Hardcoded CPU backend GUID (for now) Change ggml_backend_is_cpu logic to use GUID * Remove redundant functions Remove redundant functions `ggml_backend_i::get_name` and `ggml_backend_guid` which are not desired for future expansion * Add spaces to match style Co-authored-by: slaren * Fix brace style to match Co-authored-by: slaren * Add void to () in function signature Co-authored-by: slaren * Add back ggml_backend_guid and make CPU_GUID a local static in ggml_backend_cpu_guid * add guids to all backends ggml-ci --------- Co-authored-by: slaren --- ggml-backend-impl.h | 2 ++ ggml-backend.c | 16 ++++++++++++++-- ggml-backend.h | 2 +- ggml-cuda.cu | 8 +++++++- ggml-kompute.cpp | 8 +++++++- ggml-metal.m | 8 +++++++- ggml-sycl.cpp | 8 +++++++- ggml-vulkan.cpp | 8 +++++++- ggml.c | 4 ++++ ggml.h | 10 ++++++++++ 10 files changed, 66 insertions(+), 8 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index f95df47f72b86..0e5bf0ae1365c 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -104,6 +104,8 @@ extern "C" { }; struct ggml_backend { + ggml_guid_t guid; + struct ggml_backend_i iface; ggml_backend_context_t context; diff --git a/ggml-backend.c b/ggml-backend.c index 5076d9e5ec325..c86673b04de37 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -12,7 +12,6 @@ #define MAX(a, b) ((a) > (b) ? (a) : (b)) - // backend buffer type const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { @@ -159,6 +158,13 @@ bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml // backend +ggml_guid_t ggml_backend_guid(ggml_backend_t backend) { + if (backend == NULL) { + return NULL; + } + return backend->guid; +} + const char * ggml_backend_name(ggml_backend_t backend) { if (backend == NULL) { return "NULL"; @@ -781,6 +787,11 @@ static struct ggml_backend_i cpu_backend_i = { /* .supports_op = */ ggml_backend_cpu_supports_op, }; +static ggml_guid_t ggml_backend_cpu_guid(void) { + static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; + return &guid; +} + ggml_backend_t ggml_backend_cpu_init(void) { struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); if (ctx == NULL) { @@ -800,6 +811,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } *cpu_backend = (struct ggml_backend) { + /* .guid = */ ggml_backend_cpu_guid(), /* .interface = */ cpu_backend_i, /* .context = */ ctx }; @@ -807,7 +819,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_cpu_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); } void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { diff --git a/ggml-backend.h b/ggml-backend.h index f13c69bffb98c..8fb54bd927f8b 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -49,7 +49,7 @@ extern "C" { // Backend // - + GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend); GGML_API const char * ggml_backend_name(ggml_backend_t backend); GGML_API void ggml_backend_free(ggml_backend_t backend); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 831c84efb5286..0c6501e98a2a6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -12277,6 +12277,11 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .supports_op = */ ggml_backend_cuda_supports_op, }; +static ggml_guid_t ggml_backend_cuda_guid() { + static ggml_guid guid = { 0x2c, 0xdd, 0xe8, 0x1c, 0x65, 0xb3, 0x65, 0x73, 0x6a, 0x12, 0x88, 0x61, 0x1c, 0xc9, 0xdc, 0x25 }; + return &guid; +} + GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { ggml_init_cublas(); // TODO: remove from ggml.c @@ -12294,6 +12299,7 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { }; ggml_backend_t cuda_backend = new ggml_backend { + /* .guid = */ ggml_backend_cuda_guid(), /* .interface = */ ggml_backend_cuda_interface, /* .context = */ ctx }; @@ -12302,7 +12308,7 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { } GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_cuda_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cuda_guid()); } GGML_CALL int ggml_backend_cuda_get_device_count() { diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 51c5af8ec00a2..e740a76d1ac53 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1953,11 +1953,17 @@ static struct ggml_backend_i kompute_backend_i = { /* .supports_op = */ ggml_backend_kompute_supports_op, }; +static ggml_guid_t ggml_backend_kompute_guid() { + static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca }; + return &guid; +} + ggml_backend_t ggml_backend_kompute_init(int device) { GGML_ASSERT(s_kompute_context == nullptr); s_kompute_context = new ggml_kompute_context(device); ggml_backend_t kompute_backend = new ggml_backend { + /* .guid = */ ggml_backend_kompute_guid(), /* .interface = */ kompute_backend_i, /* .context = */ s_kompute_context, }; @@ -1966,7 +1972,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) { } bool ggml_backend_is_kompute(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_kompute_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid()); } static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) { diff --git a/ggml-metal.m b/ggml-metal.m index 9eba2f5d20375..71fcca5605914 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2771,6 +2771,11 @@ void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * ggml_metal_log_user_data = user_data; } +static ggml_guid_t ggml_backend_metal_guid(void) { + static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 }; + return &guid; +} + ggml_backend_t ggml_backend_metal_init(void) { struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); @@ -2781,6 +2786,7 @@ ggml_backend_t ggml_backend_metal_init(void) { ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); *metal_backend = (struct ggml_backend) { + /* .guid = */ ggml_backend_metal_guid(), /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; @@ -2789,7 +2795,7 @@ ggml_backend_t ggml_backend_metal_init(void) { } bool ggml_backend_is_metal(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_metal_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid()); } void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 835967fb64d9e..a054ec8b92bac 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15162,6 +15162,11 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .supports_op = */ ggml_backend_sycl_supports_op, }; +static ggml_guid_t ggml_backend_sycl_guid() { + static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; + return &guid; +} + ggml_backend_t ggml_backend_sycl_init(int device) { ggml_init_sycl(); // TODO: remove from ggml.c @@ -15179,6 +15184,7 @@ ggml_backend_t ggml_backend_sycl_init(int device) { }; ggml_backend_t sycl_backend = new ggml_backend { + /* .guid = */ ggml_backend_sycl_guid(), /* .interface = */ ggml_backend_sycl_interface, /* .context = */ ctx }; @@ -15187,7 +15193,7 @@ ggml_backend_t ggml_backend_sycl_init(int device) { } bool ggml_backend_is_sycl(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_sycl_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); } static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 6caafb82279ae..314e3d7a950ca 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5244,6 +5244,11 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .supports_op = */ ggml_backend_vk_supports_op, }; +static ggml_guid_t ggml_backend_vk_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + return &guid; +} + GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { if (vk_instance.initialized[idx]) { return vk_instance.backends[idx]; @@ -5262,6 +5267,7 @@ GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { vk_instance.initialized[idx] = true; ggml_backend_t vk_backend = new ggml_backend { + /* .guid = */ ggml_backend_vk_guid(), /* .interface = */ ggml_backend_vk_interface, /* .context = */ &vk_instance.contexts[ctx->idx], }; @@ -5272,7 +5278,7 @@ GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { } GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_vk_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); } GGML_CALL int ggml_backend_vk_get_device_count() { diff --git a/ggml.c b/ggml.c index 4591644adbdd4..68ac6201ee5ea 100644 --- a/ggml.c +++ b/ggml.c @@ -355,6 +355,10 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { } } +bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) { + return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0; +} + // // timing // diff --git a/ggml.h b/ggml.h index 23b7686407895..0a6d3c051fe72 100644 --- a/ggml.h +++ b/ggml.h @@ -672,6 +672,16 @@ extern "C" { GGML_NUMA_STRATEGY_COUNT }; + // + // GUID + // + + // GUID types + typedef uint8_t ggml_guid[16]; + typedef ggml_guid * ggml_guid_t; + + GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b); + // misc GGML_API void ggml_time_init(void); // call this once at the beginning of the program From 2774b0c97427ee3ad3e2ee121354d078794e89d9 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 25 Feb 2024 20:41:35 +0100 Subject: [PATCH 124/401] add google magika inference example (ggml/748) * add magika inference example * ggml : fix unaligned accesses in custom ops * ggml : fix FP32 GELU for values that exceed the FP16 range * use ggml_pool_1d * add README * Update README.md * pad inputs if the files are too small * cleanup ggml-ci --- ggml.c | 54 ++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 34 insertions(+), 20 deletions(-) diff --git a/ggml.c b/ggml.c index 68ac6201ee5ea..f29b9f13fbcaf 100644 --- a/ggml.c +++ b/ggml.c @@ -1608,9 +1608,15 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + if (x[i] <= -10.0f) { + y[i] = 0.0f; + } else if (x[i] >= 10.0f) { + y[i] = x[i]; + } else { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + } } } #else @@ -5780,11 +5786,13 @@ struct ggml_tensor * ggml_pool_1d( is_node = true; } - const int64_t ne[2] = { + const int64_t ne[4] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], + a->ne[2], + a->ne[3], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, params, sizeof(params)); @@ -15081,9 +15089,10 @@ static void ggml_compute_forward_map_custom1( return; } - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params; + struct ggml_map_custom1_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, params->ith, params->nth, p->userdata); + p.fun(dst, a, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom2 @@ -15099,9 +15108,10 @@ static void ggml_compute_forward_map_custom2( return; } - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params; + struct ggml_map_custom2_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, b, params->ith, params->nth, p->userdata); + p.fun(dst, a, b, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom3 @@ -15118,9 +15128,10 @@ static void ggml_compute_forward_map_custom3( return; } - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params; + struct ggml_map_custom3_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, b, c, params->ith, params->nth, p->userdata); + p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); } // ggml_compute_forward_cross_entropy_loss @@ -17386,29 +17397,32 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; case GGML_OP_MAP_CUSTOM1: { - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom1_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM2: { - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom2_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM3: { - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom3_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_CROSS_ENTROPY_LOSS: From 8c0e8f4e73e275756ad69f9c99b26ead085ca9f0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Feb 2024 11:17:32 +0200 Subject: [PATCH 125/401] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 59de343706f2a..389c0bdfe9eb6 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -8cdf783f288a98eddf521b0ab1b4d405be9e18ba +b458250b736a7473f7ff3560d47c93f1644f3290 From 78aacf36344df724cdca9f1e1af849b2d2519cb8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Feb 2024 17:36:53 +0200 Subject: [PATCH 126/401] awq-py : remove (#5768) --- awq-py/README.md | 116 ------------------ awq-py/awq/apply_awq.py | 254 ---------------------------------------- awq-py/requirements.txt | 2 - 3 files changed, 372 deletions(-) delete mode 100644 awq-py/README.md delete mode 100644 awq-py/awq/apply_awq.py delete mode 100644 awq-py/requirements.txt diff --git a/awq-py/README.md b/awq-py/README.md deleted file mode 100644 index 16e68d027e239..0000000000000 --- a/awq-py/README.md +++ /dev/null @@ -1,116 +0,0 @@ -# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp -[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)] - -**Supported models:** - -- [X] LLaMA -- [x] LLaMA 2 -- [X] MPT -- [X] Mistral AI v0.1 -- [ ] Bloom -- [ ] Mixtral MoE - -**TODO:** -- [x] Update version work with both MPT and MPT-AWQ model -- [ ] Add OPT model -- [ ] Add Bloom model -- [ ] Add Mixtral MoE -- [ ] Support w3, w2 - - -## Contents - -- [Install](##Install) -- [Convert](##Convert) -- [Quantize](##Quantize) -- [Test](##Test) -- [Benchmark](##Benchmark) -- [Results](##Results) - -## Install -Install requirements -```bash -pip install -r requirements.txt -``` -Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT -```bash -git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache -``` - -## Convert -Example for llama model -```bash -# For llama7b and llama2 models -python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf -# For mistral and mpt models -python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf -``` - -## Quantize -```bash -# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types. -./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0 -``` - -## Test -```bash -# For all models. -./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time" -``` - -## Benchmark -The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. -```bash -# For llama and llama2, and mistral models. -./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw -``` - -## Results -Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison -We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k - -### Llama 7B (Build with OpenBLAS) - -| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | -|-----------:|--------------|-------:|-------:|-------:|-------:| -|Llama 7B | perplexity | 5.9066 | 6.1214 | 6.0643 | 6.5808 | -|Llama 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | -|Llama 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | -|AWQ-LLama 7B| perplexity | 5.9175 | 6.0252 | 5.9987 | 6.3692 | -|AWQ-LLama 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | -|AWQ-LLama 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | - - -### Llama2 7B (Build with CuBLAS) - -| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | -|------------:|--------------|-------:|-------:|-------:|-------:| -|Llama2 7B | perplexity | 5.8664 | 6.0260 | 6.0656 | 6.4496 | -|Llama2 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | -|Llama2 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | -|AWQ-LLama2 7B| perplexity | 5.8801 | 6.0054 | 5.9849 | 6.3650 | -|AWQ-LLama2 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | -|AWQ-LLama2 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | - - -### Mistral 7B v0.1 (Build with CuBLAS) - -| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | -|-------------:|--------------|-------:|-------:|-------:|-------:| -|Mistral 7B | perplexity | 5.6931 | 5.8202 | 5.8268 | 6.1645 | -|Mistral 7B | file size | 14.5G | 4.1G | 4.5G | 3.1G | -|Mistral 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | -|AWQ-Mistral 7B| perplexity | 5.6934 | 5.8020 | 5.7691 | 6.0426 | -|AWQ-Mistral 7B| file size | 14.5G | 4.1G | 4.5G | 3.1G | -|AWQ-Mistral 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | - -### MPT 7B (Build with OpenBLAS) - -| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | -|---------:|--------------|-------:|-------:|-------:|--------:| -|MPT 7B | perplexity | 8.4369 | 8.7956 | 8.6265 | 11.4913 | -|MPT 7B | file size | 13.7G | 3.9G | 4.3G | 2.8G | -|MPT 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | -|AWQ-MPT 7B| perplexity | 8.4944 | 8.7053 | 8.6750 | 10.2873| -|AWQ-MPT 7B| file size | 13.7G | 3.9G | 4.3G | 2.8G | -|AWQ-MPT 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | diff --git a/awq-py/awq/apply_awq.py b/awq-py/awq/apply_awq.py deleted file mode 100644 index 11132c5d26e0c..0000000000000 --- a/awq-py/awq/apply_awq.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -Implements the AWQ for llama.cpp use cases. -Original paper: https://arxiv.org/abs/2306.00978 - -This code is based on versions of the AWQ implementation found in the following repositories: -* https://github.com/mit-han-lab/llm-awq -* https://github.com/casper-hansen/AutoAWQ -""" - -import os -import torch -import torch.nn as nn - -from transformers import AutoModelForCausalLM, AutoConfig -from transformers.models.bloom.modeling_bloom import BloomGelu -from transformers.models.llama.modeling_llama import LlamaRMSNorm -from transformers.activations import GELUActivation - - -class ScaledActivation(nn.Module): - """ - ScaledActivation module wraps an existing activation function and applies a - scale factor to its output. - - Args: - module (nn.Module): The activation function to be scaled. - scales (torch.Tensor): A tensor of size (num_features,) containing the initial - scale factors for each feature. - - Returns: - torch.Tensor: The scaled output of the activation function. - """ - - def __init__(self, module, scales): - super().__init__() - self.act = module - self.scales = nn.Parameter(scales.data) - - def forward(self, x): - return self.act(x) / self.scales.view(1, 1, -1).to(x.device) - - -def set_op_by_name(layer, name, new_module): - """ - Set the new module for given module's name. - - Args: - layer (nn.Module): The layer in which to replace the submodule. - name (str): The path to the submodule to be replaced, using dot notation - to access nested modules. - new_module (nn.Module): The new module to replace the existing one. - """ - levels = name.split(".") - if len(levels) > 1: - mod_ = layer - for l_idx in range(len(levels) - 1): - if levels[l_idx].isdigit(): - mod_ = mod_[int(levels[l_idx])] - else: - mod_ = getattr(mod_, levels[l_idx]) - setattr(mod_, levels[-1], new_module) - else: - setattr(layer, name, new_module) - - -def get_op_by_name(module, op_name): - """ - Retrieves a submodule within a given layer based on its name. - - Args: - module (nn.Module): The layer containing the submodule to find. - op_name (str): The name of the submodule. - - Returns: - nn.Module: The requested submodule found within the given layer. - - Raises: - ValueError: If the specified submodule cannot be found within the layer. - """ - for name, m in module.named_modules(): - if name == op_name: - return m - raise ValueError(f"Cannot find op {op_name} in module {module}") - - -@torch.no_grad() -def scale_ln_fcs(ln, fcs, scales): - """ - Scales the weights of a LayerNorm and a list of fully-connected layers proportionally. - - Args: - ln (nn.LayerNorm): The LayerNorm module to be scaled. - fcs (List[nn.Linear]): A list of fully-connected layers to be scaled. - scales (torch.Tensor): A 1D tensor of size (num_features,). - """ - - if not isinstance(fcs, list): - fcs = [fcs] - - scales = scales.to(ln.weight.device) - - ln.weight.div_(scales) - if hasattr(ln, "bias") and ln.bias is not None: - ln.bias.div_(scales) - - for fc in fcs: - fc.weight.mul_(scales.view(1, -1)) - - for p in ln.parameters(): - assert torch.isnan(p).sum() == 0 - for fc in fcs: - for p in fc.parameters(): - assert torch.isnan(p).sum() == 0 - - -@torch.no_grad() -def scale_fc_fc(fc1, fc2, scales): - """ - Scales the weights of two fully-connected layers in a specific pattern. - - Args: - fc1 (nn.Linear): The first fully-connected layer to be scaled. - fc2 (nn.Linear): The second fully-connected layer to be scaled. - scales (torch.Tensor): A 1D tensor of size (num_features,). - """ - assert isinstance(fc1, nn.Linear) - assert isinstance(fc2, nn.Linear) - - scales = scales.to(fc1.weight.device) - - fc1.weight[-scales.size(0):].div_(scales.view(-1, 1)) - if fc1.bias is not None: - fc1.bias.div_(scales.view(-1)) - - fc2.weight.mul_(scales.view(1, -1)) - - for p in fc1.parameters(): - assert torch.isnan(p).sum() == 0 - for p in fc2.parameters(): - assert torch.isnan(p).sum() == 0 - - -@torch.no_grad() -def scale_gelu_fc(gelu, fc, scales): - """ - Scales the weight of a GELU activation and a fully-connected layer proportionally. - - Args: - gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled. - fc (nn.Linear): The fully-connected layer to be scaled. - scales (torch.Tensor): A 1D tensor of size (num_features,). - - Raises: - TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`. - TypeError: If the `fc` module is not of type `nn.Linear`. - """ - assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation)) - assert isinstance(fc, nn.Linear) - - fc.weight.mul_(scales.view(1, -1).to(fc.weight.device)) - - for p in fc.parameters(): - assert torch.isnan(p).sum() == 0 - - -def apply_scale(module, scales_list, input_feat_dict=None): - """ - Applies different scaling strategies to layers based on their type and hierarchy within a given module. - - Args: - module (nn.Module): The module containing the layers to be scaled. - scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing: - * prev_op_name (str): The name of the preceding operation or module, - relative to which the layers to be scaled are located. - * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation. - * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature. - input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding - input features (optional). - """ - for prev_op_name, layer_names, scales in scales_list: - prev_op = get_op_by_name(module, prev_op_name) - layers = [get_op_by_name(module, name) for name in layer_names] - - prev_op.cuda() - for layer in layers: - layer.cuda() - scales.cuda() - - if isinstance(prev_op, nn.Linear): - assert len(layers) == 1 - scale_fc_fc(prev_op, layers[0], scales) - elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower(): - scale_ln_fcs(prev_op, layers, scales) - elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)): - new_module = ScaledActivation(prev_op, scales) - set_op_by_name(module, prev_op_name, new_module) - scale_gelu_fc(prev_op, layers[0], scales) - else: - raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!") - - # apply the scaling to input feat if given; prepare it for clipping - if input_feat_dict is not None: - for layer_name in layer_names: - inp = input_feat_dict[layer_name] - inp.div_(scales.view(1, -1).to(inp.device)) - - prev_op.cpu() - for layer in layers: - layer.cpu() - scales.cpu() - - -@torch.no_grad() -def apply_clip(module, clip_list): - """ - Applies element-wise clipping to the weight of a specific layer within a given module. - - Args: - module (nn.Module): The module containing the layer to be clipped. - clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing: - * name (str): The name of the layer to be clipped, relative to the root of the module. - * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight. - """ - for name, max_val in clip_list: - layer = get_op_by_name(module, name) - layer.cuda() - max_val = max_val.to(layer.weight.device) - org_shape = layer.weight.shape - layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1) - layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val) - layer.weight.data = layer.weight.data.reshape(org_shape) - layer.cpu() - - -def add_scale_weights(model_path, scale_path, tmp_path): - """ - Adds pre-computed Activation Weight Quantization (AWQ) results to a model, - including scaling factors and clipping bounds. - - Args: - model_path (str): Path to the pre-trained model to be equipped with AWQ. - scale_path (str): Path to the AWQ scale factors (.pt file). - tmp_path (str): Path to the temporary directory where the equipped model will be saved. - """ - config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) - model = AutoModelForCausalLM.from_pretrained( - model_path, config=config, trust_remote_code=True - ) - model.eval() - awq_results = torch.load(str(scale_path), map_location="cpu") - apply_scale(model, awq_results["scale"]) - apply_clip(model, awq_results["clip"]) - model.save_pretrained(str(tmp_path)) - os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}") diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt deleted file mode 100644 index 9918961160774..0000000000000 --- a/awq-py/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -torch>=2.1.1 -transformers>=4.32.0 From 08c5ee87e4cceb603ecceac90734fcdade57311b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Feb 2024 18:43:38 +0200 Subject: [PATCH 127/401] llama : remove deprecated API (#5770) ggml-ci --- llama.cpp | 88 +------------------------------------------------------ llama.h | 45 ---------------------------- 2 files changed, 1 insertion(+), 132 deletions(-) diff --git a/llama.cpp b/llama.cpp index 893bcdbc0147d..30d5eb32dab83 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7894,9 +7894,9 @@ static int llama_decode_internal( const auto n_batch = cparams.n_batch; GGML_ASSERT(n_tokens <= n_batch); + GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; - GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT const int64_t t_start_us = ggml_time_us(); @@ -10062,10 +10062,6 @@ void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * cand } } -void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) { - llama_sample_temp(ctx, candidates_p, temp); -} - void llama_sample_repetition_penalties( struct llama_context * ctx, llama_token_data_array * candidates, @@ -10192,38 +10188,6 @@ void llama_sample_apply_guidance( ctx->t_sample_us += ggml_time_us() - t_start_sample_us; } -void llama_sample_classifier_free_guidance( - struct llama_context * ctx, - llama_token_data_array * candidates, - struct llama_context * guidance_ctx, - float scale) { - GGML_ASSERT(ctx); - int64_t t_start_sample_us; - - t_start_sample_us = ggml_time_us(); - const size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); - - GGML_ASSERT(n_vocab == candidates->size); - GGML_ASSERT(!candidates->sorted); - - std::vector logits_base(n_vocab); - for (size_t i = 0; i < n_vocab; ++i) { - logits_base[i] = candidates->data[i].logit; - } - - float * logits_guidance = llama_get_logits(guidance_ctx); - - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale); - t_start_sample_us = ggml_time_us(); - - for (size_t i = 0; i < n_vocab; ++i) { - candidates->data[i].logit = logits_base[i]; - } - - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; -} - llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) { GGML_ASSERT(ctx); @@ -11724,15 +11688,6 @@ bool llama_supports_gpu_offload(void) { #endif } -// deprecated: -bool llama_mmap_supported(void) { - return llama_supports_mmap(); -} - -bool llama_mlock_supported(void) { - return llama_supports_mlock(); -} - void llama_backend_init(void) { ggml_time_init(); @@ -12244,15 +12199,6 @@ uint32_t llama_model_quantize( } } -int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) { - try { - return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads); - } catch (const std::exception & err) { - LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what()); - return 1; - } -} - int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) { try { return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads); @@ -12802,38 +12748,6 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi return true; } -int llama_eval( - struct llama_context * ctx, - llama_token * tokens, - int32_t n_tokens, - int32_t n_past) { - llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1); - - const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0)); - if (ret < 0) { - LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); - } - - return ret; -} - -int llama_eval_embd( - struct llama_context * ctx, - float * embd, - int32_t n_tokens, - int32_t n_past) { - llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1); - - llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, }; - - const int ret = llama_decode_internal(*ctx, batch); - if (ret < 0) { - LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); - } - - return ret; -} - void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) { ctx->cparams.n_threads = n_threads; ctx->cparams.n_threads_batch = n_threads_batch; diff --git a/llama.h b/llama.h index 16e28e91deb54..a6823bb2bd5b9 100644 --- a/llama.h +++ b/llama.h @@ -364,9 +364,6 @@ extern "C" { LLAMA_API bool llama_supports_mlock (void); LLAMA_API bool llama_supports_gpu_offload(void); - LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead"); - LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead"); - LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); @@ -423,14 +420,6 @@ extern "C" { // The model needs to be reloaded before applying a new adapter, otherwise the adapter // will be applied on top of the previous one // Returns 0 on success - LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file( - struct llama_context * ctx, - const char * path_lora, - float scale, - const char * path_base_model, - int32_t n_threads), - "use llama_model_apply_lora_from_file instead"); - LLAMA_API int32_t llama_model_apply_lora_from_file( const struct llama_model * model, const char * path_lora, @@ -606,27 +595,6 @@ extern "C" { // Decoding // - // Run the llama inference to obtain the logits and probabilities for the next token(s). - // tokens + n_tokens is the provided batch of new tokens to process - // n_past is the number of tokens to use from previous eval calls - // Returns 0 on success - // DEPRECATED: use llama_decode() instead - LLAMA_API DEPRECATED(int llama_eval( - struct llama_context * ctx, - llama_token * tokens, - int32_t n_tokens, - int32_t n_past), - "use llama_decode() instead"); - - // Same as llama_eval, but use float matrix input directly. - // DEPRECATED: use llama_decode() instead - LLAMA_API DEPRECATED(int llama_eval_embd( - struct llama_context * ctx, - float * embd, - int32_t n_tokens, - int32_t n_past), - "use llama_decode() instead"); - // Return batch for single sequence of tokens starting at pos_0 // // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it @@ -800,13 +768,6 @@ extern "C" { float * logits_guidance, float scale); - LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance( - struct llama_context * ctx, - llama_token_data_array * candidates, - struct llama_context * guidance_ctx, - float scale), - "use llama_sample_apply_guidance() instead"); - /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. LLAMA_API void llama_sample_softmax( struct llama_context * ctx, @@ -860,12 +821,6 @@ extern "C" { llama_token_data_array * candidates, float temp); - LLAMA_API DEPRECATED(void llama_sample_temperature( - struct llama_context * ctx, - llama_token_data_array * candidates, - float temp), - "use llama_sample_temp instead"); - /// @details Apply constraints from grammar LLAMA_API void llama_sample_grammar( struct llama_context * ctx, From 317709b2a81dbaf87850202686ec5bb2602a504e Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 28 Feb 2024 19:33:37 +0000 Subject: [PATCH 128/401] make portability_enumeration_ext apple only (#5757) --- ggml-vulkan.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 314e3d7a950ca..896c290b243ab 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1106,7 +1106,9 @@ void ggml_vk_instance_init() { const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions); +#ifdef __APPLE__ const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); +#endif std::vector layers; @@ -1117,13 +1119,17 @@ void ggml_vk_instance_init() { if (validation_ext) { extensions.push_back("VK_EXT_validation_features"); } +#ifdef __APPLE__ if (portability_enumeration_ext) { extensions.push_back("VK_KHR_portability_enumeration"); } +#endif vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); +#ifdef __APPLE__ if (portability_enumeration_ext) { instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; } +#endif std::vector features_enable; vk::ValidationFeaturesEXT validation_features; From 87c91c07663b707e831c59ec373b5e665ff9d64a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Feb 2024 21:44:21 +0200 Subject: [PATCH 129/401] ci : reduce 3b ppl chunks to 1 to avoid timeout (#5771) ggml-ci --- ci/run.sh | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index f3a29c2e9be0b..35eb3c7aa00ef 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -272,19 +272,19 @@ function gg_run_open_llama_3b_v2 { (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log @@ -343,17 +343,17 @@ function gg_run_open_llama_3b_v2 { python3 ../convert-lora-to-ggml.py ${path_lora} # f16 - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log # q8_0 - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log # q8_0 + f16 lora-base - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log set +e From d5ab29757ebc59a30f03e408294ec20628a6374e Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Thu, 29 Feb 2024 00:17:23 -0800 Subject: [PATCH 130/401] llama : constified `llama_set_state_data`'s `src` (#5774) --- llama.cpp | 6 +++--- llama.h | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 30d5eb32dab83..62699ce52e197 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12545,8 +12545,8 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) { } // Sets the state reading from the specified source address -size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { - uint8_t * inp = src; +size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { + const uint8_t * inp = src; // set rng { @@ -12555,7 +12555,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE); - std::string rng_str((char *)inp, rng_size); inp += rng_size; + std::string rng_str((const char *)inp, rng_size); inp += rng_size; std::istringstream rng_ss(rng_str); rng_ss >> ctx->rng; diff --git a/llama.h b/llama.h index a6823bb2bd5b9..4d0ebe37d3f9b 100644 --- a/llama.h +++ b/llama.h @@ -575,7 +575,7 @@ extern "C" { // Returns the number of bytes read LLAMA_API size_t llama_set_state_data( struct llama_context * ctx, - uint8_t * src); + const uint8_t * src); // Save/load session file LLAMA_API bool llama_load_session_file( From 052051d8ae4639a1c3c61e7da3237bcc572469d4 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 29 Feb 2024 21:42:11 +0100 Subject: [PATCH 131/401] Server: normalize naming (#5779) * server: normalize naming * fix spacing --- examples/server/server.cpp | 370 ++++++++++++++++--------------------- examples/server/utils.hpp | 186 ++++++++++++------- 2 files changed, 277 insertions(+), 279 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 080fa9bd5702c..bf20e0cf1c9c7 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -33,8 +33,7 @@ using json = nlohmann::json; -struct server_params -{ +struct server_params { std::string hostname = "127.0.0.1"; std::vector api_keys; std::string public_path = "examples/server/public"; @@ -49,103 +48,50 @@ struct server_params bool server_verbose = false; bool server_log_json = true; -static size_t common_part(const std::vector &a, const std::vector &b) -{ - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) - { - } - return i; -} - -enum stop_type -{ +enum stop_type { STOP_FULL, STOP_PARTIAL, }; -static bool ends_with(const std::string &str, const std::string &suffix) -{ - return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); -} +// TODO: can become bool if we can't find use of more states +enum slot_state { + IDLE, + PROCESSING, +}; -static size_t find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { - const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { - const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { - return text.size() - char_index - 1; - } - } - } - } - return std::string::npos; -} +enum slot_command { + NONE, + LOAD_PROMPT, + RELEASE, +}; -// TODO: reuse llama_detokenize -template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ - std::string ret; - for (; begin != end; ++begin) - { - ret += llama_token_to_piece(ctx, *begin); - } - return ret; -} +struct slot_params { + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt -// format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ - std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); - // if the size is 1 and first bit is 1, meaning it's a partial character - // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { - std::stringstream ss; - ss << std::hex << (out[0] & 0xff); - std::string res(ss.str()); - out = "byte: \\x" + res; - } - return out; -} + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict -// convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) -{ - json out = json::array(); - for (const auto &prob : probs) - { - json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { - {"tok_str", tok_str}, - {"prob", p.prob}, - }); - } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ - {"content", tok_str}, - {"probs", probs_for_token}, - }); - } - return out; -} + std::vector antiprompt; -struct llama_client_slot -{ + json input_prefix; + json input_suffix; +}; + +struct slot_image { + int32_t id; + + bool request_encode_image = false; + float * image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 * img_data; + + std::string prefix_prompt; // before of this image +}; + +struct server_slot { int id; int task_id = -1; @@ -165,8 +111,8 @@ struct llama_client_slot int32_t i_batch = -1; int32_t n_predict = -1; - int32_t num_prompt_tokens = 0; - int32_t num_prompt_tokens_processed = 0; + int32_t n_prompt_tokens = 0; + int32_t n_prompt_tokens_processed = 0; json prompt; std::string generated_text; @@ -201,8 +147,8 @@ struct llama_client_slot std::vector images; // stats - size_t sent_count = 0; - size_t sent_token_probs_index = 0; + size_t n_sent_text = 0; // number of sent text character + size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_genereration; @@ -214,7 +160,7 @@ struct llama_client_slot int multitask_id = -1; void reset() { - num_prompt_tokens = 0; + n_prompt_tokens = 0; generated_text = ""; truncated = false; stopped_eos = false; @@ -222,16 +168,15 @@ struct llama_client_slot stopped_limit = false; stopping_word = ""; n_past = 0; - sent_count = 0; - sent_token_probs_index = 0; + n_sent_text = 0; + n_sent_token_probs = 0; infill = false; ga_i = 0; n_past_se = 0; generated_token_probs.clear(); - for (slot_image & img : images) - { + for (slot_image & img : images) { free(img.image_embedding); if (img.img_data) { clip_image_u8_free(img.img_data); @@ -243,19 +188,15 @@ struct llama_client_slot } bool has_budget(gpt_params &global_params) { - if (params.n_predict == -1 && global_params.n_predict == -1) - { + if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } n_remaining = -1; - if (params.n_predict != -1) - { + if (params.n_predict != -1) { n_remaining = params.n_predict - n_decoded; - } - else if (global_params.n_predict != -1) - { + } else if (global_params.n_predict != -1) { n_remaining = global_params.n_predict - n_decoded; } @@ -271,8 +212,7 @@ struct llama_client_slot } void add_token_string(const completion_token_output &token) { - if (command == RELEASE) - { + if (command == RELEASE) { return; } cache_tokens.push_back(token.tok); @@ -290,10 +230,10 @@ struct llama_client_slot json get_formated_timings() { return json { - {"prompt_n", num_prompt_tokens_processed}, + {"prompt_n", n_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, - {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed}, + {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, {"predicted_n", n_decoded}, {"predicted_ms", t_token_generation}, @@ -304,18 +244,18 @@ struct llama_client_slot void print_timings() const { char buffer[512]; - double t_token = t_prompt_processing / num_prompt_tokens_processed; - double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed; + double t_token = t_prompt_processing / n_prompt_tokens_processed; + double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", - t_prompt_processing, num_prompt_tokens_processed, + t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_prompt_processing", t_prompt_processing}, - {"num_prompt_tokens_processed", num_prompt_tokens_processed}, - {"t_token", t_token}, - {"n_tokens_second", n_tokens_second}, + {"slot_id", id}, + {"task_id", task_id}, + {"t_prompt_processing", t_prompt_processing}, + {"n_prompt_tokens_processed", n_prompt_tokens_processed}, + {"t_token", t_token}, + {"n_tokens_second", n_tokens_second}, }); t_token = t_token_generation / n_decoded; @@ -343,7 +283,7 @@ struct llama_client_slot } }; -struct llama_metrics { +struct server_metrics { uint64_t n_prompt_tokens_processed_total = 0; uint64_t n_tokens_predicted_total = 0; @@ -354,18 +294,16 @@ struct llama_metrics { uint64_t t_tokens_generation = 0; - void on_prompt_eval(const llama_client_slot &slot) { - n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed; - - n_prompt_tokens_processed += slot.num_prompt_tokens_processed; - t_prompt_processing += slot.t_prompt_processing; + void on_prompt_eval(const server_slot &slot) { + n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; + n_prompt_tokens_processed += slot.n_prompt_tokens_processed; + t_prompt_processing += slot.t_prompt_processing; } - void on_prediction(const llama_client_slot &slot) { + void on_prediction(const server_slot &slot) { n_tokens_predicted_total += slot.n_decoded; - - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; } void reset_bucket() { @@ -404,13 +342,13 @@ struct llama_server_context std::string name_assistant; // slots / clients - std::vector slots; + std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; + llama_server_queue queue_tasks; llama_server_response queue_results; - llama_metrics metrics; + server_metrics metrics; ~llama_server_context() { @@ -487,7 +425,7 @@ struct llama_server_context LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); for (int i = 0; i < params.n_parallel; i++) { - llama_client_slot slot; + server_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; @@ -579,11 +517,11 @@ struct llama_server_context return prompt_tokens; } - llama_client_slot* get_slot(int id) { + server_slot* get_slot(int id) { int64_t t_last = ggml_time_us(); - llama_client_slot *last_used = nullptr; + server_slot *last_used = nullptr; - for (llama_client_slot & slot : slots) + for (server_slot & slot : slots) { if (slot.id == id && slot.available()) { @@ -600,7 +538,7 @@ struct llama_server_context return last_used; } - bool launch_slot_with_data(llama_client_slot* &slot, json data) { + bool launch_slot_with_data(server_slot* &slot, json data) { slot_params default_params; llama_sampling_params default_sparams; @@ -888,7 +826,7 @@ struct llama_server_context clean_kv_cache = false; } - void update_system_prompt() { + void system_prompt_update() { kv_cache_clear(); system_tokens.clear(); @@ -933,9 +871,9 @@ struct llama_server_context system_need_update = false; } - void notify_system_prompt_changed() { + void system_prompt_notify() { // release all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.release(); } @@ -943,17 +881,17 @@ struct llama_server_context system_need_update = true; } - void process_system_prompt_data(const json &sys_props) { + void system_prompt_process(const json &sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); - notify_system_prompt_changed(); + system_prompt_notify(); } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, llama_client_slot &slot) + const stop_type type, server_slot &slot) { size_t stop_pos = std::string::npos; @@ -975,8 +913,8 @@ struct llama_server_context { if (type == STOP_FULL) { - slot.stopped_word = true; - slot.stopping_word = word; + slot.stopped_word = true; + slot.stopping_word = word; slot.has_next_token = false; } stop_pos = pos; @@ -986,7 +924,7 @@ struct llama_server_context return stop_pos; } - bool process_token(completion_token_output &result, llama_client_slot &slot) { + bool process_token(completion_token_output &result, server_slot &slot) { // remember which tokens were sampled - used for repetition penalties during sampling const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; @@ -1032,7 +970,7 @@ struct llama_server_context if (!incomplete) { - size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); @@ -1042,7 +980,7 @@ struct llama_server_context slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); - pos = std::min(slot.sent_count, slot.generated_text.size()); + pos = std::min(slot.n_sent_text, slot.generated_text.size()); } else { @@ -1055,7 +993,7 @@ struct llama_server_context { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); - slot.sent_count += result.text_to_send.size(); + slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache } slot.add_token_string(result); @@ -1099,7 +1037,7 @@ struct llama_server_context return slot.has_next_token; // continue } - bool process_images(llama_client_slot &slot) const + bool process_images(server_slot &slot) const { for (slot_image &img : slot.images) { @@ -1132,7 +1070,7 @@ struct llama_server_context queue_results.send(res); } - json get_formated_generation(llama_client_slot &slot) + json get_formated_generation(server_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && @@ -1179,7 +1117,7 @@ struct llama_server_context }; } - void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + void send_partial_response(server_slot &slot, completion_token_output tkn) { task_result res; res.id = slot.task_id; @@ -1199,13 +1137,13 @@ struct llama_server_context { std::vector probs_output = {}; const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); - size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); + size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); + size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); if (probs_pos < probs_stop_pos) { probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); } - slot.sent_token_probs_index = probs_stop_pos; + slot.n_sent_token_probs = probs_stop_pos; res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } @@ -1218,7 +1156,7 @@ struct llama_server_context queue_results.send(res); } - void send_final_response(llama_client_slot &slot) + void send_final_response(server_slot &slot) { task_result res; res.id = slot.task_id; @@ -1233,7 +1171,7 @@ struct llama_server_context {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, - {"tokens_evaluated", slot.num_prompt_tokens}, + {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, {"prompt", slot.prompt}, {"truncated", slot.truncated}, @@ -1271,7 +1209,7 @@ struct llama_server_context queue_results.send(res); } - void send_embedding(llama_client_slot &slot) + void send_embedding(server_slot &slot) { task_result res; res.id = slot.task_id; @@ -1282,9 +1220,7 @@ struct llama_server_context const int n_embd = llama_n_embd(model); if (!params.embedding) { - LOG_WARNING("embedding disabled", { - {"params.embedding", params.embedding}, - }); + LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); res.result_json = json { {"embedding", std::vector(n_embd, 0.0f)}, @@ -1296,7 +1232,7 @@ struct llama_server_context std::vector embedding(data, data + n_embd); res.result_json = json { - {"embedding", embedding }, + {"embedding", embedding}, }; } queue_results.send(res); @@ -1345,7 +1281,7 @@ struct llama_server_context } // for multiple images processing - bool ingest_images(llama_client_slot &slot, int n_batch) + bool ingest_images(server_slot &slot, int n_batch) { int image_idx = 0; @@ -1384,7 +1320,17 @@ struct llama_server_context } const int n_embd = llama_n_embd(model); - llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + llama_batch batch_img = { + n_eval, + nullptr, + (img.image_embedding + i * n_embd), + nullptr, + nullptr, + nullptr, + nullptr, + slot.n_past, + 1, 0 + }; if (llama_decode(ctx, batch_img)) { LOG_TEE("%s : failed to eval image\n", __func__); @@ -1454,7 +1400,7 @@ struct llama_server_context switch (task.type) { case TASK_TYPE_COMPLETION: { - llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { // if no slot is available, we defer this task for processing later @@ -1469,10 +1415,10 @@ struct llama_server_context send_error(task, "system prompt can only be updated when all slots are idle"); break; } - process_system_prompt_data(task.data["system_prompt"]); + system_prompt_process(task.data["system_prompt"]); // reset cache_tokens for all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.cache_tokens.clear(); slot.n_past = 0; @@ -1512,20 +1458,20 @@ struct llama_server_context int n_idle_slots = 0; int n_processing_slots = 0; - for (llama_client_slot &slot: slots) { + for (server_slot &slot: slots) { json slot_data = get_formated_generation(slot); slot_data["id"] = slot.id; slot_data["task_id"] = slot.task_id; slot_data["state"] = slot.state; slot_data["prompt"] = slot.prompt; slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, }; if (slot_data["state"] == IDLE) { n_idle_slots++; @@ -1563,10 +1509,10 @@ struct llama_server_context { "n_tokens_predicted", metrics.n_tokens_predicted}, { "t_tokens_generation", metrics.t_tokens_generation}, - { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, - { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, + { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, + { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, - { "slots", slots_data }, + { "slots", slots_data }, }; metrics.reset_bucket(); queue_results.send(res); @@ -1597,7 +1543,7 @@ struct llama_server_context if (system_need_update) { LOG_INFO("updating system prompt", {}); - update_system_prompt(); + system_prompt_update(); } llama_batch_clear(batch); @@ -1618,7 +1564,7 @@ struct llama_server_context task.target_id = -1; queue_tasks.post(task); - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { if (slot.ga_n == 1) { @@ -1754,45 +1700,50 @@ struct llama_server_context prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt } - slot.num_prompt_tokens = prompt_tokens.size(); + slot.n_prompt_tokens = prompt_tokens.size(); if (slot.params.n_keep < 0) { - slot.params.n_keep = slot.num_prompt_tokens; + slot.params.n_keep = slot.n_prompt_tokens; } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) + if (slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; - const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + std::vector new_tokens( + prompt_tokens.begin(), + prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert( + new_tokens.end(), + prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, + prompt_tokens.end()); LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); slot.truncated = true; prompt_tokens = new_tokens; - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + slot.n_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); - slot.n_past = 0; + slot.n_past = 0; slot.n_past_se = 0; - slot.ga_i = 0; - slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + slot.ga_i = 0; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens; } else { @@ -1811,7 +1762,7 @@ struct llama_server_context slot.n_past -= 1; } - slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; if (slot.ga_n != 1) { @@ -1836,13 +1787,13 @@ struct llama_server_context { "slot_id", slot.id }, { "task_id", slot.task_id }, { "n_past", slot.n_past }, - { "num_prompt_tokens_processed", slot.num_prompt_tokens_processed } + { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } }); } slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_INFO("we have to evaluate at least 1 token to generate logits", { @@ -1898,8 +1849,8 @@ struct llama_server_context if (has_images && !ingest_images(slot, n_batch)) { LOG_ERROR("failed processing images", { - "slot_id", slot.id, - "task_id", slot.task_id, + {"slot_id", slot.id}, + {"task_id", slot.task_id}, }); // FIXME @phymbert: to be properly tested // early returning without changing the slot state will block the slot for ever @@ -2049,10 +2000,6 @@ struct llama_server_context LOG_VERBOSE("slots updated", {}); return true; } - - void run_on_all_tasks_finished() { - update_slots(); - } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -2561,7 +2508,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::istreambuf_iterator(), std::back_inserter(systm_content) ); - llama.process_system_prompt_data(json::parse(systm_content)); + llama.system_prompt_process(json::parse(systm_content)); } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; @@ -2692,7 +2639,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, /* llama.cpp completion api semantics */ static json format_partial_response( - llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs + llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector &probs ) { json res = json { @@ -2748,14 +2695,7 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -struct token_translator -{ - llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } -}; - -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot) +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_slot *slot) { auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; @@ -3526,8 +3466,8 @@ int main(int argc, char **argv) &llama_server_context::process_single_task, &llama, std::placeholders::_1)); llama.queue_tasks.on_finish_multitask(std::bind( &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_all_tasks_finished(std::bind( - &llama_server_context::run_on_all_tasks_finished, &llama)); + llama.queue_tasks.on_run_slots(std::bind( + &llama_server_context::update_slots, &llama)); llama.queue_results.on_multitask_update(std::bind( &llama_server_queue::update_multitask, &llama.queue_tasks, diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d7abd7cbba71c..d98541f26d123 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -37,10 +37,6 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -// -// parallel -// - enum server_state { SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet SERVER_STATE_READY, // Server is ready and model is loaded @@ -78,51 +74,8 @@ struct task_multi { std::vector results{}; }; -// TODO: can become bool if we can't find use of more states -enum slot_state -{ - IDLE, - PROCESSING, -}; - -enum slot_command -{ - NONE, - LOAD_PROMPT, - RELEASE, -}; - -struct slot_params -{ - bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt - - uint32_t seed = -1; // RNG seed - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_predict = -1; // new tokens to predict - - std::vector antiprompt; - - json input_prefix; - json input_suffix; -}; - -struct slot_image -{ - int32_t id; - - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; - - clip_image_u8 * img_data; - - std::string prefix_prompt; // before of this image -}; - // completion token output with probabilities -struct completion_token_output -{ +struct completion_token_output { struct token_prob { llama_token tok; @@ -134,8 +87,13 @@ struct completion_token_output std::string text_to_send; }; -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) -{ +struct token_translator { + llama_context * ctx; + std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } + std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } +}; + +static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); json log = nlohmann::ordered_json{ @@ -183,8 +141,7 @@ static inline void server_log(const char *level, const char *function, int line, // template -static T json_value(const json &body, const std::string &key, const T &default_value) -{ +static T json_value(const json &body, const std::string &key, const T &default_value) { // Fallback null to default value return body.contains(key) && !body.at(key).is_null() ? body.value(key, default_value) @@ -200,8 +157,7 @@ inline bool verify_custom_template(const std::string & tmpl) { } // Format given chat. If tmpl is empty, we take the template from model metadata -inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) -{ +inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { size_t alloc_size = 0; // vector holding all allocated string to be passed to llama_chat_apply_template std::vector str(messages.size() * 2); @@ -250,7 +206,7 @@ struct llama_server_queue { // callback functions std::function callback_new_task; std::function callback_finish_multitask; - std::function callback_all_task_finished; + std::function callback_run_slots; // Add a new task to the end of the queue int post(task_server task) { @@ -283,14 +239,14 @@ struct llama_server_queue { callback_new_task = callback; } - // Register function to process a multitask + // Register function to process a multitask when it is finished void on_finish_multitask(std::function callback) { callback_finish_multitask = callback; } - // Register the function to be called when the batch of tasks is finished - void on_all_tasks_finished(std::function callback) { - callback_all_task_finished = callback; + // Register the function to be called when all slots data is ready to be processed + void on_run_slots(std::function callback) { + callback_run_slots = callback; } // Call when the state of one slot is changed @@ -312,7 +268,13 @@ struct llama_server_queue { condition_tasks.notify_all(); } - // Start the main loop. + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Run all slots + */ void start_loop() { running = true; while (true) { @@ -331,8 +293,8 @@ struct llama_server_queue { LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); callback_new_task(task); } - LOG_VERBOSE("callback_all_task_finished", {}); - // process and update all the multitasks + LOG_VERBOSE("update_multitasks", {}); + // check if we have any finished multitasks auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { @@ -349,8 +311,9 @@ struct llama_server_queue { ++queue_iterator; } } - // all tasks in the current loop is finished - callback_all_task_finished(); + // all tasks in the current loop is processed, slots data is now ready + LOG_VERBOSE("callback_run_slots", {}); + callback_run_slots(); } LOG_VERBOSE("wait for new task", {}); // wait for new task @@ -408,12 +371,14 @@ struct llama_server_response { std::mutex mutex_results; std::condition_variable condition_results; + // add the task_id to the list of tasks waiting for response void add_waiting_task_id(int task_id) { LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); waiting_task_ids.insert(task_id); } + // when the request is finished, we can remove task associated with it void remove_waiting_task_id(int task_id) { LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); @@ -574,3 +539,96 @@ static std::string gen_chatcmplid() chatcmplid << "chatcmpl-" << random_string(); return chatcmplid.str(); } + +// +// other common utils +// + +static size_t common_part(const std::vector &a, const std::vector &b) +{ + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) + { + } + return i; +} + +static bool ends_with(const std::string &str, const std::string &suffix) +{ + return str.size() >= suffix.size() && + 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); +} + +static size_t find_partial_stop_string(const std::string &stop, + const std::string &text) +{ + if (!text.empty() && !stop.empty()) + { + const char text_last_char = text.back(); + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) + { + if (stop[char_index] == text_last_char) + { + const std::string current_partial = stop.substr(0, char_index + 1); + if (ends_with(text, current_partial)) + { + return text.size() - char_index - 1; + } + } + } + } + return std::string::npos; +} + +// TODO: reuse llama_detokenize +template +static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) +{ + std::string ret; + for (; begin != end; ++begin) + { + ret += llama_token_to_piece(ctx, *begin); + } + return ret; +} + +// format incomplete utf-8 multibyte character for output +static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) +{ + std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character + // (size > 1 meaning it's already a known token) + if (out.size() == 1 && (out[0] & 0x80) == 0x80) + { + std::stringstream ss; + ss << std::hex << (out[0] & 0xff); + std::string res(ss.str()); + out = "byte: \\x" + res; + } + return out; +} + +// convert a vector of completion_token_output to json +static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) +{ + json out = json::array(); + for (const auto &prob : probs) + { + json probs_for_token = json::array(); + for (const auto &p : prob.probs) + { + std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json + { + {"tok_str", tok_str}, + {"prob", p.prob}, + }); + } + std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json{ + {"content", tok_str}, + {"probs", probs_for_token}, + }); + } + return out; +} From 38d152160898b0173ffe4dc7df5daadcbd2eceb0 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 1 Mar 2024 07:36:47 +0000 Subject: [PATCH 132/401] [SYCL] Use batched mul_mat pathway (#5591) * Use batched mul_mat pathway * rm extra line * Explicitly state scaled data type --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 107 +++++++++++++++++++++----------------------------- 1 file changed, 44 insertions(+), 63 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a054ec8b92bac..6f391b0c650ad 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -12726,6 +12726,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -13269,31 +13270,23 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { +static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - - GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb); - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - - GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); + GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13312,11 +13305,16 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; // convert src1 to fp16 - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - - sycl_pool_alloc src1_as_f16(ne1); - to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream); + sycl_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf + : src1_f16_alloc.get(); sycl_pool_alloc dst_f16; char * dst_t; @@ -13337,20 +13335,12 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, const void * alpha = &alpha_f16; const void * beta = &beta_f16; - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); - - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); - } else { - dst_t = (char *) dst_ddf; - - cu_compute_type = dpct::library_data_t::real_float; - cu_data_type = dpct::library_data_t::real_float; + // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway + // once oneMKL open source supports half, half, float, float: datatypes + dst_t = (char *) dst_f16.alloc(ne_dst); - alpha = &alpha_f32; - beta = &beta_f32; - } + nbd2 /= sizeof(float) / sizeof(sycl::half); + nbd3 /= sizeof(float) / sizeof(sycl::half); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13386,10 +13376,10 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, - nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half), - (const char *)src1_as_f16.get(), dpct::library_data_t::real_half, - nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta, - (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float), + nb01 / nb00, nb02 / nb00, + (const char *)src1_f16, dpct::library_data_t::real_half, + nb11 / nb10, nb12 / nb10, beta, + (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { // use syclGemmBatchedEx @@ -13409,44 +13399,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, {sycl::aspect::fp16}); main_stream->submit([&](sycl::handler &cgh) { - const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get(); - const void **ptrs_src_get_ct3 = ptrs_src.get(); - void **ptrs_dst_get_ct4 = ptrs_dst.get(); - + const void **ptrs_src_get = ptrs_src.get(); + void **ptrs_dst_get = ptrs_dst.get(); + size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2; + size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2; cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_compute_batched_ptrs( - src0_as_f16, src1_as_f16_get_ct1, - dst_t, ptrs_src_get_ct3, - ptrs_dst_get_ct4, ne12, ne13, ne23, - nb02, nb03, nb12, nb13, nbd2, nbd3, r2, - r3, item_ct1); + src0_as_f16, src1_f16, + dst_t, ptrs_src_get, + ptrs_dst_get, ne12, ne13, ne23, + nb02, nb03, nb12_scaled, nb13_scaled, + nbd2, nbd3, r2, r3, item_ct1); }); }); } - /* - DPCT1010:95: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this - code. - */ - SYCL_CHECK(0); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), - dpct::library_data_t::real_half, nb01 / sizeof(sycl::half), + dpct::library_data_t::real_half, nb01 / nb00, (const void **)(ptrs_src.get() + 1 * ne23), - dpct::library_data_t::real_half, nb11 / sizeof(float), beta, + dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); } #endif - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream); - } + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -13491,10 +13472,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 // KQV single-batch // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n"); - ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst); + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); + ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); From f105471ef6aa4727afac8240da398590d7277f45 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 09:59:43 +0200 Subject: [PATCH 133/401] server : fix newlines in help (#5785) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index bf20e0cf1c9c7..45c4aec4de502 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2080,8 +2080,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); From 6ea0f010ff6967034528d9e0b8330b9b0f0b7c13 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Fri, 1 Mar 2024 08:54:53 +0000 Subject: [PATCH 134/401] ci : add Ubuntu 22 Vulkan CI run (#5789) --- .github/workflows/build.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 66ad85938ca16..9144f926628a5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -145,6 +145,28 @@ jobs: cd build ctest -L main --verbose + ubuntu-22-cmake-vulkan: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libvulkan-dev + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake -DLLAMA_VULKAN=ON .. + cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 From 5cb02b4a012bb16c6c699c0c62c05ffa653eee0f Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 10:08:08 +0100 Subject: [PATCH 135/401] server: allow to override threads server pool with --threads-http (#5794) --- examples/server/README.md | 1 + examples/server/server.cpp | 16 ++++++++++++++++ 2 files changed, 17 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index 0e9bd7fd404ba..ad35306c60c4e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,6 +18,7 @@ The project is under active development, and we are [looking for feedback and co - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 45c4aec4de502..eea98796663d6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -43,6 +43,7 @@ struct server_params { int32_t write_timeout = 600; bool slots_endpoint = true; bool metrics_endpoint = false; + int n_threads_http = -1; }; bool server_verbose = false; @@ -2012,6 +2013,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); + printf(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -2298,6 +2300,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads_batch = std::stoi(argv[i]); } + else if (arg == "--threads-http") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + sparams.n_threads_http = std::stoi(argv[i]); + } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -3449,6 +3460,11 @@ int main(int argc, char **argv) }*/ //); + if (sparams.n_threads_http > 0) { + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + } + LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() From 9600d59e010c18f5872580a21734ea1bf1968d04 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Fri, 1 Mar 2024 03:15:36 -0600 Subject: [PATCH 136/401] unicode : switch to multimap based nfd_map (#5799) * switch to multimap based nfd_map due to compile time issues * simplify multimap keys * dont construct new locale every time --- llama.cpp | 11 +- unicode.h | 566 +++++++++++++++++++++++++++++------------------------- 2 files changed, 312 insertions(+), 265 deletions(-) diff --git a/llama.cpp b/llama.cpp index 62699ce52e197..a35f07aa47cda 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8947,10 +8947,10 @@ struct llm_tokenizer_wpm { std::vector codepoints = codepoints_from_utf8(text); std::vector nfd_codepoints; for (uint32_t code : codepoints) { - auto it = nfd_map.find(code); - if (it != nfd_map.end()) { - for (uint32_t c : it->second) { - nfd_codepoints.push_back(c); + auto it = nfd_map.equal_range(code); + if (it.first != it.second) { + for (auto jt = it.first; jt != it.second; jt++) { + nfd_codepoints.push_back(jt->second); } } else { nfd_codepoints.push_back(code); @@ -9001,12 +9001,13 @@ struct llm_tokenizer_wpm { } uint32_t to_lower(uint32_t code) { + static const std::locale locale("en_US.UTF-8"); #if defined(_WIN32) if (code > 0xFFFF) { return code; } #endif - return std::tolower(wchar_t(code), std::locale("en_US.UTF-8")); + return std::tolower(wchar_t(code), locale); } bool is_ascii_punct(uint32_t code) { diff --git a/unicode.h b/unicode.h index 620e2b5806c24..f6be4549bff10 100644 --- a/unicode.h +++ b/unicode.h @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include #include @@ -223,266 +224,311 @@ static const std::vector> control_ranges = { {0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, }; -static const std::unordered_map> nfd_map = { -{0xC0, {0x41, 0x300}}, {0xC1, {0x41, 0x301}}, {0xC2, {0x41, 0x302}}, {0xC3, {0x41, 0x303}}, {0xC4, {0x41, 0x308}}, {0xC5, {0x41, 0x30A}}, {0xC7, {0x43, 0x327}}, {0xC8, {0x45, 0x300}}, -{0xC9, {0x45, 0x301}}, {0xCA, {0x45, 0x302}}, {0xCB, {0x45, 0x308}}, {0xCC, {0x49, 0x300}}, {0xCD, {0x49, 0x301}}, {0xCE, {0x49, 0x302}}, {0xCF, {0x49, 0x308}}, {0xD1, {0x4E, 0x303}}, -{0xD2, {0x4F, 0x300}}, {0xD3, {0x4F, 0x301}}, {0xD4, {0x4F, 0x302}}, {0xD5, {0x4F, 0x303}}, {0xD6, {0x4F, 0x308}}, {0xD9, {0x55, 0x300}}, {0xDA, {0x55, 0x301}}, {0xDB, {0x55, 0x302}}, -{0xDC, {0x55, 0x308}}, {0xDD, {0x59, 0x301}}, {0xE0, {0x61, 0x300}}, {0xE1, {0x61, 0x301}}, {0xE2, {0x61, 0x302}}, {0xE3, {0x61, 0x303}}, {0xE4, {0x61, 0x308}}, {0xE5, {0x61, 0x30A}}, -{0xE7, {0x63, 0x327}}, {0xE8, {0x65, 0x300}}, {0xE9, {0x65, 0x301}}, {0xEA, {0x65, 0x302}}, {0xEB, {0x65, 0x308}}, {0xEC, {0x69, 0x300}}, {0xED, {0x69, 0x301}}, {0xEE, {0x69, 0x302}}, -{0xEF, {0x69, 0x308}}, {0xF1, {0x6E, 0x303}}, {0xF2, {0x6F, 0x300}}, {0xF3, {0x6F, 0x301}}, {0xF4, {0x6F, 0x302}}, {0xF5, {0x6F, 0x303}}, {0xF6, {0x6F, 0x308}}, {0xF9, {0x75, 0x300}}, -{0xFA, {0x75, 0x301}}, {0xFB, {0x75, 0x302}}, {0xFC, {0x75, 0x308}}, {0xFD, {0x79, 0x301}}, {0xFF, {0x79, 0x308}}, {0x100, {0x41, 0x304}}, {0x101, {0x61, 0x304}}, {0x102, {0x41, 0x306}}, -{0x103, {0x61, 0x306}}, {0x104, {0x41, 0x328}}, {0x105, {0x61, 0x328}}, {0x106, {0x43, 0x301}}, {0x107, {0x63, 0x301}}, {0x108, {0x43, 0x302}}, {0x109, {0x63, 0x302}}, {0x10A, {0x43, 0x307}}, -{0x10B, {0x63, 0x307}}, {0x10C, {0x43, 0x30C}}, {0x10D, {0x63, 0x30C}}, {0x10E, {0x44, 0x30C}}, {0x10F, {0x64, 0x30C}}, {0x112, {0x45, 0x304}}, {0x113, {0x65, 0x304}}, {0x114, {0x45, 0x306}}, -{0x115, {0x65, 0x306}}, {0x116, {0x45, 0x307}}, {0x117, {0x65, 0x307}}, {0x118, {0x45, 0x328}}, {0x119, {0x65, 0x328}}, {0x11A, {0x45, 0x30C}}, {0x11B, {0x65, 0x30C}}, {0x11C, {0x47, 0x302}}, -{0x11D, {0x67, 0x302}}, {0x11E, {0x47, 0x306}}, {0x11F, {0x67, 0x306}}, {0x120, {0x47, 0x307}}, {0x121, {0x67, 0x307}}, {0x122, {0x47, 0x327}}, {0x123, {0x67, 0x327}}, {0x124, {0x48, 0x302}}, -{0x125, {0x68, 0x302}}, {0x128, {0x49, 0x303}}, {0x129, {0x69, 0x303}}, {0x12A, {0x49, 0x304}}, {0x12B, {0x69, 0x304}}, {0x12C, {0x49, 0x306}}, {0x12D, {0x69, 0x306}}, {0x12E, {0x49, 0x328}}, -{0x12F, {0x69, 0x328}}, {0x130, {0x49, 0x307}}, {0x134, {0x4A, 0x302}}, {0x135, {0x6A, 0x302}}, {0x136, {0x4B, 0x327}}, {0x137, {0x6B, 0x327}}, {0x139, {0x4C, 0x301}}, {0x13A, {0x6C, 0x301}}, -{0x13B, {0x4C, 0x327}}, {0x13C, {0x6C, 0x327}}, {0x13D, {0x4C, 0x30C}}, {0x13E, {0x6C, 0x30C}}, {0x143, {0x4E, 0x301}}, {0x144, {0x6E, 0x301}}, {0x145, {0x4E, 0x327}}, {0x146, {0x6E, 0x327}}, -{0x147, {0x4E, 0x30C}}, {0x148, {0x6E, 0x30C}}, {0x14C, {0x4F, 0x304}}, {0x14D, {0x6F, 0x304}}, {0x14E, {0x4F, 0x306}}, {0x14F, {0x6F, 0x306}}, {0x150, {0x4F, 0x30B}}, {0x151, {0x6F, 0x30B}}, -{0x154, {0x52, 0x301}}, {0x155, {0x72, 0x301}}, {0x156, {0x52, 0x327}}, {0x157, {0x72, 0x327}}, {0x158, {0x52, 0x30C}}, {0x159, {0x72, 0x30C}}, {0x15A, {0x53, 0x301}}, {0x15B, {0x73, 0x301}}, -{0x15C, {0x53, 0x302}}, {0x15D, {0x73, 0x302}}, {0x15E, {0x53, 0x327}}, {0x15F, {0x73, 0x327}}, {0x160, {0x53, 0x30C}}, {0x161, {0x73, 0x30C}}, {0x162, {0x54, 0x327}}, {0x163, {0x74, 0x327}}, -{0x164, {0x54, 0x30C}}, {0x165, {0x74, 0x30C}}, {0x168, {0x55, 0x303}}, {0x169, {0x75, 0x303}}, {0x16A, {0x55, 0x304}}, {0x16B, {0x75, 0x304}}, {0x16C, {0x55, 0x306}}, {0x16D, {0x75, 0x306}}, -{0x16E, {0x55, 0x30A}}, {0x16F, {0x75, 0x30A}}, {0x170, {0x55, 0x30B}}, {0x171, {0x75, 0x30B}}, {0x172, {0x55, 0x328}}, {0x173, {0x75, 0x328}}, {0x174, {0x57, 0x302}}, {0x175, {0x77, 0x302}}, -{0x176, {0x59, 0x302}}, {0x177, {0x79, 0x302}}, {0x178, {0x59, 0x308}}, {0x179, {0x5A, 0x301}}, {0x17A, {0x7A, 0x301}}, {0x17B, {0x5A, 0x307}}, {0x17C, {0x7A, 0x307}}, {0x17D, {0x5A, 0x30C}}, -{0x17E, {0x7A, 0x30C}}, {0x1A0, {0x4F, 0x31B}}, {0x1A1, {0x6F, 0x31B}}, {0x1AF, {0x55, 0x31B}}, {0x1B0, {0x75, 0x31B}}, {0x1CD, {0x41, 0x30C}}, {0x1CE, {0x61, 0x30C}}, {0x1CF, {0x49, 0x30C}}, -{0x1D0, {0x69, 0x30C}}, {0x1D1, {0x4F, 0x30C}}, {0x1D2, {0x6F, 0x30C}}, {0x1D3, {0x55, 0x30C}}, {0x1D4, {0x75, 0x30C}}, {0x1D5, {0x55, 0x308, 0x304}}, {0x1D6, {0x75, 0x308, 0x304}}, -{0x1D7, {0x55, 0x308, 0x301}}, {0x1D8, {0x75, 0x308, 0x301}}, {0x1D9, {0x55, 0x308, 0x30C}}, {0x1DA, {0x75, 0x308, 0x30C}}, {0x1DB, {0x55, 0x308, 0x300}}, {0x1DC, {0x75, 0x308, 0x300}}, -{0x1DE, {0x41, 0x308, 0x304}}, {0x1DF, {0x61, 0x308, 0x304}}, {0x1E0, {0x41, 0x307, 0x304}}, {0x1E1, {0x61, 0x307, 0x304}}, {0x1E2, {0xC6, 0x304}}, {0x1E3, {0xE6, 0x304}}, {0x1E6, {0x47, 0x30C}}, -{0x1E7, {0x67, 0x30C}}, {0x1E8, {0x4B, 0x30C}}, {0x1E9, {0x6B, 0x30C}}, {0x1EA, {0x4F, 0x328}}, {0x1EB, {0x6F, 0x328}}, {0x1EC, {0x4F, 0x328, 0x304}}, {0x1ED, {0x6F, 0x328, 0x304}}, -{0x1EE, {0x1B7, 0x30C}}, {0x1EF, {0x292, 0x30C}}, {0x1F0, {0x6A, 0x30C}}, {0x1F4, {0x47, 0x301}}, {0x1F5, {0x67, 0x301}}, {0x1F8, {0x4E, 0x300}}, {0x1F9, {0x6E, 0x300}}, {0x1FA, {0x41, 0x30A, 0x301}}, -{0x1FB, {0x61, 0x30A, 0x301}}, {0x1FC, {0xC6, 0x301}}, {0x1FD, {0xE6, 0x301}}, {0x1FE, {0xD8, 0x301}}, {0x1FF, {0xF8, 0x301}}, {0x200, {0x41, 0x30F}}, {0x201, {0x61, 0x30F}}, {0x202, {0x41, 0x311}}, -{0x203, {0x61, 0x311}}, {0x204, {0x45, 0x30F}}, {0x205, {0x65, 0x30F}}, {0x206, {0x45, 0x311}}, {0x207, {0x65, 0x311}}, {0x208, {0x49, 0x30F}}, {0x209, {0x69, 0x30F}}, {0x20A, {0x49, 0x311}}, -{0x20B, {0x69, 0x311}}, {0x20C, {0x4F, 0x30F}}, {0x20D, {0x6F, 0x30F}}, {0x20E, {0x4F, 0x311}}, {0x20F, {0x6F, 0x311}}, {0x210, {0x52, 0x30F}}, {0x211, {0x72, 0x30F}}, {0x212, {0x52, 0x311}}, -{0x213, {0x72, 0x311}}, {0x214, {0x55, 0x30F}}, {0x215, {0x75, 0x30F}}, {0x216, {0x55, 0x311}}, {0x217, {0x75, 0x311}}, {0x218, {0x53, 0x326}}, {0x219, {0x73, 0x326}}, {0x21A, {0x54, 0x326}}, -{0x21B, {0x74, 0x326}}, {0x21E, {0x48, 0x30C}}, {0x21F, {0x68, 0x30C}}, {0x226, {0x41, 0x307}}, {0x227, {0x61, 0x307}}, {0x228, {0x45, 0x327}}, {0x229, {0x65, 0x327}}, {0x22A, {0x4F, 0x308, 0x304}}, -{0x22B, {0x6F, 0x308, 0x304}}, {0x22C, {0x4F, 0x303, 0x304}}, {0x22D, {0x6F, 0x303, 0x304}}, {0x22E, {0x4F, 0x307}}, {0x22F, {0x6F, 0x307}}, {0x230, {0x4F, 0x307, 0x304}}, -{0x231, {0x6F, 0x307, 0x304}}, {0x232, {0x59, 0x304}}, {0x233, {0x79, 0x304}}, {0x340, {0x300}}, {0x341, {0x301}}, {0x343, {0x313}}, {0x344, {0x308, 0x301}}, {0x374, {0x2B9}}, {0x37E, {0x3B}}, -{0x385, {0xA8, 0x301}}, {0x386, {0x391, 0x301}}, {0x387, {0xB7}}, {0x388, {0x395, 0x301}}, {0x389, {0x397, 0x301}}, {0x38A, {0x399, 0x301}}, {0x38C, {0x39F, 0x301}}, {0x38E, {0x3A5, 0x301}}, -{0x38F, {0x3A9, 0x301}}, {0x390, {0x3B9, 0x308, 0x301}}, {0x3AA, {0x399, 0x308}}, {0x3AB, {0x3A5, 0x308}}, {0x3AC, {0x3B1, 0x301}}, {0x3AD, {0x3B5, 0x301}}, {0x3AE, {0x3B7, 0x301}}, -{0x3AF, {0x3B9, 0x301}}, {0x3B0, {0x3C5, 0x308, 0x301}}, {0x3CA, {0x3B9, 0x308}}, {0x3CB, {0x3C5, 0x308}}, {0x3CC, {0x3BF, 0x301}}, {0x3CD, {0x3C5, 0x301}}, {0x3CE, {0x3C9, 0x301}}, -{0x3D3, {0x3D2, 0x301}}, {0x3D4, {0x3D2, 0x308}}, {0x400, {0x415, 0x300}}, {0x401, {0x415, 0x308}}, {0x403, {0x413, 0x301}}, {0x407, {0x406, 0x308}}, {0x40C, {0x41A, 0x301}}, {0x40D, {0x418, 0x300}}, -{0x40E, {0x423, 0x306}}, {0x419, {0x418, 0x306}}, {0x439, {0x438, 0x306}}, {0x450, {0x435, 0x300}}, {0x451, {0x435, 0x308}}, {0x453, {0x433, 0x301}}, {0x457, {0x456, 0x308}}, {0x45C, {0x43A, 0x301}}, -{0x45D, {0x438, 0x300}}, {0x45E, {0x443, 0x306}}, {0x476, {0x474, 0x30F}}, {0x477, {0x475, 0x30F}}, {0x4C1, {0x416, 0x306}}, {0x4C2, {0x436, 0x306}}, {0x4D0, {0x410, 0x306}}, {0x4D1, {0x430, 0x306}}, -{0x4D2, {0x410, 0x308}}, {0x4D3, {0x430, 0x308}}, {0x4D6, {0x415, 0x306}}, {0x4D7, {0x435, 0x306}}, {0x4DA, {0x4D8, 0x308}}, {0x4DB, {0x4D9, 0x308}}, {0x4DC, {0x416, 0x308}}, {0x4DD, {0x436, 0x308}}, -{0x4DE, {0x417, 0x308}}, {0x4DF, {0x437, 0x308}}, {0x4E2, {0x418, 0x304}}, {0x4E3, {0x438, 0x304}}, {0x4E4, {0x418, 0x308}}, {0x4E5, {0x438, 0x308}}, {0x4E6, {0x41E, 0x308}}, {0x4E7, {0x43E, 0x308}}, -{0x4EA, {0x4E8, 0x308}}, {0x4EB, {0x4E9, 0x308}}, {0x4EC, {0x42D, 0x308}}, {0x4ED, {0x44D, 0x308}}, {0x4EE, {0x423, 0x304}}, {0x4EF, {0x443, 0x304}}, {0x4F0, {0x423, 0x308}}, {0x4F1, {0x443, 0x308}}, -{0x4F2, {0x423, 0x30B}}, {0x4F3, {0x443, 0x30B}}, {0x4F4, {0x427, 0x308}}, {0x4F5, {0x447, 0x308}}, {0x4F8, {0x42B, 0x308}}, {0x4F9, {0x44B, 0x308}}, {0x622, {0x627, 0x653}}, {0x623, {0x627, 0x654}}, -{0x624, {0x648, 0x654}}, {0x625, {0x627, 0x655}}, {0x626, {0x64A, 0x654}}, {0x6C0, {0x6D5, 0x654}}, {0x6C2, {0x6C1, 0x654}}, {0x6D3, {0x6D2, 0x654}}, {0x929, {0x928, 0x93C}}, {0x931, {0x930, 0x93C}}, -{0x934, {0x933, 0x93C}}, {0x958, {0x915, 0x93C}}, {0x959, {0x916, 0x93C}}, {0x95A, {0x917, 0x93C}}, {0x95B, {0x91C, 0x93C}}, {0x95C, {0x921, 0x93C}}, {0x95D, {0x922, 0x93C}}, {0x95E, {0x92B, 0x93C}}, -{0x95F, {0x92F, 0x93C}}, {0x9CB, {0x9C7, 0x9BE}}, {0x9CC, {0x9C7, 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{0x865C}}, {0x2F9B5, {0x8667}}, {0x2F9B6, {0x8669}}, {0x2F9B7, {0x86A9}}, {0x2F9B8, {0x8688}}, -{0x2F9B9, {0x870E}}, {0x2F9BA, {0x86E2}}, {0x2F9BB, {0x8779}}, {0x2F9BC, {0x8728}}, {0x2F9BD, {0x876B}}, {0x2F9BE, {0x8786}}, {0x2F9BF, {0x45D7}}, {0x2F9C0, {0x87E1}}, {0x2F9C1, {0x8801}}, -{0x2F9C2, {0x45F9}}, {0x2F9C3, {0x8860}}, {0x2F9C4, {0x8863}}, {0x2F9C5, {0x27667}}, {0x2F9C6, {0x88D7}}, {0x2F9C7, {0x88DE}}, {0x2F9C8, {0x4635}}, {0x2F9C9, {0x88FA}}, {0x2F9CA, {0x34BB}}, -{0x2F9CB, {0x278AE}}, {0x2F9CC, {0x27966}}, {0x2F9CD, {0x46BE}}, {0x2F9CE, {0x46C7}}, {0x2F9CF, {0x8AA0}}, {0x2F9D0, {0x8AED}}, {0x2F9D1, {0x8B8A}}, {0x2F9D2, {0x8C55}}, {0x2F9D3, {0x27CA8}}, -{0x2F9D4, {0x8CAB}}, {0x2F9D5, {0x8CC1}}, {0x2F9D6, {0x8D1B}}, {0x2F9D7, {0x8D77}}, {0x2F9D8, {0x27F2F}}, {0x2F9D9, {0x20804}}, {0x2F9DA, {0x8DCB}}, {0x2F9DB, {0x8DBC}}, {0x2F9DC, {0x8DF0}}, -{0x2F9DD, {0x208DE}}, {0x2F9DE, {0x8ED4}}, {0x2F9DF, {0x8F38}}, {0x2F9E0, {0x285D2}}, {0x2F9E1, {0x285ED}}, {0x2F9E2, {0x9094}}, {0x2F9E3, {0x90F1}}, {0x2F9E4, {0x9111}}, {0x2F9E5, {0x2872E}}, -{0x2F9E6, {0x911B}}, {0x2F9E7, {0x9238}}, {0x2F9E8, {0x92D7}}, {0x2F9E9, {0x92D8}}, {0x2F9EA, {0x927C}}, {0x2F9EB, {0x93F9}}, {0x2F9EC, {0x9415}}, {0x2F9ED, {0x28BFA}}, {0x2F9EE, {0x958B}}, -{0x2F9EF, {0x4995}}, {0x2F9F0, {0x95B7}}, {0x2F9F1, {0x28D77}}, {0x2F9F2, {0x49E6}}, {0x2F9F3, {0x96C3}}, {0x2F9F4, {0x5DB2}}, {0x2F9F5, {0x9723}}, {0x2F9F6, {0x29145}}, {0x2F9F7, {0x2921A}}, -{0x2F9F8, {0x4A6E}}, {0x2F9F9, {0x4A76}}, {0x2F9FA, {0x97E0}}, {0x2F9FB, {0x2940A}}, {0x2F9FC, {0x4AB2}}, {0x2F9FD, {0x29496}}, {0x2F9FE, {0x980B}}, {0x2F9FF, {0x980B}}, {0x2FA00, {0x9829}}, -{0x2FA01, {0x295B6}}, {0x2FA02, {0x98E2}}, {0x2FA03, {0x4B33}}, {0x2FA04, {0x9929}}, {0x2FA05, {0x99A7}}, {0x2FA06, {0x99C2}}, {0x2FA07, {0x99FE}}, {0x2FA08, {0x4BCE}}, {0x2FA09, {0x29B30}}, -{0x2FA0A, {0x9B12}}, {0x2FA0B, {0x9C40}}, {0x2FA0C, {0x9CFD}}, {0x2FA0D, {0x4CCE}}, {0x2FA0E, {0x4CED}}, {0x2FA0F, {0x9D67}}, {0x2FA10, {0x2A0CE}}, {0x2FA11, {0x4CF8}}, {0x2FA12, {0x2A105}}, -{0x2FA13, {0x2A20E}}, {0x2FA14, {0x2A291}}, {0x2FA15, {0x9EBB}}, {0x2FA16, {0x4D56}}, {0x2FA17, {0x9EF9}}, {0x2FA18, {0x9EFE}}, {0x2FA19, {0x9F05}}, {0x2FA1A, {0x9F0F}}, {0x2FA1B, {0x9F16}}, -{0x2FA1D, {0x2A600}}, +static const std::multimap nfd_map = { +{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43}, +{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301}, +{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F}, +{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301}, +{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63}, +{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301}, +{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F}, +{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301}, +{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61}, +{0x105, 0x328}, {0x106, 0x43}, {0x106, 0x301}, {0x107, 0x63}, {0x107, 0x301}, {0x108, 0x43}, {0x108, 0x302}, {0x109, 0x63}, {0x109, 0x302}, {0x10A, 0x43}, {0x10A, 0x307}, {0x10B, 0x63}, +{0x10B, 0x307}, {0x10C, 0x43}, {0x10C, 0x30C}, {0x10D, 0x63}, {0x10D, 0x30C}, {0x10E, 0x44}, {0x10E, 0x30C}, {0x10F, 0x64}, {0x10F, 0x30C}, {0x112, 0x45}, {0x112, 0x304}, {0x113, 0x65}, +{0x113, 0x304}, {0x114, 0x45}, {0x114, 0x306}, {0x115, 0x65}, {0x115, 0x306}, {0x116, 0x45}, {0x116, 0x307}, {0x117, 0x65}, {0x117, 0x307}, {0x118, 0x45}, {0x118, 0x328}, {0x119, 0x65}, +{0x119, 0x328}, {0x11A, 0x45}, {0x11A, 0x30C}, {0x11B, 0x65}, {0x11B, 0x30C}, {0x11C, 0x47}, {0x11C, 0x302}, {0x11D, 0x67}, {0x11D, 0x302}, {0x11E, 0x47}, {0x11E, 0x306}, {0x11F, 0x67}, +{0x11F, 0x306}, {0x120, 0x47}, {0x120, 0x307}, {0x121, 0x67}, {0x121, 0x307}, {0x122, 0x47}, {0x122, 0x327}, {0x123, 0x67}, {0x123, 0x327}, {0x124, 0x48}, {0x124, 0x302}, {0x125, 0x68}, +{0x125, 0x302}, {0x128, 0x49}, {0x128, 0x303}, {0x129, 0x69}, {0x129, 0x303}, {0x12A, 0x49}, {0x12A, 0x304}, {0x12B, 0x69}, {0x12B, 0x304}, {0x12C, 0x49}, {0x12C, 0x306}, {0x12D, 0x69}, +{0x12D, 0x306}, {0x12E, 0x49}, {0x12E, 0x328}, {0x12F, 0x69}, {0x12F, 0x328}, {0x130, 0x49}, {0x130, 0x307}, {0x134, 0x4A}, {0x134, 0x302}, {0x135, 0x6A}, {0x135, 0x302}, {0x136, 0x4B}, +{0x136, 0x327}, {0x137, 0x6B}, {0x137, 0x327}, {0x139, 0x4C}, {0x139, 0x301}, {0x13A, 0x6C}, {0x13A, 0x301}, {0x13B, 0x4C}, {0x13B, 0x327}, {0x13C, 0x6C}, {0x13C, 0x327}, {0x13D, 0x4C}, +{0x13D, 0x30C}, {0x13E, 0x6C}, {0x13E, 0x30C}, {0x143, 0x4E}, {0x143, 0x301}, {0x144, 0x6E}, {0x144, 0x301}, {0x145, 0x4E}, {0x145, 0x327}, {0x146, 0x6E}, {0x146, 0x327}, {0x147, 0x4E}, +{0x147, 0x30C}, {0x148, 0x6E}, {0x148, 0x30C}, {0x14C, 0x4F}, {0x14C, 0x304}, {0x14D, 0x6F}, {0x14D, 0x304}, {0x14E, 0x4F}, {0x14E, 0x306}, {0x14F, 0x6F}, {0x14F, 0x306}, {0x150, 0x4F}, +{0x150, 0x30B}, {0x151, 0x6F}, {0x151, 0x30B}, {0x154, 0x52}, {0x154, 0x301}, {0x155, 0x72}, {0x155, 0x301}, {0x156, 0x52}, {0x156, 0x327}, {0x157, 0x72}, {0x157, 0x327}, {0x158, 0x52}, +{0x158, 0x30C}, {0x159, 0x72}, {0x159, 0x30C}, {0x15A, 0x53}, {0x15A, 0x301}, {0x15B, 0x73}, {0x15B, 0x301}, {0x15C, 0x53}, {0x15C, 0x302}, {0x15D, 0x73}, {0x15D, 0x302}, {0x15E, 0x53}, +{0x15E, 0x327}, {0x15F, 0x73}, {0x15F, 0x327}, {0x160, 0x53}, {0x160, 0x30C}, {0x161, 0x73}, {0x161, 0x30C}, {0x162, 0x54}, {0x162, 0x327}, {0x163, 0x74}, {0x163, 0x327}, {0x164, 0x54}, +{0x164, 0x30C}, {0x165, 0x74}, {0x165, 0x30C}, {0x168, 0x55}, {0x168, 0x303}, {0x169, 0x75}, {0x169, 0x303}, {0x16A, 0x55}, {0x16A, 0x304}, {0x16B, 0x75}, {0x16B, 0x304}, {0x16C, 0x55}, +{0x16C, 0x306}, {0x16D, 0x75}, {0x16D, 0x306}, {0x16E, 0x55}, {0x16E, 0x30A}, {0x16F, 0x75}, {0x16F, 0x30A}, {0x170, 0x55}, {0x170, 0x30B}, {0x171, 0x75}, {0x171, 0x30B}, {0x172, 0x55}, +{0x172, 0x328}, {0x173, 0x75}, {0x173, 0x328}, {0x174, 0x57}, {0x174, 0x302}, {0x175, 0x77}, {0x175, 0x302}, {0x176, 0x59}, {0x176, 0x302}, {0x177, 0x79}, {0x177, 0x302}, {0x178, 0x59}, +{0x178, 0x308}, {0x179, 0x5A}, {0x179, 0x301}, {0x17A, 0x7A}, {0x17A, 0x301}, {0x17B, 0x5A}, {0x17B, 0x307}, {0x17C, 0x7A}, {0x17C, 0x307}, {0x17D, 0x5A}, {0x17D, 0x30C}, {0x17E, 0x7A}, +{0x17E, 0x30C}, {0x1A0, 0x4F}, {0x1A0, 0x31B}, {0x1A1, 0x6F}, {0x1A1, 0x31B}, {0x1AF, 0x55}, {0x1AF, 0x31B}, {0x1B0, 0x75}, {0x1B0, 0x31B}, {0x1CD, 0x41}, {0x1CD, 0x30C}, {0x1CE, 0x61}, +{0x1CE, 0x30C}, {0x1CF, 0x49}, {0x1CF, 0x30C}, {0x1D0, 0x69}, {0x1D0, 0x30C}, {0x1D1, 0x4F}, {0x1D1, 0x30C}, {0x1D2, 0x6F}, {0x1D2, 0x30C}, {0x1D3, 0x55}, {0x1D3, 0x30C}, {0x1D4, 0x75}, +{0x1D4, 0x30C}, {0x1D5, 0x55}, {0x1D5, 0x308}, {0x1D5, 0x304}, {0x1D6, 0x75}, {0x1D6, 0x308}, {0x1D6, 0x304}, {0x1D7, 0x55}, {0x1D7, 0x308}, {0x1D7, 0x301}, {0x1D8, 0x75}, {0x1D8, 0x308}, +{0x1D8, 0x301}, {0x1D9, 0x55}, {0x1D9, 0x308}, {0x1D9, 0x30C}, {0x1DA, 0x75}, {0x1DA, 0x308}, {0x1DA, 0x30C}, {0x1DB, 0x55}, {0x1DB, 0x308}, {0x1DB, 0x300}, {0x1DC, 0x75}, {0x1DC, 0x308}, +{0x1DC, 0x300}, {0x1DE, 0x41}, {0x1DE, 0x308}, {0x1DE, 0x304}, {0x1DF, 0x61}, {0x1DF, 0x308}, {0x1DF, 0x304}, {0x1E0, 0x41}, {0x1E0, 0x307}, {0x1E0, 0x304}, {0x1E1, 0x61}, {0x1E1, 0x307}, +{0x1E1, 0x304}, {0x1E2, 0xC6}, {0x1E2, 0x304}, {0x1E3, 0xE6}, {0x1E3, 0x304}, {0x1E6, 0x47}, {0x1E6, 0x30C}, {0x1E7, 0x67}, {0x1E7, 0x30C}, {0x1E8, 0x4B}, {0x1E8, 0x30C}, {0x1E9, 0x6B}, +{0x1E9, 0x30C}, {0x1EA, 0x4F}, {0x1EA, 0x328}, {0x1EB, 0x6F}, {0x1EB, 0x328}, {0x1EC, 0x4F}, {0x1EC, 0x328}, {0x1EC, 0x304}, {0x1ED, 0x6F}, {0x1ED, 0x328}, {0x1ED, 0x304}, {0x1EE, 0x1B7}, +{0x1EE, 0x30C}, {0x1EF, 0x292}, {0x1EF, 0x30C}, {0x1F0, 0x6A}, {0x1F0, 0x30C}, {0x1F4, 0x47}, {0x1F4, 0x301}, {0x1F5, 0x67}, {0x1F5, 0x301}, {0x1F8, 0x4E}, {0x1F8, 0x300}, {0x1F9, 0x6E}, +{0x1F9, 0x300}, {0x1FA, 0x41}, {0x1FA, 0x30A}, {0x1FA, 0x301}, {0x1FB, 0x61}, {0x1FB, 0x30A}, {0x1FB, 0x301}, {0x1FC, 0xC6}, {0x1FC, 0x301}, {0x1FD, 0xE6}, {0x1FD, 0x301}, {0x1FE, 0xD8}, +{0x1FE, 0x301}, {0x1FF, 0xF8}, {0x1FF, 0x301}, {0x200, 0x41}, {0x200, 0x30F}, {0x201, 0x61}, {0x201, 0x30F}, {0x202, 0x41}, {0x202, 0x311}, {0x203, 0x61}, {0x203, 0x311}, {0x204, 0x45}, +{0x204, 0x30F}, {0x205, 0x65}, {0x205, 0x30F}, {0x206, 0x45}, {0x206, 0x311}, {0x207, 0x65}, {0x207, 0x311}, {0x208, 0x49}, {0x208, 0x30F}, {0x209, 0x69}, {0x209, 0x30F}, {0x20A, 0x49}, +{0x20A, 0x311}, {0x20B, 0x69}, {0x20B, 0x311}, {0x20C, 0x4F}, {0x20C, 0x30F}, {0x20D, 0x6F}, {0x20D, 0x30F}, {0x20E, 0x4F}, {0x20E, 0x311}, {0x20F, 0x6F}, {0x20F, 0x311}, {0x210, 0x52}, +{0x210, 0x30F}, {0x211, 0x72}, {0x211, 0x30F}, {0x212, 0x52}, {0x212, 0x311}, {0x213, 0x72}, {0x213, 0x311}, {0x214, 0x55}, {0x214, 0x30F}, {0x215, 0x75}, {0x215, 0x30F}, {0x216, 0x55}, +{0x216, 0x311}, {0x217, 0x75}, {0x217, 0x311}, {0x218, 0x53}, {0x218, 0x326}, {0x219, 0x73}, {0x219, 0x326}, {0x21A, 0x54}, {0x21A, 0x326}, {0x21B, 0x74}, {0x21B, 0x326}, {0x21E, 0x48}, +{0x21E, 0x30C}, {0x21F, 0x68}, {0x21F, 0x30C}, {0x226, 0x41}, {0x226, 0x307}, {0x227, 0x61}, {0x227, 0x307}, {0x228, 0x45}, {0x228, 0x327}, {0x229, 0x65}, {0x229, 0x327}, {0x22A, 0x4F}, +{0x22A, 0x308}, {0x22A, 0x304}, {0x22B, 0x6F}, {0x22B, 0x308}, {0x22B, 0x304}, {0x22C, 0x4F}, {0x22C, 0x303}, {0x22C, 0x304}, {0x22D, 0x6F}, {0x22D, 0x303}, {0x22D, 0x304}, {0x22E, 0x4F}, +{0x22E, 0x307}, {0x22F, 0x6F}, {0x22F, 0x307}, {0x230, 0x4F}, {0x230, 0x307}, {0x230, 0x304}, {0x231, 0x6F}, {0x231, 0x307}, {0x231, 0x304}, {0x232, 0x59}, {0x232, 0x304}, {0x233, 0x79}, +{0x233, 0x304}, {0x340, 0x300}, {0x341, 0x301}, {0x343, 0x313}, {0x344, 0x308}, {0x344, 0x301}, {0x374, 0x2B9}, {0x37E, 0x3B}, {0x385, 0xA8}, {0x385, 0x301}, {0x386, 0x391}, {0x386, 0x301}, +{0x387, 0xB7}, {0x388, 0x395}, {0x388, 0x301}, {0x389, 0x397}, {0x389, 0x301}, {0x38A, 0x399}, {0x38A, 0x301}, {0x38C, 0x39F}, {0x38C, 0x301}, {0x38E, 0x3A5}, {0x38E, 0x301}, {0x38F, 0x3A9}, +{0x38F, 0x301}, {0x390, 0x3B9}, {0x390, 0x308}, {0x390, 0x301}, {0x3AA, 0x399}, {0x3AA, 0x308}, {0x3AB, 0x3A5}, {0x3AB, 0x308}, {0x3AC, 0x3B1}, {0x3AC, 0x301}, {0x3AD, 0x3B5}, {0x3AD, 0x301}, +{0x3AE, 0x3B7}, {0x3AE, 0x301}, {0x3AF, 0x3B9}, {0x3AF, 0x301}, {0x3B0, 0x3C5}, {0x3B0, 0x308}, {0x3B0, 0x301}, {0x3CA, 0x3B9}, {0x3CA, 0x308}, {0x3CB, 0x3C5}, {0x3CB, 0x308}, {0x3CC, 0x3BF}, +{0x3CC, 0x301}, {0x3CD, 0x3C5}, {0x3CD, 0x301}, {0x3CE, 0x3C9}, {0x3CE, 0x301}, {0x3D3, 0x3D2}, {0x3D3, 0x301}, {0x3D4, 0x3D2}, {0x3D4, 0x308}, {0x400, 0x415}, {0x400, 0x300}, {0x401, 0x415}, +{0x401, 0x308}, {0x403, 0x413}, {0x403, 0x301}, {0x407, 0x406}, {0x407, 0x308}, {0x40C, 0x41A}, {0x40C, 0x301}, {0x40D, 0x418}, {0x40D, 0x300}, {0x40E, 0x423}, {0x40E, 0x306}, {0x419, 0x418}, +{0x419, 0x306}, {0x439, 0x438}, {0x439, 0x306}, {0x450, 0x435}, {0x450, 0x300}, {0x451, 0x435}, {0x451, 0x308}, {0x453, 0x433}, {0x453, 0x301}, {0x457, 0x456}, {0x457, 0x308}, {0x45C, 0x43A}, +{0x45C, 0x301}, {0x45D, 0x438}, {0x45D, 0x300}, {0x45E, 0x443}, {0x45E, 0x306}, {0x476, 0x474}, {0x476, 0x30F}, {0x477, 0x475}, {0x477, 0x30F}, {0x4C1, 0x416}, {0x4C1, 0x306}, {0x4C2, 0x436}, +{0x4C2, 0x306}, {0x4D0, 0x410}, {0x4D0, 0x306}, {0x4D1, 0x430}, 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0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E}, +{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995}, +{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76}, +{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33}, +{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE}, +{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9}, +{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600}, }; static std::string codepoint_to_utf8(uint32_t cp) { From 3ab8b3a92ede46df88bc5a2dfca3777de4a2b2b6 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 12:39:06 +0100 Subject: [PATCH 137/401] llama : cleanup unused mmq flags (#5772) * cleanup unused --no-mul-mat-q,-nommq, -mmq, --mul-mat-q, mul_mat_q * remove: mul_mat_q in compare llama bench and usage * update llama-bench --------- Co-authored-by: slaren --- common/common.cpp | 2 -- common/common.h | 1 - examples/batched-bench/batched-bench.cpp | 18 +++++--------- examples/llama-bench/README.md | 1 - examples/llama-bench/llama-bench.cpp | 30 +++--------------------- examples/server/server.cpp | 8 ------- llama.cpp | 3 --- llama.h | 1 - scripts/compare-llama-bench.py | 2 +- 9 files changed, 10 insertions(+), 56 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 18289755c9ceb..bf1ed8a66fea6 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1281,7 +1281,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_batch = params.n_batch; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; cparams.seed = params.seed; cparams.logits_all = params.logits_all; cparams.embedding = params.embedding; @@ -1725,7 +1724,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); diff --git a/common/common.h b/common/common.h index 25003df2600d1..ab62bdb822d71 100644 --- a/common/common.h +++ b/common/common.h @@ -115,7 +115,6 @@ struct gpt_params { bool kl_divergence = false; // compute KL-divergence - bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index b4b8a38e1bcf5..19aff18aefde7 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -32,16 +32,15 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n" , argv[0]); + printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] \n" , argv[0]); printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); + printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); return 1 ; } int n_kv_max = 2048; int is_pp_shared = 0; int n_gpu_layers = 0; - int mmq = 0; std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; std::vector n_tg = { 128, 256, }; @@ -65,19 +64,15 @@ int main(int argc, char ** argv) { } if (argc >= 6) { - mmq = std::atoi(argv[5]); + n_pp = parse_list(argv[5]); } if (argc >= 7) { - n_pp = parse_list(argv[6]); + n_tg = parse_list(argv[6]); } if (argc >= 8) { - n_tg = parse_list(argv[7]); - } - - if (argc >= 9) { - n_pl = parse_list(argv[8]); + n_pl = parse_list(argv[7]); } // init LLM @@ -106,7 +101,6 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_max; ctx_params.n_batch = 512; - ctx_params.mul_mat_q = mmq; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -159,7 +153,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 374e40a7dc108..10f37b4418897 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -35,7 +35,6 @@ options: -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -mmq, --mul-mat-q <0|1> (default: 1) -ts, --tensor_split (default: 0) -r, --repetitions (default: 5) -o, --output (default: md) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8fec3d43ddfdd..c2155b2ac4696 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -176,7 +176,6 @@ struct cmd_params { std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; - std::vector mul_mat_q; std::vector> tensor_split; std::vector use_mmap; int reps; @@ -196,7 +195,6 @@ static const cmd_params cmd_params_defaults = { /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, - /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* reps */ 5, @@ -221,7 +219,6 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -383,13 +380,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); - } else if (arg == "-mmq" || arg == "--mul-mat-q") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = split(argv[i], split_delim); - params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; @@ -466,7 +456,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -486,7 +475,6 @@ struct cmd_params_instance { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; @@ -518,7 +506,6 @@ struct cmd_params_instance { cparams.n_batch = n_batch; cparams.type_k = type_k; cparams.type_v = type_v; - cparams.mul_mat_q = mul_mat_q; cparams.offload_kqv = !no_kv_offload; return cparams; @@ -538,7 +525,6 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { @@ -557,7 +543,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -580,7 +565,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -616,7 +600,6 @@ struct test { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; int n_prompt; @@ -639,7 +622,6 @@ struct test { split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; - mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; @@ -713,7 +695,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -733,7 +715,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q" || field == "use_mmap") { + field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -767,7 +749,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -931,9 +913,6 @@ struct markdown_printer : public printer { if (field == "n_threads") { return "threads"; } - if (field == "mul_mat_q") { - return "mmq"; - } if (field == "no_kv_offload") { return "nkvo"; } @@ -974,9 +953,6 @@ struct markdown_printer : public printer { if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { fields.emplace_back("split_mode"); } - if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.emplace_back("mul_mat_q"); - } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eea98796663d6..2b2f4a0f4a48b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2390,14 +2390,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUBLAS - } - else if (arg == "--no-mul-mat-q" || arg == "-nommq") - { -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - params.mul_mat_q = false; -#else - LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") diff --git a/llama.cpp b/llama.cpp index a35f07aa47cda..073fd3b703037 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1645,7 +1645,6 @@ struct llama_cparams { float yarn_beta_slow; float defrag_thold; - bool mul_mat_q; bool offload_kqv; bool do_pooling; @@ -11633,7 +11632,6 @@ struct llama_context_params llama_context_default_params() { /*.cb_eval_user_data =*/ nullptr, /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, - /*.mul_mat_q =*/ true, /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, @@ -11785,7 +11783,6 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; - cparams.mul_mat_q = params.mul_mat_q; cparams.offload_kqv = params.offload_kqv; cparams.do_pooling = params.do_pooling; diff --git a/llama.h b/llama.h index 4d0ebe37d3f9b..ed51f478a7b21 100644 --- a/llama.h +++ b/llama.h @@ -255,7 +255,6 @@ extern "C" { enum ggml_type type_v; // data type for V cache // Keep the booleans together to avoid misalignment during copy-by-value. - bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 70737f976c5b5..39c3e52e52789 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -31,7 +31,7 @@ "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", - "mul_mat_q": "MMQ", "tensor_split": "Tensor split" + "tensor_split": "Tensor split" } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. From f49a5356865ced0eca1df9f9d84631dfef71b9dc Mon Sep 17 00:00:00 2001 From: Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Date: Fri, 1 Mar 2024 22:48:56 +0900 Subject: [PATCH 138/401] common : fix flag `--logits-all` to `--all-logits` (#5805) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index bf1ed8a66fea6..938c428cf0fa2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1015,7 +1015,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n"); + printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); From e7433867288d2f142cffe596f3751bda5d7ee2c7 Mon Sep 17 00:00:00 2001 From: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Date: Fri, 1 Mar 2024 06:08:08 -0800 Subject: [PATCH 139/401] gemma : fix bfloat16 -> float16 conversion issue (#5810) --- convert-hf-to-gguf.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ae30b2a76971a..d3e8ec1f60c70 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1811,16 +1811,15 @@ def write_tensors(self): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 data = data_torch.squeeze().numpy() # map tensor names From c2224f003bf9cf558b1a3c57033563e11a4de9a5 Mon Sep 17 00:00:00 2001 From: ddpasa <112642920+ddpasa@users.noreply.github.com> Date: Fri, 1 Mar 2024 18:00:00 +0100 Subject: [PATCH 140/401] ggml-vulkan: fix VULKAN_CHECK_RESULTS flag, which was previously broken (#5813) --- ggml-vulkan.cpp | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 896c290b243ab..ae9cb3c1c1c6e 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5428,7 +5428,8 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -5540,7 +5541,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5550,10 +5552,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu->size) { - src0_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src0_size >= buffer_gpu->size) { + src0_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5583,7 +5586,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5593,10 +5597,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu->size) { - src1_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src1_size >= buffer_gpu->size) { + src1_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5643,11 +5648,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { - if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); - } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); - } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5753,11 +5754,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu->size) { - tensor_size = extra->buffer_gpu->size - (extra->offset); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (extra->offset + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; From 38d16b142624bdd7c41d9955752b7f7b59c5e048 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 20:00:58 +0200 Subject: [PATCH 141/401] server : remove api_like_OAI.py proxy script (#5808) --- README.md | 1 + examples/server/README.md | 17 +-- examples/server/api_like_OAI.py | 228 -------------------------------- 3 files changed, 3 insertions(+), 243 deletions(-) delete mode 100755 examples/server/api_like_OAI.py diff --git a/README.md b/README.md index 5401e197f67d7..67717c1e38b34 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) - Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 - Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 diff --git a/examples/server/README.md b/examples/server/README.md index ad35306c60c4e..397ee825278bd 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -326,7 +326,7 @@ Notice that each `probs` is an array of length `n_probs`. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. *Options:* @@ -528,20 +528,7 @@ bash chat.sh ### API like OAI -API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) -This example must be used with server.cpp - -```sh -python api_like_OAI.py -``` - -After running the API server, you can use it in Python by setting the API base URL. - -```python -openai.api_base = "http://:port" -``` - -Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API +The HTTP server supports OAI-like API ### Extending or building alternative Web Front End diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py deleted file mode 100755 index 607fe49d3ff15..0000000000000 --- a/examples/server/api_like_OAI.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -import argparse -from flask import Flask, jsonify, request, Response -import urllib.parse -import requests -import time -import json - - -app = Flask(__name__) -slot_id = -1 - -parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") -parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.') -parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ") -parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ") -parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ") -parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") -parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') -parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") -parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') -parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) - -args = parser.parse_args() - -def is_present(json, key): - try: - buf = json[key] - except KeyError: - return False - if json[key] == None: - return False - return True - -#convert chat to prompt -def convert_chat(messages): - - system_n = args.system_name - user_n = args.user_name - ai_n = args.ai_name - stop = args.stop - - prompt = "" + args.chat_prompt + stop - - for line in messages: - if (line["role"] == "system"): - prompt += f"{system_n}{line['content']}{stop}" - if (line["role"] == "user"): - prompt += f"{user_n}{line['content']}{stop}" - if (line["role"] == "assistant"): - prompt += f"{ai_n}{line['content']}{stop}" - prompt += ai_n.rstrip() - - return prompt - -def make_postData(body, chat=False, stream=False): - postData = {} - if (chat): - postData["prompt"] = convert_chat(body["messages"]) - else: - postData["prompt"] = body["prompt"] - if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] - if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] - if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] - if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] - if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] - if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] - if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] - if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] - if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] - if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] - if(is_present(body, "seed")): postData["seed"] = body["seed"] - if(is_present(body, "grammar")): postData["grammar"] = body["grammar"] - if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] - if (args.stop != ""): - postData["stop"] = [args.stop] - else: - postData["stop"] = [] - if(is_present(body, "stop")): postData["stop"] += body["stop"] - postData["n_keep"] = -1 - postData["stream"] = stream - postData["cache_prompt"] = True - postData["slot_id"] = slot_id - return postData - -def make_resData(data, chat=False, promptToken=[]): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion" if (chat) else "text_completion", - "created": int(time.time()), - "truncated": data["truncated"], - "model": "LLaMA_CPP", - "usage": { - "prompt_tokens": data["tokens_evaluated"], - "completion_tokens": data["tokens_predicted"], - "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] - } - } - if (len(promptToken) != 0): - resData["promptToken"] = promptToken - if (chat): - #only one choice is supported - resData["choices"] = [{ - "index": 0, - "message": { - "role": "assistant", - "content": data["content"], - }, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - else: - #only one choice is supported - resData["choices"] = [{ - "text": data["content"], - "index": 0, - "logprobs": None, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - return resData - -def make_resData_stream(data, chat=False, time_now = 0, start=False): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", - "created": time_now, - "model": "LLaMA_CPP", - "choices": [ - { - "finish_reason": None, - "index": 0 - } - ] - } - slot_id = data.get("slot_id") - if (chat): - if (start): - resData["choices"][0]["delta"] = { - "role": "assistant" - } - else: - resData["choices"][0]["delta"] = { - "content": data["content"] - } - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - else: - resData["choices"][0]["text"] = data["content"] - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - - return resData - - -@app.route('/chat/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS']) -def chat_completions(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=True, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=True, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) - yield 'data: {}\n\n'.format(json.dumps(resData)) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - - -@app.route('/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/completions', methods=['POST', 'OPTIONS']) -def completion(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=False, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=False, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - -if __name__ == '__main__': - app.run(args.host, port=args.port) From c29af7e2252d288f2ea58a7d437c1cb7c0abf160 Mon Sep 17 00:00:00 2001 From: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Date: Sat, 2 Mar 2024 01:00:46 +0530 Subject: [PATCH 142/401] llama : add StarCoder2 support (#5795) * Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 8 +- gguf-py/gguf/constants.py | 21 ++++ gguf-py/gguf/tensor_mapping.py | 2 + llama.cpp | 199 +++++++++++++++++++++++++++++++++ 4 files changed, 229 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index d3e8ec1f60c70..28b92ac385367 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -96,9 +96,11 @@ def set_gguf_parameters(self): if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None: + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) @@ -220,6 +222,8 @@ def from_model_architecture(model_architecture): return NomicBertModel if model_architecture == "GemmaForCausalLM": return GemmaModel + if model_architecture == "Starcoder2ForCausalLM": + return Model return Model def _is_model_safetensors(self) -> bool: @@ -281,6 +285,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.NOMIC_BERT if arch == "GemmaForCausalLM": return gguf.MODEL_ARCH.GEMMA + if arch == "Starcoder2ForCausalLM": + return gguf.MODEL_ARCH.STARCODER2 raise NotImplementedError(f'Architecture "{arch}" not supported!') diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8f9139d1b7eca..5db760cb14900 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -112,6 +112,7 @@ class MODEL_ARCH(IntEnum): INTERNLM2 = auto() MINICPM = auto() GEMMA = auto() + STARCODER2 = auto() class MODEL_TENSOR(IntEnum): @@ -169,6 +170,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.STARCODER2: "starcoder2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -526,6 +528,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -554,6 +571,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8610037767fb6..db2ec9704a441 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -210,6 +210,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "model.layers.{bid}.mlp.c_fc", # starcoder2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -256,6 +257,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert + "model.layers.{bid}.mlp.c_proj", # starcoder2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 073fd3b703037..b1db5b1797dc5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -211,6 +211,7 @@ enum llm_arch { LLM_ARCH_INTERNLM2, LLM_ARCH_MINICPM, LLM_ARCH_GEMMA, + LLM_ARCH_STARCODER2, LLM_ARCH_UNKNOWN, }; @@ -238,6 +239,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_INTERNLM2, "internlm2" }, { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, }; enum llm_kv { @@ -779,6 +781,24 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_STARCODER2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -3320,6 +3340,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_STARCODER2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 30: model.type = e_model::MODEL_3B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_15B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4490,6 +4520,56 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); } } break; + case LLM_ARCH_STARCODER2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + // optional bias tensors + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + + // optional bias tensors + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -7559,6 +7639,120 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_starcoder2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -7705,6 +7899,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_gemma(); } break; + case LLM_ARCH_STARCODER2: + { + result = llm.build_starcoder2(); + } break; default: GGML_ASSERT(false); } @@ -12084,6 +12282,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN2: case LLM_ARCH_PHI2: case LLM_ARCH_GEMMA: + case LLM_ARCH_STARCODER2: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here From da3b9ba2b710c0f8b44398a0eb9e5a7ae2ad967a Mon Sep 17 00:00:00 2001 From: nold Date: Fri, 1 Mar 2024 22:51:12 +0100 Subject: [PATCH 143/401] convert-hf-to-gguf : require einops for InternLM2ForCausalLM (#5792) --- requirements/requirements-convert-hf-to-gguf.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt index 6ac4026107fbe..6ce840d73cb73 100644 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -1,2 +1,3 @@ -r ./requirements-convert.txt torch~=2.1.1 +einops~=0.7.0 From cb5e8f7fc4ee57d4bcccafbe04a82cededd35486 Mon Sep 17 00:00:00 2001 From: Tushar Date: Sat, 2 Mar 2024 04:48:26 +0530 Subject: [PATCH 144/401] build(nix): Introduce flake.formatter for `nix fmt` (#5687) * build(nix): Introduce flake.formatter for `nix fmt` * chore: Switch to pkgs.nixfmt-rfc-style --- .devops/nix/sif.nix | 2 +- flake.nix | 10 +++++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix index 7535ca0f3088e..7a5e1dd0ffc4c 100644 --- a/.devops/nix/sif.nix +++ b/.devops/nix/sif.nix @@ -7,7 +7,7 @@ }: let - optionalInt = cond: x: if cond then x else 0; + optionalInt = cond: x: if cond then x else 0; in singularity-tools.buildImage rec { inherit (llama-cpp) name; diff --git a/flake.nix b/flake.nix index dc4e503c33061..45f9deda0beec 100644 --- a/flake.nix +++ b/flake.nix @@ -107,11 +107,12 @@ # ``` # # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format - flake.overlays.default = - (final: prev: { + flake.overlays.default = ( + final: prev: { llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; inherit (final.llamaPackages) llama-cpp; - }); + } + ); systems = [ "aarch64-darwin" @@ -131,6 +132,9 @@ ... }: { + # For standardised reproducible formatting with `nix fmt` + formatter = pkgs.nixfmt-rfc-style; + # Unlike `.#packages`, legacyPackages may contain values of # arbitrary types (including nested attrsets) and may even throw # exceptions. This attribute isn't recursed into by `nix flake From 9bf297a02bfbd474e51912409a470dd797e2fe13 Mon Sep 17 00:00:00 2001 From: crasm Date: Sat, 2 Mar 2024 00:11:06 -0500 Subject: [PATCH 145/401] workflows : remove nocleanup arg for check-requirements.sh (#5826) Reduces peak tmpfs usage and should prevent the check from failing from running out of space. Fixes the 'No space left on device' issue mentioned in #5703. --- .github/workflows/python-check-requirements.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml index 92e1108b3af88..b822059923ebe 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -3,12 +3,14 @@ name: Python check requirements.txt on: push: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - 'requirements.txt' - 'requirements/*.txt' pull_request: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - 'requirements.txt' @@ -26,4 +28,4 @@ jobs: with: python-version: "3.11" - name: Run check-requirements.sh script - run: bash scripts/check-requirements.sh nocleanup + run: bash scripts/check-requirements.sh From 715641391dda1ff9762dc5d99d9a30acce99f2c6 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Sat, 2 Mar 2024 19:49:30 +0800 Subject: [PATCH 146/401] Support multiple GPUs (split mode) on SYCL backend (#5806) * suport multiple cards: split-mode - layer|row * rm warning * rebase with master, support tow new OPs, close feature for -sm=row, fix for unit test * update news * fix merge error * update according to review comments --- README-sycl.md | 21 + common/common.cpp | 4 + examples/llama-bench/llama-bench.cpp | 17 +- examples/sycl/ls-sycl-device.cpp | 2 +- examples/sycl/run-llama2.sh | 17 +- ggml-sycl.cpp | 2261 ++++++++++++++++---------- ggml-sycl.h | 5 + llama.cpp | 49 +- 8 files changed, 1534 insertions(+), 842 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index dd5bf9dead5bf..85eb16f2be340 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,6 +1,7 @@ # llama.cpp for SYCL - [Background](#background) +- [News](#news) - [OS](#os) - [Intel GPU](#intel-gpu) - [Docker](#docker) @@ -25,6 +26,21 @@ The llama.cpp for SYCL is used to support Intel GPUs. For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). +## News + +- 2024.3 + - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. + - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. + - Support detecting all GPUs with level-zero and same top **Max compute units**. + - Support OPs + - hardsigmoid + - hardswish + - pool2d + +- 2024.1 + - Create SYCL backend for Intel GPU. + - Support Windows build + ## OS |OS|Status|Verified| @@ -449,6 +465,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |-|-|-| |GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| |GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| +|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer| ## Known Issue @@ -458,6 +475,10 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Solution: add **--no-mmap** or **--mmap 0**. +- Split-mode: [row] is not supported + + It's on developing. + ## Q&A - Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. diff --git a/common/common.cpp b/common/common.cpp index 938c428cf0fa2..1c0b7c403b936 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -640,6 +640,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { +#ifdef GGML_USE_SYCL + fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); + exit(1); +#endif // GGML_USE_SYCL params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c2155b2ac4696..aa79d002a0875 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -123,20 +123,15 @@ static std::string get_gpu_info() { } #endif #ifdef GGML_USE_SYCL - int device_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); - - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { - if (device_list[i] >0 ){ - char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; + int count = ggml_backend_sycl_get_device_count(); + for (int i = 0; i < count; i++) { + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + if (i < count - 1) { id += "/"; } } - if (id.length() >2 ) { - id.pop_back(); - } #endif // TODO: other backends return id; diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 52442e4ca80fa..74a8b7fd814d9 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -7,7 +7,7 @@ #include "ggml-sycl.h" -int main(int argc, char ** argv) { +int main() { ggml_backend_sycl_print_sycl_devices(); return 0; } diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index f5f4c1e980de4..52f7c01a4bd90 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -8,12 +8,19 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" source /opt/intel/oneapi/setvars.sh if [ $# -gt 0 ]; then - export GGML_SYCL_DEVICE=$1 + GGML_SYCL_DEVICE=$1 else - export GGML_SYCL_DEVICE=0 + GGML_SYCL_DEVICE=0 fi -echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE +echo "use $GGML_SYCL_DEVICE as main GPU" #export GGML_SYCL_DEBUG=1 -./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0 + + +#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. + +#use all GPUs with same max compute units +ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 + +#use main GPU only +#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 6f391b0c650ad..cad08d610310f 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -661,26 +661,29 @@ namespace dpct /// \param [out] total_memory The number of bytes of total memory on the SYCL device. void get_memory_info(size_t &free_memory, size_t &total_memory) { + total_memory = get_device_info().get_global_mem_size(); + const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not " + "supported (export/set ZES_ENABLE_SYSMAN=1 to support), " + "use total memory as free memory"; #if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) if (!has(sycl::aspect::ext_intel_free_memory)) { - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; } else { free_memory = get_info(); } #else - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; #if defined(_MSC_VER) && !defined(__clang__) #pragma message("Querying the number of bytes of free memory is not supported") #else #warning "Querying the number of bytes of free memory is not supported" #endif #endif - total_memory = get_device_info().get_global_mem_size(); } void get_device_info(device_info &out) const @@ -738,15 +741,25 @@ namespace dpct #endif // DPCT_USM_LEVEL_NONE } - sycl::queue *create_in_order_queue(bool enable_exception_handler = false) - { + sycl::queue *create_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + return create_in_order_queue(context, device, enable_exception_handler); + } + + sycl::queue *create_in_order_queue(bool enable_exception_handler = false) { std::lock_guard lock(m_mutex); return create_queue_impl(enable_exception_handler, - sycl::property::queue::in_order()); + sycl::property::queue::in_order()); } - sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) - { + sycl::queue *create_in_order_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(context, device, enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) { std::lock_guard lock(m_mutex); return create_queue_impl(enable_exception_handler); } @@ -809,6 +822,25 @@ namespace dpct return _queues.back().get(); } + template + sycl::queue *create_queue_impl(sycl::context context, sycl::device device, + bool enable_exception_handler, + Properties... properties) { + sycl::async_handler eh = {}; + if (enable_exception_handler) { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + context, device, eh, + sycl::property_list( + #ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), + #endif + properties...))); + + return _queues.back().get(); + } + void get_version(int &major, int &minor) const { detail::get_version(*this, major, minor); @@ -2943,14 +2975,11 @@ bool ggml_sycl_loaded(void); void * ggml_sycl_host_malloc(size_t size); void ggml_sycl_host_free(void * ptr); bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -void ggml_sycl_set_tensor_split(const float * tensor_split); -void ggml_sycl_transform_tensor(void * data, struct ggml_tensor * tensor); void ggml_sycl_free_data(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor); -void ggml_sycl_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); @@ -2963,6 +2992,14 @@ int get_main_device(); void print_ggml_tensor(const char*name, struct ggml_tensor *src); void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); +void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, + const void *ptr_src, size_t size) { + char *host_buf = (char *)malloc(size); + q_src.memcpy(host_buf, (const char *)ptr_src, size).wait(); + q_dst.memcpy((char *)ptr_dst, host_buf, size).wait(); + free(host_buf); +} + static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) { const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment @@ -3180,6 +3217,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_SILU_BLOCK_SIZE 256 #define SYCL_TANH_BLOCK_SIZE 256 #define SYCL_RELU_BLOCK_SIZE 256 +#define SYCL_HARDSIGMOID_BLOCK_SIZE 256 +#define SYCL_HARDSWISH_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 @@ -3196,6 +3235,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_PAD_BLOCK_SIZE 256 #define SYCL_ACC_BLOCK_SIZE 256 #define SYCL_IM2COL_BLOCK_SIZE 256 +#define SYCL_POOL2D_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_SYCL_DMMV_X @@ -3218,8 +3258,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MUL_MAT_SRC1_COL_STRIDE 128 #define MAX_STREAMS 8 -static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = { - {0}}; +static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}}; struct ggml_tensor_extra_gpu { void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -3228,30 +3267,108 @@ struct ggml_tensor_extra_gpu { [MAX_STREAMS]; // events for synchronizing multiple GPUs }; -inline dpct::err0 ggml_sycl_set_device(const int device) try { - int current_device; +class sycl_gpu_mgr { + public: + std::vector gpus; + std::vector devices; + sycl::queue *first_queue; + sycl::context co_ctx; + int max_compute_units = 0; + int work_group_size = 0; + std::string gpus_list = ""; + + sycl_gpu_mgr() { + detect_sycl_gpu_list_with_max_cu(); + get_allow_gpus(); + create_context_with_gpus(); + } + + void create_context_with_gpus() { + sycl::context ctx = sycl::context(devices); + assert(gpus.size() > 0); + first_queue = dpct::get_current_device().create_queue(ctx, devices[0]); + co_ctx = first_queue->get_context(); + } + + sycl::context &get_co_ctx() { return co_ctx; } + + void get_allow_gpus() { + gpus_list = ""; + for (size_t i = 0; i < gpus.size(); ++i) { + gpus_list += std::to_string(gpus[i]); + gpus_list += ","; + } + if (gpus_list.length() > 2) { + gpus_list.pop_back(); + } + } - SYCL_CHECK(CHECK_TRY_ERROR( - current_device = dpct::dev_mgr::instance().current_device_id())); + bool is_allowed_gpu(int device_id) { + return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end(); + } - // GGML_SYCL_DEBUG("ggml_sycl_set_device device=%d, current_device=%d\n", device, current_device); - if (device == current_device) { - return 0; - } + void detect_sycl_gpu_list_with_max_cu() try { + int device_count = dpct::dev_mgr::instance().device_count(); - return CHECK_TRY_ERROR(dpct::select_device(device)); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - crash(); - std::exit(1); -} + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units < prop.get_max_compute_units()) + max_compute_units = prop.get_max_compute_units(); + } + + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units == prop.get_max_compute_units() && + prop.get_major_version() == 1) { + gpus.push_back(id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + } + } + return; + } catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + int get_gpu_count() { return (int)gpus.size(); } + + int get_index(int id) { + for (int i = 0; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } + + int get_next_index(int id) { + int cur_index = get_index(id); + for (int i = cur_index + 1; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } +}; +static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL; static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; -static int g_main_device_index = -1; +static int g_main_device_id = -1; + +static std::array g_default_tensor_split = {}; static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; @@ -3268,8 +3385,6 @@ struct sycl_device_id2index { int index; }; -static sycl_device_id2index g_sycl_device_id2index[GGML_SYCL_MAX_DEVICES] = { {-1} }; - static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default static size_t g_scratch_offset = 0; @@ -3290,6 +3405,63 @@ static void bad_arch(const sycl::stream &stream_ct1) { (void) bad_arch; // suppress unused function warning } +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +void check_allow_gpu_index(const int device_index) { + if (device_index >= g_device_count) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", __func__, + device_index, g_device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} + +/* +device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices(). + It is only used to set current working device. +*/ +void check_allow_gpu_id(const int device_id) { + if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "error: cannot set device=%d, which is not allowed. Please " + "set GPU ID in: [%s]", + device_id, g_sycl_gpu_mgr->gpus_list.c_str()); + fprintf(stderr, "%s\n", error_buf); + throw std::invalid_argument(error_buf); + } +} + +int get_current_device_id() { + return dpct::dev_mgr::instance().current_device_id(); +} + +inline dpct::err0 ggml_sycl_set_device(const int device) try { + + int device_id = g_sycl_gpu_mgr->gpus[device]; + check_allow_gpu_id(device_id); + + int current_device_id; + SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); + + // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d, + // current_device_id=%d\n", device, current_device); + if (device_id == current_device_id) { + return 0; + } + + return CHECK_TRY_ERROR(dpct::select_device(device_id)); +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + crash(); + std::exit(1); +} + void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){ if(!g_ggml_sycl_debug) return; if(!src){ @@ -3302,22 +3474,18 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo size_t total_size = total_elements*sizeof(float); float *local_buf = NULL; - // printf("total_size %d2, src_on_device %d\n", total_size, src_on_device); if(src_on_device) { local_buf = (float *) ggml_sycl_host_malloc(total_size); - // printf("local buf %p size %d bytes\n", local_buf, total_size); ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; main_stream->memcpy(local_buf, src, total_size); } else { local_buf = (float *)src; - // printf("local buf from src-> data %p\n", local_buf); } std::ofstream logfile; logfile.open(filename); - // printf("local buf element %d\n", total_elements); for(size_t i=0; iextra; - src_data = (float*)src_extra->data_device[g_main_device_index]; + src_data = (float*)src_extra->data_device[g_main_device]; } else { src_data = (float *)src->data; @@ -3359,10 +3527,6 @@ void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cn sprintf(filename, "%s_%07d", name, log_file_name_idx); log_file_name_idx++; print_ggml_tensor(filename, src); - // print_ggml_tensor("ggml_sycl_rms_norm_src0", (ggml_tensor *)src0); - // print_ggml_tensor("ggml_sycl_rms_norm_src1", (ggml_tensor *)src1); - // int *ptr = NULL; - // *ptr = 0; } static __dpct_inline__ float warp_reduce_sum(float x, @@ -3583,6 +3747,28 @@ static void relu_f32(const float * x, float * dst, const int k, dst[i] = sycl::fmax((float)(x[i]), (float)0); } +static void hardsigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static void hardswish_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + @@ -4964,8 +5150,8 @@ static void k_get_rows_float( template static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - 2 * item_ct1.get_local_id(2); + const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2)); if (i >= k) { return; @@ -7695,7 +7881,7 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { static void cpy_1_f16_f32(const char * cxi, char * cdsti) { const sycl::half *xi = (const sycl::half *)cxi; - float *dsti = (float *)cdsti; + float * dsti = (float *) cdsti; *dsti = *xi; } @@ -8297,6 +8483,62 @@ static void im2col_kernel(const float *x, T *dst, int offset_delta, } } +template +static void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op, + const sycl::nd_item<3> &item_ct1) { + int idx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = sycl::max(0, start_h); + const int eh = sycl::min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = sycl::max(0, start_w); + const int ew = sycl::min(iw, start_w + kw); + + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { +#if DPCT_COMPATIBILITY_TEMP >= 350 + /* + DPCT1098:106: The '*' expression is used instead of the __ldg + call. These two expressions do not provide the exact same + functionality. Check the generated code for potential precision + and/or performance issues. + */ + Ti cur = *(i_ptr + i * iw + j); +#else + Ti cur = i_ptr[i * iw + j]; +#endif + switch (op) { + case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break; + case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const void *src0_dd, @@ -8585,6 +8827,30 @@ static void relu_f32_sycl(const float *x, float *dst, const int k, }); } +static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardsigmoid_f32(x, dst, k, item_ct1); + }); +} + +static void hardswish_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardswish_f32(x, dst, k, item_ct1); + }); +} + static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, const float negative_slope, dpct::queue_ptr stream) { @@ -8811,11 +9077,10 @@ template static void dequantize_block_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, dpct::queue_ptr stream) { - const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE); { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( sycl::nd_range<3>( sycl::range<3>(1, 1, num_blocks) * @@ -9208,24 +9473,6 @@ static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, }); } -int get_device_index_by_id(int id){ - int res = g_sycl_device_id2index[id].index; - // GGML_SYCL_DEBUG("get_device_index_by_id id=%d device_index=%d\n", id, res); - GGML_ASSERT(res>=0); - return res; -} - -int get_device_id_by_index(int index){ - int res = g_device_caps[index].device_id; - GGML_ASSERT(res>=0); - return res; -} - - -int get_current_device_index(){ - return get_device_index_by_id(dpct::dev_mgr::instance().current_device_id()); -} - static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, @@ -9234,7 +9481,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9349,7 +9596,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9464,7 +9711,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9579,7 +9826,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9694,7 +9941,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9809,7 +10056,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9932,7 +10179,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10060,7 +10307,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10181,7 +10428,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10302,7 +10549,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10458,6 +10705,31 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( } } +static void +ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, + const int ne01, const int ne02, const int nb00, + const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, + const int nb10, const int nb11, const int nb12, + const int nb13, dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, + nb01, nb02, nb03, ne10, ne11, ne12, + nb10, nb11, nb12, nb13, item_ct1); + }); + } +} + static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, @@ -11014,12 +11286,9 @@ struct sycl_buffer { static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS]; static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg index %d\n", id); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size); #ifdef DEBUG_SYCL_MALLOC int nnz = 0; size_t max_size = 0; @@ -11027,7 +11296,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { size_t best_diff = 1ull << 36; int ibest = -1; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr != nullptr) { #ifdef DEBUG_SYCL_MALLOC ++nnz; @@ -11043,7 +11312,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr); return ptr; } } @@ -11051,30 +11320,30 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { } } if (ibest >= 0) { - sycl_buffer& b = g_sycl_buffer_pool[id][ibest]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest]; void * ptr = b.ptr; *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr); return ptr; } void * ptr; size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; SYCL_CHECK( CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( look_ahead_size, *stream))); *actual_size = look_ahead_size; - g_sycl_pool_size[id] += look_ahead_size; + g_sycl_pool_size[device_index] += look_ahead_size; #ifdef DEBUG_SYCL_MALLOC fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); return ptr; } catch (sycl::exception const &exc) { @@ -11083,15 +11352,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr == nullptr) { b.ptr = ptr; b.size = size; @@ -11100,7 +11365,7 @@ static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { } fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n"); SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream))); - g_sycl_pool_size[id] -= size; + g_sycl_pool_size[device_index] -= size; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11117,7 +11382,8 @@ DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported. static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0}; static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try { + GGML_UNUSED(device_index); GGML_UNUSED(size); GGML_UNUSED(actual_size); return NULL; @@ -11128,20 +11394,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_vmm(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = dpct::dev_mgr::instance().current_device_id())); - #ifdef DEBUG_SYCL_MALLOC - printf("sycl pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); + printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr); #endif - g_sycl_pool_used[id] -= size; + g_sycl_pool_used[device_index] -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[id] + g_sycl_pool_used[id])); + GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index])); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11149,14 +11411,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void *ggml_sycl_pool_malloc(size_t size, size_t *actual_size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - return ggml_sycl_pool_malloc_vmm(size, actual_size); +static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try { + if (g_device_caps[device_index].vmm) { + return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size); } else { - return ggml_sycl_pool_malloc_leg(size, actual_size); + return ggml_sycl_pool_malloc_leg(device_index, size, actual_size); } } catch (sycl::exception const &exc) { @@ -11165,14 +11424,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free(void *ptr, size_t size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - ggml_sycl_pool_free_vmm(ptr, size); +static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try { + if (g_device_caps[device_index].vmm) { + ggml_sycl_pool_free_vmm(device_index, ptr, size); } else { - ggml_sycl_pool_free_leg(ptr, size); + ggml_sycl_pool_free_leg(device_index, ptr, size); } } catch (sycl::exception const &exc) { @@ -11184,13 +11440,17 @@ catch (sycl::exception const &exc) { template struct sycl_pool_alloc { + int device_index = -1; + int device_id = -1; T * ptr = nullptr; size_t actual_size = 0; // size is in number of elements T * alloc(size_t size) { GGML_ASSERT(ptr == nullptr); - ptr = (T *) ggml_sycl_pool_malloc(size * sizeof(T), &this->actual_size); + device_id = get_current_device_id(); + device_index = g_sycl_gpu_mgr->get_index(device_id); + ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); return ptr; } @@ -11201,7 +11461,7 @@ struct sycl_pool_alloc { ~sycl_pool_alloc() { if (ptr != nullptr) { - ggml_sycl_pool_free(ptr, actual_size); + ggml_sycl_pool_free(device_index, ptr, actual_size); } } @@ -11222,44 +11482,57 @@ bool ggml_sycl_loaded(void) { return g_sycl_loaded; } -void ggml_backend_sycl_print_sycl_devices(){ +void print_device_detail(int id) { + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_device_info(prop, dpct::dev_mgr::instance().get_device(id)))); + sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); + std::string version; + version += std::to_string(prop.get_major_version()); + version += "."; + version += std::to_string(prop.get_minor_version()); + + fprintf(stderr, "|%2d|%45s|%18s|%17d|%14d|%13d|%15lu|\n", id, + prop.get_name(), version.c_str(), prop.get_max_compute_units(), + prop.get_max_work_group_size(), prop.get_max_sub_group_size(), + prop.get_global_mem_size()); +} + +void ggml_backend_sycl_print_sycl_devices() { int device_count = dpct::dev_mgr::instance().device_count(); fprintf(stderr, "found %d SYCL devices:\n", device_count); + fprintf(stderr, "|ID| Name |compute capability|Max compute units|Max work group|Max sub group|Global mem size|\n"); + fprintf(stderr, "|--|---------------------------------------------|------------------|-----------------|--------------|-------------|---------------|\n"); for (int id = 0; id < device_count; ++id) { - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); - sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); - fprintf(stderr, " Device %d: %s,\tcompute capability %d.%d,\n\tmax compute_units %d,\tmax work group size %d,\tmax sub group size %d,\tglobal mem size %lu\n", id, - prop.get_name(), prop.get_major_version(), - prop.get_minor_version(), - prop.get_max_compute_units(), - prop.get_max_work_group_size(), - prop.get_max_sub_group_size(), - prop.get_global_mem_size() - ); - } - // fprintf(stderr, "\n"); -} - -int get_sycl_env(const char* env_name, int default_val){ - char * user_device_string = getenv(env_name); + print_device_detail(id); + } +} + +void print_gpu_device_list() { + fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", + g_sycl_gpu_mgr->get_gpu_count(), + g_sycl_gpu_mgr->gpus_list.c_str(), + g_sycl_gpu_mgr->max_compute_units); +} + +int get_sycl_env(const char *env_name, int default_val) { + char *user_device_string = getenv(env_name); int user_number = default_val; unsigned n; - if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number=default_val; - } + if (user_device_string != NULL && + sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number = default_val; + } return user_number; } -int get_work_group_size(int user_device_id){ +int get_work_group_size(int user_device_id) { dpct::device_info prop; - dpct::get_device_info( - prop, - dpct::dev_mgr::instance().get_device(user_device_id)); + dpct::get_device_info(prop, + dpct::dev_mgr::instance().get_device(user_device_id)); return prop.get_max_work_group_size(); } @@ -11268,113 +11541,81 @@ void ggml_init_sycl() try { if (!initialized) { g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - printf("GGML_SYCL_DEBUG=%d\n", g_ggml_sycl_debug); - - int user_device_id = get_sycl_env("GGML_SYCL_DEVICE", 0); - +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); +#endif if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != - 0) { + dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; g_sycl_loaded = false; return; } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - int64_t total_vram = 0; + ggml_backend_sycl_print_sycl_devices(); -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; + print_gpu_device_list(); + + int64_t total_vram = 0; + +/* NOT REMOVE, keep it for next optimize for XMX. #if defined(SYCL_USE_XMX) fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); #else fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); #endif - ggml_backend_sycl_print_sycl_devices(); +*/ for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_sycl_device_id2index[id].index = -1; g_device_caps[id].vmm = 0; g_device_caps[id].device_id = -1; g_device_caps[id].cc = 0; g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; } - int device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - - device_inx++; - - g_device_caps[device_inx].vmm = 0; - g_device_caps[device_inx].device_id = id; - g_sycl_device_id2index[id].index = device_inx; + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); - g_tensor_split[device_inx] = total_vram; + g_default_tensor_split[i] = total_vram; total_vram += prop.get_global_mem_size(); - g_device_caps[device_inx].cc = + g_device_caps[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - g_tensor_split[device_inx] /= total_vram; + + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - SYCL_CHECK(ggml_sycl_set_device(id)); + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // create sycl streams for (int is = 0; is < MAX_STREAMS; ++is) { - /* - DPCT1025:88: The SYCL queue is created ignoring the flag and - priority options. - */ SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[device_inx][is] = - dpct::get_current_device().create_queue())); + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); } - const dpct::queue_ptr stream = g_syclStreams[device_inx][0]; + const dpct::queue_ptr stream = g_syclStreams[i][0]; // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[device_inx] = - stream)); - /* - DPCT1027:89: The call to syclSetMathMode was replaced with 0 - because this functionality is redundant in SYCL. - */ - SYCL_CHECK(0); + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } - // configure logging to stdout - // SYCL_CHECK(syclLoggerConfigure(1, 1, 0, nullptr)); - - //hardcode, force set to 1 device - g_device_count = 1; - ggml_sycl_set_main_device(user_device_id); - ggml_sycl_set_device(user_device_id); - g_work_group_size = get_work_group_size(user_device_id); - // fprintf(stderr, "Using Device %d\n", user_device_id); - - // for (int id = 0; id < g_all_sycl_device_count; ++id) { - // GGML_SYCL_DEBUG("id=%d g_device_caps[%d].device_id=%d g_sycl_device_id2index[%d].index=%d ", id, id, - // g_device_caps[id].device_id, id, g_sycl_device_id2index[id].index); - // } - initialized = true; g_sycl_loaded = true; } @@ -11385,31 +11626,6 @@ catch (sycl::exception const &exc) { std::exit(1); } - -void ggml_sycl_set_tensor_split(const float * tensor_split) { - if (tensor_split == nullptr) { - return; - } - bool all_zero = true; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] != 0.0f) { - all_zero = false; - break; - } - } - if (all_zero) { - return; - } - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] /= split_sum; - } -} - void *ggml_sycl_host_malloc(size_t size) try { if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { return nullptr; @@ -11419,28 +11635,14 @@ void *ggml_sycl_host_malloc(size_t size) try { //allow to use dpct::get_in_order_queue() for host malloc dpct::err0 err = CHECK_TRY_ERROR( ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); - /* - DPCT1000:82: Error handling if-stmt was detected but could not be rewritten. - */ + if (err != 0) { // clear the error - /* - DPCT1026:83: The call to syclGetLastError was removed because this - functionality is redundant in SYCL. - */ - /* - DPCT1001:81: The statement could not be removed. - */ fprintf( stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", - /* - DPCT1009:84: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ size / 1024.0 / 1024.0, - "syclGetErrorString is not supported" /*syclGetErrorString(err)*/); + "syclGetErrorString is not supported"); return nullptr; } @@ -11480,7 +11682,7 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; SYCL_CHECK(CHECK_TRY_ERROR( - id = get_current_device_index())); + id = get_current_device_id())); // GGML_SYCL_DEBUG("current device index %d\n", id); src_ptr = (char *) extra->data_device[id]; } else { @@ -11714,7 +11916,6 @@ inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); (void) src1; @@ -11737,44 +11938,75 @@ inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const dpct::queue_ptr &main_stream) { +static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); + hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); (void) src1; (void) dst; (void) src1_dd; } -inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { +static void ggml_sycl_op_hardswish(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); (void) src1; (void) dst; (void) src1_dd; } -inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { +inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -11905,7 +12137,7 @@ inline void ggml_sycl_op_mul_mat_q( int device_id; SYCL_CHECK( - CHECK_TRY_ERROR(device_id = dpct::dev_mgr::instance().current_device_id())); + CHECK_TRY_ERROR(device_id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into @@ -11957,16 +12189,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static int64_t get_row_rounding(ggml_type type) { +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; - for (int64_t id = 0; id < g_device_count; ++id) { - if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > g_device_caps[id].cc) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > g_device_caps[i].cc) { + min_compute_capability = g_device_caps[i].cc; } - if (max_compute_capability < g_device_caps[id].cc) { - max_compute_capability = g_device_caps[id].cc; + if (max_compute_capability < g_device_caps[i].cc) { + max_compute_capability = g_device_caps[i].cc; } } } @@ -11986,12 +12218,16 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: return max_compute_capability >= VER_GEN9 ? 128 : 64; case GGML_TYPE_Q6_K: return 64; default: GGML_ASSERT(false); } + } inline void ggml_sycl_op_mul_mat_vec_q( @@ -12176,27 +12412,22 @@ inline void ggml_sycl_op_mul_mat_sycl( const int64_t row_diff = row_high - row_low; int id; - int device_id = dpct::dev_mgr::instance().current_device_id(); SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; + int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else bool use_fp16 = false; #endif - // if (compute_capability >= VER_GEN9 && (src0->type == GGML_TYPE_F16 || - // ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == - // src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { - // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); sycl_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { @@ -12225,7 +12456,6 @@ inline void ggml_sycl_op_mul_mat_sycl( const sycl::half alpha_f16 = 1.0f; const sycl::half beta_f16 = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( *g_sycl_handles[id], oneapi::mkl::transpose::trans, @@ -12241,14 +12471,21 @@ inline void ggml_sycl_op_mul_mat_sycl( else { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); sycl_pool_alloc src0_ddq_as_f32; - + sycl_pool_alloc src1_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); GGML_ASSERT(to_fp32_sycl != nullptr); src0_ddq_as_f32.alloc(row_diff*ne00); to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type); + GGML_ASSERT(to_fp32_sycl != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -12261,7 +12498,6 @@ inline void ggml_sycl_op_mul_mat_sycl( src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); } - (void) dst; (void) src1_ddq_i; (void) src1_padded_row_size; @@ -12382,6 +12618,48 @@ inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } +static void ggml_sycl_op_pool2d(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE; + sycl::range<3> block_nums(1, 1, num_blocks); + main_stream->parallel_for( + sycl::nd_range<3>(block_nums * + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, + parallel_elements, src0_dd, dst_dd, op, + item_ct1); + }); + + (void) src1; + (void) src1_dd; +} + inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -12606,12 +12884,12 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, sycl_pool_alloc dst_f; ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; - // GGML_SYCL_DEBUG("g_main_device_index=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // g_main_device_index, main_stream, src0_on_device, src1_on_device, dst_on_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device); if (src0_on_device) { - src0_ddf = (float *) src0_extra->data_device[g_main_device_index]; + src0_ddf = (float *) src0_extra->data_device[g_main_device]; } else { src0_ddf = src0_f.alloc(ggml_nelements(src0)); // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0); @@ -12620,15 +12898,14 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, if (use_src1) { if (src1_on_device) { - src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + src1_ddf = (float *) src1_extra->data_device[g_main_device]; } else { src1_ddf = src1_f.alloc(ggml_nelements(src1)); SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); } } if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; - // printf("zjy dst_ddf=%p main_stream=%p g_main_device_index=%d\n", dst_ddf, main_stream, g_main_device_index); + dst_ddf = (float *) dst_extra->data_device[g_main_device]; } else { dst_ddf = dst_f.alloc(ggml_nelements(dst)); } @@ -12672,21 +12949,19 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { } #ifdef NDEBUG - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // SYCL_CHECK(syclDeviceSynchronize()); } - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - int device_id = g_device_caps[id].device_id; + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); for (int id_other = 0; id_other < g_device_count; ++id_other) { - int device_id_other = g_device_caps[id_other].device_id; - if (device_id == id_other) { + if (i == id_other) { continue; } - if (device_id != g_main_device && device_id_other != g_main_device) { + if (i != g_main_device && id_other != g_main_device) { continue; } @@ -12706,6 +12981,10 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { peer_access_enabled = enable_peer_access; } +struct ggml_backend_sycl_split_buffer_type_context { + std::array tensor_split; +}; + static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, @@ -12752,80 +13031,90 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); - // dd = data device - char * src0_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; - float * src1_ddf[GGML_SYCL_MAX_DEVICES] = {nullptr}; // float - char * src1_ddq[GGML_SYCL_MAX_DEVICES] = {nullptr}; // q8_1 - float * dst_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; + std::array tensor_split; + if (split) { + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check + // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } + + struct dev_data { + sycl_pool_alloc src0_dd_alloc; + sycl_pool_alloc src1_ddf_alloc; + sycl_pool_alloc src1_ddq_alloc; + sycl_pool_alloc dst_dd_alloc; - // as = actual size - size_t src0_as[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asf[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asq[GGML_SYCL_MAX_DEVICES] = {0}; - size_t dst_as[GGML_SYCL_MAX_DEVICES] = {0}; + char *src0_dd = nullptr; + float *src1_ddf = nullptr; // float + char *src1_ddq = nullptr; // q8_1 + float *dst_dd = nullptr; + + int64_t row_low; + int64_t row_high; + }; - int64_t row_low[GGML_SYCL_MAX_DEVICES]; - int64_t row_high[GGML_SYCL_MAX_DEVICES]; + dev_data dev[GGML_SYCL_MAX_DEVICES]; int used_devices = 0; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int i = 0; i < g_device_count; ++i) { // by default, use all rows - row_low[id] = 0; - row_high[id] = ne01; + dev[i].row_low = 0; + dev[i].row_high = ne01; // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type); + const int64_t rounding = get_row_rounding(src0->type, tensor_split); - if (id != 0) { - row_low[id] = ne01*g_tensor_split[id]; - if (row_low[id] < ne01) { - row_low[id] -= row_low[id] % rounding; + if (i != 0) { + dev[i].row_low = ne01*tensor_split[i]; + if (dev[i].row_low < ne01) { + dev[i].row_low -= dev[i].row_low % rounding; } } - if (id != g_device_count - 1) { - row_high[id] = ne01*g_tensor_split[id + 1]; - if (row_high[id] < ne01) { - row_high[id] -= row_high[id] % rounding; + if (i != g_device_count - 1) { + dev[i].row_high = ne01*tensor_split[i + 1]; + if (dev[i].row_high < ne01) { + dev[i].row_high -= dev[i].row_high % rounding; } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][0]; if (src0_on_device && src0_is_contiguous) { - src0_dd[id] = (char *) src0_extra->data_device[id]; + dev[i].src0_dd = (char *) src0_extra->data_device[i]; } else { - // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_sycl_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0)); } if (src1_on_device && src1_is_contiguous) { - src1_ddf[id] = (float *) src1_extra->data_device[id]; + dev[i].src1_ddf = (float *) src1_extra->data_device[i]; } else { - src1_ddf[id] = (float *) ggml_sycl_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1)); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_sycl_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_sycl(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); + quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); /* DPCT1010:90: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12836,25 +13125,25 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (dst_on_device) { - dst_dd[id] = (float *) dst_extra->data_device[id]; + dev[i].dst_dd = (float *) dst_extra->data_device[i]; } else { - const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_sycl_pool_malloc(size_dst_ddf, &dst_as[id]); + const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst); + dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf); } } // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data if (split && used_devices > 1) { - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + ggml_sycl_set_device(g_main_device); /* DPCT1024:91: The original code returned the error code that was further consumed by the program logic. This original code was replaced with 0. You may need to rewrite the program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[g_main_device_index][0] = - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier())); + *src0_extra->events[g_main_device][0] = + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier())); } const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; @@ -12862,22 +13151,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const int64_t row_diff = row_high[id] - row_low[id]; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const int64_t row_diff = dev[i].row_high - dev[i].row_low; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][is]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][is]; // wait for main GPU data if necessary - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { + /* + DPCT1009:163: SYCL uses exceptions to report errors and does not + use the error codes. The original code was commented out and a + warning string was inserted. You need to rewrite this code. + */ SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( - {*src0_extra->events[g_main_device_index][0]}))); + {*src0_extra->events[g_main_device][0]}))); } for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { @@ -12887,30 +13181,32 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset; - float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); + char * src0_dd_i = dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = dev[i].src1_ddq + src1_ddq_i_offset; + float * dst_dd_i = dev[i].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) { - dst_dd_i += row_low[id]; // offset is 0 if no tensor split + if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) { + dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { - if (id != g_main_device_index) { + if (i != g_main_device) { if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / q8_1_bs))); } else { - float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device_index]; + + float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + + SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, src1_ddf_i, src1_ddf_i_source, src1_ncols * ne10 * sizeof(float)))); } @@ -12933,14 +13229,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream)); } if (src1->type == GGML_TYPE_F16) { src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; } // do the computation op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream); + dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream); /* DPCT1010:93: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12956,7 +13252,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dst_off_device = dst->data; kind = dpct::device_to_host; } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { - dst_off_device = dst_extra->data_device[g_main_device_index]; + dst_off_device = dst_extra->data_device[g_main_device]; kind = dpct::device_to_device; } else { GGML_ASSERT(false); @@ -12969,11 +13265,29 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + row_low[id]; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, kind, *stream))); + dhf_dst_i += src1_col_0*ne0 + dev[i].row_low; + + //todo, dirty solution. Need be updated when device2device memcpy() is supported. + if (kind == dpct::device_to_device) { + size_t dst_size = ggml_nbytes_pad(dst); + float *host_buf = (float *)malloc(dst_size); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + host_buf, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::device_to_host, *stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw(); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), host_buf, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::host_to_device, *main_stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw(); + free(host_buf); + } else { + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, kind, *stream))); + } } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -12985,7 +13299,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } // add event for the main device to wait on until other device is done - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { /* DPCT1024:94: The original code returned the error code that was further consumed by the program logic. This original @@ -12993,48 +13307,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[id][is] = + *src0_extra->events[i][is] = stream->ext_oneapi_submit_barrier())); } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { - continue; - } - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - - // free buffers again when done - if (dst_as[id] > 0) { - ggml_sycl_pool_free(dst_dd[id], dst_as[id]); - } - if (src1_asq[id] > 0) { - ggml_sycl_pool_free(src1_ddq[id], src1_asq[id]); - } - if (src1_asf[id] > 0) { - ggml_sycl_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src0_as[id] > 0) { - ggml_sycl_pool_free(src0_dd[id], src0_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - for (int64_t id = 0; id < g_device_count; ++id) { - if (row_low[id] == row_high[id]) { + ggml_sycl_set_device(g_main_device); + for (int i = 0; i < g_device_count; ++i) { + if (dev[i].row_low == dev[i].row_high) { continue; } for (int64_t is = 0; is < is_max; ++is) { SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier( - {*src0_extra->events[id][is]}))); + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier( + {*src0_extra->events[i][is]}))); } } } @@ -13051,110 +13344,132 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add); - // log_tensor_with_cnt("log_ggml_sycl_add_src0", (struct ggml_tensor *) src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul); - // log_tensor_with_cnt("log_ggml_sycl_mul_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_dst", dst, 6); - + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { @@ -13189,16 +13504,16 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); } @@ -13228,16 +13543,16 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; const int64_t row_stride_x = nb01 / sizeof(sycl::half); const int64_t channel_stride_x = nb02 / sizeof(sycl::half); @@ -13280,38 +13595,37 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne_dst = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(g_sycl_handles[g_main_device_index] = main_stream)); + CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream)); ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 sycl_pool_alloc src1_f16_alloc; if (src1->type != GGML_TYPE_F16) { - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - const int64_t ne_src1 = ggml_nelements(src1); - src1_f16_alloc.alloc(ne_src1); - GGML_ASSERT(to_fp16_sycl != nullptr); - to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); } sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf : src1_f16_alloc.get(); @@ -13358,7 +13672,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, int i02 = i12 / r2; SYCL_CHECK( - syclGemmEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), @@ -13371,9 +13685,8 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, #else if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - // use syclGemmStridedBatchedEx SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00, @@ -13382,7 +13695,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { - // use syclGemmBatchedEx const int ne23 = ne12*ne13; sycl_pool_alloc ptrs_src(2*ne23); @@ -13415,7 +13727,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, }); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00, @@ -13435,6 +13747,7 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && @@ -13444,9 +13757,9 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int64_t id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) { + min_compute_capability = g_device_caps[i].cc; } } @@ -13587,30 +13900,30 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { const int64_t ne = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - syclStream_t main_stream = g_syclStreams[g_main_device_index][0]; + syclStream_t main_stream = g_syclStreams[g_main_device][0]; - SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device_index], main_stream)); + SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream)); //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - //void * src0_ddq = src0_extra->data_device[g_main_device_index]; + //void * src0_ddq = src0_extra->data_device[g_main_device]; //half * src0_as_f16 = (half *) src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); GGML_ASSERT(to_fp16_sycl != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(ne1 * sizeof(half), &src1_as); + half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as); to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_sycl_pool_malloc(ne * sizeof(half), &dst_as); + half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13631,14 +13944,14 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { size_t ptrs_src_s = 0; size_t ptrs_dst_s = 0; - ptrs_src = (const void **) ggml_sycl_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_sycl_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s); int64_t src0_ne = ggml_nelements(src00); half * src0_as_f16 = nullptr; size_t src0_as = 0; if (src00->type != GGML_TYPE_F16) { - src0_as_f16 = (half *) ggml_sycl_pool_malloc(src0_ne * sizeof(half), &src0_as); + src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as); } static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); @@ -13653,16 +13966,16 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { r2, r3, src00->type, src0_as_f16, src0_ne, src1_as_f16, dst_f16, - (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index], id, - dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device_index] : nullptr + (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, + dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, + dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, + dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, + dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr ); SYCL_CHECK(syclGetLastError()); SYCL_CHECK( - syclGemmBatchedEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00, (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10, @@ -13672,20 +13985,20 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (src0_as != 0) { - ggml_sycl_pool_free(src0_as_f16, src0_as); + ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as); } if (ptrs_src_s != 0) { - ggml_sycl_pool_free(ptrs_src, ptrs_src_s); + ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s); } if (ptrs_dst_s != 0) { - ggml_sycl_pool_free(ptrs_dst, ptrs_dst_s); + ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s); } const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream); - ggml_sycl_pool_free(src1_as_f16, src1_as); - ggml_sycl_pool_free(dst_f16, dst_as); + ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as); + ggml_sycl_pool_free(g_main_device, dst_f16, dst_as); } #endif @@ -13706,10 +14019,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, std::vector ids_host(ggml_nbytes(ids)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (ids->backend == GGML_BACKEND_TYPE_GPU) { - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index]; + const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); @@ -13733,9 +14046,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row.extra = &dst_row_extra; char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? - (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index]; + (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? - (char *) dst->data : (char *) dst_extra->data_device[g_main_device_index]; + (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); @@ -13752,10 +14065,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - src1_row_extra.data_device[g_main_device_index] = src1_original + i01*src1->nb[1]; + src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? - dst_row_extra.data_device[g_main_device_index] = dst_original + i01*dst->nb[1]; + dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); @@ -13764,8 +14077,8 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); sycl_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); - src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); - dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); + src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); + dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; @@ -13853,13 +14166,13 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, GGML_TENSOR_BINARY_OP_LOCALS; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; + char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); @@ -13871,6 +14184,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { @@ -13914,6 +14229,10 @@ static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); } +static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); +} + static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col); } @@ -13940,93 +14259,6 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { - const int64_t nrows = ggml_nrows(tensor); - - const int64_t ne0 = tensor->ne[0]; - - const size_t nb1 = tensor->nb[1]; - - ggml_backend_type backend = tensor->backend; - ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - - for (int64_t id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) { - continue; - } - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; - - int64_t row_low, row_high; - if (backend == GGML_BACKEND_TYPE_GPU) { - row_low = 0; - row_high = nrows; - } else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - const int64_t rounding = get_row_rounding(tensor->type); - - row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; - row_low -= row_low % rounding; - - if (id == g_device_count - 1) { - row_high = nrows; - } else { - row_high = nrows*g_tensor_split[id + 1]; - row_high -= row_high % rounding; - } - } else { - GGML_ASSERT(false); - } - if (row_low == row_high) { - continue; - } - - int64_t nrows_split = row_high - row_low; - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf; - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - char * buf_host = (char *)data + offset_split; - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(buf, buf_host, original_size) - .wait())); - - extra->data_device[id] = buf; - - if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] = - new sycl::event())); - } - } - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - void ggml_sycl_free_data(struct ggml_tensor *tensor) try { if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) { return; @@ -14034,18 +14266,18 @@ void ggml_sycl_free_data(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - for (int64_t id = 0; id < g_device_count; ++id) { - const dpct::queue_ptr stream = g_syclStreams[id][0]; - if (extra->data_device[id] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[id], *stream))); + for (int i = 0; i < g_device_count; ++i) { + const dpct::queue_ptr stream = g_syclStreams[i][0]; + if (extra->data_device[i] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream))); } for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + if (extra->events[i][is] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[id][is]))); + dpct::destroy_event(extra->events[i][is]))); } } } @@ -14105,22 +14337,22 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, const size_t size = ggml_nbytes(tensor); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t offset = 0; if (tensor->op == GGML_OP_VIEW) { memcpy(&offset, tensor->op_params, sizeof(size_t)); } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src0_ddc + offset; + extra->data_device[g_main_device] = src0_ddc + offset; } else if (tensor->op == GGML_OP_CPY) { ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device_index]; + void * src1_ddv = src1_extra->data_device[g_main_device]; extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src1_ddv; + extra->data_device[g_main_device] = src1_ddv; } else if (scratch) { GGML_ASSERT(size <= g_scratch_size); if (g_scratch_offset + size > g_scratch_size) { @@ -14135,7 +14367,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, g_scratch_buffer = data; } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = data + g_scratch_offset; + extra->data_device[g_main_device] = data + g_scratch_offset; g_scratch_offset += size; @@ -14148,44 +14380,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, (*stream).memset(data, 0, size).wait())); extra = new ggml_tensor_extra_gpu; memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device_index] = data; - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor, - size_t offset) try { - if (g_scratch_size == 0) { - return; - } - if (g_scratch_buffer == nullptr) { - ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(g_scratch_buffer = (void *)sycl::malloc_device( - g_scratch_size, *stream))); - } - - ggml_tensor_extra_gpu * extra = ggml_sycl_alloc_temp_tensor_extra(); - - const bool inplace = tensor->view_src != nullptr; - - if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - size_t view_offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&view_offset, tensor->op_params, sizeof(size_t)); - } - extra->data_device[g_main_device_index] = src0_ddc + view_offset; - } else { - extra->data_device[g_main_device_index] = (char *) g_scratch_buffer + offset; + extra->data_device[g_main_device] = data; } tensor->extra = extra; @@ -14202,9 +14397,9 @@ void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(extra->data_device[g_main_device_index], + .memcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor)) .wait())); } @@ -14231,21 +14426,17 @@ void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) { } void ggml_sycl_set_main_device(const int main_device) try { + if (g_main_device == main_device) return; + check_allow_gpu_index(main_device); + g_main_device = main_device; + g_main_device_id = g_sycl_gpu_mgr->gpus[main_device]; - if (main_device >= g_all_sycl_device_count) { - fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", - main_device, g_all_sycl_device_count, g_main_device); - return; - } - - if (g_main_device != main_device && g_device_count >= 1) { - g_main_device = main_device; - g_main_device_index = get_device_index_by_id(g_main_device); + if (g_ggml_sycl_debug) { dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(g_main_device)))); + prop, dpct::dev_mgr::instance().get_device(g_main_device_id)))); fprintf(stderr, "Using device %d (%s) as main device\n", - g_main_device, prop.get_name()); + g_main_device_id, prop.get_name()); } } catch (sycl::exception const &exc) { @@ -14268,7 +14459,7 @@ void ggml_sycl_free_scratch() try { return; } ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR( sycl::free(g_scratch_buffer, *stream))); @@ -14340,6 +14531,12 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_RELU: func = ggml_sycl_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_sycl_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_sycl_hardswish; + break; default: return false; } @@ -14414,6 +14611,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_IM2COL: func = ggml_sycl_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_sycl_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_sycl_sum_rows; break; @@ -14439,27 +14639,15 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ } GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { - int max_compute_units = -1; - for(int i=0;igpus.size();i++){ + if (i>=max_len) break; + id_list[i] = g_sycl_gpu_mgr->gpus[i]; } return; } @@ -14486,8 +14674,9 @@ catch (sycl::exception const &exc) { GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size) try { dpct::device_info prop; + int device_id = g_sycl_gpu_mgr->gpus[device]; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); snprintf(description, description_size, "%s", prop.get_name()); } catch (sycl::exception const &exc) { @@ -14496,17 +14685,36 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, + size_t *total) try { + ggml_sycl_set_device(device); + + /* + DPCT1009:218: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string was + inserted. You need to rewrite this code. + */ + /* + DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for + device information which may not be supported by all compilers or runtimes. + You may need to adjust the code. + */ + int device_id = g_sycl_gpu_mgr->gpus[device]; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + //////////////////////////////////////////////////////////////////////////////// // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_sycl_context { - int device; - std::string name; -}; - // sycl buffer struct ggml_backend_sycl_buffer_context { @@ -14516,7 +14724,12 @@ struct ggml_backend_sycl_buffer_context { size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr) { + check_allow_gpu_index(device); + int id = g_sycl_gpu_mgr->gpus[device]; + name = (GGML_SYCL_NAME + std::to_string(id)); + } ~ ggml_backend_sycl_buffer_context() { delete[] temp_tensor_extras; @@ -14547,10 +14760,9 @@ GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) static void ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream))); @@ -14563,13 +14775,14 @@ catch (sycl::exception const &exc) { } static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; return ctx->dev_ptr; } -static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; +GGML_CALL static void +ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { assert(tensor->view_src->buffer->buft == buffer->buft); @@ -14581,27 +14794,20 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra(); extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { // initialize padding to 0 to avoid possible NaN values - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t original_size = ggml_nbytes_split(tensor, nrows_split); + size_t original_size = ggml_nbytes(tensor); size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset( (char *)tensor->data + original_size, 0, - padded_size - original_size))); + padded_size - original_size).wait())); } } - - UNUSED(buffer); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14615,13 +14821,12 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK( CHECK_TRY_ERROR((*stream) @@ -14640,14 +14845,13 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK(CHECK_TRY_ERROR( (*stream) @@ -14660,34 +14864,94 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, - uint8_t value) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; +GGML_CALL static bool +ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *src, + ggml_tensor *dst) try { + if (ggml_backend_buffer_is_sycl(src->buffer)) { + ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + ggml_sycl_set_device(src_ctx->device); + /* + DPCT1009:198: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); + ggml_sycl_set_device(dst_ctx->device); + /* + DPCT1009:199: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); + /* + DPCT1009:200: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memset(ctx->dev_ptr, value, buffer->size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} + dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0]; + dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0]; + size_t size = ggml_nbytes(src); -static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_get_name, + //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. + dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); + +//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove +#if 0 + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( + (char *)dst->data, (const char *)src->data, size).wait())); + + /* + DPCT1009:201: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); +#endif + return true; + } + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, + uint8_t value) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memset(ctx->dev_ptr, value, buffer->size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_get_name, /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, /* .get_base = */ ggml_backend_sycl_buffer_get_base, /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ NULL, + /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, /* .clear = */ ggml_backend_sycl_buffer_clear, /* .reset = */ NULL, }; @@ -14698,29 +14962,28 @@ struct ggml_backend_sycl_buffer_type_context { std::string name; }; +struct ggml_backend_sycl_context { + int device; + std::string name; +}; + GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; return ctx->name.c_str(); } - -static ggml_backend_buffer_t +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) try { ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - int device = (int) buft_ctx->device; - - ggml_sycl_set_device(device); - int device_index = get_device_index_by_id(device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + ggml_sycl_set_device(buft_ctx->device); + const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0]; size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 void * dev_ptr; SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( size, *stream))); - - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(device, dev_ptr); - + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr); return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); } catch (sycl::exception const &exc) { @@ -14729,9 +14992,8 @@ catch (sycl::exception const &exc) { std::exit(1); } -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; - UNUSED(buft); } @@ -14741,13 +15003,8 @@ static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_typ UNUSED(buft); } -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t size = ggml_nbytes_split(tensor, nrows_split); - +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); int64_t ne0 = tensor->ne[0]; if (ggml_is_quantized(tensor->type)) { @@ -14761,10 +15018,13 @@ static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_t UNUSED(buft); } -static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_sycl(backend); - - UNUSED(buft); +GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_sycl(backend)) { + return false; + } + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + return buft_ctx->device == sycl_ctx->device; } static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { @@ -14783,10 +15043,10 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { static bool ggml_backend_sycl_buffer_type_initialized = false; if (!ggml_backend_sycl_buffer_type_initialized) { - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + for (int i = 0; i < g_device_count; i++) { ggml_backend_sycl_buffer_types[i] = { /* .iface = */ ggml_backend_sycl_buffer_type_interface, - /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i)}, + /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])}, }; } ggml_backend_sycl_buffer_type_initialized = true; @@ -14795,6 +15055,391 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { return &ggml_backend_sycl_buffer_types[device]; } +// sycl split buffer type +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + if (id == g_device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + +struct ggml_backend_sycl_split_buffer_context { + ~ggml_backend_sycl_split_buffer_context() try { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[i][is] != nullptr) { + /* + DPCT1009:206: SYCL uses exceptions to report errors and + does not use the error codes. The original code was + commented out and a warning string was inserted. You + need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[i][is]))); + } + } + if (extra->data_device[i] != nullptr) { + /* + DPCT1009:207: SYCL uses exceptions to report errors and does + not use the error codes. The original code was commented out + and a warning string was inserted. You need to rewrite this + code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( + extra->data_device[i], *g_syclStreams[i][0]))); + } + } + delete extra; + } + } + catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + std::vector tensor_extras; +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buffer); +} + +// unused at the moment +//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { +// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +//} + +GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + delete ctx; +} + +GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + UNUSED(buffer); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_sycl_set_device(i); + char * buf; + /* + DPCT1009:208: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *g_syclStreams[i][0]))); + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + /* + DPCT1009:209: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + extra->data_device[i] = buf; + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + /* + DPCT1009:210: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK( + CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); + } + } + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, const void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + /* + DPCT1009:211: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(extra->data_device[i], buf_host, original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + /* + DPCT1009:212: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(buf_host, extra->data_device[i], original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + UNUSED(buffer); + UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_split_buffer_clear, + /* .reset = */ NULL, +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buft); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_sycl(backend); + + UNUSED(buft); +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, +}; + +GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = g_default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, + /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + // host buffer type GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -14824,6 +15469,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; return buffer; @@ -14848,34 +15494,33 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { // backend -static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - return GGML_SYCL_NAME; +GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - UNUSED(backend); + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + + return sycl_ctx->name.c_str(); } -static void ggml_backend_sycl_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; delete sycl_ctx; delete backend; } -static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; return ggml_backend_sycl_buffer_type(sycl_ctx->device); } -static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( (char *)tensor->data + offset, data, size))); } @@ -14885,15 +15530,13 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( data, (const char *)tensor->data + offset, size))); } @@ -14903,12 +15546,22 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { +GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, + const ggml_tensor *src, + ggml_tensor *dst) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) { + /* + DPCT1009:215: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + dst->data, src->data, ggml_nbytes(dst)))); + return true; + } - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); - - UNUSED(backend); + return false; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14916,32 +15569,20 @@ catch (sycl::exception const &exc) { std::exit(1); } -static ggml_backend_graph_plan_t ggml_backend_sycl_graph_plan_create(ggml_backend_t backend, const ggml_cgraph * cgraph) { - GGML_ASSERT(!"not implemented"); - - return nullptr; - - UNUSED(backend); - UNUSED(cgraph); -} - -static void ggml_backend_sycl_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); +static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); UNUSED(backend); - UNUSED(plan); } - -static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); } -static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_sycl_set_main_device(sycl_ctx->device); ggml_compute_params params = {}; @@ -14949,63 +15590,41 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - - if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; - - assert(node->backend == GGML_BACKEND_TYPE_GPU); + } +#ifndef NDEBUG + assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU); + assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->src[j]->extra != nullptr); } } - +#endif bool ok = ggml_sycl_compute_forward(¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); - -#if 0 - if (node->type == GGML_TYPE_F32) { - syclDeviceSynchronize(); - std::vector tmp(ggml_nelements(node), 0.0f); - syclMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), syclMemcpyDeviceToHost); - printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), - ggml_type_name(node->src[0]->type), - node->src[1] ? ggml_type_name(node->src[1]->type) : "none", - node->src[0]->name, - node->src[1] ? node->src[1]->name : "none"); - double sum = 0.0; - double sq_sum = 0.0; - for (int i = 0; i < ggml_nelements(node); i++) { - printf("%f ", tmp[i]); - sum += tmp[i]; - sq_sum += tmp[i]*tmp[i]; - } - printf("\n"); - printf("sum: %f, ", sum); - printf("sq_sum: %f\n", sq_sum); - } -#endif } - UNUSED(backend); return true; } -static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -15081,16 +15700,17 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { return true; } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return true; + } return false; } break; + case GGML_OP_DUP: + case GGML_OP_REPEAT: case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_F32) { - return true; - } else { - return false; - } + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_NONE: case GGML_OP_RESHAPE: @@ -15098,8 +15718,6 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NORM: - case GGML_OP_REPEAT: - case GGML_OP_DUP: case GGML_OP_ADD: case GGML_OP_MUL: case GGML_OP_DIV: @@ -15113,6 +15731,7 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: @@ -15134,11 +15753,11 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, + /* .cpy_tensor_async = */ ggml_backend_sycl_cpy_tensor_async, /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ ggml_backend_sycl_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_sycl_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_sycl_graph_plan_compute, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, }; @@ -15148,20 +15767,17 @@ static ggml_guid_t ggml_backend_sycl_guid() { return &guid; } -ggml_backend_t ggml_backend_sycl_init(int device) { +GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) { ggml_init_sycl(); // TODO: remove from ggml.c - if (device < 0 || device >= ggml_sycl_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); - return nullptr; - } + check_allow_gpu_index(device); // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_sycl_set_main_device(device); - + int id = g_sycl_gpu_mgr->gpus[device]; ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context { /* .device = */ device, - /* .name = */ GGML_SYCL_NAME + std::to_string(device), + /* .name = */ GGML_SYCL_NAME + std::to_string(id), }; ggml_backend_t sycl_backend = new ggml_backend { @@ -15177,22 +15793,33 @@ bool ggml_backend_is_sycl(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); } -static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { +GGML_CALL int ggml_backend_sycl_get_device_count() { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + return g_sycl_gpu_mgr->get_gpu_count(); +} + +GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data); return sycl_backend; UNUSED(params); } +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { + return g_sycl_gpu_mgr->get_index(device_id); +} + extern "C" int ggml_backend_sycl_reg_devices(); int ggml_backend_sycl_reg_devices() { - int device_count = ggml_sycl_get_device_count(); - - for (int i = 0; i < device_count; i++) { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + assert(g_device_count>0); + for (int i = 0; i < g_device_count; i++) { + int id = g_sycl_gpu_mgr->gpus[i]; char name[128]; - snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i); + snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id); ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i); } - return device_count; + return g_device_count; } diff --git a/ggml-sycl.h b/ggml-sycl.h index 891f2d00a9457..bf5b11b369d19 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -24,6 +24,11 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); GGML_API void ggml_backend_sycl_print_sycl_devices(void); GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len); GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_count(); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); +GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); + #ifdef __cplusplus } #endif diff --git a/llama.cpp b/llama.cpp index b1db5b1797dc5..cb6266a433ed5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -104,6 +104,7 @@ #define LLAMA_MAX_NODES 8192 #define LLAMA_MAX_EXPERTS 8 + // // logging // @@ -1429,7 +1430,9 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer buft = ggml_backend_cuda_host_buffer_type(); } #elif defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_host_buffer_type(); + if (host_buffer) { + buft = ggml_backend_sycl_host_buffer_type(); + } #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #elif defined(GGML_USE_VULKAN) @@ -1483,6 +1486,12 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g } #endif +#ifdef GGML_USE_SYCL + if (ggml_backend_sycl_get_device_count() > 1) { + buft = ggml_backend_sycl_split_buffer_type(tensor_split); + } +#endif + if (buft == nullptr) { buft = llama_default_buffer_type_offload(fallback_gpu); } @@ -1494,6 +1503,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g static size_t llama_get_device_count() { #if defined(GGML_USE_CUBLAS) return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + return ggml_backend_sycl_get_device_count(); #elif defined(GGML_USE_VULKAN) return ggml_backend_vk_get_device_count(); #else @@ -1507,6 +1518,11 @@ static size_t llama_get_device_memory(int device) { size_t free; ggml_backend_cuda_get_device_memory(device, &total, &free); return free; +#elif defined(GGML_USE_SYCL) + size_t total; + size_t free; + ggml_backend_sycl_get_device_memory(device, &total, &free); + return free; #elif defined(GGML_USE_VULKAN) size_t total; size_t free; @@ -12075,13 +12091,31 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { - ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu); - llama_free(ctx); - return nullptr; + // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { + int main_gpu_index = ggml_backend_sycl_get_device_index(model->main_gpu); + ggml_backend_t backend = ggml_backend_sycl_init(main_gpu_index); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, model->main_gpu, main_gpu_index); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_LAYER requires a backend for each GPU + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { + int device_id = id_list[i]; + ggml_backend_t backend = ggml_backend_sycl_init(i); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } } - ctx->backends.push_back(backend); } #elif defined(GGML_USE_KOMPUTE) if (model->n_gpu_layers > 0) { @@ -12161,7 +12195,6 @@ struct llama_context * llama_new_context_with_model( ggml_set_name(ctx->inp_cls, "inp_cls"); ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); - LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(ctx->buf_input), ggml_backend_buffer_get_size(ctx->buf_input) / 1024.0 / 1024.0); From 802da0091ba646ecf02e1a8fae2da0b8e76409bd Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Sat, 2 Mar 2024 08:42:56 -0500 Subject: [PATCH 147/401] llama : fix segfault from unknown model arch name (#5820) * llama : fix segfault from unknown model arch name * llama : make all LLM maps const This also requires using `std::map::at` instead of its `operator[]` which does not exist for const maps. * llama : name LLM_ARCH_UNKNOWN to "(unknown)" This avoids errors from `std::map::at` when getting the general name of the model architecture. Using "(unknown)" instead of an empty string as per suggestion https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284 * llama : remove redundant inner const for LLM_TENSOR_NAMES The extra const won't do anything here as const maps return const references to values. Co-authored-by: Jared Van Bortel * llama : remove redundant nullptr check in llm_arch_from_string Since LLM_ARCH_NAMES is a const map, no spurious elements with a NULL name are inserted anymore, so this check is dead code. --------- Co-authored-by: Jared Van Bortel --- llama.cpp | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/llama.cpp b/llama.cpp index cb6266a433ed5..790c2740fba19 100644 --- a/llama.cpp +++ b/llama.cpp @@ -216,7 +216,7 @@ enum llm_arch { LLM_ARCH_UNKNOWN, }; -static std::map LLM_ARCH_NAMES = { +static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -241,6 +241,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_UNKNOWN, "(unknown)" }, }; enum llm_kv { @@ -301,7 +302,7 @@ enum llm_kv { LLM_KV_TOKENIZER_RWKV, }; -static std::map LLM_KV_NAMES = { +static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, @@ -365,7 +366,7 @@ struct LLM_KV { llm_arch arch; std::string operator()(llm_kv kv) const { - return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]); + return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch)); } }; @@ -400,7 +401,7 @@ enum llm_tensor { LLM_TENSOR_LAYER_OUT_NORM, }; -static std::map> LLM_TENSOR_NAMES = { +static const std::map> LLM_TENSOR_NAMES = { { LLM_ARCH_LLAMA, { @@ -833,38 +834,38 @@ struct LLM_TN { llm_arch arch; std::string operator()(llm_tensor tensor) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return LLM_TENSOR_NAMES[arch].at(tensor); + return LLM_TENSOR_NAMES.at(arch).at(tensor); } std::string operator()(llm_tensor tensor, const std::string & suffix) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix; + return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix; } std::string operator()(llm_tensor tensor, int bid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid); + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid); } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix; + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix; } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const { - if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { return "__missing__"; } - return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix; + return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix; } }; @@ -872,7 +873,7 @@ struct LLM_TN { // gguf helpers // -static std::map LLAMA_ROPE_SCALING_TYPES = { +static const std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, From 6c32d8c7ad8ba7b6ad2a162e929a21dd04fcdca0 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 2 Mar 2024 15:19:09 +0100 Subject: [PATCH 148/401] llama : refactor internal quantization functions (#5830) --- llama.cpp | 81 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 43 insertions(+), 38 deletions(-) diff --git a/llama.cpp b/llama.cpp index 790c2740fba19..697e85e89e19c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10836,7 +10836,7 @@ struct quantize_state_internal { {} }; -static void llama_convert_tensor_internal( +static void llama_tensor_dequantize_internal( struct ggml_tensor * tensor, std::vector> & output, std::vector & workers, const size_t nelements, const int nthread ) { @@ -11177,6 +11177,46 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty return new_type; } +static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, int64_t * hist_cur, const float * imatrix, std::vector & workers, const int nthread) { + std::mutex mutex; + int counter = 0; + size_t new_size = 0; + if (nthread < 2) { + // single-thread + return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur, imatrix); + } + auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + nrows, n_per_row, imatrix]() { + std::array local_hist = {}; + const int nrows_per_chunk = chunk_size / n_per_row; + size_t local_size = 0; + while (true) { + std::unique_lock lock(mutex); + int first_row = counter; counter += nrows_per_chunk; + if (first_row >= nrows) { + if (local_size > 0) { + for (int j=0; jftype; @@ -11289,7 +11329,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector workers; workers.reserve(nthread); - std::mutex mutex; int idx = 0; @@ -11403,7 +11442,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) { throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type))); } else { - llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread); + llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread); f32_data = (float *) f32_conv_buf.data(); } @@ -11424,41 +11463,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; - if (nthread_use < 2) { - new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur.data(), imatrix); - } else { - int counter = 0; - new_size = 0; - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, - nrows, n_per_row, imatrix]() { - std::array local_hist = {}; - const int nrows_per_chunk = chunk_size / n_per_row; - size_t local_size = 0; - while (true) { - std::unique_lock lock(mutex); - int first_row = counter; counter += nrows_per_chunk; - if (first_row >= nrows) { - if (local_size > 0) { - for (int j=0; j %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); int64_t tot_count = 0; From ef2cd694c4155fbf25bae61c5178c47eb3676dba Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 2 Mar 2024 16:54:08 +0200 Subject: [PATCH 149/401] scripts : add pod-llama.sh --- scripts/pod-llama.sh | 213 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 213 insertions(+) create mode 100644 scripts/pod-llama.sh diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh new file mode 100644 index 0000000000000..6cf1ab4f352a6 --- /dev/null +++ b/scripts/pod-llama.sh @@ -0,0 +1,213 @@ +#!/bin/bash +# +# Use this script only on fresh pods (runpod.io)! +# Otherwise, it can break your environment! +# + +if [ -z "$1" ]; then + echo "Usage: $0 " + echo " 0: no models" + echo " 1: tinyllama-1b" + echo " 2: codellama-7b" + echo " 3: codellama-13b" + echo " 4: codellama-34b" + echo " 5: codellama-7b-instruct" + echo " 6: codellama-13b-instruct" + echo " 7: codellama-34b-instruct" + + exit 1 +fi + +set -x + +# setup deps +apt-get update +apt-get install -y git-lfs cmake cmake-curses-gui vim ruby +git-lfs install + +if [ ! -d "/workspace" ]; then + ln -sfn $(pwd) /workspace +fi + +# download data +cd /workspace + +# this is useful to git clone repos without doubling the disk size due to .git +git clone https://github.com/iboB/git-lfs-download +ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download + +# llama.cpp +cd /workspace +git clone https://github.com/ggerganov/llama.cpp + +cd llama.cpp + +LLAMA_CUBLAS=1 make -j + +ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3 ./models/tinyllama-1b +ln -sfn /workspace/CodeLlama-7b-hf ./models/codellama-7b +ln -sfn /workspace/CodeLlama-13b-hf ./models/codellama-13b +ln -sfn /workspace/CodeLlama-34b-hf ./models/codellama-34b +ln -sfn /workspace/CodeLlama-7b-Instruct-hf ./models/codellama-7b-instruct +ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct +ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct + +pip install -r requirements.txt + +# cmake +cd /workspace/llama.cpp + +mkdir build-cublas +cd build-cublas + +cmake -DLLAMA_CUBLAS=1 ../ +make -j + +if [ "$1" -eq "0" ]; then + exit 0 +fi + +# more models +if [ "$1" -eq "1" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3 + + cd /workspace/llama.cpp + + python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "2" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf --without *safetensors* + rm -v ./CodeLlama-7b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "3" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors* + rm -v ./CodeLlama-13b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "4" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors* + rm -v ./CodeLlama-34b-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "5" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-7b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "6" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-13b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "7" ]; then + cd /workspace + + git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors* + rm -v ./CodeLlama-34b-Instruct-hf/*safetensors* + + cd /workspace/llama.cpp + + python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 + + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k + ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0 +fi + +if [ "$1" -eq "1" ]; then + # perf + perplexity + cd /workspace/llama.cpp/build-cublas + + make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128" + + ../scripts/get-wikitext-2.sh + unzip wikitext-2-raw-v1.zip + + make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32 + + # batched + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999 + + # batched-bench + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32 + + # parallel + cd /workspace/llama.cpp + + LLAMA_CUBLAS=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb + +fi + +# speculative +#if [ "$1" -eq "7" ]; then +# cd /workspace/llama.cpp +# +# LLAMA_CUBLAS=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0 +#fi + +# more benches +#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 +#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 + From bbde6eb2561153aabbdfac5001c690fe00cad639 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 2 Mar 2024 17:00:51 +0200 Subject: [PATCH 150/401] ggml : IQ3_S improvements (#5829) * iq3_s: somewhat faster AVX2 dot product On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using 16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s. PP-512 increases to 28.5 t/s from 23.8 t/s. * iq3_s: somewhat faster ARM_NEON dot product Still dog slow - 10.7 t/s up from 9.9 t/s. * iq3_s: another small ARM_NEON improvement 10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick that works best on AVX2. * iq3_s: minor improvement on Metal 49.4 t/s -> 50.3 t/s * iq3_s: PPL improvement E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653. * iq3_s: use new grid everywhere * Fix ARM_NEON --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 143 ++++++++++++------------ ggml-metal.metal | 152 ++++++++++++------------- ggml-quants.c | 280 +++++++++++++++++++++++++++-------------------- 3 files changed, 310 insertions(+), 265 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0c6501e98a2a6..7ed97430f4fa4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2018,74 +2018,73 @@ static const __device__ uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const __device__ uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const __device__ uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; - static const __device__ uint64_t iq1s_grid[512] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, @@ -2392,9 +2391,9 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); const uint8_t signs = x[i].signs[4*ib + il]; for (int j = 0; j < 4; ++j) { y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); @@ -5211,8 +5210,8 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const int8_t * q8 = bq8_1[ib32].qs; int sumi = 0; for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); - const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); @@ -5221,7 +5220,7 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( sumi = __dp4a(grid_h, *((int *)q8+1), sumi); q8 += 8; } - const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f; + const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); return d * sumi; #else assert(false); diff --git a/ggml-metal.metal b/ggml-metal.metal index 74a5e0b039fc7..8b9488437494d 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -4087,71 +4087,71 @@ constexpr constant static uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -constexpr constant static uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +constexpr constant static uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ1S 512 @@ -4742,7 +4742,7 @@ void kernel_mul_mv_iq3_s_f32_impl( { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i]; + for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4769,12 +4769,14 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; row++) { const float db = dh[0]; - const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf)); + const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf)); float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; + const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); for (int j = 0; j < 4; ++j) { sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]); sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]); @@ -4795,7 +4797,7 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; } } } @@ -5685,15 +5687,15 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & device const uint8_t * qs = xb->qs + 8*ib32; device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256))); + const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); + constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); for (int i = 0; i < 4; ++i) { reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); } - grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256))); + grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); for (int i = 0; i < 4; ++i) { reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); diff --git a/ggml-quants.c b/ggml-quants.c index 371826f14719d..492a1b9a6ed20 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3818,71 +3818,71 @@ static const uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ2XXS 512 @@ -4162,11 +4162,11 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in const uint8_t * signs = x[i].signs; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f; - const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f; + const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf)); + const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >> 4)); for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -4176,8 +4176,8 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in qs += 8; signs += 4; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -10089,18 +10089,34 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v #if defined(__ARM_NEON) + typedef union { + uint16x8_t vec_index; + uint16_t index[8]; + } vec_index_t; + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 }; static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); + static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; + + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); uint8x16x2_t vs; ggml_int8x16x4_t q3s; ggml_int8x16x4_t q8b; + vec_index_t idx; + +#if QK_K == 256 + uint32_t scales32[2]; + const uint8_t * scales8 = (const uint8_t *)scales32; +#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -10109,47 +10125,63 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint8_t * restrict qh = x[i].qh; const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; + +#if QK_K == 256 + memcpy(scales32, x[i].scales, 4); + scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; + scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; +#endif + int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]}; - const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]}; - const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]}; - const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]}; - qs += 16; + + const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; + idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); + const uint32x4_t aux32x4_0 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_1 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); + const uint32x4_t aux32x4_2 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_3 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); signs += 4; - q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); +#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; + sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; +#else sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); +#endif } sumf += d*(sumi1 + sumi2); } - *s = 0.25f * sumf; + *s = sumf; #elif defined(__AVX2__) @@ -10164,6 +10196,16 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = _mm256_set1_epi32(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + __m256 accumf = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; @@ -10176,24 +10218,25 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]); - qs += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]); - qs += 8; + const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; + idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); + idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); + idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); + idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = _mm256_set_epi32( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = _mm256_set_epi32( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); @@ -10221,7 +10264,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } - *s = 0.25f * hsum_float_8(accumf); + *s = hsum_float_8(accumf); #else @@ -10238,8 +10281,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; int32_t sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -10251,8 +10294,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v bsum += sumi * ls1; sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -10265,7 +10308,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } sumf += d * bsum; } - *s = 0.25f * sumf; + *s = sumf; #endif } @@ -11912,7 +11955,8 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float best = 0; float scale = max/(2*kMaxQ-1); - for (int is = -15; is <= 15; ++is) { + for (int k = 0; k < bs4; ++k) is_on_grid[k] = false; + for (int is = -9; is <= 9; ++is) { float id = (2*kMaxQ-1+is*0.2f)/max; float this_scale = 1/id; for (int k = 0; k < bs4; ++k) { @@ -11948,7 +11992,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo if (n_not_ongrid > 0 && scale > 0) { float id = 1/scale; for (int k = 0; k < bs4; ++k) { - if (is_on_grid[k]) continue; + //if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 4; ++i) { int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); @@ -12004,7 +12048,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float d = max_scale/31; - y[ibl].d = GGML_FP32_TO_FP16(d); + y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f); float id = 1/d; for (int ib = 0; ib < QK_K/block_size; ib += 2) { int l1 = nearest_int(0.5f*(id*scales[ib+0]-1)); From c7a0ad8ec9ebb5ddb1c1c80c82f2ee041c525d47 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 2 Mar 2024 12:21:47 -0500 Subject: [PATCH 151/401] convert-hf : make model class definitions self-contained (#5825) --- convert-hf-to-gguf.py | 204 ++++++++++++++++++------------------ gguf-py/gguf/gguf_writer.py | 2 +- 2 files changed, 101 insertions(+), 105 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 28b92ac385367..fa9d4f22fd33c 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -8,9 +8,10 @@ import os import re import sys +from abc import ABC, abstractmethod from enum import IntEnum from pathlib import Path -from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast import numpy as np import torch @@ -35,8 +36,11 @@ class SentencePieceTokenTypes(IntEnum): UNUSED = 5 BYTE = 6 +AnyModel = TypeVar("AnyModel", bound="type[Model]") + +class Model(ABC): + _model_classes: dict[str, type[Model]] = {} -class Model: def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): self.dir_model = dir_model self.ftype = ftype @@ -47,10 +51,14 @@ def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) - self.model_arch = self._get_model_architecture() self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) + @property + @abstractmethod + def model_arch(self) -> gguf.MODEL_ARCH: + pass + def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: key = next((k for k in keys if k in self.hparams), None) if key is not None: @@ -176,55 +184,21 @@ def load_hparams(dir_model): with open(dir_model / "config.json", "r", encoding="utf-8") as f: return json.load(f) - @staticmethod - def from_model_architecture(model_architecture): - if model_architecture == "GPTNeoXForCausalLM": - return GPTNeoXModel - if model_architecture == "BloomForCausalLM": - return BloomModel - if model_architecture == "MPTForCausalLM": - return MPTModel - if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return BaichuanModel - if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): - return FalconModel - if model_architecture == "GPTBigCodeForCausalLM": - return StarCoderModel - if model_architecture == "GPTRefactForCausalLM": - return RefactModel - if model_architecture == "PersimmonForCausalLM": - return PersimmonModel - if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return StableLMModel - if model_architecture == "QWenLMHeadModel": - return QwenModel - if model_architecture == "Qwen2ForCausalLM": - return Model - if model_architecture == "MixtralForCausalLM": - return MixtralModel - if model_architecture == "GPT2LMHeadModel": - return GPT2Model - if model_architecture == "PhiForCausalLM": - return Phi2Model - if model_architecture == "PlamoForCausalLM": - return PlamoModel - if model_architecture == "CodeShellForCausalLM": - return CodeShellModel - if model_architecture == "OrionForCausalLM": - return OrionModel - if model_architecture == "InternLM2ForCausalLM": - return InternLM2Model - if model_architecture == "MiniCPMForCausalLM": - return MiniCPMModel - if model_architecture == "BertModel": - return BertModel - if model_architecture == "NomicBertModel": - return NomicBertModel - if model_architecture == "GemmaForCausalLM": - return GemmaModel - if model_architecture == "Starcoder2ForCausalLM": - return Model - return Model + @classmethod + def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: + assert names + def func(modelcls: type[Model]): + for name in names: + cls._model_classes[name] = modelcls + return modelcls + return func + + @classmethod + def from_model_architecture(cls, arch): + try: + return cls._model_classes[arch] + except KeyError: + raise NotImplementedError(f'Architecture {arch!r} not supported!') from None def _is_model_safetensors(self) -> bool: return Model.count_model_parts(self.dir_model, ".safetensors") > 0 @@ -239,57 +213,6 @@ def _get_part_names(self): return ("pytorch_model.bin",) return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - def _get_model_architecture(self) -> gguf.MODEL_ARCH: - arch = self.hparams["architectures"][0] - if arch == "GPTNeoXForCausalLM": - return gguf.MODEL_ARCH.GPTNEOX - if arch == "BloomForCausalLM": - return gguf.MODEL_ARCH.BLOOM - if arch == "MPTForCausalLM": - return gguf.MODEL_ARCH.MPT - if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return gguf.MODEL_ARCH.BAICHUAN - if arch in ("FalconForCausalLM", "RWForCausalLM"): - return gguf.MODEL_ARCH.FALCON - if arch == "GPTBigCodeForCausalLM": - return gguf.MODEL_ARCH.STARCODER - if arch == "GPTRefactForCausalLM": - return gguf.MODEL_ARCH.REFACT - if arch == "PersimmonForCausalLM": - return gguf.MODEL_ARCH.PERSIMMON - if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return gguf.MODEL_ARCH.STABLELM - if arch == "QWenLMHeadModel": - return gguf.MODEL_ARCH.QWEN - if arch == "Qwen2ForCausalLM": - return gguf.MODEL_ARCH.QWEN2 - if arch == "MixtralForCausalLM": - return gguf.MODEL_ARCH.LLAMA - if arch == "GPT2LMHeadModel": - return gguf.MODEL_ARCH.GPT2 - if arch == "PhiForCausalLM": - return gguf.MODEL_ARCH.PHI2 - if arch == "PlamoForCausalLM": - return gguf.MODEL_ARCH.PLAMO - if arch == "CodeShellForCausalLM": - return gguf.MODEL_ARCH.CODESHELL - if arch == "OrionForCausalLM": - return gguf.MODEL_ARCH.ORION - if arch == "InternLM2ForCausalLM": - return gguf.MODEL_ARCH.INTERNLM2 - if arch == "MiniCPMForCausalLM": - return gguf.MODEL_ARCH.MINICPM - if arch == "BertModel": - return gguf.MODEL_ARCH.BERT - if arch == "NomicBertModel": - return gguf.MODEL_ARCH.NOMIC_BERT - if arch == "GemmaForCausalLM": - return gguf.MODEL_ARCH.GEMMA - if arch == "Starcoder2ForCausalLM": - return gguf.MODEL_ARCH.STARCODER2 - - raise NotImplementedError(f'Architecture "{arch}" not supported!') - def _set_vocab_gpt2(self): dir_model = self.dir_model hparams = self.hparams @@ -457,7 +380,10 @@ def _set_vocab_hf(self): special_vocab.add_to_gguf(self.gguf_writer) +@Model.register("GPTNeoXForCausalLM") class GPTNeoXModel(Model): + model_arch = gguf.MODEL_ARCH.GPTNEOX + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] @@ -474,7 +400,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) +@Model.register("BloomForCausalLM") class BloomModel(Model): + model_arch = gguf.MODEL_ARCH.BLOOM + def set_gguf_parameters(self): self.gguf_writer.add_name("Bloom") n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) @@ -566,7 +495,10 @@ def write_tensors(self): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("MPTForCausalLM") class MPTModel(Model): + model_arch = gguf.MODEL_ARCH.MPT + def set_gguf_parameters(self): block_count = self.hparams["n_layers"] self.gguf_writer.add_name(self.dir_model.name) @@ -629,7 +561,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("OrionForCausalLM") class OrionModel(Model): + model_arch = gguf.MODEL_ARCH.ORION + def set_vocab(self): self._set_vocab_sentencepiece() @@ -708,7 +643,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM") class BaichuanModel(Model): + model_arch = gguf.MODEL_ARCH.BAICHUAN + def set_vocab(self): self._set_vocab_sentencepiece() @@ -823,7 +761,10 @@ def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: return weights[r * n_part:r * n_part + r, ...] +@Model.register("FalconForCausalLM", "RWForCausalLM") class FalconModel(Model): + model_arch = gguf.MODEL_ARCH.FALCON + def set_gguf_parameters(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: @@ -916,7 +857,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("GPTBigCodeForCausalLM") class StarCoderModel(Model): + model_arch = gguf.MODEL_ARCH.STARCODER + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -931,7 +875,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_file_type(self.ftype) +@Model.register("GPTRefactForCausalLM") class RefactModel(Model): + model_arch = gguf.MODEL_ARCH.REFACT + def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim @@ -1015,7 +962,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("PersimmonForCausalLM") class PersimmonModel(Model): + model_arch = gguf.MODEL_ARCH.PERSIMMON + def set_gguf_parameters(self): block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) head_count = self.hparams["num_attention_heads"] @@ -1063,7 +1013,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): + model_arch = gguf.MODEL_ARCH.STABLELM + def set_vocab(self): if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() @@ -1087,12 +1040,18 @@ def set_gguf_parameters(self): self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) +@Model.register("MixtralForCausalLM") class MixtralModel(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + def set_vocab(self): self._set_vocab_sentencepiece() +@Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): + model_arch = gguf.MODEL_ARCH.MINICPM + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] self.gguf_writer.add_name("MiniCPM") @@ -1169,7 +1128,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("QWenLMHeadModel") class QwenModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN + @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode @@ -1249,7 +1211,15 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Qwen2ForCausalLM") +class Qwen2Model(Model): + model_arch = gguf.MODEL_ARCH.QWEN2 + + +@Model.register("GPT2LMHeadModel") class GPT2Model(Model): + model_arch = gguf.MODEL_ARCH.GPT2 + def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_block_count(self.hparams["n_layer"]) @@ -1311,7 +1281,10 @@ def write_tensors(self): self.gguf_writer.add_tensor("output.weight", data) +@Model.register("PhiForCausalLM") class Phi2Model(Model): + model_arch = gguf.MODEL_ARCH.PHI2 + def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) @@ -1333,7 +1306,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_add_bos_token(False) +@Model.register("PlamoForCausalLM") class PlamoModel(Model): + model_arch = gguf.MODEL_ARCH.PLAMO + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1412,7 +1388,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("CodeShellForCausalLM") class CodeShellModel(Model): + model_arch = gguf.MODEL_ARCH.CODESHELL + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -1477,7 +1456,10 @@ def write_tensors(self): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("InternLM2ForCausalLM") class InternLM2Model(Model): + model_arch = gguf.MODEL_ARCH.INTERNLM2 + def set_vocab(self): # (TODO): Is there a better way? # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character @@ -1649,7 +1631,10 @@ def write_tensors(self): self.post_write_tensors(tensor_map, name, data_torch) +@Model.register("BertModel") class BertModel(Model): + model_arch = gguf.MODEL_ARCH.BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.vocab_size = None @@ -1679,7 +1664,7 @@ def set_gguf_parameters(self): else: raise NotImplementedError("Only MEAN and CLS pooling types supported") - self.gguf_writer.add_pooling_type(pooling_type.value) + self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): path = self.dir_model @@ -1755,7 +1740,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("NomicBertModel") class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.NOMIC_BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -1792,7 +1780,10 @@ def get_tensors(self): yield name, data +@Model.register("GemmaForCausalLM") class GemmaModel(Model): + model_arch = gguf.MODEL_ARCH.GEMMA + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1848,6 +1839,11 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Starcoder2ForCausalLM") +class StarCoder2Model(Model): + model_arch = gguf.MODEL_ARCH.STARCODER2 + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e4681475cfcc5..8011608323c45 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -362,7 +362,7 @@ def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) def add_pooling_type(self, value: PoolingType) -> None: - self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value) + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) From 4d4d2366fc9c54d4a275065cfe9299c6cf7c5b78 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 2 Mar 2024 12:27:26 -0500 Subject: [PATCH 152/401] convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (#5821) --- README.md | 4 +- convert-llama-ggml-to-gguf.py | 6 +-- convert.py | 76 +++++++++++++++++------------------ examples/infill/infill.cpp | 4 +- 4 files changed, 43 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index 67717c1e38b34..9396467537c16 100644 --- a/README.md +++ b/README.md @@ -786,7 +786,7 @@ And after 4.45 hours, you will have the final perplexity. ### Interactive mode If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. -In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. +In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. Here is an example of a few-shot interaction, invoked with the command @@ -850,7 +850,7 @@ Sample run: ``` == Running in interactive mode. == - Press Ctrl+C to interject at any time. - - Press Return to return control to LLaMa. + - Press Return to return control to LLaMA. - If you want to submit another line, end your input in '\'. Below is an instruction that describes a task. Write a response that appropriately completes the request. diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index b331080629be0..cd9644fcb5213 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -373,7 +373,7 @@ def handle_metadata(cfg, hp): raise ValueError('Unable to load metadata') vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir) vocab_factory = convert.VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir) + vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return params, vocab, special_vocab @@ -398,8 +398,8 @@ def handle_args(): help ='Load HuggingFace/.pth vocab and metadata from the specified directory') parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm", - help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)") + parser.add_argument("--vocabtype", default="spm,hfft", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") return parser.parse_args() diff --git a/convert.py b/convert.py index 63a0a5d78075b..6e3a0319b1e46 100755 --- a/convert.py +++ b/convert.py @@ -1282,35 +1282,32 @@ def load_some_model(path: Path) -> ModelPlus: class VocabFactory: + _FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"} + def __init__(self, path: Path): self.path = path - self.files: dict[str, Path | None] = { - "tokenizer.model": None, - "vocab.json": None, - "tokenizer.json": None, - } - self._detect_files() - - def _detect_files(self): - for file in self.files.keys(): - file_path = self.path / file - parent_file_path = self.path.parent / file - if file_path.exists(): - self.files[file] = file_path - elif parent_file_path.exists(): - self.files[file] = parent_file_path - print(f"Found vocab files: {self.files}") - - def _select_file(self, vocabtype: str | None) -> Path: - if vocabtype in ["spm", "bpe"]: - for file_key in self.files.keys(): - if (file := self.files[file_key]) is not None: - return file - raise FileNotFoundError(f"{vocabtype} vocab not found.") - if vocabtype == "hfft": - # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file - return self.path - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + self.file_paths = self._detect_files() + print(f"Found vocab files: {self.file_paths}") + + def _detect_files(self) -> dict[str, Path | None]: + def locate(file: str) -> Path | None: + if (path := self.path / file).exists(): + return path + if (path := self.path.parent / file).exists(): + return path + return None + + return {vt: locate(f) for vt, f in self._FILES.items()} + + def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]: + for vtype in vocab_types: + try: + path = self.file_paths[vtype] + except KeyError: + raise ValueError(f"Unsupported vocabulary type {vtype}") from None + if path is not None: + return vtype, path + raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}") def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab: load_merges = vocabtype == "bpe" @@ -1322,30 +1319,30 @@ def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: n_vocab=n_vocab, ) - def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: - path = self._select_file(vocabtype) - print(f"Loading vocab file '{path}', type '{vocabtype}'") + def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: + vocab_type, path = self._select_file(vocab_types) + print(f"Loading vocab file {path!r}, type {vocab_type!r}") added_tokens_path = path.parent / "added_tokens.json" vocab: Vocab - if vocabtype == "bpe": + if vocab_type == "bpe": vocab = BpeVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "spm": + elif vocab_type == "spm": vocab = SentencePieceVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "hfft": + elif vocab_type == "hfft": vocab = HfVocab( - path, added_tokens_path if added_tokens_path.exists() else None + path.parent, added_tokens_path if added_tokens_path.exists() else None ) else: - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + raise ValueError(vocab_type) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, - vocabtype, + vocab_type, model_parent_path, ) return vocab, special_vocab @@ -1379,15 +1376,14 @@ def main(args_in: list[str] | None = None) -> None: if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") - vocab_types = ["spm", "bpe", "hfft"] - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") + parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") @@ -1448,7 +1444,7 @@ def main(args_in: list[str] | None = None) -> None: model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) vocab_factory = VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path) + vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path) if args.vocab_only: if not args.outfile: diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index d4b8729dd0283..91c39c5ae42e3 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -378,10 +378,10 @@ int main(int argc, char ** argv) { if (params.interactive) { const char *control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to LLaMA, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to LLaMA.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } From 494c87032613e31c0be99b2735e732871f2c4e4d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 2 Mar 2024 20:00:49 +0200 Subject: [PATCH 153/401] ggml : fix IQ3_S AVX implementation (#5834) ggml-ci --- ggml-quants.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-quants.c b/ggml-quants.c index 492a1b9a6ed20..2a8881d73422d 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -10231,7 +10231,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); const __m256i q2_1 = _mm256_set_epi32( iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] ); const __m256i q2_2 = _mm256_set_epi32( iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], From 4a6e2d6142ab815c964924896891e9ab3e050632 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Sat, 2 Mar 2024 20:52:25 +0100 Subject: [PATCH 154/401] llama : add abort_callback to interrupt computation (#5409) * using abort_callback from ggml to stop llama computation * format fix * a brief explaining comment --------- Co-authored-by: Georgi Gerganov --- llama.cpp | 18 ++++++++++++++++-- llama.h | 13 +++++++++++-- 2 files changed, 27 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 697e85e89e19c..d4c7a965bf377 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1987,6 +1987,9 @@ struct llama_context { std::vector buf_compute_meta; ggml_backend_sched_t sched = nullptr; + ggml_abort_callback abort_callback = nullptr; + void * abort_callback_data = nullptr; + // input tensors ggml_backend_buffer_t buf_input = nullptr; ggml_context * ctx_input = nullptr; @@ -8071,6 +8074,7 @@ static void llama_graph_compute( if (lctx.backend_cpu != nullptr) { ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); + ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data); } ggml_backend_sched_graph_compute(lctx.sched, gf); @@ -11856,6 +11860,8 @@ struct llama_context_params llama_context_default_params() { /*.embedding =*/ false, /*.offload_kqv =*/ true, /*.do_pooling =*/ true, + /*.abort_callback =*/ nullptr, + /*.abort_callback_data =*/ nullptr, }; return result; @@ -12038,8 +12044,11 @@ struct llama_context * llama_new_context_with_model( LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); - ctx->rng = std::mt19937(params.seed); - ctx->logits_all = params.logits_all; + ctx->abort_callback = params.abort_callback; + ctx->abort_callback_data = params.abort_callback_data; + + ctx->rng = std::mt19937(params.seed); + ctx->logits_all = params.logits_all; const ggml_type type_k = params.type_k; const ggml_type type_v = params.type_v; @@ -12989,6 +12998,11 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_ ctx->cparams.n_threads_batch = n_threads_batch; } +void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + struct llama_batch llama_batch_get_one( llama_token * tokens, int32_t n_tokens, diff --git a/llama.h b/llama.h index ed51f478a7b21..6406b52705e7d 100644 --- a/llama.h +++ b/llama.h @@ -255,10 +255,16 @@ extern "C" { enum ggml_type type_v; // data type for V cache // Keep the booleans together to avoid misalignment during copy-by-value. - bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) + bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) + + // Abort callback + // if it returns true, execution of llama_decode() will be aborted + // currently works only with CPU execution + ggml_abort_callback abort_callback; + void * abort_callback_data; }; // model quantization parameters @@ -632,7 +638,10 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); - // Token logits obtained from the last call to llama_eval() + // Set abort callback + LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); + + // Token logits obtained from the last call to llama_decode() // The logits for the last token are stored in the last row // Logits for which llama_batch.logits[i] == 0 are undefined // Rows: n_tokens provided with llama_batch From 9731134296af3a6839cd682e51d9c2109a871de5 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 2 Mar 2024 22:00:14 +0100 Subject: [PATCH 155/401] server: tests: passkey challenge / self-extend with context shift demo (#5832) * server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test --- .github/workflows/server.yml | 15 +- examples/server/server.cpp | 46 +++- examples/server/tests/README.md | 50 +++- examples/server/tests/features/environment.py | 5 +- examples/server/tests/features/issues.feature | 1 + .../server/tests/features/parallel.feature | 5 +- .../server/tests/features/passkey.feature | 55 ++++ .../server/tests/features/security.feature | 3 +- examples/server/tests/features/server.feature | 23 +- examples/server/tests/features/steps/steps.py | 259 ++++++++++++++---- .../tests/features/wrong_usages.feature | 5 +- examples/server/tests/requirements.txt | 1 + examples/server/tests/tests.sh | 2 +- examples/server/utils.hpp | 3 +- 14 files changed, 362 insertions(+), 111 deletions(-) create mode 100644 examples/server/tests/features/passkey.feature diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 0b6f6669b23c7..8c63125087d62 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -10,6 +10,8 @@ on: pull_request: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] + schedule: + - cron: '00 0 * * *' jobs: server: @@ -70,14 +72,15 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt - - name: Download models - id: download_models + - name: Tests + id: server_integration_tests run: | cd examples/server/tests - ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf + PORT=8888 ./tests.sh - - name: Tests - id: server_integration_test + - name: Slow tests + id: server_integration_tests_slow + if: github.event.schedule != '' run: | cd examples/server/tests - PORT=8888 ./tests.sh + PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2b2f4a0f4a48b..52daf9e7a3db4 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -441,8 +441,8 @@ struct llama_server_context const int ga_w = params.grp_attn_w; if (ga_n != 1) { - GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT - GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT + GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT + GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT @@ -1709,8 +1709,8 @@ struct llama_server_context } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - // if input prompt is too big, truncate it - if (slot.n_prompt_tokens >= slot.n_ctx) + // if input prompt is too big, truncate it, if group attention self-extend is disabled + if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; @@ -1785,9 +1785,11 @@ struct llama_server_context } LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, + { "slot_id", slot.id }, + { "task_id", slot.task_id }, + { "n_past", slot.n_past }, + { "n_past_se", slot.n_past_se }, + { "ga_i", slot.ga_i }, { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } }); } @@ -2001,6 +2003,17 @@ struct llama_server_context LOG_VERBOSE("slots updated", {}); return true; } + + json model_meta() { + return json{ + {"vocab_type", llama_vocab_type(model)}, + {"n_vocab", llama_n_vocab(model)}, + {"n_ctx_train", llama_n_ctx_train(model)}, + {"n_embd", llama_n_embd(model)}, + {"n_params", llama_model_n_params(model)}, + {"size", llama_model_size(model)}, + }; + } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -2911,9 +2924,10 @@ int main(int argc, char **argv) for (const auto& metric_def : metrics_def) { std::string name = metric_def["name"]; std::string help = metric_def["help"]; - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << metric_def["value"] << "\n"; + auto value = json_value(metric_def, "value", 0); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; } } @@ -2994,6 +3008,7 @@ int main(int argc, char **argv) state.store(SERVER_STATE_READY); LOG_INFO("model loaded", {}); } + const auto model_meta = llama.model_meta(); if (sparams.chat_template.empty()) { // custom chat template is not supplied // check if the template comes with the model is supported by us @@ -3143,7 +3158,7 @@ int main(int argc, char **argv) } }); - svr.Get("/v1/models", [¶ms](const httplib::Request& req, httplib::Response& res) + svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); std::time_t t = std::time(0); @@ -3152,10 +3167,11 @@ int main(int argc, char **argv) {"object", "list"}, {"data", { { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"} + {"id", params.model_alias}, + {"object", "model"}, + {"created", t}, + {"owned_by", "llamacpp"}, + {"meta", model_meta} }, }} }; diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 0b9fdc4e72678..95a0353b6a9c5 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,22 +1,30 @@ # Server tests -Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/): - * [issues.feature](./features/issues.feature) Pending issues scenario - * [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests - * [security.feature](./features/security.feature) Security, CORS and API Key - * [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... +Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) +and [behave](https://behave.readthedocs.io/en/latest/): + +* [issues.feature](./features/issues.feature) Pending issues scenario +* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests +* [security.feature](./features/security.feature) Security, CORS and API Key +* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... Tests target GitHub workflows job runners with 4 vCPU. -Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client. +Requests are +using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) +based http client. -Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. +Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. +To mitigate it, you can increase values in `n_predict`, `kv_size`. ### Install dependencies + `pip install -r requirements.txt` ### Run tests + 1. Build the server + ```shell cd ../../.. mkdir build @@ -24,24 +32,36 @@ cd build cmake ../ cmake --build . --target server ``` -2. download required models: - 1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf` -3. Start the test: `./tests.sh` + +2. Start the test: `./tests.sh` It's possible to override some scenario steps values with environment variables: - - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080` - - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server` - - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` - - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format + +| variable | description | +|--------------------------|------------------------------------------------------------------------------------------------| +| `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | +| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/server` | +| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | +| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format | +| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | ### Run @bug, @wip or @wrong_usage annotated scenario Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. + - `@bug` annotation aims to link a scenario with a GitHub issue. - `@wrong_usage` are meant to show user issue that are actually an expected behavior - `@wip` to focus on a scenario working in progress +- `@slow` heavy test, disabled by default To run a scenario annotated with `@bug`, start: -`DEBUG=ON ./tests.sh --no-skipped --tags bug` + +```shell +DEBUG=ON ./tests.sh --no-skipped --tags bug +``` After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. + +```shell +./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile" +``` diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 09e8267476135..9fd330db6ddc9 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -7,7 +7,10 @@ def before_scenario(context, scenario): - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") + context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' + if context.debug: + print("DEBUG=ON\n") + print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n") port = 8080 if 'PORT' in os.environ: port = int(os.environ['PORT']) diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature index bf5a175a357ca..7b13e44cad395 100644 --- a/examples/server/tests/features/issues.feature +++ b/examples/server/tests/features/issues.feature @@ -1,4 +1,5 @@ # List of ongoing issues +# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug @bug Feature: Issues # No confirmed issue at the moment diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 5f895cf90b966..86cdf72829f8c 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -1,11 +1,12 @@ @llama.cpp +@parallel Feature: Parallel Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And 42 as server seed + And 512 as batch size And 64 KV cache size And 2 slots And embeddings extraction diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature new file mode 100644 index 0000000000000..1bde7aab8bab0 --- /dev/null +++ b/examples/server/tests/features/passkey.feature @@ -0,0 +1,55 @@ +# run with: ./tests.sh --no-skipped --tags passkey +@passkey +@slow +Feature: Passkey / Self-extend with context shift + + Background: Server startup + Given a server listening on localhost:8080 + + # Generates a long text of junk and inserts a secret passkey number inside it. + # Then we query the LLM for the secret passkey. + # see #3856 and #4810 + Scenario Outline: Passkey + Given a model file from HF repo + And as batch size + And as number of junk + And server max tokens to predict + And 42 as seed + And KV cache size + And 1 slots + And group attention factor to extend context size through self-extend + And group attention width to extend context size through self-extend + # Can be override with N_GPU_LAYERS + And GPU offloaded layers + Then the server is starting + Then the server is healthy + Given available models + Then model 0 is trained on tokens context + Given a prefix prompt: + """ + here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there. + """ + And a passkey prompt template: + """ + The pass key is Remember it. is the pass key. + """ + And a junk suffix prompt: + """ + The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. + """ + And a suffix prompt: + """ + What is the pass key? The pass key is + """ + Given a "" passkey challenge prompt with the passkey inserted every junk + And a completion request with no api error + Then tokens are predicted matching + + Examples: + | hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b | + #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | + #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 + # 987 | + diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index db06d39775c05..42a6709a53380 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -1,9 +1,10 @@ @llama.cpp +@security Feature: Security Background: Server startup with an api key defined Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a server api key llama.cpp Then the server is starting Then the server is healthy diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index b571582a7857e..7c977bccecaad 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -1,15 +1,17 @@ @llama.cpp +@server Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a model alias tinyllama-2 And 42 as server seed # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 # see --ctx-size and #5568 And 32 KV cache size + And 512 as batch size And 1 slots And embeddings extraction And 32 server max tokens to predict @@ -29,9 +31,9 @@ Feature: llama.cpp server And prometheus metrics are exposed Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (readgoing)+ | 8 | - | Write a joke about AI | 64 | (parkfriendsscaredalways)+ | 32 | + | prompt | n_predict | re_content | n_predicted | + | I believe the meaning of life is | 8 | (read\|going)+ | 8 | + | Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 | Scenario Outline: OAI Compatibility Given a model @@ -43,9 +45,9 @@ Feature: llama.cpp server Then tokens are predicted matching Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Momwhat)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thankshappybird)+ | 32 | enabled | + | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | + | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | Scenario: Embedding When embeddings are computed for: @@ -75,10 +77,15 @@ Feature: llama.cpp server When an OAI compatible embeddings computation request for multiple inputs Then embeddings are generated - Scenario: Tokenize / Detokenize When tokenizing: """ What is the capital of France ? """ Then tokens can be detokenize + + Scenario: Models available + Given available models + Then 1 models are supported + Then model 0 is identified by tinyllama-2 + Then model 0 is trained on 128 tokens context diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 381da105e279e..3195278022ffb 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -13,6 +13,7 @@ import openai from behave import step from behave.api.async_step import async_run_until_complete +from huggingface_hub import hf_hub_download from prometheus_client import parser @@ -26,17 +27,23 @@ def step_server_config(context, server_fqdn, server_port): context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' context.model_alias = None + context.n_batch = None context.n_ctx = None + context.n_ga = None + context.n_ga_w = None + context.n_gpu_layer = None context.n_predict = None context.n_server_predict = None context.n_slots = None + context.prompt_prefix = None + context.prompt_suffix = None context.server_api_key = None context.server_continuous_batching = False context.server_embeddings = False context.server_metrics = False context.server_process = None + context.seed = None context.server_seed = None context.user_api_key = None @@ -45,9 +52,11 @@ def step_server_config(context, server_fqdn, server_port): context.prompts = [] -@step(u'a model file {model_file}') -def step_model_file(context, model_file): - context.model_file = model_file +@step(u'a model file {hf_file} from HF repo {hf_repo}') +def step_download_hf_model(context, hf_file, hf_repo): + context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) + if context.debug: + print(f"model file: {context.model_file}\n") @step(u'a model alias {model_alias}') @@ -55,24 +64,34 @@ def step_model_alias(context, model_alias): context.model_alias = model_alias -@step(u'{seed} as server seed') +@step(u'{seed:d} as server seed') def step_seed(context, seed): - context.server_seed = int(seed) + context.server_seed = seed + + +@step(u'{ngl:d} GPU offloaded layers') +def step_n_gpu_layer(context, ngl): + if 'N_GPU_LAYERS' in os.environ: + new_ngl = int(os.environ['N_GPU_LAYERS']) + if context.debug: + print(f"-ngl upgraded from {ngl} to {new_ngl}") + ngl = new_ngl + context.n_gpu_layer = ngl -@step(u'{n_ctx} KV cache size') +@step(u'{n_ctx:d} KV cache size') def step_n_ctx(context, n_ctx): - context.n_ctx = int(n_ctx) + context.n_ctx = n_ctx -@step(u'{n_slots} slots') +@step(u'{n_slots:d} slots') def step_n_slots(context, n_slots): - context.n_slots = int(n_slots) + context.n_slots = n_slots -@step(u'{n_predict} server max tokens to predict') +@step(u'{n_predict:d} server max tokens to predict') def step_server_n_predict(context, n_predict): - context.n_server_predict = int(n_predict) + context.n_server_predict = n_predict @step(u'continuous batching') @@ -116,11 +135,13 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', + timeout=10, params={'fail_on_no_slot': 0, 'include_slots': 0}, slots_idle=context.n_slots, slots_processing=0, expected_slots=[{'id': slot_id, 'state': 0} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case 'busy': await wait_for_health_status(context, context.base_url, 503, 'no slot available', @@ -128,7 +149,8 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): slots_idle=0, slots_processing=context.n_slots, expected_slots=[{'id': slot_id, 'state': 1} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case _: assert False, "unknown status" @@ -157,24 +179,24 @@ async def step_request_completion(context, api_error): context.base_url, debug=context.debug, n_predict=context.n_predict, - server_seed=context.server_seed, + seed=await completions_seed(context), expect_api_error=expect_api_error, user_api_key=context.user_api_key) context.tasks_result.append(completion) if context.debug: - print(f"Completion response: {completion}") + print(f"Completion response: {completion}\n") if expect_api_error: assert completion == 401, f"completion must be an 401 status code: {completion}" -@step(u'{predicted_n} tokens are predicted matching {re_content}') +@step(u'{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content) -@step(u'{predicted_n} tokens are predicted') +@step(u'{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n)) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n) @step(u'a user prompt {user_prompt}') @@ -192,9 +214,9 @@ def step_model(context, model): context.model = model -@step(u'{max_tokens} max tokens to predict') +@step(u'{max_tokens:d} max tokens to predict') def step_max_tokens(context, max_tokens): - context.n_predict = int(max_tokens) + context.n_predict = max_tokens @step(u'streaming is {enable_streaming}') @@ -222,11 +244,70 @@ def step_server_api_key(context, server_api_key): context.server_api_key = server_api_key +@step(u'{n_junk:d} as number of junk') +def step_n_junk(context, n_junk): + context.n_junk = n_junk + + +@step(u'{n_batch:d} as batch size') +def step_n_batch(context, n_batch): + context.n_batch = n_batch + + +@step(u'{seed:d} as seed') +def step_seed(context, seed): + context.seed = seed + + +@step(u'a prefix prompt') +def step_prompt_prefix(context): + context.prompt_prefix = context.text + + +@step(u'a junk suffix prompt') +def step_prompt_junk_suffix(context): + context.prompt_junk_suffix = context.text + + +@step(u'a suffix prompt') +def step_prompt_suffix(context): + context.prompt_suffix = context.text + + +@step(u'{n_ga:d} group attention factor' + u' to extend context size through self-extend') +def step_impl(context, n_ga): + context.n_ga = n_ga + + +@step(u'{n_ga_w:d} group attention width to extend context size through self-extend') +def step_impl(context, n_ga_w): + context.n_ga_w = n_ga_w + + +@step(u'a passkey prompt template') +def step_prompt_passkey(context): + context.prompt_passkey = context.text + + +@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') +def step_prompt_passkey(context, passkey, i_pos): + prompt = "" + for i in range(context.n_junk): + if i % context.n_junk == i_pos: + prompt += context.prompt_passkey # the passkey is already substituted + prompt += context.prompt_junk_suffix + if context.debug: + passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" + print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") + context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) + + @step(u'an OAI compatible chat completions request with {api_error} api error') @async_run_until_complete async def step_oai_chat_completions(context, api_error): if context.debug: - print(f"Submitting OAI compatible completions request...") + print(f"Submitting OAI compatible completions request...\n") expect_api_error = api_error == 'raised' completion = await oai_chat_completions(context.prompts.pop(), context.system_prompt, @@ -241,8 +322,7 @@ async def step_oai_chat_completions(context, api_error): enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, @@ -276,8 +356,10 @@ async def step_concurrent_completion_requests(context): # prompt is inserted automatically context.base_url, debug=context.debug, + prompt_prefix=context.prompt_prefix, + prompt_suffix=context.prompt_suffix, n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - server_seed=context.server_seed if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -297,8 +379,7 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -318,7 +399,9 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed + seed=context.seed + if hasattr(context, 'seed') else + context.server_seed if hasattr(context, 'server_seed') else None, user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -330,11 +413,10 @@ async def step_all_prompts_are_predicted(context): await all_prompts_are_predicted(context) -@step(u'all prompts are predicted with {n_predict} tokens') +@step(u'all prompts are predicted with {n_expected_predicted:d} tokens') @async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict): - expected_predicted_n = int(n_predict) - await all_prompts_are_predicted(context, expected_predicted_n) +async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): + await all_prompts_are_predicted(context, n_expected_predicted) async def all_prompts_are_predicted(context, expected_predicted_n=None): @@ -464,6 +546,8 @@ async def step_prometheus_metrics_exported(context): assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" metrics_raw = await metrics_response.text() metric_exported = False + if context.debug: + print(f"/metrics answer:\n{metrics_raw}\n") for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: case "llamacpp:kv_cache_usage_ratio": @@ -472,6 +556,37 @@ async def step_prometheus_metrics_exported(context): assert metric_exported, "No metrics exported" +@step(u'available models') +def step_available_models(context): + # openai client always expects an api_key + openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' + openai.api_base = f'{context.base_url}/v1' + context.models = openai.Model.list().data + + +@step(u'{n_model:d} models are supported') +def step_supported_models(context, n_model): + if context.debug: + print("server models available:", context.models) + assert len(context.models) == n_model + + +@step(u'model {i_model:d} is {param} {preposition} {param_value}') +def step_supported_models(context, i_model, param, preposition, param_value): + assert i_model < len(context.models) + model = context.models[i_model] + + param_value = param_value.split(' ', 1)[0] + match param: + case 'identified': + value = model.id + case 'trained': + value = str(model.meta.n_ctx_train) + case _: + assert False, "param {param} not supported" + assert param_value == value, f"model param {param} {value} != {param_value}" + + async def concurrent_requests(context, f_completion, *args, **kwargs): n_prompts = len(context.prompts) if context.debug: @@ -486,8 +601,10 @@ async def concurrent_requests(context, f_completion, *args, **kwargs): async def request_completion(prompt, base_url, debug=False, + prompt_prefix=None, + prompt_suffix=None, n_predict=None, - server_seed=None, + seed=None, expect_api_error=None, user_api_key=None): if debug: @@ -504,11 +621,14 @@ async def request_completion(prompt, async with aiohttp.ClientSession() as session: async with session.post(f'{base_url}/completion', json={ + "input_prefix": prompt_prefix, "prompt": prompt, - "n_predict": int(n_predict) if n_predict is not None else -1, - "seed": server_seed if server_seed is not None else 42 + "input_suffix": prompt_suffix, + "n_predict": n_predict if n_predict is not None else -1, + "seed": seed if seed is not None else 42 }, - headers=headers) as response: + headers=headers, + timeout=3600) as response: if expect_api_error is None or not expect_api_error: assert response.status == 200 assert response.headers['Access-Control-Allow-Origin'] == origin @@ -526,14 +646,14 @@ async def oai_chat_completions(user_prompt, model=None, n_predict=None, enable_streaming=None, - server_seed=None, + seed=None, user_api_key=None, expect_api_error=None): if debug: print(f"Sending OAI Chat completions request: {user_prompt}") # openai client always expects an api key user_api_key = user_api_key if user_api_key is not None else 'nope' - seed = server_seed if server_seed is not None else 42 + seed = seed if seed is not None else 42 enable_streaming = enable_streaming if enable_streaming is not None else False payload = { "messages": [ @@ -692,20 +812,32 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re content = completion_response['content'] n_predicted = completion_response['timings']['predicted_n'] assert len(content) > 0, "no token predicted" - if expected_predicted_n is not None: + if re_content is not None: + p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL) + matches = p.finditer(content) + last_match = 0 + highlighted = '' + for match in matches: + start, end = match.span() + highlighted += content[last_match: start] + highlighted += '\x1b[33m' + highlighted += content[start: end] + highlighted += '\x1b[0m' + last_match = end + highlighted += content[last_match:] + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + print(f"Checking completion response: {highlighted}\n") + assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' + if expected_predicted_n and expected_predicted_n > 0: assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' f' {n_predicted} <> {expected_predicted_n}') - if re_content is not None: - re_content = '^.*' + re_content.replace('', '|') + '.*$' - assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), ( - f'invalid tokens predicted:' - f' ```\n{content}\n``` do not match /{re_content}/') + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: - print(f"Waiting for all {n_tasks} tasks results...") + print(f"Waiting for all {n_tasks} tasks results...\n") for task_no in range(n_tasks): context.tasks_result.append(await context.concurrent_tasks.pop()) n_completions = len(context.tasks_result) @@ -716,15 +848,13 @@ async def wait_for_health_status(context, base_url, expected_http_status_code, expected_health_status, + timeout=3, params=None, slots_idle=None, slots_processing=None, expected_slots=None): if context.debug: - print(f"Starting checking for health for expected_health_status={expected_health_status}") - timeout = 3 # seconds - if expected_health_status == 'ok': - timeout = 10 # CI slow inference + print(f"Starting checking for health for expected_health_status={expected_health_status}\n") interval = 0.5 counter = 0 async with aiohttp.ClientSession() as session: @@ -734,7 +864,7 @@ async def wait_for_health_status(context, health = await health_response.json() if context.debug: print(f"HEALTH - response for expected health status='{expected_health_status}' on " - f"'{base_url}/health'?{params} is {health}") + f"'{base_url}/health'?{params} is {health}\n") if (status_code == expected_http_status_code and health['status'] == expected_health_status and (slots_idle is None or health['slots_idle'] == slots_idle) @@ -757,7 +887,7 @@ async def wait_for_health_status(context, if expected_http_status_code == 503: if len(context.tasks_result) == 0: print("\x1b[5;37;43mWARNING: forcing concurrent tasks," - " busy health check missed, probably too fast inference\x1b[0m") + " busy health check missed, probably too fast inference\x1b[0m\n") n_completions = await gather_tasks_results(context) if n_completions > 0: return @@ -791,6 +921,11 @@ def assert_slots_status(slots, expected_slots): f" = {expected[key]} != {slot[key]}") +async def completions_seed(context): + return context.seed if hasattr(context, 'seed') and context.seed is not None \ + else context.server_seed if hasattr(context, 'server_seed') else None + + def start_server_background(context): context.server_path = '../../../build/bin/server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: @@ -800,27 +935,35 @@ def start_server_background(context): '--port', context.server_port, '--model', context.model_file ] + if context.n_batch: + server_args.extend(['--batch-size', context.n_batch]) + if context.n_gpu_layer: + server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.server_continuous_batching: server_args.append('--cont-batching') if context.server_embeddings: server_args.append('--embedding') if context.server_metrics: server_args.append('--metrics') - if context.model_alias is not None: + if context.model_alias: server_args.extend(['--alias', context.model_alias]) - if context.n_ctx is not None: + if context.n_ctx: server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots is not None: + if context.n_slots: server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict is not None: + if context.n_server_predict: server_args.extend(['--n-predict', context.n_server_predict]) - if context.server_api_key is not None: + if context.server_api_key: server_args.extend(['--api-key', context.server_api_key]) + if context.n_ga: + server_args.extend(['--grp-attn-n', context.n_ga]) + if context.n_ga_w: + server_args.extend(['--grp-attn-w', context.n_ga_w]) if context.debug: server_args.append('--verbose') if 'SERVER_LOG_FORMAT_JSON' not in os.environ: server_args.extend(['--log-format', "text"]) - print(f"starting server with: {context.server_path}", *server_args) + print(f"starting server with: {context.server_path} {server_args}\n") context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], close_fds=True) diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature index e228b2371ccce..cf14b3b44e03b 100644 --- a/examples/server/tests/features/wrong_usages.feature +++ b/examples/server/tests/features/wrong_usages.feature @@ -1,4 +1,4 @@ -# run with ./test.sh --tags wrong_usage +# run with: ./tests.sh --no-skipped --tags wrong_usage @wrong_usage Feature: Wrong usage of llama.cpp server @@ -7,7 +7,7 @@ Feature: Wrong usage of llama.cpp server # or pass n_predict/max_tokens in the request. Scenario: Infinite loop Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models # Uncomment below to fix the issue #And 64 server max tokens to predict Then the server is starting @@ -18,4 +18,5 @@ Feature: Wrong usage of llama.cpp server # Uncomment below to fix the issue #And 128 max tokens to predict Given concurrent completion requests + Then the server is idle Then all prompts are predicted diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 334fa4a70ea72..5d4210164a50a 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,4 +1,5 @@ aiohttp~=3.9.3 behave~=1.2.6 +huggingface_hub~=0.20.3 openai~=0.25.0 prometheus-client~=0.20.0 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 17a4e6fc64307..1c6c5695fcf65 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -5,7 +5,7 @@ set -eu if [ $# -lt 1 ] then # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp + behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp else behave "$@" fi diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d98541f26d123..b6e49d8b98a2a 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -126,8 +126,7 @@ static inline void server_log(const char *level, const char *function, int line, for (const auto& el : log.items()) { const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); - snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str()); - ss << buf; + ss << " " << el.key() << "=" << value; } const std::string str = ss.str(); From fa974646e1a2024fc7dc9e6f27cf1f2f5d4a3763 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Mar 2024 06:11:31 +0200 Subject: [PATCH 156/401] flake.lock: Update (#5842) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) → 'github:hercules-ci/flake-parts/f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2' (2024-03-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8?dir=lib' (2024-02-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29) Co-authored-by: github-actions[bot] --- flake.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/flake.lock b/flake.lock index 9f659ba8f4cef..b1b0916566453 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1706830856, - "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=", + "lastModified": 1709336216, + "narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f", + "rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1708655239, - "narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=", + "lastModified": 1709237383, + "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a", + "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1706550542, - "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=", + "lastModified": 1709237383, + "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652", + "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", "type": "github" }, "original": { From 8ef969afcec1645d2d9c3ab1fc82263bba968989 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 3 Mar 2024 08:48:36 +0100 Subject: [PATCH 157/401] server : init http requests thread pool with --parallel if set (#5836) --- examples/server/README.md | 2 +- examples/server/server.cpp | 10 ++++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 397ee825278bd..21da7a0a04e23 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,7 +18,7 @@ The project is under active development, and we are [looking for feedback and co - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. -- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) +- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 52daf9e7a3db4..0ca388f47db7b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2026,7 +2026,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); + printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -3468,10 +3468,12 @@ int main(int argc, char **argv) }*/ //); - if (sparams.n_threads_http > 0) { - log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + if (sparams.n_threads_http < 1) { + // +2 threads for monitoring endpoints + sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below From e6029348e86c3810d4435faee54ba822cb43e2ef Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 3 Mar 2024 09:35:23 +0100 Subject: [PATCH 158/401] ci : schedule slow server tests only on Release or on demand (#5839) --- .github/workflows/server.yml | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 8c63125087d62..04e3fc0c17cf9 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -3,6 +3,11 @@ name: Server on: workflow_dispatch: # allows manual triggering + inputs: + slow_tests: + description: 'Run slow tests' + required: true + type: boolean push: branches: - master @@ -11,7 +16,7 @@ on: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] schedule: - - cron: '00 0 * * *' + - cron: '0 0 * * *' jobs: server: @@ -80,7 +85,7 @@ jobs: - name: Slow tests id: server_integration_tests_slow - if: github.event.schedule != '' + if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }} run: | cd examples/server/tests PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow From de9692a7d2db66e29e5cb373c6551acc49145ccd Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Sun, 3 Mar 2024 03:41:55 -0500 Subject: [PATCH 159/401] llama : fix llama_copy_state_data with fragmented KV cache (#5840) The row size of the saved states was based on kv_self.head while it should be based on llama_kv_cache_cell_max. Existing session files should still work. * llama : fix llama_kv_cache_cell_max inability to return 1 I've also changed its return type to uint32_t, because this function is always used to set the value of uint32_t variables, and because the index already has this type. * llama : fix state size calculation Some bytes in the state were unaccounted for in llama_get_state_size. Since the logits reserve so much space, it did not cause problems. --- llama.cpp | 47 ++++++++++++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/llama.cpp b/llama.cpp index d4c7a965bf377..41d0000da7f9e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2156,10 +2156,12 @@ static bool llama_kv_cache_find_slot( } // find how many cells are currently in use -static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { - for (uint32_t i = cache.size - 1; i > 0; --i) { - if (cache.cells[i].pos >= 0 && !cache.cells[i].is_empty()) { - return i + 1; +static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) { + for (uint32_t i = cache.size; i > 0; --i) { + const llama_kv_cell & cell = cache.cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; } } @@ -8178,7 +8180,7 @@ static int llama_decode_internal( // a heuristic, to avoid attending the full cache if it is not yet utilized // after enough generations, the benefit from this heuristic disappears // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); //kv_self.n = llama_kv_cache_cell_max(kv_self); //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); @@ -12615,9 +12617,14 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); const size_t s_embedding = ctx->embedding.size() * sizeof(float); - const size_t s_kv_size = sizeof(size_t); - const size_t s_kv_ntok = sizeof(int); + const size_t s_kv_buf_size = sizeof(size_t); + const size_t s_kv_head = sizeof(uint32_t); + const size_t s_kv_size = sizeof(uint32_t); + const size_t s_kv_used = sizeof(uint32_t); const size_t s_kv = ctx->kv_self.total_size(); + // TODO: assume the max is more than 1 seq_id per KV cell + const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + sizeof(llama_seq_id); + const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell; const size_t s_total = ( + s_rng_size @@ -12626,9 +12633,12 @@ size_t llama_get_state_size(const struct llama_context * ctx) { + s_logits + s_embedding_size + s_embedding + + s_kv_buf_size + + s_kv_head + s_kv_size - + s_kv_ntok + + s_kv_used + s_kv + + s_kv_cells ); return s_total; @@ -12728,15 +12738,13 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat { const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; - const auto & cparams = ctx->cparams; const uint32_t n_layer = hparams.n_layer; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_ctx = cparams.n_ctx; const size_t kv_buf_size = kv_self.total_size(); - const uint32_t kv_head = kv_self.head; + const uint32_t kv_head = llama_kv_cache_cell_max(kv_self); const uint32_t kv_size = kv_self.size; const uint32_t kv_used = kv_self.used; @@ -12756,7 +12764,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); tmp_buf.resize(v_row_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { @@ -12766,7 +12774,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat } } - for (uint32_t i = 0; i < kv_size; ++i) { + for (uint32_t i = 0; i < kv_head; ++i) { const auto & cell = kv_self.cells[i]; const llama_pos pos = cell.pos; @@ -12842,12 +12850,10 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { { const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; - const auto & cparams = ctx->cparams; const uint32_t n_layer = hparams.n_layer; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_ctx = cparams.n_ctx; size_t kv_buf_size; uint32_t kv_head; @@ -12870,7 +12876,7 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); - const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx); + const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size); @@ -12879,13 +12885,15 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { } } + GGML_ASSERT(kv_self.size == kv_size); + ctx->kv_self.head = kv_head; ctx->kv_self.size = kv_size; ctx->kv_self.used = kv_used; ctx->kv_self.cells.resize(kv_size); - for (uint32_t i = 0; i < kv_size; ++i) { + for (uint32_t i = 0; i < kv_head; ++i) { llama_pos pos; size_t seq_id_size; @@ -12901,6 +12909,11 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { ctx->kv_self.cells[i].seq_id.insert(seq_id); } } + + for (uint32_t i = kv_head; i < kv_size; ++i) { + ctx->kv_self.cells[i].pos = -1; + ctx->kv_self.cells[i].seq_id.clear(); + } } const size_t nread = inp - src; From 87c2e8b2797860a06af3d6c06b8488a8ff1a09ab Mon Sep 17 00:00:00 2001 From: Nindaleth Date: Sun, 3 Mar 2024 09:43:42 +0100 Subject: [PATCH 160/401] gguf-dump : support i-quants (#5841) Co-authored-by: Black_Fox --- gguf-py/gguf/constants.py | 72 ++++++++++++++++++++++++--------------- 1 file changed, 44 insertions(+), 28 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5db760cb14900..a62139811ef36 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -604,20 +604,28 @@ class PoolingType(IntEnum): class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + IQ2_XXS = 16 + IQ2_XS = 17 + IQ3_XXS = 18 + IQ1_S = 19 + IQ4_NL = 20 + IQ3_S = 21 + IQ2_S = 22 + IQ4_XS = 23 class GGUFEndian(IntEnum): @@ -662,20 +670,28 @@ def get_type(val: Any) -> GGUFValueType: QK_K = 256 # Items here are (block size, type size) GGML_QUANT_SIZES = { - GGMLQuantizationType.F32: (1, 4), - GGMLQuantizationType.F16: (1, 2), - GGMLQuantizationType.Q4_0: (32, 2 + 16), - GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), - GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), - GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), - GGMLQuantizationType.Q8_0: (32, 2 + 32), - GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), - GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), - GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), - GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), - GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), + GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), + GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), + GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), + GGMLQuantizationType.IQ4_NL: (32, 2 + 16), + GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), + GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), } From 475df1d6cf817060028d3ff763cb8097d4ec40d6 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Sun, 3 Mar 2024 04:40:27 -0600 Subject: [PATCH 161/401] llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 13 +++++++++++++ common/common.h | 7 +++++-- convert-hf-to-gguf.py | 18 +++++++++--------- llama.cpp | 44 +++++++++++++++++++++++++++---------------- llama.h | 7 +++++-- 5 files changed, 60 insertions(+), 29 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 1c0b7c403b936..dbe7e9229b770 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -335,6 +335,16 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--pooling") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } } else if (arg == "--defrag-thold" || arg == "-dt") { if (++i >= argc) { invalid_param = true; @@ -1014,6 +1024,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\n"); printf(" -dt N, --defrag-thold N\n"); printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); @@ -1296,6 +1308,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.pooling_type = params.pooling_type; cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = !params.no_kv_offload; diff --git a/common/common.h b/common/common.h index ab62bdb822d71..d3682b7adae70 100644 --- a/common/common.h +++ b/common/common.h @@ -76,8 +76,11 @@ struct gpt_params { float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold - int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; - ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; + llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // // sampling parameters struct llama_sampling_params sparams; diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index fa9d4f22fd33c..ffdba74441e19 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1644,16 +1644,17 @@ def set_gguf_parameters(self): self.gguf_writer.add_causal_attention(False) # get pooling path - with open(self.dir_model / "modules.json", encoding="utf-8") as f: - modules = json.load(f) pooling_path = None - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break # get pooling type - pooling_type = gguf.PoolingType.NONE if pooling_path is not None: with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: pooling = json.load(f) @@ -1663,8 +1664,7 @@ def set_gguf_parameters(self): pooling_type = gguf.PoolingType.CLS else: raise NotImplementedError("Only MEAN and CLS pooling types supported") - - self.gguf_writer.add_pooling_type(pooling_type) + self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): path = self.dir_model diff --git a/llama.cpp b/llama.cpp index 41d0000da7f9e..c1f015791e826 100644 --- a/llama.cpp +++ b/llama.cpp @@ -873,16 +873,16 @@ struct LLM_TN { // gguf helpers // -static const std::map LLAMA_ROPE_SCALING_TYPES = { +static const std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, }; -static int32_t llama_rope_scaling_type_from_string(const std::string & name) { +static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { - return kv.first; + return (llama_rope_scaling_type) kv.first; } } @@ -1612,7 +1612,6 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; - int32_t rope_scaling_type_train; float f_clamp_kqv = 0.0f; float f_max_alibi_bias = 0.0f; @@ -1620,8 +1619,9 @@ struct llama_hparams { bool causal_attn = true; bool need_kq_pos = false; - enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; - enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; + enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; + enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE; bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -1670,8 +1670,8 @@ struct llama_cparams { uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - float rope_freq_base; - float rope_freq_scale; + float rope_freq_base; + float rope_freq_scale; uint32_t n_yarn_orig_ctx; // These hyperparameters are not exposed in GGUF, because all @@ -1683,7 +1683,7 @@ struct llama_cparams { float defrag_thold; bool offload_kqv; - bool do_pooling; + enum llama_pooling_type pooling_type; ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; @@ -2933,7 +2933,11 @@ template<> bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { uint32_t tmp; const bool found = get_key(kid, tmp, required); - result = (enum llama_pooling_type) tmp; + if (found) { + result = (enum llama_pooling_type) tmp; + } else { + result = LLAMA_POOLING_TYPE_UNSPECIFIED; + } return found; } @@ -3210,7 +3214,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); switch (hparams.n_layer) { case 3: @@ -5175,7 +5179,7 @@ struct llm_build_context { n_kv (worst_case ? n_ctx : kv_self.n), kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), - pooling_type (cparams.do_pooling ? hparams.pooling_type : LLAMA_POOLING_TYPE_NONE), + pooling_type (cparams.pooling_type), rope_type (hparams.rope_type), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { @@ -8015,7 +8019,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { + if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -8043,7 +8047,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { + if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -11846,6 +11850,7 @@ struct llama_context_params llama_context_default_params() { /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, + /*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, /*.yarn_ext_factor =*/ -1.0f, @@ -11861,7 +11866,6 @@ struct llama_context_params llama_context_default_params() { /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, - /*.do_pooling =*/ true, /*.abort_callback =*/ nullptr, /*.abort_callback_data =*/ nullptr, }; @@ -12012,7 +12016,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = params.offload_kqv; - cparams.do_pooling = params.do_pooling; + cparams.pooling_type = params.pooling_type; cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; @@ -12038,6 +12042,14 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { + if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { + cparams.pooling_type = LLAMA_POOLING_TYPE_NONE; + } else { + cparams.pooling_type = hparams.pooling_type; + } + } + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } diff --git a/llama.h b/llama.h index 6406b52705e7d..70da4cb3f0ff6 100644 --- a/llama.h +++ b/llama.h @@ -129,6 +129,7 @@ extern "C" { }; enum llama_pooling_type { + LLAMA_POOLING_TYPE_UNSPECIFIED = -1, LLAMA_POOLING_TYPE_NONE = 0, LLAMA_POOLING_TYPE_MEAN = 1, LLAMA_POOLING_TYPE_CLS = 2, @@ -236,7 +237,10 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + + enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id + // (ignored if no pooling layer) // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model @@ -258,7 +262,6 @@ extern "C" { bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU - bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) // Abort callback // if it returns true, execution of llama_decode() will be aborted From 231ae28f078c3148d097b301f2145f1e3e816cc1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Mar 2024 12:44:03 +0200 Subject: [PATCH 162/401] readme : add API changes section --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 9396467537c16..45c5d06f3e10e 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,10 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +### Recent API changes + +- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 + ### Hot topics - The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) From 67be2ce1015d070b3b2cd488bcb041eefb61de72 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 3 Mar 2024 14:26:18 +0100 Subject: [PATCH 163/401] cuda : fix data race in soft max (#5853) --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7ed97430f4fa4..04c6cb1b8fada 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6904,6 +6904,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f // find the sum of exps in the block tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { + __syncthreads(); if (warp_id == 0) { buf_iw[lane_id] = 0.0f; } From 5a51cc1bb4592f0d71f9af89cd08b11a066ba447 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Mon, 4 Mar 2024 02:57:20 -0500 Subject: [PATCH 164/401] main : support special tokens as reverse/anti prompt (#5847) * Support special tokens as reverse/anti prompt. * Tokenize antiprompts only once. * main : minor --------- Co-authored-by: Georgi Gerganov --- examples/main/main.cpp | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 34e84d0d42f87..47059e582a0d4 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -511,6 +511,14 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + // tokenized antiprompts + std::vector> antiprompt_ids; + + antiprompt_ids.reserve(params.antiprompt.size()); + for (const std::string & antiprompt : params.antiprompt) { + antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true)); + } + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { @@ -769,6 +777,18 @@ int main(int argc, char ** argv) { } } + // check for reverse prompt using special tokens + llama_token last_token = llama_sampling_last(ctx_sampling); + for (std::vector ids : antiprompt_ids) { + if (ids.size() == 1 && last_token == ids[0]) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + break; + } + } + if (is_antiprompt) { LOG("found antiprompt: %s\n", last_output.c_str()); } From 82f3e668adafba647de703f835991e91a96b5ac4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Mon, 4 Mar 2024 03:08:19 -0500 Subject: [PATCH 165/401] common : use LLAMA_DEFAULT_SEED (#5855) --- common/common.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.h b/common/common.h index d3682b7adae70..b2868833be3e6 100644 --- a/common/common.h +++ b/common/common.h @@ -43,7 +43,7 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_threads_draft = -1; From 7d43c585dc174bb586775c22c15e5db9242b5b4b Mon Sep 17 00:00:00 2001 From: leejet Date: Sun, 3 Mar 2024 20:23:52 +0800 Subject: [PATCH 166/401] add some new ops, fix some operators and add batch operations to certain operators. (ggml/747) * cuda: fix group_norm * cuda: add batch inference support for ggml_pad/ggml_upscale * add ggml_arrange * add ggml_timestep_embedding * update ggml_arange/ggml_timestep_embedding tests * cuda: fix im2col * add ggml_arange/ggml_timestep_embbeding support for metal backend * fix some bugs * fix some bugs * Update ggml.h Co-authored-by: Georgi Gerganov * Update ggml-cuda.cu Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.metal Co-authored-by: Georgi Gerganov * modify according to the review comments * ggml : fix compile warnings + code style * ggml : normalize compute_forward calls + fix seg fault in debug * minor --------- Co-authored-by: Georgi Gerganov Co-authored-by: slaren --- ggml-cuda.cu | 229 ++++++++++++++++++++++++++++++------- ggml-metal.m | 62 +++++++++- ggml-metal.metal | 43 +++++++ ggml.c | 207 +++++++++++++++++++++++++++++++-- ggml.h | 17 +++ tests/test-backend-ops.cpp | 46 ++++++++ 6 files changed, 551 insertions(+), 53 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 04c6cb1b8fada..7d027a30a8101 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -616,6 +616,8 @@ static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + Q #define CUDA_UPSCALE_BLOCK_SIZE 256 #define CUDA_CONCAT_BLOCK_SIZE 256 #define CUDA_PAD_BLOCK_SIZE 256 +#define CUDA_ARANGE_BLOCK_SIZE 256 +#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 #define CUDA_POOL2D_BLOCK_SIZE 256 @@ -990,17 +992,21 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { int offset_src = nidx + blockIdx.y * ne0 + (blockIdx.z - ne02) * ne0 * gridDim.y; - dst[offset_dst] = y[offset_src]; + dst[offset_dst] = y[offset_src]; } } -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) { +static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { + // blockIdx.z: idx of ne02*ne03 + // blockIdx.y: idx of ne01*scale_factor， aka ne1 + // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE + // ne00xne01: ne00 * ne01 int ne0 = ne00 * scale_factor; int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { @@ -1012,7 +1018,7 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, int offset_src = i00 + i01 * ne00 + - blockIdx.z * nb02; + blockIdx.z * ne00xne01; int offset_dst = nidx + blockIdx.y * ne0 + @@ -1020,7 +1026,10 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, dst[offset_dst] = x[offset_src]; } -static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) { +static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) { + // blockIdx.z: idx of ne2*ne3, aka ne02*ne03 + // blockIdx.y: idx of ne1 + // blockIDx.x: idx of ne0 / BLOCK_SIZE int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; @@ -1031,19 +1040,53 @@ static __global__ void pad_f32(const float * x, float * dst, const int ne0, cons nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) { + if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) { int offset_src = nidx + blockIdx.y * ne00 + blockIdx.z * ne00 * ne01; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { dst[offset_dst] = 0.0f; } } +static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) { + // blockIDx.x: idx of ne0 / BLOCK_SIZE + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + dst[nidx] = start + step * nidx; +} + +static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) { + // blockIDx.y: idx of timesteps->ne[0] + // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE + int i = blockIdx.y; + int j = threadIdx.x + blockIdx.x * blockDim.x; + float * embed_data = (float *)((char *)dst + i*nb1); + + if (dim % 2 != 0 && j == ((dim + 1) / 2)) { + embed_data[dim] = 0.f; + } + + int half = dim / 2; + if (j >= half) { + return; + } + + float timestep = timesteps[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); +} + template static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) { + // blockIdx.x: num_groups idx + // threadIdx.x: block_size idx int start = blockIdx.x * group_size; int end = start + group_size; @@ -6448,7 +6491,7 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { return; @@ -6456,17 +6499,17 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; + const int64_t i03 = i/(ne00 * ne01 * ne02); + const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + + const int64_t i13 = i/(ne10 * ne11 * ne12); + const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -6956,23 +6999,23 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, template static __global__ void im2col_kernel( - const float * x, T * dst, int batch_offset, - int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, + const float * x, T * dst, int64_t batch_offset, + int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW, int s0, int s1, int p0, int p1, int d0, int d1) { - const int i = threadIdx.x + blockIdx.x * blockDim.x; + const int64_t i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { return; } - const int ksize = OW * (KH > 1 ? KW : 1); - const int kx = i / ksize; - const int kd = kx * ksize; - const int ky = (i - kd) / OW; - const int ix = i % OW; + const int64_t ksize = OW * (KH > 1 ? KW : 1); + const int64_t kx = i / ksize; + const int64_t kd = kx * ksize; + const int64_t ky = (i - kd) / OW; + const int64_t ix = i % OW; - const int oh = blockIdx.y; - const int batch = blockIdx.z / IC; - const int ic = blockIdx.z % IC; + const int64_t oh = blockIdx.y; + const int64_t batch = blockIdx.z / IC; + const int64_t ic = blockIdx.z % IC; const int64_t iiw = ix * s0 + kx * d0 - p0; const int64_t iih = oh * s1 + ky * d1 - p1; @@ -7298,19 +7341,33 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, const concat_f32<<>>(x, y, dst, ne0, ne02); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) { +static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, + const int scale_factor, cudaStream_t stream) { int ne0 = (ne00 * scale_factor); int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02); + dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); } static void pad_f32_cuda(const float * x, float * dst, - const int ne00, const int ne01, const int ne02, - const int ne0, const int ne1, const int ne2, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int ne03, + const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2); - pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02); + dim3 gridDim(num_blocks, ne1, ne2*ne3); + pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02, ne03); +} + +static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE; + arange_f32<<>>(dst, ne0, start, step); +} + +static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1, + const int dim, const int max_period, cudaStream_t stream) { + int half_ceil = (dim + 1) / 2; + int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne00, 1); + timestep_embedding_f32<<>>(x, dst, nb1, dim, max_period); } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { @@ -8443,8 +8500,8 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float * template static void im2col_cuda(const float* x, T* dst, - int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int batch, int batch_offset, int offset_delta, + int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC, + int64_t batch, int64_t batch_offset, int64_t offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; @@ -9123,7 +9180,7 @@ static void ggml_cuda_op_group_norm( int num_groups = dst->op_params[0]; int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream); + group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream); (void) src1; (void) dst; @@ -9156,7 +9213,7 @@ static void ggml_cuda_op_upscale( const int scale_factor = dst->op_params[0]; - upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); + upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream); (void) src1; (void) dst; @@ -9172,8 +9229,49 @@ static void ggml_cuda_op_pad( GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors pad_f32_cuda(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_arange( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float stop; + float step; + memcpy(&start, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *)dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *)dst->op_params + 2, sizeof(float)); + + int64_t steps = (int64_t)ceil((stop - start) / step); + GGML_ASSERT(ggml_nelements(dst) == steps); + + arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream); + + (void) src0; + (void) src1; + (void) src0_dd; + (void) src1_dd; +} + +static void ggml_cuda_op_timestep_embedding( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream); (void) src1; (void) dst; @@ -10458,6 +10556,45 @@ static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, gg ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad); } +static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; + + // dd = data device + float * src0_ddf = nullptr; + float * src1_ddf = nullptr; + float * dst_ddf = nullptr; + + cuda_pool_alloc dst_f; + + ggml_cuda_set_device(g_main_device); + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + + if (dst_on_device) { + dst_ddf = (float *) dst_extra->data_device[g_main_device]; + } else { + dst_ddf = dst_f.alloc(ggml_nelements(dst)); + } + + // do the computation + ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + CUDA_CHECK(cudaGetLastError()); + + // copy dst to host if necessary + if (!dst_on_device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); + } + + if (dst->backend == GGML_BACKEND_TYPE_CPU) { + CUDA_CHECK(cudaDeviceSynchronize()); + } +} + +static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_timestep_embedding); +} + static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } @@ -11358,6 +11495,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_PAD: func = ggml_cuda_pad; break; + case GGML_OP_ARANGE: + func = ggml_cuda_arange; + break; + case GGML_OP_TIMESTEP_EMBEDDING: + func = ggml_cuda_timestep_embedding; + break; case GGML_OP_LEAKY_RELU: func = ggml_cuda_leaky_relu; break; @@ -12253,6 +12396,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_GROUP_NORM: case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: return true; default: diff --git a/ggml-metal.m b/ggml-metal.m index 71fcca5605914..6b5a8fdf54104 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -163,6 +163,8 @@ GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_ARANGE_F32, + GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, @@ -569,6 +571,8 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); @@ -697,6 +701,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: return true; @@ -1091,7 +1097,8 @@ static bool ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) dst->op_params; + float scale; + memcpy(&scale, dst->op_params, sizeof(scale)); int64_t n = ggml_nelements(dst); @@ -1250,11 +1257,15 @@ static bool ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } - const float scale = ((float *) dst->op_params)[0]; - const float max_bias = ((float *) dst->op_params)[1]; + float scale; + float max_bias; + + memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); const int64_t nrows_x = ggml_nrows(src0); const int64_t nrows_y = src0->ne[1]; + const uint32_t n_head_kv = nrows_x/nrows_y; const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); @@ -2086,6 +2097,7 @@ static bool ggml_metal_graph_compute( //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); @@ -2300,6 +2312,50 @@ static bool ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float step; + + memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&step, ((int32_t *) dst->op_params) + 2, sizeof(float)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; + [encoder setBytes:&start length:sizeof(start) atIndex:2]; + [encoder setBytes:&step length:sizeof(step) atIndex:3]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + const int half = dim / 2; + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; + [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; + + const int nth = MIN(1024, half); + + [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(src0->type == GGML_TYPE_F32); diff --git a/ggml-metal.metal b/ggml-metal.metal index 8b9488437494d..a65d126412bfb 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1959,6 +1959,49 @@ kernel void kernel_pad_f32( } } +kernel void kernel_arange_f32( + device char * dst, + constant int64_t & ne0, + constant float & start, + constant float & step, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + device float * dst_ptr = (device float *) dst; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + dst_ptr[i0] = start + step * i0; + } +} + +kernel void kernel_timestep_embedding_f32( + device const char * src0, + device char * dst, + constant uint64_t & nb1, + constant int & dim, + constant int & max_period, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + int i = tgpig.x; + device float * embed_data = (device float *)(dst + i*nb1); + + int half_ = dim / 2; + for (int j = tpitg.x; j < half_; j += ntg.x) { + float timestep = ((device float *)src0)[i]; + float freq = (float)exp(-log((float)max_period) * j / half_); + float arg = timestep * freq; + embed_data[j ] = cos(arg); + embed_data[j + half_] = sin(arg); + } + + if (dim % 2 != 0 && tpitg.x == 0) { + embed_data[dim] = 0.f; + } +} + // bitonic sort implementation following the CUDA kernels as reference typedef void (argsort_t)( device const float * x, diff --git a/ggml.c b/ggml.c index f29b9f13fbcaf..870e41614add7 100644 --- a/ggml.c +++ b/ggml.c @@ -1822,6 +1822,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "POOL_2D", "UPSCALE", "PAD", + "ARANGE", + "TIMESTEP_EMBEDDING", "ARGSORT", "LEAKY_RELU", @@ -1850,7 +1852,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1908,6 +1910,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "pool_2d(x)", "upscale(x)", "pad(x)", + "arange(start, stop, step)", + "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", "leaky_relu(x)", @@ -1936,7 +1940,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -2895,11 +2899,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_ return ((const int32_t *)(tensor->op_params))[i]; } +static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + return ((const float *)(tensor->op_params))[i]; +} + static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); ((int32_t *)(tensor->op_params))[i] = value; } +static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + ((float *)(tensor->op_params))[i] = value; +} + struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; @@ -5898,6 +5912,55 @@ struct ggml_tensor * ggml_upscale( return ggml_upscale_impl(ctx, a, scale_factor); } +struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step) { + + GGML_ASSERT(stop > start); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); + + result->op = GGML_OP_ARANGE; + ggml_set_op_params_f32(result, 0, start); + ggml_set_op_params_f32(result, 1, stop); + ggml_set_op_params_f32(result, 2, step); + + return result; +} + +struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period) { + bool is_node = false; + + if (timesteps->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + int actual_dim = dim; + if (dim % 2 != 0) { + actual_dim = dim + 1; + } + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]); + + result->op = GGML_OP_TIMESTEP_EMBEDDING; + ggml_set_op_params_i32(result, 0, dim); + ggml_set_op_params_i32(result, 1, max_period); + + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = timesteps; + + return result; +} + // ggml_argsort struct ggml_tensor * ggml_argsort( @@ -10231,7 +10294,7 @@ static void ggml_compute_forward_group_norm_f32( int n_channels = src0->ne[2]; int n_groups = dst->op_params[0]; int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i+=nth) { + for (int i = ith; i < n_groups; i += nth) { int start = i * n_channels_per_group; int end = start + n_channels_per_group; if (end > n_channels) { @@ -10245,28 +10308,32 @@ static void ggml_compute_forward_group_norm_f32( for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; + sumr += (ggml_float)x[i00]; } + sum += sumr; } } - float mean = sum / (ne00 * ne01 * step); - ggml_float sum2 = 0.0; + const float mean = sum / (ne00 * ne01 * step); + ggml_float sum2 = 0.0; for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; - sum2 += (ggml_float)(v * v); + sumr += (ggml_float)(v * v); } + sum2 += sumr; } } - float variance = sum2 / (ne00 * ne01 * step); + const float variance = sum2 / (ne00 * ne01 * step); const float scale = 1.0f / sqrtf(variance + eps); for (int64_t i02 = start; i02 < end; i02++) { @@ -13547,6 +13614,106 @@ static void ggml_compute_forward_pad( } } + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +static void ggml_compute_forward_arange( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +static void ggml_compute_forward_timestep_embedding( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_argsort static void ggml_compute_forward_argsort_f32( @@ -15615,6 +15782,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_pad(params, tensor); } break; + case GGML_OP_ARANGE: + { + ggml_compute_forward_arange(params, tensor); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + ggml_compute_forward_timestep_embedding(params, tensor); + } break; case GGML_OP_ARGSORT: { ggml_compute_forward_argsort(params, tensor); @@ -16617,6 +16792,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(false); // TODO: not implemented @@ -17368,6 +17551,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_ARANGE: + { + n_tasks = n_threads; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + n_tasks = n_threads; + } break; case GGML_OP_ARGSORT: { n_tasks = n_threads; diff --git a/ggml.h b/ggml.h index 0a6d3c051fe72..98cfc7bf8f670 100644 --- a/ggml.h +++ b/ggml.h @@ -454,6 +454,8 @@ extern "C" { GGML_OP_POOL_2D, GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, + GGML_OP_ARANGE, + GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, @@ -1661,6 +1663,15 @@ extern "C" { int p2, int p3); + // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 + // timesteps: [N,] + // return: [N, dim] + GGML_API struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period); + // sort rows enum ggml_sort_order { GGML_SORT_ORDER_ASC, @@ -1672,6 +1683,12 @@ extern "C" { struct ggml_tensor * a, enum ggml_sort_order order); + GGML_API struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step); + // top k elements per row GGML_API struct ggml_tensor * ggml_top_k( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d4cea805f554f..8a6999f21f1f6 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1412,6 +1412,50 @@ struct test_pad : public test_case { } }; +// GGML_OP_ARANGE +struct test_arange : public test_case { + const ggml_type type; + const float start; + const float stop; + const float step; + + std::string vars() override { + return VARS_TO_STR4(type, start, stop, step); + } + + test_arange(ggml_type type = GGML_TYPE_F32, + float start = 0.f, float stop = 10.f, float step = 1.f) + : type(type), start(start), stop(stop), step(step) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * out = ggml_arange(ctx, start, stop, step); + return out; + } +}; + +// GGML_OP_TIMESTEP_EMBEDDING +struct test_timestep_embedding : public test_case { + const ggml_type type; + const std::array ne_a; + const int dim; + const int max_period; + + std::string vars() override { + return VARS_TO_STR4(type, ne_a, dim, max_period); + } + + test_timestep_embedding(ggml_type type = GGML_TYPE_F32, + std::array ne_a = {2, 1, 1, 1}, + int dim = 320, int max_period=10000) + : type(type), ne_a(ne_a), dim(dim), max_period(max_period) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period); + return out; + } +}; + // GGML_OP_LEAKY_RELU struct test_leaky_relu : public test_case { const ggml_type type; @@ -2126,6 +2170,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_group_norm()); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); + test_cases.emplace_back(new test_arange()); + test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); // these tests are disabled to save execution time, but they can be handy for debugging From a0fc62661f0fd2a9edd10ae5617345bbbf972f42 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 10:40:04 +0200 Subject: [PATCH 167/401] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 389c0bdfe9eb6..4a1b0bab4961c 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -b458250b736a7473f7ff3560d47c93f1644f3290 +274680868e12427373bab4bec87554431b954704 From 4ffcdce2ff877ebb683cd217ea38faf20faa5ffe Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 4 Mar 2024 12:22:08 +0100 Subject: [PATCH 168/401] add alias for chat template (#5858) --- examples/server/server.cpp | 2 +- llama.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0ca388f47db7b..208edd571cb0e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -413,7 +413,7 @@ struct llama_server_context int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); if (res < 0) { LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template + sparams.chat_template = "chatml"; } } diff --git a/llama.cpp b/llama.cpp index c1f015791e826..de579d9e372b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13282,7 +13282,7 @@ static int32_t llama_chat_apply_template_internal( std::string & dest, bool add_ass) { // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 std::stringstream ss; - if (tmpl.find("<|im_start|>") != std::string::npos) { + if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) { // chatml template for (auto message : chat) { ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n"; @@ -13290,7 +13290,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|im_start|>assistant\n"; } - } else if (tmpl.find("[INST]") != std::string::npos) { + } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) { // llama2 template and its variants // [variant] support system message bool support_system_message = tmpl.find("<>") != std::string::npos; @@ -13325,7 +13325,7 @@ static int32_t llama_chat_apply_template_internal( } } // llama2 templates seem to not care about "add_generation_prompt" - } else if (tmpl.find("<|user|>") != std::string::npos) { + } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) { // zephyr template for (auto message : chat) { ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n"; @@ -13333,7 +13333,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } - } else if (tmpl.find("bos_token + message['role']") != std::string::npos) { + } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) { // mlabonne/AlphaMonarch-7B template (the is included inside history) for (auto message : chat) { std::string bos = (message == chat.front()) ? "" : ""; // skip BOS for first message @@ -13342,7 +13342,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "assistant\n"; } - } else if (tmpl.find("") != std::string::npos) { + } else if (tmpl == "gemma" || tmpl.find("") != std::string::npos) { // google/gemma-7b-it std::string system_prompt = ""; for (auto message : chat) { @@ -13389,7 +13389,7 @@ LLAMA_API int32_t llama_chat_apply_template( int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); if (res < 0) { // worst case: there is no information about template, we will use chatml by default - curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal + curr_tmpl = "chatml"; // see llama_chat_apply_template_internal } else { curr_tmpl = std::string(model_template.data(), model_template.size()); } From 6d341ab6c53cd51f2921d986d0090cc8b049b39a Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Tue, 5 Mar 2024 03:24:00 +0900 Subject: [PATCH 169/401] speculative : implement stochastic speculative sampling (#5625) * (WIP) Implement stochastic speculative decoding * sample from residual distribution on draft accept failure * fix #5657: force greedy sampling with probs when temp is 0 * remove p_accept parameter * fix style * remove unused variables * add srand() in speculative.cpp * replace use of rand() with mt19937 sampling * fixes based on review (@JohannesGaessler) * fix r random generation * randomly select next sequence to verify + fix bug in memory freeing * fix bug in active_seqs sync * fix uniform int distribution initialization * remove warnings from comparison between int and size_t * check grammar in `llama_sample_probability_distribution_impl` * remove malloc code by utilizing vectors * add PR link to README --- common/common.cpp | 7 - common/common.h | 3 +- common/sampling.cpp | 79 ++++++++++ common/sampling.h | 7 + examples/speculative/README.md | 1 + examples/speculative/speculative.cpp | 216 +++++++++++++++++++++------ 6 files changed, 256 insertions(+), 57 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index dbe7e9229b770..036a981349a69 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -513,12 +513,6 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_sequences = std::stoi(argv[i]); - } else if (arg == "--p-accept" || arg == "-pa") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_accept = std::stof(argv[i]); } else if (arg == "--p-split" || arg == "-ps") { if (++i >= argc) { invalid_param = true; @@ -1044,7 +1038,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); - printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); diff --git a/common/common.h b/common/common.h index b2868833be3e6..977ce419ff93b 100644 --- a/common/common.h +++ b/common/common.h @@ -53,11 +53,10 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 8; // number of tokens to draft during speculative decoding + int32_t n_draft = 5; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) diff --git a/common/sampling.cpp b/common/sampling.cpp index e67096bea6932..823031febc7e2 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -295,6 +295,77 @@ static llama_token llama_sampling_sample_impl( return id; } +static llama_token_data_array llama_sample_probability_distribution_impl( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + const llama_sampling_params & params = ctx_sampling->params; + + const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); + + const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; + const float penalty_repeat = params.penalty_repeat; + const float penalty_freq = params.penalty_freq; + const float penalty_present = params.penalty_present; + const bool penalize_nl = params.penalize_nl; + + auto & prev = ctx_sampling->prev; + auto & cur = ctx_sampling->cur; + + // Get a pointer to the logits + float * logits = llama_get_logits_ith(ctx_main, idx); + + // Declare original_logits at the beginning of the function scope + std::vector original_logits; + + // apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + if (ctx_cfg) { + float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); + llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + } + + cur.clear(); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { cur.data(), cur.size(), false }; + + // apply penalties + const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; + const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); + if (penalty_tokens_used_size) { + const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; + + llama_sample_repetition_penalties(ctx_main, &cur_p, + penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, + penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + // apply grammar checks + if (ctx_sampling->grammar != NULL) { + llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); + } + + llama_sample_softmax(ctx_main, &cur_p); + return cur_p; +} + llama_token llama_sampling_sample( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, @@ -304,6 +375,14 @@ llama_token llama_sampling_sample( return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); } +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx); +} + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/common/sampling.h b/common/sampling.h index 95d8753942b40..48b2459d1f944 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -131,6 +131,13 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, int idx = 0); +// returns the probability that token of given id will be sampled +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + int idx = 0); + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/examples/speculative/README.md b/examples/speculative/README.md index 814efa592d94f..a6608c5fe8e3a 100644 --- a/examples/speculative/README.md +++ b/examples/speculative/README.md @@ -6,3 +6,4 @@ More info: - https://github.com/ggerganov/llama.cpp/pull/2926 - https://github.com/ggerganov/llama.cpp/pull/3624 +- https://github.com/ggerganov/llama.cpp/pull/5625 diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 3848791d475ad..85bc0a762ad08 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 @@ -18,6 +19,7 @@ struct seq_draft { std::vector i_batch_tgt; std::vector tokens; + std::vector> dists; struct llama_sampling_context * ctx_sampling; }; @@ -37,12 +39,15 @@ int main(int argc, char ** argv) { // max number of parallel drafting sequences (i.e. tree branches) const int n_seq_dft = params.n_parallel; - // probability threshold for accepting a token from the draft model - const float p_accept = params.p_accept; - // probability threshold for splitting a draft branch (only for n_seq_dft > 1) const float p_split = params.p_split; + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + std::default_random_engine rng(params.seed); + std::uniform_real_distribution<> u_dist; + #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); LOG_TEE("Log start\n"); @@ -166,7 +171,9 @@ int main(int argc, char ** argv) { std::vector drafts(n_seq_dft); params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + if (params.sparams.temp == 0) { + params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + } for (int s = 0; s < n_seq_dft; ++s) { drafts[s].ctx_sampling = llama_sampling_init(params.sparams); @@ -182,12 +189,15 @@ int main(int argc, char ** argv) { drafts[0].i_batch_tgt[0] = 0; while (true) { + std::set active_seqs = {}; + // print current draft sequences for (int s = 0; s < n_seq_dft; ++s) { if (!drafts[s].active) { continue; } + active_seqs.insert(s); const auto & tokens = drafts[s].tokens; LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str()); @@ -196,48 +206,156 @@ int main(int argc, char ** argv) { int i_dft = 0; int s_keep = 0; + llama_token token_id; + std::string token_str; + + // loop until we fail to accept a drafted token or we run out of drafted tokens while (true) { - LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); - // sample from the target model - llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + // check if the target token matches any of the drafts + // for stochastic sampling, attempt to match the token with the drafted tokens + { + bool accept = false; + if (params.sparams.temp > 0) { + // stochastic verification + + llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + float p_tgt = 0, p_dft = 0; + + // GGML_ASSERT(dist_tgt.size() == dist_dft.size()); + + while (active_seqs.size() > 0) { + // randomly select a sequence to verify from active sequences + std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); + int s = *std::next(active_seqs.begin(), u_int_dist(rng)); + if (i_dft >= (int) drafts[s].tokens.size()) { + drafts[s].active = false; + active_seqs.erase(s); + continue; + } + if (accept) { + // if we already accepted a token, we can skip the rest + if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) { + drafts[s].active = false; + active_seqs.erase(s); + } + continue; + } + LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size()); + float r = u_dist(rng); + llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true }; + // acquire the token probabilities assigned by the draft and target models + for (size_t i = 0; i < dist_tgt.size; i++) { + if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) { + p_tgt = dist_tgt.data[i].p; + } + if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) { + p_dft = dist_dft.data[i].p; + } + if (p_tgt && p_dft) { + break; + } + } + LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt); + if (r <= p_tgt / p_dft) { + s_keep = s; + accept = true; + token_id = drafts[s].tokens[i_dft]; + token_str = llama_token_to_piece(ctx_tgt, token_id); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + + LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str()); + break; + } else { + LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str()); + drafts[s].active = false; + + // calculate residual probability + GGML_ASSERT(dist_tgt.sorted); + GGML_ASSERT(dist_dft.sorted); + float sum_probs = 0.0f; + + // sort dist by id + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p); + sum_probs += dist_tgt.data[i].p; + } + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p /= sum_probs; + } - llama_sampling_accept(ctx_sampling, ctx_tgt, id, true); + // sort dist_tgt by p desc + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.p > b.p; + }); + } - //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); + active_seqs.erase(s); + for(int i = 0; i < n_seq_dft; i++) { + if (i == s) { + continue; + } + if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) { + // synchronize active status for sequences with the same drafted token + drafts[i].active = drafts[i].active && accept; + if (!drafts[i].active) { + active_seqs.erase(s); + } + } + } + } - const std::string token_str = llama_token_to_piece(ctx_tgt, id); + if (!accept) { + // all drafted tokens were rejected + // sample from the target model + LOG("all drafted tokens were rejected, sampling from residual distribution\n"); + token_id = llama_sample_token(ctx_tgt, &dist_tgt); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + token_str = llama_token_to_piece(ctx_tgt, token_id); + } - if (!params.use_color) { - printf("%s", token_str.c_str()); - } + } else { + // greedy verification - if (id == llama_token_eos(model_tgt)) { - has_eos = true; - } + // sample from the target model + LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); + token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); - ++n_predict; + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); - // check if the target token matches any of the drafts - { - bool matches = false; + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); - for (int s = 0; s < n_seq_dft; ++s) { - if (!drafts[s].active) { - continue; - } + token_str = llama_token_to_piece(ctx_tgt, token_id); - if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) { - LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str()); + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } - s_keep = s; - matches = true; - } else { - drafts[s].active = false; + if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) { + LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str()); + + s_keep = s; + accept = true; + } else { + drafts[s].active = false; + } } } - if (matches) { + if (token_id == llama_token_eos(model_tgt)) { + has_eos = true; + } + ++n_predict; + + if (accept) { ++n_accept; ++n_past_tgt; ++n_past_dft; @@ -245,17 +363,21 @@ int main(int argc, char ** argv) { if (params.use_color) { // Color token according to its origin sequence printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str()); - fflush(stdout); + } else { + printf("%s", token_str.c_str()); } + fflush(stdout); continue; + } else { + printf("%s", token_str.c_str()); + fflush(stdout); + break; } } - if (params.use_color) { - printf("%s", token_str.c_str()); - } - fflush(stdout); + } - LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + { + LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str()); // TODO: simplify { @@ -275,21 +397,21 @@ int main(int argc, char ** argv) { drafts[s].active = false; drafts[s].tokens.clear(); drafts[s].i_batch_tgt.clear(); + drafts[s].dists.clear(); } // note: will be erased after the speculation phase - drafts[0].tokens.push_back(id); + drafts[0].tokens.push_back(token_id); + drafts[0].dists.push_back(std::vector()); drafts[0].i_batch_tgt.push_back(0); llama_batch_clear(batch_dft); - llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true); + llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true); llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str()); - llama_decode (ctx_dft, batch_dft); + llama_decode(ctx_dft, batch_dft); ++n_past_dft; - - break; } if (n_predict > params.n_predict || has_eos) { @@ -334,12 +456,6 @@ int main(int argc, char ** argv) { k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str()); } - if (cur_p[0].p < p_accept) { - LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept); - drafts[s].drafting = false; - continue; - } - std::vector sa(1, s); // attempt to split the branch if the probability is high enough @@ -367,6 +483,7 @@ int main(int argc, char ** argv) { drafts[n_seq_cur].skip = true; drafts[n_seq_cur].tokens = drafts[s].tokens; + drafts[n_seq_cur].dists = drafts[s].dists; drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft; drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt; @@ -389,6 +506,8 @@ int main(int argc, char ** argv) { llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true); drafts[s].tokens.push_back(id); + // save cur_p.data into drafts[s].dists + drafts[s].dists.push_back(cur_p); // add unique drafted tokens to the target batch drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens); @@ -440,6 +559,7 @@ int main(int argc, char ** argv) { } drafts[s].tokens.erase(drafts[s].tokens.begin()); + drafts[s].dists.erase(drafts[s].dists.begin()); } } From fe52be11e35358d2fd249f19d7ef5b6f9c08b16b Mon Sep 17 00:00:00 2001 From: Dane Madsen Date: Tue, 5 Mar 2024 05:26:55 +1100 Subject: [PATCH 170/401] cmake : handle cases where git index is not found in .git (#5844) * Update CMakeLists.txt * Update CMakeLists.txt --- common/CMakeLists.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index f79acfef1d133..350bbdf7f7b1b 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") endif() endif() - set(GIT_INDEX "${GIT_DIR}/index") + if(EXISTS "${GIT_DIR}/index") + set(GIT_INDEX "${GIT_DIR}/index") + else() + message(WARNING "Git index not found in git repository.") + set(GIT_INDEX "") + endif() else() message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") set(GIT_INDEX "") From 9fa262734733573fa629ffc97dfcb971fe3f4832 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Mon, 4 Mar 2024 10:05:42 +0100 Subject: [PATCH 171/401] ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov --- ggml-backend-impl.h | 7 ++++--- ggml-backend.c | 39 ++++++++++++++++++--------------------- ggml-backend.h | 31 ++++++++++++++++--------------- ggml-cuda.cu | 4 ++-- ggml-kompute.cpp | 4 ++-- ggml-metal.m | 8 ++++---- ggml-opencl.cpp | 4 ++-- ggml-sycl.cpp | 4 ++-- ggml-vulkan.cpp | 4 ++-- ggml.c | 29 +++++++++++++++++++++++------ ggml.h | 17 +++++++++++++---- 11 files changed, 88 insertions(+), 63 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 0e5bf0ae1365c..2e9ba58a931cc 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -91,13 +91,14 @@ extern "C" { // (optional) complete all pending operations void (*GGML_CALL synchronize)(ggml_backend_t backend); - // compute graph with a plan + // create a plan for ggml_cgraph and free it ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + // compute graph with a plan + enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) - bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); diff --git a/ggml-backend.c b/ggml-backend.c index c86673b04de37..d60d984143249 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -262,11 +262,11 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla backend->iface.graph_plan_free(backend, plan); } -void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - backend->iface.graph_plan_compute(backend, plan); +enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + return backend->iface.graph_plan_compute(backend, plan); } -bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -732,15 +732,15 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g GGML_UNUSED(backend); } -GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); GGML_UNUSED(backend); } -GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -755,8 +755,7 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str cplan.abort_callback = cpu_ctx->abort_callback; cplan.abort_callback_data = cpu_ctx->abort_callback_data; - ggml_graph_compute(cgraph, &cplan); - return true; + return ggml_graph_compute(cgraph, &cplan); } GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -1437,7 +1436,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { return true; } -static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { +static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; @@ -1472,8 +1471,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); if (!sched->callback_eval) { - if (!ggml_backend_graph_compute(split_backend, &split->graph)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } //ggml_backend_synchronize(split_backend); // necessary to measure compute time } else { @@ -1494,8 +1494,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); - if (!ggml_backend_graph_compute(split_backend, &gv)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { @@ -1519,7 +1520,7 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { } #endif - return true; + return GGML_STATUS_SUCCESS; } ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { @@ -1581,7 +1582,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * return true; } -bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); if (!sched->is_reset) { @@ -1590,14 +1591,10 @@ bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cg ggml_backend_sched_split_graph(sched, graph); if (!ggml_backend_sched_alloc_splits(sched)) { - return false; + return GGML_STATUS_ALLOC_FAILED; } - if (!ggml_backend_sched_compute_splits(sched)) { - return false; - } - - return true; + return ggml_backend_sched_compute_splits(sched); } void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { diff --git a/ggml-backend.h b/ggml-backend.h index 8fb54bd927f8b..8bed22578a907 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -66,12 +66,13 @@ extern "C" { GGML_API void ggml_backend_synchronize(ggml_backend_t backend); - GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); - GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op); + GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); + + GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); @@ -157,26 +158,26 @@ extern "C" { typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); - GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // Get the number of splits of the last graph - GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); + GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); - GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); - GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); // Allocate and compute graph on the backend scheduler - GGML_API bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // Reset all assignments and allocators - must be called before changing the node backends - GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); + GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute - GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); + GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); // // Utils diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7d027a30a8101..72bcec8cdb17a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -12241,7 +12241,7 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { UNUSED(backend); } -GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -12277,7 +12277,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg GGML_ASSERT(ok); } - return true; + return GGML_STATUS_SUCCESS; } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index e740a76d1ac53..83a7822fdbe9d 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1927,10 +1927,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g return ggml_backend_kompute_buffer_type(ctx->device); } -static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { auto * ctx = static_cast(backend->context); ggml_vk_graph_compute(ctx, cgraph); - return true; + return GGML_STATUS_SUCCESS; } static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { diff --git a/ggml-metal.m b/ggml-metal.m index 6b5a8fdf54104..00df2283821b2 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -748,7 +748,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const } } -static bool ggml_metal_graph_compute( +static enum ggml_status ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @@ -2484,7 +2484,7 @@ static bool ggml_metal_graph_compute( MTLCommandBufferStatus status = [command_buffer status]; if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); - return false; + return GGML_STATUS_FAILED; } } @@ -2493,7 +2493,7 @@ static bool ggml_metal_graph_compute( } } - return true; + return GGML_STATUS_SUCCESS; } //////////////////////////////////////////////////////////////////////////////// @@ -2795,7 +2795,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe UNUSED(backend); } -GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index df619a884842c..aa73d67df84b0 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2231,7 +2231,7 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg GGML_UNUSED(backend); } -static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { +static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { for (int i = 0; i < graph->n_nodes; ++i) { ggml_tensor * node = graph->nodes[i]; switch (node->op) { @@ -2246,7 +2246,7 @@ static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgrap } } - return true; + return GGML_STATUS_SUCCESS; GGML_UNUSED(backend); } diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index cad08d610310f..47a605b01a07a 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15581,7 +15581,7 @@ catch (sycl::exception const &exc) { std::exit(1); } -GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; ggml_sycl_set_main_device(sycl_ctx->device); @@ -15613,7 +15613,7 @@ GGML_CALL static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, gg GGML_ASSERT(ok); } - return true; + return GGML_STATUS_SUCCESS; } GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index ae9cb3c1c1c6e..bc316c3f3944d 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5092,7 +5092,7 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } -GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { @@ -5135,7 +5135,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml ggml_vk_graph_cleanup(ctx); - return true; + return GGML_STATUS_SUCCESS; UNUSED(backend); } diff --git a/ggml.c b/ggml.c index 870e41614add7..3c2e94c1b8925 100644 --- a/ggml.c +++ b/ggml.c @@ -320,6 +320,16 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; +const char * ggml_status_to_string(enum ggml_status status) { + switch (status) { + case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; + default: GGML_ASSERT(false); + } +} + // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { @@ -17400,6 +17410,7 @@ struct ggml_compute_state { ggml_thread_t thrd; int ith; struct ggml_compute_state_shared * shared; + enum ggml_status ec; }; static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { @@ -17693,7 +17704,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { state->shared->node_n += 1; - return (thread_ret_t) GGML_EXIT_ABORTED; + state->ec = GGML_STATUS_ABORTED; + return 0; } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -17815,7 +17827,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } } - return GGML_EXIT_SUCCESS; + return 0; } struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { @@ -18011,7 +18023,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa return cplan; } -int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { +enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); @@ -18055,6 +18067,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { .thrd = 0, .ith = j, .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, }; const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); @@ -18065,12 +18078,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { workers[0].ith = 0; workers[0].shared = &state_shared; + workers[0].ec = GGML_STATUS_SUCCESS; const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); // this is a work thread too - int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]); + ggml_graph_compute_thread(&workers[0]); + enum ggml_status compute_status = workers[0].ec; // don't leave affinity set on the main thread clear_numa_thread_affinity(); @@ -18080,6 +18095,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { for (int j = 1; j < n_threads; j++) { const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); + if (workers[j].ec != GGML_STATUS_SUCCESS) + compute_status = workers[j].ec; } } @@ -18107,14 +18124,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { return compute_status; } -void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { +enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - ggml_graph_compute(cgraph, &cplan); + return ggml_graph_compute(cgraph, &cplan); } struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { diff --git a/ggml.h b/ggml.h index 98cfc7bf8f670..0ea4f8847795c 100644 --- a/ggml.h +++ b/ggml.h @@ -315,6 +315,16 @@ extern "C" { #endif + enum ggml_status { + GGML_STATUS_ALLOC_FAILED = -2, + GGML_STATUS_FAILED = -1, + GGML_STATUS_SUCCESS = 0, + GGML_STATUS_ABORTED = 1, + }; + + // get ggml_status name string + GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status); + typedef uint16_t ggml_fp16_t; // convert FP16 <-> FP32 @@ -1940,12 +1950,11 @@ extern "C" { // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - + GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); + GGML_API enum ggml_status ggml_graph_compute ( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data - GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); + GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); From efd8533ef8d0752cef7119eb5dbee412c4dba270 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 11:06:39 +0200 Subject: [PATCH 172/401] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 4a1b0bab4961c..afe68077d0ec9 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -274680868e12427373bab4bec87554431b954704 +8695910a39102609073d0e099aa7c97d6bcb3bf9 From a1c6d96ed8f906aa1cda439f7386b1171a22bf9f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 20:53:27 +0200 Subject: [PATCH 173/401] ggml : fix unknown status (#0) --- ggml.c | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ggml.c b/ggml.c index 3c2e94c1b8925..6a10bbcb45e45 100644 --- a/ggml.c +++ b/ggml.c @@ -323,11 +323,12 @@ float ggml_table_f32_f16[1 << 16]; const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; - case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; - case GGML_STATUS_SUCCESS: return "GGML status: success"; - case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; - default: GGML_ASSERT(false); + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; } + + return "GGML status: unknown"; } // note: do not use these inside ggml.c From e0843afe1b37890b631bc7d3d2da2ed36c862b91 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 21:50:50 +0200 Subject: [PATCH 174/401] flake : fix --- convert-hf-to-gguf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ffdba74441e19..f6369af38081d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -36,8 +36,10 @@ class SentencePieceTokenTypes(IntEnum): UNUSED = 5 BYTE = 6 + AnyModel = TypeVar("AnyModel", bound="type[Model]") + class Model(ABC): _model_classes: dict[str, type[Model]] = {} @@ -187,6 +189,7 @@ def load_hparams(dir_model): @classmethod def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: assert names + def func(modelcls: type[Model]): for name in names: cls._model_classes[name] = modelcls From 29ae62d2ae163e2b68aa0ad3bf2ab4636de0c957 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 4 Mar 2024 22:31:20 +0200 Subject: [PATCH 175/401] llama : fix embeddings (#5796) * llama : fix embeddings ggml-ci * llama : do not use KV cache for non-causal models ggml-ci * embeddings : fix llama_batch_init arg * llama : add pooling switch * llama : distinguish token vs sequence embeddings ggml-ci * llama : assert pooling tensor * llama : simplify causal mask condition ggml-ci * llama : assert input batch with pooling enabled * readme : update API changes list --- README.md | 1 + common/common.cpp | 2 +- examples/embedding/embedding.cpp | 28 ++- examples/server-embd.py | 34 +++ examples/server/server.cpp | 53 ++++- llama.cpp | 355 +++++++++++++++++++++---------- llama.h | 18 +- 7 files changed, 358 insertions(+), 133 deletions(-) create mode 100644 examples/server-embd.py diff --git a/README.md b/README.md index 45c5d06f3e10e..f754022de894d 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 ### Hot topics diff --git a/common/common.cpp b/common/common.cpp index 036a981349a69..c244db6443eaa 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1292,7 +1292,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; + cparams.embeddings = params.embedding; cparams.rope_scaling_type = params.rope_scaling_type; cparams.rope_freq_base = params.rope_freq_base; cparams.rope_freq_scale = params.rope_freq_scale; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index acff715e99d05..ff5883da6ba27 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -19,11 +19,11 @@ static std::vector split_lines(const std::string & s) { static void batch_add_seq(llama_batch & batch, const std::vector & tokens, int seq_id) { for (size_t i = 0; i < tokens.size(); i++) { - llama_batch_add(batch, tokens[i], i, { seq_id }, false); + llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1); } } -static void normalize(float * vec, float * out, int n) { +static void normalize(const float * vec, float * out, int n) { float norm = 0; for (int i = 0; i < n; i++) { norm += vec[i] * vec[i]; @@ -45,10 +45,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } // normalize on copy - for (int k = 0; k < n_seq; k++) { - float * emb = llama_get_embeddings_ith(ctx, k); - float * out = output + k * n_embd; - normalize(emb, out, n_embd); + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + // try to get sequence embeddings - supported only when pooling_type is not NONE + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i); + continue; + } + } + + float * out = output + batch.seq_id[i][0] * n_embd; + normalize(embd, out, n_embd); } } @@ -132,7 +145,7 @@ int main(int argc, char ** argv) { // initialize batch const int n_prompts = prompts.size(); - struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); // allocate output const int n_embd = llama_n_embd(model); @@ -145,6 +158,7 @@ int main(int argc, char ** argv) { for (int k = 0; k < n_prompts; k++) { // clamp to n_batch tokens auto & inp = inputs[k]; + const uint64_t n_toks = inp.size(); // encode if at capacity diff --git a/examples/server-embd.py b/examples/server-embd.py new file mode 100644 index 0000000000000..c5c4ea87b09fc --- /dev/null +++ b/examples/server-embd.py @@ -0,0 +1,34 @@ +import asyncio +import requests +import numpy as np + +n = 8 + +result = [] + +async def requests_post_async(*args, **kwargs): + return await asyncio.to_thread(requests.post, *args, **kwargs) + +async def main(): + model_url = "http://127.0.0.1:6900" + responses: list[requests.Response] = await asyncio.gather(*[requests_post_async( + url= f"{model_url}/embedding", + json= {"content": str(i)*1024} + ) for i in range(n)]) + + for response in responses: + embedding = response.json()["embedding"] + print(embedding[-8:]) + result.append(embedding) + +asyncio.run(main()) + +# compute cosine similarity + +for i in range(n-1): + for j in range(i+1, n): + embedding1 = np.array(result[i]) + embedding2 = np.array(result[j]) + similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) + print(f"Similarity between {i} and {j}: {similarity:.2f}") + diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 208edd571cb0e..8fe5e0b19668f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1210,7 +1210,7 @@ struct llama_server_context queue_results.send(res); } - void send_embedding(server_slot &slot) + void send_embedding(server_slot & slot, const llama_batch & batch) { task_result res; res.id = slot.task_id; @@ -1219,6 +1219,7 @@ struct llama_server_context res.stop = true; const int n_embd = llama_n_embd(model); + if (!params.embedding) { LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); @@ -1229,12 +1230,29 @@ struct llama_server_context } else { - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - res.result_json = json - { - {"embedding", embedding}, - }; + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; + } + + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + LOG_ERROR("failed to get embeddings for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}}); + res.result_json = json + { + {"embedding", std::vector(n_embd, 0.0f)}, + }; + continue; + } + } + + res.result_json = json + { + {"embedding", std::vector(embd, embd + n_embd)}, + }; + } } queue_results.send(res); } @@ -1845,7 +1863,7 @@ struct llama_server_context ga_i += ga_w/ga_n; } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); slot_npast++; } @@ -1881,7 +1899,7 @@ struct llama_server_context for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); for (auto & slot : slots) { @@ -1954,7 +1972,7 @@ struct llama_server_context // prompt evaluated for embedding if (slot.embedding) { - send_embedding(slot); + send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; continue; @@ -2036,6 +2054,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\n"); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); @@ -2276,6 +2296,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.yarn_beta_slow = std::stof(argv[i]); } + else if (arg == "--pooling") + { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } + } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) @@ -2330,7 +2362,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_batch = std::stoi(argv[i]); - params.n_batch = std::min(512, params.n_batch); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { diff --git a/llama.cpp b/llama.cpp index de579d9e372b4..76afcbc135f4c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1665,7 +1665,7 @@ struct llama_hparams { }; struct llama_cparams { - uint32_t n_ctx; // context size used during inference + uint32_t n_ctx; // context size used during inference uint32_t n_batch; uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -1682,7 +1682,9 @@ struct llama_cparams { float yarn_beta_slow; float defrag_thold; + bool embeddings; bool offload_kqv; + enum llama_pooling_type pooling_type; ggml_backend_sched_eval_callback cb_eval; @@ -1972,7 +1974,7 @@ struct llama_context { int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1) int32_t n_eval = 0; // number of eval calls - // decode output (2-dimensional array: [n_tokens][n_vocab]) + // logits output (2-dimensional array: [n_tokens][n_vocab]) std::vector logits; #ifndef NDEBUG // guard against access to unset logits @@ -1980,8 +1982,13 @@ struct llama_context { #endif bool logits_all = false; - // input embedding (1-dimensional array: [n_embd]) - std::vector embedding; + // embeddings output (2-dimensional array: [n_tokens][n_embd]) + // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE + std::vector embd; + + // sequence embeddings output (map of [n_embd] vectors) + // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE + std::map> embd_seq; // memory buffers used to evaluate the model std::vector buf_compute_meta; @@ -5092,6 +5099,7 @@ static struct ggml_tensor * llm_build_kv( llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il); struct ggml_tensor * cur; + cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b, q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); @@ -6085,6 +6093,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; @@ -6092,9 +6101,10 @@ struct llm_build_context { // get input vectors with right size const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type); - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0); - struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); + struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); // construct input embeddings (token, type, position) inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); @@ -6112,39 +6122,38 @@ struct llm_build_context { cb(inpL, "inp_norm", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); // [n_kv, n_tokens] + struct ggml_tensor * KQ_mask = ggml_cont(ctx0, ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_tokens, n_tokens, n_tokens*ggml_type_size(lctx.inp_KQ_mask->type), 0)); + cb(KQ_mask, "KQ_mask", -1); // [n_tokens, n_tokens] // iterate layers for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * cur = inpL; + struct ggml_tensor * Qcur; + struct ggml_tensor * Kcur; + struct ggml_tensor * Vcur; + // self-attention if (model.arch == LLM_ARCH_BERT) { - struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); + Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); cb(Qcur, "Qcur", il); - struct ggml_tensor * Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); cb(Kcur, "Kcur", il); - struct ggml_tensor * Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); + Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); cb(Vcur, "Vcur", il); - // seems like we just need to do this for Q? - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); } else { // compute Q and K and RoPE them cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); cb(cur, "wqkv", il); - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); @@ -6163,12 +6172,40 @@ struct llm_build_context { ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); + } - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); + + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); + if (model.layers[il].bo) { + cb(cur, "kqv_wo", il); + } + + if (model.layers[il].bo) { + cur = ggml_add(ctx0, cur, model.layers[il].bo); } + cb(cur, "kqv_out", il); // re-add the layer input cur = ggml_add(ctx0, cur, inpL); @@ -6209,16 +6246,29 @@ struct llm_build_context { // final output cur = inpL; + cb(cur, "result_embd", -1); // pooling layer - if (pooling_type == LLAMA_POOLING_TYPE_MEAN) { - cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); - } else if (pooling_type == LLAMA_POOLING_TYPE_CLS) { - cur = ggml_get_rows(ctx0, cur, inp_cls); - } else { - GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type"); + switch (pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // nop + } break; + case LLAMA_POOLING_TYPE_MEAN: + { + cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); + cb(cur, "result_embd_pooled", -1); + } break; + case LLAMA_POOLING_TYPE_CLS: + { + cur = ggml_get_rows(ctx0, cur, inp_cls); + cb(cur, "result_embd_pooled", -1); + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ASSERT(false && "Invalid pooling type"); + } break; } - cb(cur, "result_embd", -1); ggml_build_forward_expand(gf, cur); @@ -7980,7 +8030,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } - { + if (hparams.causal_attn) { const int64_t n_kv = kv_self.n; const int64_t n_tokens = batch.n_tokens; @@ -7995,16 +8045,40 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { for (int i = 0; i < n_kv; ++i) { float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || - (hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) { + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { f = -INFINITY; } else { - f = 0; + f = 0.0f; } data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } } + } else { + // non-causal attention attends only the tokens within the batch (i.e. the KV cache is not used) + const int64_t n_tokens = batch.n_tokens; + + assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + + float * data = (float *) lctx.inp_KQ_mask->data; + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_seq_id seq_id = batch.seq_id[j][0]; + + for (int i = 0; i < n_tokens; ++i) { + float f = -INFINITY; + for (int s = 0; s < batch.n_seq_id[i]; ++s) { + if (batch.seq_id[i][s] == seq_id) { + f = 0.0f; + break; + } + } + + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = f; + } + } + } } if (hparams.need_kq_pos) { @@ -8023,13 +8097,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); - float * data = (float *) lctx.inp_mean->data; + float * data = (float *) lctx.inp_mean->data; memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean)); std::vector sum(n_tokens, 0); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); + sum[seq_id] += 1; } @@ -8051,11 +8128,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); + uint32_t * data = (uint32_t *) lctx.inp_cls->data; + memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; - const llama_pos pos = batch.pos[i]; + const llama_pos pos = batch.pos[i]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS"); + if (pos == 0) { data[seq_id] = i; } @@ -8169,23 +8251,26 @@ static int llama_decode_internal( batch.seq_id = seq_id_arr.data(); } - llama_kv_cache_update(&lctx); + // non-causal masks do not use the KV cache + if (hparams.causal_attn) { + llama_kv_cache_update(&lctx); - // if we have enough unused cells before the current head -> - // better to start searching from the beginning of the cache, hoping to fill it - if (kv_self.head > kv_self.used + 2*n_tokens) { - kv_self.head = 0; - } + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (kv_self.head > kv_self.used + 2*n_tokens) { + kv_self.head = 0; + } - if (!llama_kv_cache_find_slot(kv_self, batch)) { - return 1; - } + if (!llama_kv_cache_find_slot(kv_self, batch)) { + return 1; + } - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); - //kv_self.n = llama_kv_cache_cell_max(kv_self); + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + //kv_self.n = llama_kv_cache_cell_max(kv_self); + } //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); @@ -8195,20 +8280,26 @@ static int llama_decode_internal( ggml_cgraph * gf = llama_build_graph(lctx, batch, false); // the output is always the last tensor in the graph - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; - - if (strcmp(res->name, "result_output") == 0) { - // the embeddings could be the second to last tensor, or the third to last tensor - if (strcmp(embeddings->name, "result_norm") != 0) { - embeddings = gf->nodes[gf->n_nodes - 3]; - GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); - } - } else if (strcmp(res->name, "result_embd") == 0) { - embeddings = res; - res = nullptr; + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; + + if (!hparams.causal_attn) { + res = nullptr; // do not extract logits for embedding models such as BERT + + // token or sequence embeddings + embd = gf->nodes[gf->n_nodes - 1]; + + GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0); } else { - GGML_ASSERT(false); + if (strcmp(res->name, "result_output") == 0) { + // the token embeddings could be the second to last tensor, or the third to last tensor + if (strcmp(embd->name, "result_norm") != 0) { + embd = gf->nodes[gf->n_nodes - 3]; + GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + } + } else { + GGML_ASSERT(false && "missing result_output tensor"); + } } // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); @@ -8275,46 +8366,82 @@ static int llama_decode_internal( logits_out.clear(); #endif - ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res); - GGML_ASSERT(res_backend != nullptr); + ggml_backend_t backend_res = ggml_backend_sched_get_node_backend(lctx.sched, res); + GGML_ASSERT(backend_res != nullptr); + if (batch.logits) { logits_out.resize(n_vocab * n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { if (batch.logits[i] == 0) { continue; } - ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[i] = true; #endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); - ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); #endif } else { logits_out.resize(n_vocab); - ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[0] = true; #endif } - ggml_backend_synchronize(res_backend); + ggml_backend_synchronize(backend_res); } // extract embeddings - if (!lctx.embedding.empty()) { - auto & embedding_out = lctx.embedding; + if (cparams.embeddings && embd) { + ggml_backend_t backend_embd = ggml_backend_sched_get_node_backend(lctx.sched, embd); + GGML_ASSERT(backend_embd != nullptr); - const int64_t embd_pos = res ? n_embd * (n_tokens-1) : 0; - const int64_t embd_size = res ? n_embd : n_embd * n_tokens; + switch (cparams.pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // extract token embeddings + auto & embd_out = lctx.embd; + + if (batch.logits) { + embd_out.resize(n_embd * n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + if (batch.logits[i] == 0) { + continue; + } - embedding_out.resize(embd_size); - ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); - ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float)); - ggml_backend_synchronize(embeddings_backend); + ggml_backend_tensor_get_async(backend_embd, embd, embd_out.data() + (n_embd*i), (n_embd*i)*sizeof(float), n_embd*sizeof(float)); + } + } + } break; + case LLAMA_POOLING_TYPE_CLS: + case LLAMA_POOLING_TYPE_MEAN: + { + GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0); + + // extract sequence embeddings + auto & embd_seq_out = lctx.embd_seq; + embd_seq_out.clear(); + + for (uint32_t i = 0; i < n_tokens; i++) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { + continue; + } + embd_seq_out[seq_id].resize(n_embd); + ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); + } + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ASSERT(false && "unknown pooling type"); + } break; + } + ggml_backend_synchronize(backend_embd); } // measure the performance only for the single-token evals @@ -8608,19 +8735,19 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { GGML_ASSERT(llama_is_byte_token(vocab, id)); const auto& token_data = vocab.id_to_token.at(id); switch (llama_vocab_get_type(vocab)) { - case LLAMA_VOCAB_TYPE_SPM: { - auto buf = token_data.text.substr(3, 2); - return strtol(buf.c_str(), NULL, 16); - } - case LLAMA_VOCAB_TYPE_BPE: { - GGML_ASSERT(false); - return unicode_to_bytes_bpe(token_data.text); - } - case LLAMA_VOCAB_TYPE_WPM: { - GGML_ASSERT(false); - } - default: - GGML_ASSERT(false); + case LLAMA_VOCAB_TYPE_SPM: { + auto buf = token_data.text.substr(3, 2); + return strtol(buf.c_str(), NULL, 16); + } + case LLAMA_VOCAB_TYPE_BPE: { + GGML_ASSERT(false); + return unicode_to_bytes_bpe(token_data.text); + } + case LLAMA_VOCAB_TYPE_WPM: { + GGML_ASSERT(false); + } + default: + GGML_ASSERT(false); } } @@ -11864,7 +11991,7 @@ struct llama_context_params llama_context_default_params() { /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, /*.logits_all =*/ false, - /*.embedding =*/ false, + /*.embeddings =*/ false, /*.offload_kqv =*/ true, /*.abort_callback =*/ nullptr, /*.abort_callback_data =*/ nullptr, @@ -12015,6 +12142,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; + cparams.embeddings = params.embeddings; cparams.offload_kqv = params.offload_kqv; cparams.pooling_type = params.pooling_type; @@ -12192,8 +12320,8 @@ struct llama_context * llama_new_context_with_model( // resized during inference, reserve maximum ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); - if (params.embedding) { - ctx->embedding.resize(hparams.n_embd); + if (params.embeddings) { + ctx->embd.reserve(hparams.n_embd*cparams.n_batch); } // graph inputs @@ -12628,7 +12756,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { // assume worst case for logits although only currently set ones are serialized const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); - const size_t s_embedding = ctx->embedding.size() * sizeof(float); + const size_t s_embedding = ctx->embd.capacity() * sizeof(float); const size_t s_kv_buf_size = sizeof(size_t); const size_t s_kv_head = sizeof(uint32_t); const size_t s_kv_size = sizeof(uint32_t); @@ -12737,12 +12865,12 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat // copy embeddings { - const size_t embedding_size = ctx->embedding.size(); + const size_t embeddings_size = ctx->embd.size(); - data_ctx->write(&embedding_size, sizeof(embedding_size)); + data_ctx->write(&embeddings_size, sizeof(embeddings_size)); - if (embedding_size) { - data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float)); + if (embeddings_size) { + data_ctx->write(ctx->embd.data(), embeddings_size * sizeof(float)); } } @@ -12846,15 +12974,17 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { // set embeddings { - size_t embedding_size; + size_t embeddings_size; + + memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size); - memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size); + GGML_ASSERT(ctx->embd.capacity() == embeddings_size); - GGML_ASSERT(ctx->embedding.capacity() == embedding_size); + if (embeddings_size) { + ctx->embd.resize(embeddings_size); - if (embedding_size) { - memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float)); - inp += embedding_size * sizeof(float); + memcpy(ctx->embd.data(), inp, embeddings_size * sizeof(float)); + inp += embeddings_size * sizeof(float); } } @@ -13104,11 +13234,20 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { } float * llama_get_embeddings(struct llama_context * ctx) { - return ctx->embedding.data(); + return ctx->embd.data(); } float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) { - return ctx->embedding.data() + i*ctx->model.hparams.n_embd; + return ctx->embd.data() + i*ctx->model.hparams.n_embd; +} + +float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) { + auto it = ctx->embd_seq.find(seq_id); + if (it == ctx->embd_seq.end()) { + return nullptr; + } + + return it->second.data(); } const char * llama_token_get_text(const struct llama_model * model, llama_token token) { diff --git a/llama.h b/llama.h index 70da4cb3f0ff6..3dc162b078d30 100644 --- a/llama.h +++ b/llama.h @@ -163,7 +163,7 @@ extern "C" { // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence // - seq_id : the sequence to which the respective token belongs - // - logits : if zero, the logits for the respective token will not be output + // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output // typedef struct llama_batch { int32_t n_tokens; @@ -173,7 +173,7 @@ extern "C" { llama_pos * pos; int32_t * n_seq_id; llama_seq_id ** seq_id; - int8_t * logits; + int8_t * logits; // TODO: rename this to "output" // NOTE: helpers for smooth API transition - can be deprecated in the future // for future-proof code, use the above fields instead and ignore everything below @@ -260,7 +260,7 @@ extern "C" { // Keep the booleans together to avoid misalignment during copy-by-value. bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) - bool embedding; // embedding mode only + bool embeddings; // if true, extract embeddings (together with logits) bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU // Abort callback @@ -655,14 +655,20 @@ extern "C" { // llama_get_logits(ctx) + i*n_vocab LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); - // Get the embeddings for the input - // shape: [n_embd] (1-dimensional) + // Get all output token embeddings + // shape: [n_tokens*n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); - // Get the embeddings for the ith sequence + // Get the embeddings for the ith token // llama_get_embeddings(ctx) + i*n_embd + // shape: [n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); + // Get the embeddings for a sequence id + // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE + // shape: [n_embd] (1-dimensional) + LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); + // // Vocab // From 1d41d6f7c2a666eb9c18a686a4684c4b03289bf3 Mon Sep 17 00:00:00 2001 From: hutli <6594598+hutli@users.noreply.github.com> Date: Tue, 5 Mar 2024 02:33:08 +0100 Subject: [PATCH 176/401] nix: static build (#5814) --- .devops/nix/package.nix | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 815db6a2d8c20..01c99185b86ac 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -1,5 +1,6 @@ { lib, + glibc, config, stdenv, mkShell, @@ -30,6 +31,11 @@ useRocm ? config.rocmSupport, useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv, + enableStatic ? effectiveStdenv.hostPlatform.isStatic }@inputs: let @@ -40,11 +46,8 @@ let strings versionOlder ; - - # It's necessary to consistently use backendStdenv when building with CUDA support, - # otherwise we get libstdc++ errors downstream. + stdenv = throw "Use effectiveStdenv instead"; - effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv; suffices = lib.optionals useBlas [ "BLAS" ] @@ -167,6 +170,9 @@ effectiveStdenv.mkDerivation ( # TODO: Replace with autoAddDriverRunpath # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged cudaPackages.autoAddOpenGLRunpathHook + ] + ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ + glibc.static ]; buildInputs = @@ -181,7 +187,7 @@ effectiveStdenv.mkDerivation ( [ (cmakeBool "LLAMA_NATIVE" false) (cmakeBool "LLAMA_BUILD_SERVER" true) - (cmakeBool "BUILD_SHARED_LIBS" true) + (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic)) (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) (cmakeBool "LLAMA_BLAS" useBlas) (cmakeBool "LLAMA_CLBLAST" useOpenCL) @@ -190,6 +196,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) (cmakeBool "LLAMA_VULKAN" useVulkan) + (cmakeBool "LLAMA_STATIC" enableStatic) ] ++ optionals useCuda [ ( From 29eee404746e4696143a4f3a642660a4793a15d8 Mon Sep 17 00:00:00 2001 From: Jeffrey Quesnelle Date: Mon, 4 Mar 2024 19:23:06 -0800 Subject: [PATCH 177/401] fix speculative decoding build on windows (#5874) --- examples/speculative/speculative.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 85bc0a762ad08..e991b88460713 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -226,7 +226,7 @@ int main(int argc, char ** argv) { while (active_seqs.size() > 0) { // randomly select a sequence to verify from active sequences - std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); + std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); int s = *std::next(active_seqs.begin(), u_int_dist(rng)); if (i_dft >= (int) drafts[s].tokens.size()) { drafts[s].active = false; From 6a87ac3a52668e117d97bcea07b529c93188b303 Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Tue, 5 Mar 2024 15:12:23 +0900 Subject: [PATCH 178/401] fix editorconfig check break (#5879) --- .devops/nix/package.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 01c99185b86ac..c7fa2203a0751 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -46,7 +46,7 @@ let strings versionOlder ; - + stdenv = throw "Use effectiveStdenv instead"; suffices = From 21b08674331e1ea1b599f17c5ca91f0ed173be31 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Tue, 5 Mar 2024 16:08:35 +0800 Subject: [PATCH 179/401] [SYCL] fix mul_mat fault in CI/unit-test (#5862) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix mul_mat fault in cpy_f32_f16 * rm unused function * add wait() for memcpy * restore ci/run.sh, rename struct defination, fix bug in ggml_sycl_op_mul_mat_sycl * fix format issue * llama : fix segfault from unknown model arch name (#5820) * llama : fix segfault from unknown model arch name * llama : make all LLM maps const This also requires using `std::map::at` instead of its `operator[]` which does not exist for const maps. * llama : name LLM_ARCH_UNKNOWN to "(unknown)" This avoids errors from `std::map::at` when getting the general name of the model architecture. Using "(unknown)" instead of an empty string as per suggestion https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284 * llama : remove redundant inner const for LLM_TENSOR_NAMES The extra const won't do anything here as const maps return const references to values. Co-authored-by: Jared Van Bortel * llama : remove redundant nullptr check in llm_arch_from_string Since LLM_ARCH_NAMES is a const map, no spurious elements with a NULL name are inserted anymore, so this check is dead code. --------- Co-authored-by: Jared Van Bortel * llama : refactor internal quantization functions (#5830) * scripts : add pod-llama.sh * ggml : IQ3_S improvements (#5829) * iq3_s: somewhat faster AVX2 dot product On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using 16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s. PP-512 increases to 28.5 t/s from 23.8 t/s. * iq3_s: somewhat faster ARM_NEON dot product Still dog slow - 10.7 t/s up from 9.9 t/s. * iq3_s: another small ARM_NEON improvement 10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick that works best on AVX2. * iq3_s: minor improvement on Metal 49.4 t/s -> 50.3 t/s * iq3_s: PPL improvement E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653. * iq3_s: use new grid everywhere * Fix ARM_NEON --------- Co-authored-by: Iwan Kawrakow * convert-hf : make model class definitions self-contained (#5825) * convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (#5821) * ggml : fix IQ3_S AVX implementation (#5834) ggml-ci * llama : add abort_callback to interrupt computation (#5409) * using abort_callback from ggml to stop llama computation * format fix * a brief explaining comment --------- Co-authored-by: Georgi Gerganov * server: tests: passkey challenge / self-extend with context shift demo (#5832) * server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test * flake.lock: Update (#5842) Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) → 'github:hercules-ci/flake-parts/f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2' (2024-03-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8?dir=lib' (2024-02-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23) → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29) Co-authored-by: github-actions[bot] * server : init http requests thread pool with --parallel if set (#5836) * ci : schedule slow server tests only on Release or on demand (#5839) * llama : fix llama_copy_state_data with fragmented KV cache (#5840) The row size of the saved states was based on kv_self.head while it should be based on llama_kv_cache_cell_max. Existing session files should still work. * llama : fix llama_kv_cache_cell_max inability to return 1 I've also changed its return type to uint32_t, because this function is always used to set the value of uint32_t variables, and because the index already has this type. * llama : fix state size calculation Some bytes in the state were unaccounted for in llama_get_state_size. Since the logits reserve so much space, it did not cause problems. * gguf-dump : support i-quants (#5841) Co-authored-by: Black_Fox * llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov * readme : add API changes section * cuda : fix data race in soft max (#5853) * main : support special tokens as reverse/anti prompt (#5847) * Support special tokens as reverse/anti prompt. * Tokenize antiprompts only once. * main : minor --------- Co-authored-by: Georgi Gerganov * common : use LLAMA_DEFAULT_SEED (#5855) * add some new ops, fix some operators and add batch operations to certain operators. (ggml/747) * cuda: fix group_norm * cuda: add batch inference support for ggml_pad/ggml_upscale * add ggml_arrange * add ggml_timestep_embedding * update ggml_arange/ggml_timestep_embedding tests * cuda: fix im2col * add ggml_arange/ggml_timestep_embbeding support for metal backend * fix some bugs * fix some bugs * Update ggml.h Co-authored-by: Georgi Gerganov * Update ggml-cuda.cu Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.m Co-authored-by: Georgi Gerganov * Update ggml-metal.metal Co-authored-by: Georgi Gerganov * modify according to the review comments * ggml : fix compile warnings + code style * ggml : normalize compute_forward calls + fix seg fault in debug * minor --------- Co-authored-by: Georgi Gerganov Co-authored-by: slaren * sync : ggml * add alias for chat template (#5858) * speculative : implement stochastic speculative sampling (#5625) * (WIP) Implement stochastic speculative decoding * sample from residual distribution on draft accept failure * fix #5657: force greedy sampling with probs when temp is 0 * remove p_accept parameter * fix style * remove unused variables * add srand() in speculative.cpp * replace use of rand() with mt19937 sampling * fixes based on review (@JohannesGaessler) * fix r random generation * randomly select next sequence to verify + fix bug in memory freeing * fix bug in active_seqs sync * fix uniform int distribution initialization * remove warnings from comparison between int and size_t * check grammar in `llama_sample_probability_distribution_impl` * remove malloc code by utilizing vectors * add PR link to README * cmake : handle cases where git index is not found in .git (#5844) * Update CMakeLists.txt * Update CMakeLists.txt * ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov * sync : ggml ggml-ci * ggml : fix unknown status (#0) * flake : fix * llama : fix embeddings (#5796) * llama : fix embeddings ggml-ci * llama : do not use KV cache for non-causal models ggml-ci * embeddings : fix llama_batch_init arg * llama : add pooling switch * llama : distinguish token vs sequence embeddings ggml-ci * llama : assert pooling tensor * llama : simplify causal mask condition ggml-ci * llama : assert input batch with pooling enabled * readme : update API changes list * nix: static build (#5814) * fix speculative decoding build on windows (#5874) * rebase and rm tailing space --------- Co-authored-by: LiangtaoJin Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> Co-authored-by: Jared Van Bortel Co-authored-by: Xuan Son Nguyen Co-authored-by: Georgi Gerganov Co-authored-by: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Co-authored-by: Iwan Kawrakow Co-authored-by: Jared Van Bortel Co-authored-by: Michael Podvitskiy Co-authored-by: Pierrick Hymbert Co-authored-by: github-actions[bot] Co-authored-by: Nindaleth Co-authored-by: Black_Fox Co-authored-by: Douglas Hanley Co-authored-by: slaren Co-authored-by: DAN™ Co-authored-by: leejet Co-authored-by: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Co-authored-by: Dane Madsen Co-authored-by: hutli <6594598+hutli@users.noreply.github.com> Co-authored-by: Jeffrey Quesnelle --- ggml-sycl.cpp | 1966 +++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 1810 insertions(+), 156 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 47a605b01a07a..477f5cb02db52 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2894,6 +2894,254 @@ namespace dpct using err0 = detail::generic_error_type; using err1 = detail::generic_error_type; + static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) { + detail::dpct_free(ptr, q); + } + + /// dpct accessor used as device function parameter. + template class accessor; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<3>; + accessor(pointer_t data, const sycl::range<3> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<3> &in_range) + : accessor(acc.get_pointer(), in_range) {} + accessor operator[](size_t index) const { + sycl::range<2> sub(_range.get(1), _range.get(2)); + return accessor(_data + index * sub.size(), sub); + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<3> _range; + }; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<2>; + accessor(pointer_t data, const sycl::range<2> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<2> &in_range) + : accessor(acc.get_pointer(), in_range) {} + + pointer_t operator[](size_t index) const { + return _data + _range.get(1) * index; + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<2> _range; + }; + + namespace detail { + /// Device variable with address space of shared, global or constant. + template class device_memory { + public: + using accessor_t = + typename detail::memory_traits::template accessor_t; + using value_t = typename detail::memory_traits::value_t; + using dpct_accessor_t = dpct::accessor; + + device_memory() : device_memory(sycl::range(1)) {} + + /// Constructor of 1-D array with initializer list + device_memory(const sycl::range &in_range, + std::initializer_list &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range.size()); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T)); + } + + /// Constructor of 2-D array with initializer list + template + device_memory( + const typename std::enable_if>::type &in_range, + std::initializer_list> &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range[0]); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + auto tmp_data = _host_ptr; + for (auto sub_list : init_list) { + assert(sub_list.size() <= in_range[1]); + std::memcpy(tmp_data, sub_list.begin(), + sub_list.size() * sizeof(T)); + tmp_data += in_range[1]; + } + } + + /// Constructor with range + device_memory(const sycl::range &range_in) + : _size(range_in.size() * sizeof(T)), _range(range_in), + _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) { + static_assert( + (Memory == global) || (Memory == constant) || (Memory == shared), + "device memory region should be global, constant or shared"); + // Make sure that singleton class mem_mgr and dev_mgr will destruct + // later than this. + detail::mem_mgr::instance(); + dev_mgr::instance(); + } + + /// Constructor with range + template + device_memory(Args... Arguments) + : device_memory(sycl::range(Arguments...)) {} + + ~device_memory() { + if (_device_ptr && !_reference) + dpct::dpct_free(_device_ptr); + if (_host_ptr) + std::free(_host_ptr); + } + + /// Allocate memory with default queue, and init memory if has initial + /// value. + void init() { init(dpct::get_default_queue()); } + /// Allocate memory with specified queue, and init memory if has initial + /// value. + void init(sycl::queue &q) { + if (_device_ptr) + return; + if (!_size) + return; + allocate_device(q); + if (_host_ptr) + detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size, + host_to_device); + } + + /// The variable is assigned to a device pointer. + void assign(value_t *src, size_t size) { + this->~device_memory(); + new (this) device_memory(src, size); + } + + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr() { return get_ptr(get_default_queue()); } + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr(sycl::queue &q) { + init(q); + return _device_ptr; + } + + /// Get the device memory object size in bytes. + size_t get_size() { return _size; } + + template + typename std::enable_if::type &operator[](size_t index) { + init(); + #ifdef DPCT_USM_LEVEL_NONE + return dpct::get_buffer::type>( + _device_ptr) + .template get_access()[index]; + #else + return _device_ptr[index]; + #endif // DPCT_USM_LEVEL_NONE + } + + #ifdef DPCT_USM_LEVEL_NONE + /// Get sycl::accessor for the device memory object when usm is not used. + accessor_t get_access(sycl::handler &cgh) { + return get_buffer(_device_ptr) + .template reinterpret(_range) + .template get_access::mode, + detail::memory_traits::target>(cgh); + } + #else + /// Get dpct::accessor with dimension info for the device memory object + /// when usm is used and dimension is greater than 1. + template + typename std::enable_if::type + get_access(sycl::handler &cgh) { + return dpct_accessor_t((T *)_device_ptr, _range); + } + #endif // DPCT_USM_LEVEL_NONE + + private: + device_memory(value_t *memory_ptr, size_t size) + : _size(size), _range(size / sizeof(T)), _reference(true), + _device_ptr(memory_ptr) {} + + void allocate_device(sycl::queue &q) { + #ifndef DPCT_USM_LEVEL_NONE + if (Memory == shared) { + _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(), + q.get_context()); + return; + } + #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY + if (Memory == constant) { + _device_ptr = (value_t *)sycl::malloc_device( + _size, q.get_device(), q.get_context(), + sycl::ext::oneapi::property::usm::device_read_only()); + return; + } + #endif + #endif + _device_ptr = (value_t *)detail::dpct_malloc(_size, q); + } + + size_t _size; + sycl::range _range; + bool _reference; + value_t *_host_ptr; + value_t *_device_ptr; + }; + template + class device_memory : public device_memory { + public: + using base = device_memory; + using value_t = typename base::value_t; + using accessor_t = + typename detail::memory_traits::template accessor_t<0>; + + /// Constructor with initial value. + device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {} + + /// Default constructor + device_memory() : base(1) {} + + #ifdef DPCT_USM_LEVEL_NONE + /// Get sycl::accessor for the device memory object when usm is not used. + accessor_t get_access(sycl::handler &cgh) { + auto buf = get_buffer(base::get_ptr()) + .template reinterpret(sycl::range<1>(1)); + return accessor_t(buf, cgh); + } + #endif // DPCT_USM_LEVEL_NONE + }; + } // namespace detail + + template + using global_memory = detail::device_memory; + template + using constant_memory = detail::device_memory; + template + using shared_memory = detail::device_memory; + + } // COPY from DPCT head files @@ -2938,6 +3186,15 @@ static int g_work_group_size = 0; #pragma warning(disable: 4244 4267) // possible loss of data #endif +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_SYCL_DMMV_X +#define GGML_SYCL_DMMV_X 32 +#endif +#ifndef GGML_SYCL_MMV_Y +#define GGML_SYCL_MMV_Y 1 +#endif + + static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); static void crash(){ @@ -3060,7 +3317,7 @@ typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0, #define QK4_0 32 #define QR4_0 2 #define QI4_0 (QK4_0 / (4 * QR4_0)) -typedef struct dpct_type_471834 { +typedef struct dpct_type_block_q4_0 { sycl::half d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants } block_q4_0; @@ -3069,7 +3326,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 #define QK4_1 32 #define QR4_1 2 #define QI4_1 (QK4_1 / (4 * QR4_1)) -typedef struct dpct_type_143705 { +typedef struct dpct_type_block_q4_1 { sycl::half2 dm; // dm.x = delta, dm.y = min uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; @@ -3078,7 +3335,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong #define QK5_0 32 #define QR5_0 2 #define QI5_0 (QK5_0 / (4 * QR5_0)) -typedef struct dpct_type_673649 { +typedef struct dpct_type_block_q5_0 { sycl::half d; // delta uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_0 / 2]; // nibbles / quants @@ -3088,7 +3345,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5 #define QK5_1 32 #define QR5_1 2 #define QI5_1 (QK5_1 / (4 * QR5_1)) -typedef struct dpct_type_135589 { +typedef struct dpct_type_block_q5_1 { sycl::half2 dm; // dm.x = delta, dm.y = min uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_1 / 2]; // nibbles / quants @@ -3098,7 +3355,7 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + #define QK8_0 32 #define QR8_0 1 #define QI8_0 (QK8_0 / (4 * QR8_0)) -typedef struct dpct_type_122878 { +typedef struct dpct_type_block_q8_0 { sycl::half d; // delta int8_t qs[QK8_0]; // quants } block_q8_0; @@ -3107,7 +3364,7 @@ static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 blo #define QK8_1 32 #define QR8_1 1 #define QI8_1 (QK8_1 / (4 * QR8_1)) -typedef struct dpct_type_143721 { +typedef struct dpct_type_block_q8_1 { sycl::half2 ds; // ds.x = delta, ds.y = sum int8_t qs[QK8_0]; // quants } block_q8_1; @@ -3141,7 +3398,7 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define QR2_K 4 #define QI2_K (QK_K / (4*QR2_K)) -typedef struct dpct_type_619598 { +typedef struct dpct_type_block_q2_K { uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits uint8_t qs[QK_K/4]; // quants sycl::half2 dm; // super-block scale for quantized scales/mins @@ -3150,7 +3407,7 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "w #define QR3_K 4 #define QI3_K (QK_K / (4*QR3_K)) -typedef struct dpct_type_138576 { +typedef struct dpct_type_block_q3_K { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits #ifdef GGML_QKK_64 @@ -3166,13 +3423,13 @@ typedef struct dpct_type_138576 { #define QI4_K (QK_K / (4*QR4_K)) #ifdef GGML_QKK_64 typedef struct { - half dm[2]; // super-block scales/mins + sycl::half dm[2]; // super-block scales/mins uint8_t scales[2]; // 4-bit block scales/mins uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; -static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); +static_assert(sizeof(block_q4_K) == sizeof(sycl::half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); #else -typedef struct dpct_type_154943 { +typedef struct dpct_type_block_q4_K { sycl::half2 dm; // super-block scale for quantized scales/mins uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits uint8_t qs[QK_K/2]; // 4--bit quants @@ -3184,14 +3441,14 @@ static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, #define QI5_K (QK_K / (4*QR5_K)) #ifdef GGML_QKK_64 typedef struct { - half d; // super-block scale + sycl::half d; // super-block scale int8_t scales[QK_K/16]; // block scales uint8_t qh[QK_K/8]; // quants, high bit uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); #else -typedef struct dpct_type_866817 { +typedef struct dpct_type_block_q5_K { sycl::half2 dm; // super-block scale for quantized scales/mins uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits uint8_t qh[QK_K/8]; // quants, high bit @@ -3202,7 +3459,7 @@ static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/ #define QR6_K 2 #define QI6_K (QK_K / (4*QR6_K)) -typedef struct dpct_type_107281 { +typedef struct dpct_type_block_q6_K { uint8_t ql[QK_K/2]; // quants, lower 4 bits uint8_t qh[QK_K/4]; // quants, upper 2 bits int8_t scales[QK_K/16]; // scales @@ -3210,6 +3467,31 @@ typedef struct dpct_type_107281 { } block_q6_K; static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); +#define QR2_XXS 8 +#define QI2_XXS (QK_K / (4*QR2_XXS)) +typedef struct dpct_type_block_iq2_xxs { + sycl::half d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); + +#define QR2_XS 8 +#define QI2_XS (QK_K / (4*QR2_XS)) +typedef struct dpct_type_block_iq2_xs { + sycl::half d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); + +#define QR3_XXS 8 +#define QI3_XXS (QK_K / (4*QR3_XXS)) +typedef struct dpct_type_block_iq3_xxs { + sycl::half d; + uint8_t qs[3*(QK_K/8)]; +} block_iq3_xxs; +static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -3478,7 +3760,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo local_buf = (float *) ggml_sycl_host_malloc(total_size); ggml_sycl_set_device(g_main_device); dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - main_stream->memcpy(local_buf, src, total_size); + main_stream->memcpy(local_buf, src, total_size).wait(); } else { local_buf = (float *)src; @@ -4129,6 +4411,66 @@ static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib, #endif // GGML_SYCL_F16 } +template +static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + + // assume 32 threads + const int tid = item_ct1.get_local_id(2); + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_0 * x = (const block_q4_0 *)vx + ib; + const float d = sycl::vec(x->d) + .convert()[0]; + const float dm = -8*d; + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d * (q[l] & 0xF) + dm; + y[l+16] = d * (q[l] >> 4) + dm; + } +} + +template +static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + + // assume 32 threads + const int tid = item_ct1.get_local_id(2); + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_1 * x = (const block_q4_1 *)vx + ib; + const sycl::float2 d = + x->dm.convert(); + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l + 0] = d.x() * (q[l] & 0xF) + d.y(); + y[l + 16] = d.x() * (q[l] >> 4) + d.y(); + } +} + + //================================== k-quants template @@ -4158,8 +4500,9 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri const int il = tid%16; // 0...15 const uint8_t q = x[i].qs[il] >> (2*is); dst_t * y = yy + i*QK_K + 16*is + il; - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); + + float dall = x[i].dm[0]; + float dmin = x[i].dm[1]; y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); #endif @@ -4198,7 +4541,7 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const int is = tid/16; // 0 or 1 const int il = tid%16; // 0...15 const int im = il/8; // 0...1 @@ -4264,7 +4607,7 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri y[l +32] = d2 * (q[l] >> 4) - m2; } #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const uint8_t * q = x[i].qs; dst_t * y = yy + i*QK_K; const float d = (float)x[i].dm[0]; @@ -4309,7 +4652,7 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; #else - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const uint8_t q = x[i].qs[tid]; const int im = tid/8; // 0...3 const int in = tid%8; // 0...7 @@ -4351,7 +4694,7 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #else // assume 32 threads - const int tid = threadIdx.x; + const int tid = item_ct1.get_local_id(2); const int ip = tid/16; // 0 or 1 const int il = tid - 16*ip; // 0...15 @@ -4368,6 +4711,474 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #endif } +static dpct::global_memory + iq2xxs_grid(sycl::range<1>(256), + { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, + 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, + 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, + 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, + 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b082b2b, 0x080808082b2b082b, + 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, + 0x080808192b192b08, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b082b082b, 0x0808082b2b08082b, 0x0808190808080819, + 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, + 0x0808190819082b08, 0x08081908192b0808, 0x080819082b080819, + 0x080819082b081908, 0x080819082b190808, 0x080819082b2b1908, + 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, + 0x080819192b080808, 0x080819192b190819, 0x0808192b08082b19, + 0x0808192b08190808, 0x0808192b19080808, 0x0808192b2b081908, + 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, + 0x08082b0819080819, 0x08082b0819081908, 0x08082b0819190808, + 0x08082b081919082b, 0x08082b082b082b08, 0x08082b1908081908, + 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, + 0x08190808082b0819, 0x0819080819080808, 0x08190808192b0808, + 0x081908082b081908, 0x081908082b190808, 0x081908082b191919, + 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, + 0x0819082b082b1908, 0x0819082b19081919, 0x0819190808080808, + 0x0819190808082b08, 0x08191908082b0808, 0x08191908082b1919, + 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, + 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, + 0x08192b0819080808, 0x08192b082b080819, 0x08192b1908080808, + 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, + 0x082b080819081908, 0x082b0808192b0819, 0x082b08082b080808, + 0x082b08082b08082b, 0x082b0819082b2b19, 0x082b081919082b08, + 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, + 0x082b19081919192b, 0x082b191908080808, 0x082b191919080819, + 0x082b1919192b1908, 0x082b192b2b190808, 0x082b2b0808082b08, + 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, + 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x1908080819082b08, 0x190808081919192b, + 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, + 0x19080819192b0819, 0x190808192b080808, 0x190808192b081919, + 0x1908082b08080819, 0x1908082b08190808, 0x1908082b19082b08, + 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, + 0x190819082b192b19, 0x190819190819082b, 0x19081919082b1908, + 0x1908192b08080808, 0x19082b0808080819, 0x19082b0808081908, + 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, + 0x19082b192b08082b, 0x19082b2b19081919, 0x19082b2b2b190808, + 0x1919080808080808, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, + 0x191908192b2b1908, 0x1919082b2b190819, 0x191919082b190808, + 0x191919082b19082b, 0x1919191908082b2b, 0x1919192b08080819, + 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, + 0x19192b2b08082b08, 0x192b080808081908, 0x192b080808190808, + 0x192b080819080808, 0x192b0808192b2b08, 0x192b081908080808, + 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, + 0x192b19190819082b, 0x192b19192b081908, 0x192b2b081908082b, + 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808082b2b, + 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, + 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, + 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, + 0x2b082b080808082b, 0x2b082b1908081908, 0x2b082b2b08190819, + 0x2b19080808081908, 0x2b19080808190808, 0x2b190808082b1908, + 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, + 0x2b191908082b082b, 0x2b19190819081908, 0x2b19191919190819, + 0x2b192b082b080819, 0x2b192b19082b0808, 0x2b2b08080808082b, + 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, + 0x2b2b2b1908081908, + }); + +static dpct::global_memory + iq2xs_grid(sycl::range<1>(512), + { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, + 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, + 0x0808080808191908, 0x080808080819192b, 0x0808080808192b19, + 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, + 0x080808081908192b, 0x0808080819082b19, 0x0808080819190808, + 0x080808081919082b, 0x0808080819191919, 0x0808080819192b08, + 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, + 0x080808082b190819, 0x080808082b191908, 0x080808082b192b19, + 0x080808082b2b0808, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, + 0x0808081908192b2b, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, + 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x0808082b08080808, + 0x0808082b0808082b, 0x0808082b08081919, 0x0808082b08082b08, + 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, + 0x0808082b19191919, 0x0808082b2b080808, 0x0808082b2b082b2b, + 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, + 0x0808190819081919, 0x0808190819082b08, 0x0808190819190819, + 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x0808191908080808, 0x080819190808082b, 0x0808191908081919, + 0x0808191908082b08, 0x0808191908190819, 0x0808191908191908, + 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, + 0x0808192b08080819, 0x0808192b08081908, 0x0808192b08190808, + 0x0808192b082b192b, 0x0808192b19080808, 0x0808192b1908082b, + 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, + 0x08082b0808190819, 0x08082b0808191908, 0x08082b08082b0808, + 0x08082b08082b1919, 0x08082b0819080819, 0x08082b0819081908, + 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b1919080808, + 0x08082b192b080819, 0x08082b192b082b19, 0x08082b2b08080808, + 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, + 0x081908080819082b, 0x0819080808191919, 0x0819080808192b08, + 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, + 0x0819080819190819, 0x0819080819191908, 0x08190808192b0808, + 0x08190808192b2b2b, 0x081908082b080819, 0x081908082b081908, + 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, + 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, + 0x0819081908191908, 0x08190819082b0808, 0x0819081919080819, + 0x0819081919081908, 0x0819081919190808, 0x081908192b080808, + 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, + 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, + 0x0819082b19080808, 0x0819082b192b0808, 0x0819190808080808, + 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, + 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, + 0x0819190819190808, 0x08191908192b1908, 0x081919082b080808, + 0x0819191908080819, 0x0819191908081908, 0x0819191908190808, + 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, + 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, + 0x08192b0808190808, 0x08192b080819082b, 0x08192b0819080808, + 0x08192b0819191908, 0x08192b082b08192b, 0x08192b1908080808, + 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, + 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, + 0x082b080808081919, 0x082b080808082b08, 0x082b080808082b2b, + 0x082b080808190819, 0x082b080808191908, 0x082b0808082b0808, + 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, + 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, + 0x082b081908081908, 0x082b081908190808, 0x082b081919080808, + 0x082b081919082b08, 0x082b0819192b1919, 0x082b082b08080808, + 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, + 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, + 0x082b1908082b2b19, 0x082b190819080808, 0x082b191908080808, + 0x082b191919080819, 0x082b19191919082b, 0x082b19192b192b19, + 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, + 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, + 0x082b2b08082b0808, 0x082b2b0819191919, 0x082b2b082b082b08, + 0x082b2b082b2b082b, 0x082b2b19192b2b08, 0x082b2b192b190808, + 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, + 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, + 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, + 0x1908080808190808, 0x190808080819082b, 0x1908080808191919, + 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, + 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, + 0x1908080819082b08, 0x1908080819082b2b, 0x1908080819190819, + 0x1908080819191908, 0x19080808192b0808, 0x19080808192b1919, + 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, + 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, + 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, + 0x19080819082b0808, 0x1908081919080819, 0x1908081919081908, + 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, + 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, + 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b082b2b19, + 0x1908082b19080808, 0x1908190808080808, 0x190819080808082b, + 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, + 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, + 0x1908190819080819, 0x1908190819081908, 0x1908190819190808, + 0x190819082b080808, 0x190819082b191908, 0x1908191908080819, + 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, + 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, + 0x1908192b08082b2b, 0x1908192b19081908, 0x1908192b19190808, + 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808190808, + 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, + 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, + 0x19082b1919081908, 0x19082b1919190808, 0x19082b19192b2b19, + 0x19082b2b08081908, 0x1919080808080808, 0x191908080808082b, + 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, + 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, + 0x1919080819080819, 0x1919080819081908, 0x1919080819190808, + 0x191908082b080808, 0x1919081908080819, 0x1919081908081908, + 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, + 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, + 0x1919082b2b2b2b2b, 0x1919190808080819, 0x1919190808081908, + 0x1919190808190808, 0x19191908082b0819, 0x1919190819080808, + 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, + 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, + 0x191919192b082b08, 0x1919192b082b0819, 0x1919192b192b2b08, + 0x1919192b2b2b0819, 0x19192b0808080808, 0x19192b0808191908, + 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, + 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, + 0x19192b2b2b081919, 0x192b080808080819, 0x192b080808081908, + 0x192b080808190808, 0x192b080819080808, 0x192b080819191908, + 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, + 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, + 0x192b082b2b19082b, 0x192b190808080808, 0x192b19080819192b, + 0x192b191908190808, 0x192b191919080808, 0x192b191919081919, + 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, + 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, + 0x192b2b2b192b082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, + 0x2b0808082b080808, 0x2b0808082b08082b, 0x2b0808082b2b2b08, + 0x2b0808082b2b2b2b, 0x2b08081908080819, 0x2b08081908081908, + 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, + 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, + 0x2b08082b082b0808, 0x2b08082b2b080808, 0x2b08082b2b08082b, + 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, + 0x2b0819082b082b19, 0x2b08191908080808, 0x2b08191919081908, + 0x2b0819192b2b1919, 0x2b08192b08192b08, 0x2b08192b192b2b2b, + 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, + 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, + 0x2b082b082b2b2b08, 0x2b082b190808192b, 0x2b082b2b082b082b, + 0x2b082b2b2b080808, 0x2b082b2b2b082b08, 0x2b082b2b2b19192b, + 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, + 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, + 0x2b1908082b081908, 0x2b19081908080808, 0x2b190819082b082b, + 0x2b190819192b1908, 0x2b19082b1919192b, 0x2b19082b2b082b19, + 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, + 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, + 0x2b1919192b190808, 0x2b1919192b19082b, 0x2b19192b19080819, + 0x2b192b0819190819, 0x2b192b082b2b192b, 0x2b192b1919082b19, + 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, + 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, + 0x2b2b0808082b0808, 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, + 0x2b2b081919190819, 0x2b2b081919192b19, 0x2b2b08192b2b192b, + 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, + 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, + 0x2b2b190819080808, 0x2b2b19082b191919, 0x2b2b192b192b1919, + 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, 0x2b2b2b08082b0808, + 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, + 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, + 0x2b2b2b192b08192b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, + 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, + }); + +static dpct::global_memory iq3xxs_grid( + sycl::range<1>(256), + { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, + 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, + 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, 0x040c140c, 0x040c142c, + 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, + 0x04141c1c, 0x04141c3e, 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, + 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, 0x041c3e04, 0x04240c1c, + 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, + 0x043e0c24, 0x043e0c34, 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, + 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, 0x0c041c04, 0x0c041c14, + 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, + 0x0c14140c, 0x0c141c04, 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, + 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, 0x0c24042c, 0x0c242c04, + 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, + 0x14041414, 0x14041434, 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, + 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, 0x140c1c04, 0x140c341c, + 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, + 0x141c0c04, 0x141c0c24, 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, + 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, 0x143e040c, 0x143e041c, + 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, + 0x1c0c1404, 0x1c0c1c0c, 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, + 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, 0x1c1c0c0c, 0x1c1c1c1c, + 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, + 0x24040424, 0x24040c3e, 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, + 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, 0x24143404, 0x24143434, + 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, + 0x2c040c14, 0x2c04240c, 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, + 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, 0x2c1c0414, 0x2c1c2c1c, + 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, + 0x34043424, 0x340c140c, 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, + 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, 0x34341c1c, 0x343e041c, + 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, + 0x3e1c0404, 0x3e1c0c2c, 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, + 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, + }); + +static dpct::global_memory ksigns_iq2xs( + sycl::range<1>(128), + { + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, + 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, + 154, 27, 156, 29, 30, 159, 160, 33, 34, 163, 36, 165, 166, + 39, 40, 169, 170, 43, 172, 45, 46, 175, 48, 177, 178, 51, + 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, 192, + 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, + 78, 207, 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, + 219, 92, 221, 222, 95, 96, 225, 226, 99, 228, 101, 102, 231, + 232, 105, 106, 235, 108, 237, 238, 111, 240, 113, 114, 243, 116, + 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, + }); + +static dpct::global_memory + ksigns64(sycl::range<1>(128), + { + 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, + 0x000000000000ffff, 0xff00000000ff0000, 0x0000000000ff00ff, + 0x0000000000ffff00, 0xff00000000ffffff, 0xff000000ff000000, + 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, + 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, + 0x00000000ffffffff, 0xff0000ff00000000, 0x000000ff000000ff, + 0x000000ff0000ff00, 0xff0000ff0000ffff, 0x000000ff00ff0000, + 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, + 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, + 0x000000ffff00ffff, 0xff0000ffffff0000, 0x000000ffffff00ff, + 0x000000ffffffff00, 0xff0000ffffffffff, 0xff00ff0000000000, + 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, + 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, + 0x0000ff0000ffffff, 0x0000ff00ff000000, 0xff00ff00ff0000ff, + 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, 0xff00ff00ffff0000, + 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, + 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, + 0x0000ffff0000ffff, 0xff00ffff00ff0000, 0x0000ffff00ff00ff, + 0x0000ffff00ffff00, 0xff00ffff00ffffff, 0xff00ffffff000000, + 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, + 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, + 0x0000ffffffffffff, 0xffff000000000000, 0x00ff0000000000ff, + 0x00ff00000000ff00, 0xffff00000000ffff, 0x00ff000000ff0000, + 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, + 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, + 0x00ff0000ff00ffff, 0xffff0000ffff0000, 0x00ff0000ffff00ff, + 0x00ff0000ffffff00, 0xffff0000ffffffff, 0x00ff00ff00000000, + 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, + 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, + 0xffff00ff00ffffff, 0xffff00ffff000000, 0x00ff00ffff0000ff, + 0x00ff00ffff00ff00, 0xffff00ffff00ffff, 0x00ff00ffffff0000, + 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, + 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, + 0x00ffff000000ffff, 0xffffff0000ff0000, 0x00ffff0000ff00ff, + 0x00ffff0000ffff00, 0xffffff0000ffffff, 0xffffff00ff000000, + 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, + 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, + 0x00ffff00ffffffff, 0xffffffff00000000, 0x00ffffff000000ff, + 0x00ffffff0000ff00, 0xffffffff0000ffff, 0x00ffffff00ff0000, + 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, + 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, + 0x00ffffffff00ffff, 0xffffffffffff0000, 0x00ffffffffff00ff, + 0x00ffffffffffff00, 0xffffffffffffffff, + }); +//#endif + +static dpct::global_memory + kmask_iq2xs(sycl::range<1>(8), {1, 2, 4, 8, 16, 32, 64, 128}); + +template +static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xxs_grid_ptr, + const uint8_t *ksigns_iq2xs_ptr, + const uint8_t *kmask_iq2xs_ptr) { + + const int i = item_ct1.get_group(2); + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + +template +static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xs_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq2_xs * x = (const block_iq2_xs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + +template +static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * q3 = x[i].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } +#else + assert(false); +#endif + +} + /* DPCT1110:4: The total declared local variable size in device function dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register @@ -4446,13 +5257,16 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int tid = item_ct1.get_local_id(2) / + (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = item_ct1.get_local_id(2) % + (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3 const int offset = tid * K_QUANTS_PER_ITERATION; uint32_t uaux[2]; const uint8_t * d = (const uint8_t *)uaux; + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { const float * y = yy + i * QK_K + offset; @@ -4462,7 +5276,8 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, uaux[0] = s[0] & 0x0f0f0f0f; uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - const float2 dall = __half22float2(x[i].dm); + const sycl::float2 dall = + x[i].dm.convert(); float sum1 = 0, sum2 = 0; for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { @@ -4473,8 +5288,9 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, + y[l+48] * d[3] * ((ql >> 6) & 3); sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; } - tmp += dall.x * sum1 - dall.y * sum2; + tmp += dall.x() * sum1 - dall.y() * sum2; } + #endif // sum up partial sums and write back result @@ -4569,8 +5385,8 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 const int in = offset/8; // 0 or 1 const int im = offset%8; // 0...7 @@ -4719,8 +5535,8 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); const int step = tid * K_QUANTS_PER_ITERATION; @@ -4860,8 +5676,8 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, } #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); const int step = tid * K_QUANTS_PER_ITERATION; const int im = step/8; const int in = step%8; @@ -4969,8 +5785,8 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 + const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7 + const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3 const int step = tid * K_QUANTS_PER_ITERATION; @@ -5170,6 +5986,21 @@ static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y[iybs + iqs + y_offset] = v.y(); } +template +static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + const src_t * x = (src_t *) vx; + + y[i] = x[i]; +} + // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -6588,8 +7419,8 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const float dall = bq4_K->dm[0]; const float dmin = bq4_K->dm[1]; - const float d8_1 = __low2float(bq8_1[0].ds); - const float d8_2 = __low2float(bq8_1[1].ds); + const float d8_1 = bq8_1[0].ds[0]; + const float d8_2 = bq8_1[1].ds[1]; const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); @@ -6600,10 +7431,10 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const int v1 = q4[0]; const int v2 = q4[4]; - const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); - const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); + const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0)); + const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); + const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0)); + const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0)); sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); @@ -6772,8 +7603,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const float d = bq5_K->d; - const float d8_1 = __low2half(bq8_1[0].ds); - const float d8_2 = __low2half(bq8_1[1].ds); + const float d8_1 = bq8_1[0].ds[0]; + const float d8_2 = bq8_1[1].ds[1]; const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); @@ -6794,8 +7625,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); + const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1]) + + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]); return d * sumf_d; @@ -7051,6 +7882,150 @@ static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat( return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); } + +static __dpct_inline__ float +vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { +#if QK_K == 256 + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; + +#if QR2_XXS == 8 + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = q2[2] | (q2[3] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[aux32 & 127]; + for (int j = 0; j < 8; ++j) { + sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f; + return d * sumi; +#else + // iqs is 0...15 + const int ib32 = iqs/2; + const int il = iqs%2; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f; + const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; + const int8_t * q8 = bq8_1[ib32].qs + 16*il; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); + sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); + } + return d * (sumi1 + sumi2); +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __dpct_inline__ float +vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint64_t *iq2xs_grid, const uint64_t *ksigns64) { +#if DPCT_COMPATIBILITY_TEMP >= \ + MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; + + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = dpct::vectorized_binary( + grid[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid[1] ^ signs[1], signs[1], std::minus<>()); + sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = dpct::vectorized_binary( + grid[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid[1] ^ signs[1], signs[1], std::minus<>()); + sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __dpct_inline__ float +vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) { +#if DPCT_COMPATIBILITY_TEMP >= \ + MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + + const int ib32 = iqs; + const uint8_t * q3 = bq2->qs + 8*ib32; + const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = gas[0] | (gas[1] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; + const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; + const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); + const int grid_l = dpct::vectorized_binary( + grid1[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid2[0] ^ signs[1], signs[1], std::minus<>()); + sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + + template @@ -7632,7 +8607,8 @@ template static void template static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, - const sycl::nd_item<3> &item_ct1) { + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid_ptr, const uint64_t *ksigns64_ptr) { const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); @@ -7649,12 +8625,11 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = 0; i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row * blocks_per_row + i + - item_ct1.get_local_id(2) / (qi / vdr); // x block index + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index - const int iby = (i + item_ct1.get_local_id(2) / (qi / vdr)) * - (qk / QK8_1); // y block index that aligns with ibx + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iqs = vdr * @@ -7676,6 +8651,145 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ } } +template +static void mul_mat_vec_q_iq2_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xxs_grid_ptr, const uint8_t *ksigns_iq2xs_ptr, + const uint8_t *kmask_iq2xs_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid_ptr, ksigns_iq2xs_ptr, kmask_iq2xs_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void mul_mat_vec_q_iq2_xs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq2xs_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void mul_mat_vec_q_iq3_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3xxs_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + template static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows, const sycl::nd_item<3> &item_ct1) { @@ -9109,7 +10223,18 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q2_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q2_K(vx, y, item_ct1); + }); + } + #endif } @@ -9130,10 +10255,57 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q3_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q3_K(vx, y, item_ct1); + }); + } #endif } +template +static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_0(vx, y, nb32, item_ct1); + }); + } +} + +template +static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_1(vx, y, nb32, item_ct1); + }); + } +} + + template static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { @@ -9168,7 +10340,18 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q5_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q5_K(vx, y, item_ct1); + }); + } + #endif } @@ -9189,11 +10372,132 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, }); } #else - dequantize_block_q6_K<<>>(vx, y); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q6_K(vx, y, item_ct1); + }); + } + #endif } -static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) { + +template +static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq2xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xxs_grid_ptr_ct1 = iq2xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xxs( + vx, y, item_ct1, iq2xxs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq2xs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xs_grid_ptr_ct1 = iq2xs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xs( + vx, y, item_ct1, iq2xs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq3xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq3_xxs( + vx, y, item_ct1, iq3xxs_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void convert_unary_sycl(const void *__restrict__ vx, + dst_t *__restrict__ y, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>( + sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + convert_unary(vx, y, k, item_ct1); + }); + } +} + + +static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try { + int id; switch (type) { case GGML_TYPE_Q4_0: return dequantize_block_sycl; @@ -9215,19 +10519,30 @@ static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) { return dequantize_row_q5_K_sycl; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_sycl; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_sycl; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_sycl; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_sycl; case GGML_TYPE_F32: - return dequantize_block_sycl<1, 1, convert_f32>; + return convert_unary_sycl; default: return nullptr; } } +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_sycl; + return dequantize_row_q4_0_sycl; case GGML_TYPE_Q4_1: - return dequantize_block_sycl; + return dequantize_row_q4_1_sycl; case GGML_TYPE_Q5_0: return dequantize_block_sycl; case GGML_TYPE_Q5_1: @@ -9244,8 +10559,14 @@ static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { return dequantize_row_q5_K_sycl; case GGML_TYPE_Q6_K: return dequantize_row_q6_K_sycl; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_sycl; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_sycl; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_sycl; case GGML_TYPE_F16: - return dequantize_block_sycl<1, 1, convert_f16>; + return convert_unary_sycl; default: return nullptr; } @@ -9455,24 +10776,385 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, } } -template -static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, + +static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK8_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq2xxs_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + + stream->submit([&](sycl::handler &cgh) { + auto iq2xxs_grid_ptr_ct1 = iq2xxs_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq2_xxs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq2xxs_grid_ptr_ct1, ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, dpct::queue_ptr stream) { - GGML_ASSERT(ncols % QK4_0 == 0); - const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; - const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), [= - ](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq2xs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq2xs_grid_ptr_ct1 = iq2xs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq2_xs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq2xs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3xxs_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3xxs_grid_ptr_ct1 = iq3xxs_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq3_xxs_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } } + static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, @@ -11451,7 +13133,7 @@ struct sycl_pool_alloc { device_id = get_current_device_id(); device_index = g_sycl_gpu_mgr->get_index(device_id); ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); - // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); + // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); return ptr; } @@ -12242,63 +13924,46 @@ inline void ggml_sycl_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; - // TODO: support these quantization types - GGML_ASSERT(!(src0->type == GGML_TYPE_IQ2_XXS || - src0->type == GGML_TYPE_IQ2_XS || - src0->type == GGML_TYPE_IQ3_XXS || - src0->type == GGML_TYPE_IQ1_S)); - switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_1: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_1: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q8_0: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q2_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q3_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q4_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q5_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; case GGML_TYPE_Q6_K: - mul_mat_vec_q_sycl_submitter( - src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); - break; + mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ2_XXS: + mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ2_XS: + mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ3_XXS: + mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -12311,6 +13976,7 @@ inline void ggml_sycl_op_mul_mat_vec_q( (void) src1_padded_row_size; } + inline void ggml_sycl_op_dequantize_mul_mat_vec( const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, @@ -12318,10 +13984,11 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( const int64_t src1_ncols, const int64_t src1_padded_row_size, const dpct::queue_ptr &stream) { - GGML_TENSOR_BINARY_OP_LOCALS; - + const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + GGML_ASSERT(src1->type == GGML_TYPE_F32); + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_SYCL_F16 sycl_pool_alloc src1_dfloat_a; @@ -12333,15 +14000,10 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; if (src1_convert_f16) { - if (src1->type == GGML_TYPE_F16) { - src1_dfloat = (sycl::half *)src1->data + src1_padded_row_size; - } else { - src1_dfloat = src1_dfloat_a.alloc(ne00); - ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat, - ne00, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, - nb13, stream); - } + src1_dfloat = src1_dfloat_a.alloc(ne00); + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream); } #else const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion @@ -12495,7 +14157,7 @@ inline void ggml_sycl_op_mul_mat_sycl( *g_sycl_handles[id], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, - src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), + src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); } (void) dst; @@ -12923,7 +14585,7 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, // copy dst to host if necessary if (!dst_on_device) { SYCL_CHECK(CHECK_TRY_ERROR( - main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)))); + main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait())); } if (dst->backend == GGML_BACKEND_TYPE_CPU) { @@ -13200,7 +14862,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / - q8_1_bs))); + q8_1_bs).wait())); } else { float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; @@ -13294,7 +14956,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dhf_dst_i += src1_col_0*ne0; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(dhf_dst_i, dst_dd_i, - src1_ncols * ne0 * sizeof(float)))); + src1_ncols * ne0 * sizeof(float)).wait())); } } @@ -13852,13 +15514,13 @@ static __global__ void k_compute_batched_ptrs_id( src0_f16 = (half *) srcs_ar[i]; } else { src0_f16 = src0_as_f16; - if (threadIdx.x == 0 && threadIdx.y == 0) { + if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) { const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type); to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget); } } - int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2); int i12 = blockIdx.y * blockDim.y + threadIdx.y; if (i13 >= ne13 || i12 >= ne12) { @@ -14024,8 +15686,8 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, if (ids->backend == GGML_BACKEND_TYPE_GPU) { const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); + stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)).wait())); + // SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); } else { memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); } @@ -14095,7 +15757,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, - src1_original + i01 * nb11, nb11))); + src1_original + i01 * nb11, nb11).wait())); num_src1_rows++; } @@ -14128,7 +15790,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( dst_original + i01 * nb1, - dst_contiguous.get() + num_src1_rows * nb1, nb1))); + dst_contiguous.get() + num_src1_rows * nb1, nb1).wait())); num_src1_rows++; } } @@ -15522,7 +17184,7 @@ GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - (char *)tensor->data + offset, data, size))); + (char *)tensor->data + offset, data, size).wait())); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15538,7 +17200,7 @@ GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - data, (const char *)tensor->data + offset, size))); + data, (const char *)tensor->data + offset, size).wait())); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15557,7 +17219,7 @@ GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, was inserted. You need to rewrite this code. */ SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( - dst->data, src->data, ggml_nbytes(dst)))); + dst->data, src->data, ggml_nbytes(dst)).wait())); return true; } @@ -15647,20 +17309,12 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons if (a->ne[3] != b->ne[3]) { return false; } - - if (a->type == GGML_TYPE_IQ1_S) { - return false; - } - if (a->type == GGML_TYPE_IQ3_XXS) { - return false; - } - if (a->type == GGML_TYPE_IQ2_XXS) { - return false; - } - if (a->type == GGML_TYPE_IQ2_XS) { + ggml_type a_type = a->type; + if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || + a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { return false; } - return true; } break; case GGML_OP_GET_ROWS: @@ -15705,15 +17359,15 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons } return false; } break; - case GGML_OP_DUP: - case GGML_OP_REPEAT: case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; + case GGML_OP_DUP: case GGML_OP_NONE: case GGML_OP_RESHAPE: + case GGML_OP_REPEAT: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: From 61d1c88e155515dd03940913a5707ea84a8b119b Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Tue, 5 Mar 2024 13:33:42 +0100 Subject: [PATCH 180/401] Vulkan Improvements (#5835) * Improve dequant shaders, add fast q4_0 dequant * Optimize dmmv non-kquants for GCN Remove unnecessary SPIR-V shader duplication * Fix q4_0 dequant dispatch sizes Fix backend free bug * Optimize dequant shaders for q4_1, q5_0, q5_1 and q8_0 * Add unary and binary op shader templates * Fix Vulkan check results * Enable non-contiguous support for simple ops * Add argsort Basic q4_0 mmq shader and unit test * Speed up q4_0 dequant code, enable mmq for q4_0 * Rework matmul pipeline selection * Add soft_max alibi support * Add q4_1, q5_0, q5_1 and q8_0 dequant mat mat mul shaders * Add environment variable GGML_VK_FORCE_MAX_ALLOCATION_SIZE to limit max buffer size Rename GGML_VULKAN_DISABLE_F16 to GGML_VK_DISABLE_F16 for consistency --- ggml-vulkan-shaders.hpp | 82797 ++++++++++++++++------------------ ggml-vulkan.cpp | 2076 +- ggml-vulkan.h | 1 + ggml_vk_generate_shaders.py | 1171 +- llama.cpp | 4 +- 5 files changed, 41365 insertions(+), 44684 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e5e7a8414040b..1eca4d6e0c80a 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -2,1051 +2,2081 @@ unsigned char add_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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clamp_f32_len = 1448; +const uint64_t argsort_f32_len = 3752; -unsigned char cpy_f16_f16_data[] = { +unsigned char clamp_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x9f,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00, 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0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q5_0_len = 13952; +const uint64_t dequant_q5_0_len = 6644; unsigned char dequant_q5_1_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x95,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x55,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 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+0x3c,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0x34,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x01,0x00,0x00, +0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q8_0_len = 8868; +const uint64_t dequant_q8_0_len = 4780; unsigned char diag_mask_inf_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -7890,144 +6689,6 @@ unsigned char diag_mask_inf_f32_data[] = { }; const uint64_t diag_mask_inf_f32_len = 1480; -unsigned char f32_to_f16_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x13,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x47,0x00,0x00,0x00, -0x1a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x4a,0x00,0x00,0x00, -0x4b,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x2b,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x3e,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x73,0x00,0x04,0x00, -0x32,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, -0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0x31,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x31,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t f32_to_f16_len = 1596; - unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -9897,9 +8558,9 @@ unsigned char get_rows_q5_1_data[] = { 0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00, 0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x20,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x70,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x80,0x00,0x00,0x00, @@ -9971,68 +8632,66 @@ unsigned char get_rows_q5_1_data[] = { 0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x73,0x00,0x04,0x00, 0x1c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x41,0x00,0x07,0x00,0x69,0x00,0x00,0x00,0x6a,0x00,0x00,0x00, +0x41,0x00,0x07,0x00,0x67,0x00,0x00,0x00,0x68,0x00,0x00,0x00, 0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00, 0x12,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xc2,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6b,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0xc2,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00, 0x4a,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x6f,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00, -0x6f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, -0x10,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00, 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+0x5c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xb4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q5_1_len = 2796; +const uint64_t get_rows_q5_1_len = 2780; unsigned char get_rows_q5_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -10135,9 +8794,9 @@ unsigned char get_rows_q5_1_f32_data[] = { 0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00, 0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x20,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -10671,9 +9329,9 @@ unsigned char matmul_f16_aligned_l_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00, -0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x10,0x00,0x06,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, +0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00, +0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -10712,44 +9370,38 @@ unsigned char matmul_f16_aligned_l_data[] = { 0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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matmul_f16_aligned_s_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00, -0x58,0x01,0x00,0x00,0x53,0x02,0x00,0x00,0x9c,0x02,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00, 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+0x61,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x42,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00, +0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_m_fp32_len = 10464; +const uint64_t matmul_f16_len = 10316; -unsigned char matmul_f16_f32_aligned_s_data[] = { +unsigned char matmul_f16_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x09,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -20091,9 +10193,9 @@ unsigned char matmul_f16_f32_aligned_s_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8f,0x01,0x00,0x00, -0xc6,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x00,0x06,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00, +0x69,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -20132,44 +10234,44 @@ unsigned char matmul_f16_f32_aligned_s_data[] = { 0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00, +0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00, +0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00, 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-unsigned char matmul_f16_f32_aligned_s_fp32_data[] = { +unsigned char matmul_f16_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -21104,8 +11131,8 @@ unsigned char matmul_f16_f32_aligned_s_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00, -0x59,0x01,0x00,0x00,0x51,0x02,0x00,0x00,0x9a,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x43,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_s_fp32_len = 10464; +const uint64_t matmul_f16_f32_len = 10332; -unsigned char matmul_f16_f32_l_data[] = { +unsigned char matmul_f16_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x13,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -21981,9 +12859,9 @@ unsigned char matmul_f16_f32_l_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00, -0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x74,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -22022,38 +12900,44 @@ unsigned char matmul_f16_f32_l_data[] = { 0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 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= 11360; -unsigned char matmul_f16_f32_l_fp32_data[] = { +unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -22833,8 +13810,8 @@ unsigned char matmul_f16_f32_l_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, -0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00, 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-const uint64_t matmul_f16_f32_m_len = 10172; +const uint64_t matmul_f16_f32_fp32_len = 10260; -unsigned char matmul_f16_f32_m_fp32_data[] = { +unsigned char matmul_f16_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -24531,8 +15528,8 @@ unsigned char matmul_f16_f32_m_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, -0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00, +0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -24571,38 +15568,38 @@ unsigned char matmul_f16_f32_m_fp32_data[] = { 0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00, +0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00, +0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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= { +unsigned char matmul_q4_1_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xca,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, -0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_s_fp32_len = 10116; +const uint64_t matmul_q4_1_f32_aligned_len = 11224; -unsigned char matmul_f32_aligned_l_data[] = { +unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00, -0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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-const uint64_t matmul_f32_aligned_l_len = 12168; +const uint64_t matmul_q4_1_f32_aligned_fp32_len = 10476; -unsigned char matmul_f32_aligned_l_fp32_data[] = { +unsigned char matmul_q4_1_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, -0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, -0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, -0x46,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x4a,0x02,0x00,0x00, -0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xe5,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x81,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f32_aligned_m_len = 12168; +const uint64_t matmul_q5_0_f32_len = 11076; -unsigned char matmul_f32_aligned_m_fp32_data[] = { +unsigned char matmul_q5_0_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x62,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 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-const uint64_t matmul_f32_aligned_s_len = 12168; +const uint64_t matmul_q5_1_f32_len = 10956; -unsigned char matmul_f32_aligned_s_fp32_data[] = { +unsigned char matmul_q5_1_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x3a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, -0x46,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x4a,0x02,0x00,0x00, -0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, +0x81,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x97,0x02,0x00,0x00, +0xe0,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -37003,37 +31768,48 @@ unsigned char matmul_f32_aligned_s_fp32_data[] = { 0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00, +0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 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+const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452; + +unsigned char matmul_q8_0_f32_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00, +0x28,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x64,0x01,0x00,0x00, +0x4a,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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matmul_q8_0_f32_fp32_len = 10488; unsigned char mul_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x27,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x16,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00, +0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, 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+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xac,0x00,0x00,0x00, +0xab,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00, +0xad,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x28,0x00,0x00,0x00, +0xaa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xad,0x00,0x00,0x00, +0xac,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_f16_f32_len = 2372; +const uint64_t mul_mat_vec_f16_f32_len = 2776; unsigned char mul_mat_vec_nc_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -43707,7 +39152,7 @@ const uint64_t mul_mat_vec_p021_f16_f32_len = 2768; unsigned char mul_mat_vec_q2_K_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xdc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 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0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_q4_K_f32_len = 9176; +const uint64_t mul_mat_vec_q4_K_f32_len = 9128; unsigned char mul_mat_vec_q5_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00, -0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0xa5,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 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+0xbc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb1,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_q8_0_f32_len = 3120; +const uint64_t mul_mat_vec_q8_0_f32_len = 3124; unsigned char norm_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -50921,142 +46355,314 @@ unsigned char rope_neox_f32_data[] = { 0x9d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00, 0xef,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00, -0xf5,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, -0xb8,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x41,0x00,0x06,0x00, -0xa0,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x42,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, 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scale_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00, +0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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+0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, +0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00, +0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t sqr_f32_len = 1188; +const uint64_t sqr_f32_len = 3252; diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index bc316c3f3944d..5a1b3f4776181 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -69,6 +69,33 @@ struct vk_queue { vk::PipelineStageFlags stage_flags; }; +struct vk_pipeline_struct { + std::string name; + vk::ShaderModule shader_module; + vk::DescriptorSetLayout dsl; + std::vector descriptor_pools; + std::vector descriptor_sets; + uint32_t descriptor_set_idx; + vk::PipelineLayout layout; + vk::Pipeline pipeline; + uint32_t push_constant_size; + uint32_t parameter_count; + std::array wg_denoms; + uint32_t align; +}; + +typedef std::shared_ptr vk_pipeline; +typedef std::weak_ptr vk_pipeline_ref; + +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline); + +struct vk_matmul_pipeline_struct { + vk_pipeline l, m, s; + vk_pipeline a_l, a_m, a_s; +}; + +typedef std::shared_ptr vk_matmul_pipeline; + struct vk_device { vk::PhysicalDevice physical_device; vk::PhysicalDeviceProperties properties; @@ -84,10 +111,61 @@ struct vk_device { uint32_t subgroup_size; bool uma; + bool initialized; + size_t idx; + + vk_matmul_pipeline pipeline_matmul_f32; + vk_matmul_pipeline pipeline_matmul_f16; + vk_matmul_pipeline pipeline_matmul_f16_f32; + vk_pipeline pipeline_matmul_split_k_reduce; + + vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES]; + + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; + vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_add_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_sqr_f32; + vk_pipeline pipeline_clamp_f32; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_gelu_f32; + vk_pipeline pipeline_silu_f32; + vk_pipeline pipeline_relu_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; + vk_pipeline pipeline_argsort_f32; + + std::vector pipelines; + ~vk_device() { #ifdef GGML_VULKAN_DEBUG std::cerr << "destroy device " << name << std::endl; #endif + device.destroyCommandPool(compute_queue.pool); + if (!single_queue) { + device.destroyCommandPool(transfer_queue.pool); + } + + for (auto& pipeline : pipelines) { + if (pipeline.expired()) { + continue; + } + + vk_pipeline pl = pipeline.lock(); + ggml_vk_destroy_pipeline(device, pl); + } + pipelines.clear(); + device.destroy(); } }; @@ -125,21 +203,6 @@ struct vk_subbuffer { uint64_t size; }; -struct vk_pipeline { - std::string name; - vk::ShaderModule shader_module; - vk::DescriptorSetLayout dsl; - std::vector descriptor_pools; - std::vector descriptor_sets; - uint32_t descriptor_set_idx; - vk::PipelineLayout layout; - vk::Pipeline pipeline; - uint32_t push_constant_size; - uint32_t parameter_count; - std::array wg_denoms; - uint32_t align; -}; - struct vk_semaphore { vk::Semaphore s; uint64_t value; @@ -160,11 +223,21 @@ struct vk_op_push_constants { float param2; }; -struct vk_op_cpy_push_constants { +struct vk_op_unary_push_constants { uint32_t ne; - uint32_t ne00; uint32_t ne01; uint32_t nb00; uint32_t nb01; uint32_t nb02; - uint32_t ne10; uint32_t ne11; uint32_t nb10; uint32_t nb11; uint32_t nb12; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; uint32_t d_offset; + float param1; float param2; +}; + +struct vk_op_binary_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; + uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23; + uint32_t d_offset; + float param1; float param2; }; struct vk_op_diag_mask_push_constants { @@ -196,6 +269,22 @@ struct vk_op_rope_neox_push_constants { float inv_ndims; }; +struct vk_op_soft_max_push_constants { + uint32_t KX; + uint32_t KY; + uint32_t KZ; + float scale; + float max_bias; + float m0; + float m1; + uint32_t n_head_log2; +}; + +struct vk_op_argsort_push_constants { + uint32_t ncols; + bool ascending; +}; + // Allow pre-recording command buffers struct vk_staging_memcpy { vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} @@ -236,7 +325,6 @@ struct ggml_tensor_extra_gpu { }; struct ggml_vk_garbage_collector { - std::vector pipelines; std::vector tl_semaphores; std::vector semaphores; std::vector events; @@ -247,35 +335,7 @@ struct ggml_vk_garbage_collector { struct ggml_backend_vk_context { std::string name; - std::weak_ptr device; - vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s; - vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s; - vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s; - vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s; - vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s; - vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s; - vk_pipeline pipeline_matmul_split_k_reduce; - vk_pipeline pipeline_dequant[VK_NUM_TYPES]; - vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; - vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; - vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; - vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; - vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; - vk_pipeline pipeline_mul_f32; - vk_pipeline pipeline_add_f32; - vk_pipeline pipeline_scale_f32; - vk_pipeline pipeline_sqr_f32; - vk_pipeline pipeline_clamp_f32; - vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; - vk_pipeline pipeline_norm_f32; - vk_pipeline pipeline_rms_norm_f32; - vk_pipeline pipeline_gelu_f32; - vk_pipeline pipeline_silu_f32; - vk_pipeline pipeline_relu_f32; - vk_pipeline pipeline_diag_mask_inf_f32; - vk_pipeline pipeline_soft_max_f32; - vk_pipeline pipeline_rope_f32, pipeline_rope_f16; - vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; + std::shared_ptr device; size_t semaphore_idx, event_idx; ggml_vk_garbage_collector gc; @@ -304,13 +364,31 @@ struct vk_instance { std::vector device_indices; - std::shared_ptr devices[GGML_VK_MAX_DEVICES]; ggml_backend_t backends[GGML_VK_MAX_DEVICES]; ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES]; ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES]; bool initialized[GGML_VK_MAX_DEVICES]; }; +static std::shared_ptr ggml_vk_get_device(size_t idx) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl; +#endif + static std::weak_ptr devices[GGML_VK_MAX_DEVICES]; + + if (devices[idx].expired()) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "Initializing new vk_device" << std::endl; +#endif + std::shared_ptr device = std::make_shared(); + device->initialized = false; + devices[idx] = device; + return device; + } + + return devices[idx].lock(); +} + #ifdef GGML_VULKAN_CHECK_RESULTS static size_t vk_skip_checks; static size_t vk_output_tensor; @@ -334,14 +412,15 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - pipeline.name = name; - pipeline.parameter_count = parameter_count; - pipeline.push_constant_size = push_constant_size; - pipeline.wg_denoms = wg_denoms; - pipeline.align = align; + pipeline = std::make_shared(); + pipeline->name = name; + pipeline->parameter_count = parameter_count; + pipeline->push_constant_size = push_constant_size; + pipeline->wg_denoms = wg_denoms; + pipeline->align = align; vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info); + pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info); std::vector dsl_binding; std::vector dsl_binding_flags; @@ -355,49 +434,49 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& vk::PushConstantRange pcr( vk::ShaderStageFlagBits::eCompute, 0, - pipeline.push_constant_size + pipeline->push_constant_size ); vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info( {}, dsl_binding); descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info); + pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info); // Check if device supports multiple descriptors per pool - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { const uint32_t alloc_count = 2; // Try allocating multiple sets from one pool // This fails on AMD for some reason, so add a fall back to allocating one pool per set - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info); + vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline.dsl; + layouts[i] = pipeline->dsl; } try { vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); } catch(vk::OutOfPoolMemoryError const&) { - ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; } - ctx->device.lock()->device.destroyDescriptorPool(pool); + ctx->device->device.destroyDescriptorPool(pool); } - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); + pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info)); } - pipeline.descriptor_set_idx = 0; + pipeline->descriptor_set_idx = 0; - vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); - pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info); + vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr); + pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info); std::vector specialization_entries(specialization_constants.size()); @@ -417,72 +496,75 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( vk::PipelineShaderStageCreateFlags(), vk::ShaderStageFlagBits::eCompute, - pipeline.shader_module, + pipeline->shader_module, entrypoint.c_str(), &specialization_info); vk::ComputePipelineCreateInfo compute_pipeline_create_info( vk::PipelineCreateFlags(), pipeline_shader_create_info, - pipeline.layout); - pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + pipeline->layout); + pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - ctx->gc.pipelines.push_back(&pipeline); + ctx->device->pipelines.push_back(pipeline); } -static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) { +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl; +#endif for (auto& pool : pipeline->descriptor_pools) { - ctx->device.lock()->device.destroyDescriptorPool(pool); + device.destroyDescriptorPool(pool); } pipeline->descriptor_pools.clear(); pipeline->descriptor_sets.clear(); pipeline->descriptor_set_idx = 0; - ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl); + device.destroyDescriptorSetLayout(pipeline->dsl); - ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout); + device.destroyPipelineLayout(pipeline->layout); - ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module); + device.destroyShaderModule(pipeline->shader_module); - ctx->device.lock()->device.destroyPipeline(pipeline->pipeline); + device.destroyPipeline(pipeline->pipeline); } static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; + std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl; #endif - if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { + if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { // Enough descriptors are available return; } - if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); + if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size(); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline.dsl; + layouts[i] = pipeline->dsl; } - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data()); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end()); } else { - for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); + pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info)); - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); - std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline.descriptor_sets.push_back(sets[0]); + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl); + std::vector sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline->descriptor_sets.push_back(sets[0]); } } } static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl; + std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl; #endif - pipeline.descriptor_set_idx = 0; + pipeline->descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { @@ -498,7 +580,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx q.pool, vk::CommandBufferLevel::ePrimary, 1); - const std::vector cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info); + const std::vector cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info); auto buf = cmd_buffers.front(); q.cmd_buffers.push_back(buf); @@ -643,11 +725,11 @@ static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uin q.queue_family_index = queue_family_index; vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index); - q.pool = ctx->device.lock()->device.createCommandPool(command_pool_create_info_compute); + q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute); q.cmd_buffer_idx = 0; - q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index); + q.queue = ctx->device->device.getQueue(queue_family_index, queue_index); q.stage_flags = stage_flags; } @@ -671,7 +753,7 @@ static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); ctx->gc.semaphores.push_back({ semaphore, 0 }); return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; } @@ -684,7 +766,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); } return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; @@ -692,7 +774,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { if (ctx->event_idx >= ctx->gc.events.size()) { - ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({})); + ctx->gc.events.push_back(ctx->device->device.createEvent({})); } return ctx->gc.events[ctx->event_idx++]; } @@ -703,7 +785,7 @@ static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { #endif // Requires command buffers to be done - ctx->device.lock()->device.resetCommandPool(q.pool); + ctx->device->device.resetCommandPool(q.pool); q.cmd_buffer_idx = 0; } @@ -740,11 +822,11 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz nullptr, }; - buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info); + buf->buffer = ctx->device->device.createBuffer(buffer_create_info); - vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer); + vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer); - vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties(); + vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties(); uint32_t memory_type_index = UINT32_MAX; @@ -757,30 +839,30 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz } if (memory_type_index == UINT32_MAX) { - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.destroyBuffer(buf->buffer); buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } try { - buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index }); + buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index }); } catch (const vk::SystemError& e) { // Out of Host/Device memory, clean up buffer - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.destroyBuffer(buf->buffer); buf->size = 0; throw e; } buf->ptr = nullptr; if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); + buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } - ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); + ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); buf->ctx = ctx; - buf->device = ctx->device.lock(); + buf->device = ctx->device; #ifdef GGML_VULKAN_DEBUG std::cerr << "Created buffer " << buf->buffer << std::endl; @@ -802,7 +884,7 @@ static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { // Fall back to host memory type buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { @@ -883,10 +965,16 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; #endif + const std::shared_ptr device = ctx->device; + // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size }; - std::initializer_list warptile_s = { ctx->device.lock()->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + std::initializer_list warptile_s = { device->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + + std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + std::initializer_list warptile_mmq_s = { device->subgroup_size, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -896,126 +984,206 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { uint32_t m_align = 64; uint32_t s_align = 32; - if (ctx->device.lock()->fp16) { - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ctx->device->pipeline_matmul_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f16_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f16 = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared(); + ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared(); + + if (device->fp16) { + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); } else { - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - } - - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + } + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); // dequant shaders - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), { 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); // get_rows - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); } static void ggml_vk_print_gpu_info(size_t idx) { @@ -1057,8 +1225,8 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } - const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); + bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; @@ -1188,140 +1356,152 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { throw std::runtime_error("Device not found"); } - vk_instance.devices[idx] = std::make_shared(); - ctx->device = vk_instance.devices[idx]; - ctx->device.lock()->physical_device = devices[dev_num]; - const std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); + ctx->device = ggml_vk_get_device(idx); + if (!ctx->device->initialized) { + ctx->device->physical_device = devices[dev_num]; + const std::vector ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties(); - bool maintenance4_support = false; + bool maintenance4_support = false; - // Check if maintenance4 is supported - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { - maintenance4_support = true; + // Check if maintenance4 is supported + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { + maintenance4_support = true; + } } - } - vk::PhysicalDeviceProperties2 props2; - vk::PhysicalDeviceMaintenance3Properties props3; - vk::PhysicalDeviceMaintenance4Properties props4; - vk::PhysicalDeviceSubgroupProperties subgroup_props; - props2.pNext = &props3; - props3.pNext = &subgroup_props; - if (maintenance4_support) { - subgroup_props.pNext = &props4; - } - ctx->device.lock()->physical_device.getProperties2(&props2); - ctx->device.lock()->properties = props2.properties; + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceMaintenance4Properties props4; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + if (maintenance4_support) { + subgroup_props.pNext = &props4; + } + ctx->device->physical_device.getProperties2(&props2); + ctx->device->properties = props2.properties; - if (maintenance4_support) { - ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); - } else { - ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize; - } + const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE"); - ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID; - ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize; - ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) { + ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE); + } else if (maintenance4_support) { + ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + } else { + ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize; + } - bool fp16_storage = false; - bool fp16_compute = false; + ctx->device->vendor_id = ctx->device->properties.vendorID; + ctx->device->subgroup_size = subgroup_props.subgroupSize; + ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { - fp16_storage = true; - } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { - fp16_compute = true; + bool fp16_storage = false; + bool fp16_compute = false; + + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } } - } - const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); + const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; - ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - std::vector queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties(); + std::vector queue_family_props = ctx->device->physical_device.getQueueFamilyProperties(); - // Try to find a non-graphics compute queue and transfer-focused queues - const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); - const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); + // Try to find a non-graphics compute queue and transfer-focused queues + const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); + const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); - const float priorities[] = { 1.0f, 1.0f }; - ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + const float priorities[] = { 1.0f, 1.0f }; + ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; - std::vector device_queue_create_infos; - if (compute_queue_family_index != transfer_queue_family_index) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!ctx->device.lock()->single_queue) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); - } else { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - } - vk::DeviceCreateInfo device_create_info; - std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures(); + std::vector device_queue_create_infos; + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); + } else if(!ctx->device->single_queue) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + } + vk::DeviceCreateInfo device_create_info; + std::vector device_extensions; + vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures(); - VkPhysicalDeviceFeatures2 device_features2; - device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; - device_features2.pNext = nullptr; - device_features2.features = (VkPhysicalDeviceFeatures)device_features; + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; - VkPhysicalDeviceVulkan11Features vk11_features; - vk11_features.pNext = nullptr; - vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; - device_features2.pNext = &vk11_features; + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; - VkPhysicalDeviceVulkan12Features vk12_features; - vk12_features.pNext = nullptr; - vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; - vk11_features.pNext = &vk12_features; + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; - vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2); + vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2); - ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16; + ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16; - if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; - throw std::runtime_error("Unsupported device"); - } + if (!vk11_features.storageBuffer16BitAccess) { + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); + } - device_extensions.push_back("VK_KHR_16bit_storage"); + device_extensions.push_back("VK_KHR_16bit_storage"); #ifdef GGML_VULKAN_VALIDATE - device_extensions.push_back("VK_KHR_shader_non_semantic_info"); + device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif - if (ctx->device.lock()->fp16) { - device_extensions.push_back("VK_KHR_shader_float16_int8"); - } - ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data(); + if (ctx->device->fp16) { + device_extensions.push_back("VK_KHR_shader_float16_int8"); + } + ctx->device->name = ctx->device->properties.deviceName.data(); - device_create_info = { - vk::DeviceCreateFlags(), - device_queue_create_infos, - {}, - device_extensions - }; - device_create_info.setPNext(&device_features2); - ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info); + device_create_info = { + vk::DeviceCreateFlags(), + device_queue_create_infos, + {}, + device_extensions + }; + device_create_info.setPNext(&device_features2); + ctx->device->device = ctx->device->physical_device.createDevice(device_create_info); - ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; - // Shaders - ggml_vk_load_shaders(ctx); + // Queues + ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - // Queues - ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - if (!ctx->device.lock()->single_queue) { - const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); - } else { - // TODO: Use pointer or reference to avoid copy - ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue; + // Shaders + ggml_vk_load_shaders(ctx); + + if (!ctx->device->single_queue) { + const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; + ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + } else { + // TODO: Use pointer or reference to avoid copy + ctx->device->transfer_queue = ctx->device->compute_queue; + } + + ctx->device->idx = dev_num; + ctx->device->initialized = true; + } else if (ctx->device->idx != dev_num) { + std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl; + throw std::runtime_error("Device already initialized"); } - ctx->fence = ctx->device.lock()->device.createFence({}); + ctx->fence = ctx->device->device.createFence({}); ctx->compute_ctx = nullptr; ctx->transfer_ctx = nullptr; @@ -1339,7 +1519,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { #endif } -static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { +static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif @@ -1360,10 +1540,36 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type return nullptr; } - return &ctx->pipeline_dequant[type]; + return ctx->device->pipeline_dequant[type]; } -static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl; +#endif + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16; + } + + GGML_ASSERT(src1_type == GGML_TYPE_F32); + + switch (src0_type) { + case GGML_TYPE_Q4_0: + break; + default: + return nullptr; + } + + return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; +} + +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif @@ -1384,7 +1590,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - return &ctx->pipeline_dequant_mul_mat_vec_f32[type]; + return ctx->device->pipeline_dequant_mul_mat_vec_f32[type]; } static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { @@ -1463,8 +1669,8 @@ static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", size/1024.0/1024.0); - ctx->device.lock()->device.freeMemory(buf->device_memory); - ctx->device.lock()->device.destroyBuffer(buf->buffer); + ctx->device->device.freeMemory(buf->device_memory); + ctx->device->device.destroyBuffer(buf->buffer); return nullptr; } @@ -1528,30 +1734,30 @@ static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_ } static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { - const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); - const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); - const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); + const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); + const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); + const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; + std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; std::vector write_descriptor_sets; - GGML_ASSERT(pipeline.descriptor_set_idx < pipeline.descriptor_sets.size()); - GGML_ASSERT(buffers.size() == pipeline.parameter_count); - vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; - for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size()); + GGML_ASSERT(buffers.size() == pipeline->parameter_count); + vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++]; + for (uint32_t i = 0; i < pipeline->parameter_count; i++) { descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); } - for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + for (uint32_t i = 0; i < pipeline->parameter_count; i++) { write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); } - ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {}); + ctx->device->device.updateDescriptorSets(write_descriptor_sets, {}); - subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, - pipeline.layout, + pipeline->layout, 0, { descriptor_set }, {}); @@ -1810,7 +2016,7 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); } } else { - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true); ggml_vk_ctx_end(subctx); @@ -1820,8 +2026,9 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds } ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); } } @@ -1906,18 +2113,19 @@ static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, s memcpy(dst, (uint8_t *) src->ptr + offset, size); } else { - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); } } @@ -1941,15 +2149,13 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr // Copy within the device ggml_backend_vk_context * ctx = src->ctx; - VkBufferCopy bc{ src_offset, dst_offset, size }; - - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); } else { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; @@ -1977,14 +2183,14 @@ static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, // Make sure ctx owns the buffer GGML_ASSERT(dst->ctx == ctx); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, subctx); subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); } static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { @@ -2045,176 +2251,63 @@ static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * su static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; + std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " = 4" << std::endl; -#endif return 4; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " = 1" << std::endl; -#endif return 1; } -static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; -#endif +static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { if (m <= 32 || n <= 32) { - return ctx->pipeline_matmul_f32_aligned_s.align; + return aligned ? mmp->a_s : mmp->s; } - if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { - return ctx->pipeline_matmul_f32_aligned_m.align; - } - return ctx->pipeline_matmul_f32_aligned_l.align; + return aligned ? mmp->a_m : mmp->m; + + GGML_UNUSED(ctx); } -static vk_pipeline* ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { - if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } - if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } +static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { + return aligned ? mmp->a_m : mmp->m; - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; + GGML_UNUSED(ctx); } -static vk_pipeline* ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - if (bit16_x && bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } - if (bit16_x && !bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; -} +static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { + return aligned ? mmp->a_s : mmp->s; -static vk_pipeline* ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - if (bit16_x && bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } - if (bit16_x && !bit16_y) { - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); - } - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; + GGML_UNUSED(ctx); } -static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; + std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl; #endif - switch (ctx->device.lock()->vendor_id) { + switch (ctx->device->vendor_id) { case VK_VENDOR_ID_AMD: - return ggml_vk_guess_matmul_pipeline_amd(ctx, bit16_x, bit16_y, m, n, aligned); + return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned); case VK_VENDOR_ID_APPLE: - return ggml_vk_guess_matmul_pipeline_apple(ctx, bit16_x, bit16_y, aligned); + return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned); case VK_VENDOR_ID_INTEL: - return ggml_vk_guess_matmul_pipeline_intel(ctx, bit16_x, bit16_y, aligned); - } - - if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; - } - if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; - } - if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; - } - if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l; - } - if (!bit16_x && bit16_y) { - GGML_ASSERT(false); + return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned); } if (m <= 32 || n <= 32) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " S" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; + return aligned ? mmp->a_s : mmp->s; } if (m <= 64 || n <= 64) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << " M" << std::endl; -#endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; + return aligned ? mmp->a_m : mmp->m; } + return aligned ? mmp->a_l : mmp->l; +} + +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << " L" << std::endl; + std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif - return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l; + return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, false)->align; } static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { @@ -2232,10 +2325,10 @@ static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, v const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); ggml_vk_sync_buffers(subctx); const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -2245,41 +2338,39 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { +static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return &ctx->pipeline_cpy_f32_f32; + return ctx->device->pipeline_cpy_f32_f32; } if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return &ctx->pipeline_cpy_f32_f16; + return ctx->device->pipeline_cpy_f32_f16; } if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return &ctx->pipeline_cpy_f16_f16; + return ctx->device->pipeline_cpy_f16_f16; } std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; GGML_ASSERT(false); } -static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); - const int dst_type_size = ggml_type_size(buffer_type); - - const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; - const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; + const uint32_t ne = ggml_nelements(tensor); - const vk_op_cpy_push_constants pc = { + const vk_op_unary_push_constants pc = { (uint32_t)ne, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], 1 , (uint32_t)tensor->ne[0] , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]), 0, + 0.0f, 0.0f, }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 }); } static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -2319,7 +2410,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su bool src0_uma = false; bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; @@ -2332,10 +2423,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); - const bool f16_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - const bool qx_needs_dequant = src0->type != GGML_TYPE_F16 || x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); + + const bool qx_needs_dequant = mmp == nullptr || x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; + + if (mmp == nullptr) { + // Fall back to dequant + f16 mulmat + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16); + } // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT @@ -2344,17 +2442,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); const bool aligned = ne10 == kpad; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = sizeof(ggml_fp16_t) * x_ne; - const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -2385,7 +2483,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } else { d_X = d_Qx; x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); // NOLINT + GGML_ASSERT(qx_sz == x_sz); } if (qy_needs_dequant) { d_Y = ctx->prealloc_y; @@ -2396,8 +2494,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline * to_fp16_vk_0 = nullptr; - vk_pipeline * to_fp16_vk_1 = nullptr; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); @@ -2413,19 +2511,19 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1); } if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1); } if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (load_x || qx_needs_dequant) { if (load_x) { // copy data to device @@ -2434,13 +2532,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } if (qx_needs_dequant) { - const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; + const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } } if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } else if (load_y) { ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } @@ -2457,7 +2555,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } // compute - ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul(ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2505,7 +2603,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context bool src0_uma = false; bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; @@ -2527,9 +2625,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; @@ -2569,8 +2667,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline * to_fp16_vk_0 = nullptr; - vk_pipeline* to_fp16_vk_1 = nullptr; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); } @@ -2579,30 +2677,30 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); // Allocate descriptor sets if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } - ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13); if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type); + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (load_x) { // copy data to device ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); } if (y_non_contig) { GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } else if (load_y) { ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } @@ -2619,22 +2717,22 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_buffer_offset = (y_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t y_shader_offset = y_offset - y_buffer_offset; - const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_offset - d_buffer_offset; if (!y_non_contig && qy_needs_dequant) { - const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; + const std::vector pc = { (uint32_t)ne11, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(y_ne / 32) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)y_ne, 1, 1}); } // compute - const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; + const std::array pc = { (uint32_t)ne00, (uint32_t)(y_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type))}; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2680,7 +2778,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2691,7 +2789,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const uint64_t y_ne = ne10 * ne11 * ne12; const uint64_t d_ne = ne01 * ne11 * ne12; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; @@ -2710,12 +2808,12 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c } // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { @@ -2725,7 +2823,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2772,7 +2870,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con bool src1_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2803,12 +2901,12 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con } // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { @@ -2818,7 +2916,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2856,6 +2954,10 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, } } +// static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +// +// } + static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat const uint64_t ne0 = dst->ne[0]; @@ -2927,40 +3029,40 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_add_f32; + return ctx->device->pipeline_add_f32; } return nullptr; case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); if (dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_get_rows[src0->type]; + return ctx->device->pipeline_get_rows[src0->type]; } if (dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_get_rows_f32[src0->type]; + return ctx->device->pipeline_get_rows_f32[src0->type]; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_mul_f32; + return ctx->device->pipeline_mul_f32; } return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_scale_f32; + return ctx->device->pipeline_scale_f32; } return nullptr; case GGML_OP_SQR: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_sqr_f32; + return ctx->device->pipeline_sqr_f32; } return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_clamp_f32; + return ctx->device->pipeline_clamp_f32; } return nullptr; case GGML_OP_CPY: @@ -2969,29 +3071,29 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_norm_f32; + return ctx->device->pipeline_norm_f32; } return nullptr; case GGML_OP_RMS_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rms_norm_f32; + return ctx->device->pipeline_rms_norm_f32; } return nullptr; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_silu_f32; + return ctx->device->pipeline_silu_f32; } break; case GGML_UNARY_OP_GELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_gelu_f32; + return ctx->device->pipeline_gelu_f32; } break; case GGML_UNARY_OP_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_relu_f32; + return ctx->device->pipeline_relu_f32; } break; default: @@ -3000,12 +3102,12 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_DIAG_MASK_INF: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_diag_mask_inf_f32; + return ctx->device->pipeline_diag_mask_inf_f32; } return nullptr; case GGML_OP_SOFT_MAX: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_soft_max_f32; + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_soft_max_f32; } return nullptr; case GGML_OP_ROPE: @@ -3020,21 +3122,26 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rope_neox_f32; + return ctx->device->pipeline_rope_neox_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_rope_neox_f16; + return ctx->device->pipeline_rope_neox_f16; } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &ctx->pipeline_rope_f32; + return ctx->device->pipeline_rope_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &ctx->pipeline_rope_f16; + return ctx->device->pipeline_rope_f16; } } return nullptr; } + case GGML_OP_ARGSORT: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) { + return ctx->device->pipeline_argsort_f32; + } + return nullptr; default: return nullptr; } @@ -3050,17 +3157,19 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; } + if (src2 != nullptr) { + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + } std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; #endif GGML_ASSERT(!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))); // NOLINT GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT - GGML_ASSERT(src1 == nullptr || ggml_vk_dim01_contiguous(src1)); // NOLINT GGML_ASSERT(dst->extra != nullptr); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3077,7 +3186,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); + const bool use_src2 = src2 != nullptr; + const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3098,29 +3210,39 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; + vk_buffer d_Z = nullptr; + size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool src2_uma = false; - if (ctx->device.lock()->uma) { + if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); src0_uma = d_X != nullptr; if (use_src1) { ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } + if (use_src2) { + ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset); + src2_uma = d_Z != nullptr; + } } const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma; const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma; + const bool transfer_src2 = use_src2 && src2->backend != GGML_BACKEND_TYPE_GPU && !src2_uma; - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3131,7 +3253,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if (transfer_src0) { d_X = ctx->prealloc_qx; @@ -3148,6 +3270,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c GGML_ASSERT(d_Y != nullptr); } + GGML_ASSERT(!transfer_src2); + if (use_src2 && !src2_uma) { + d_Z = extra_src2->buffer_gpu.lock(); + z_buf_offset = extra_src2->offset; + GGML_ASSERT(d_Z != nullptr); + } + if (op == GGML_OP_CPY) { GGML_ASSERT(!transfer_src0); GGML_ASSERT(!transfer_src1); @@ -3175,7 +3304,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c // Single call if dimension 2 is contiguous if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); switch (dst->op) { case GGML_OP_NORM: @@ -3204,16 +3333,30 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } } - if (!use_src1 && op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer + if (op == GGML_OP_SOFT_MAX) { + // Empty src1 and src2 are possible on soft_max, but the shader needs buffers + vk_subbuffer subbuf_y; + if (use_src1) { + subbuf_y = { d_Y, y_buf_offset, y_sz }; + } else { + subbuf_y = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } + + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } + ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) { ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); @@ -3223,7 +3366,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); } } else { - ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03); + GGML_ASSERT(op != GGML_OP_SOFT_MAX); + + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: @@ -3248,16 +3393,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint32_t y_offset = y_sz * it_idx1; const uint32_t d_offset = d_sz * it_idx0; - if (!use_src1 && op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src1) { + if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -3269,69 +3410,141 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + op_params[0], 0.0f + }); } static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); } static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + op_params[0], op_params[1], + }); } static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - const int src0_type_size = ggml_type_size(src0->type); - const int dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, d_offset, + 0.0f, 0.0f, }); } static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); } static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); + + float scale = op_params[0]; + float max_bias = op_params[1]; + + const uint32_t ncols = (uint32_t)src0->ne[0]; + const uint32_t nrows_x = (uint32_t)ggml_nrows(src0); + const uint32_t nrows_y = (uint32_t)src0->ne[1]; + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { + ncols, + nrows_y, + src2 != nullptr ? (uint32_t)1 : (uint32_t)0, + scale, max_bias, + m0, m1, + n_head_log2, + }); } static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3357,12 +3570,17 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); } } +static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + int32_t * op_params = (int32_t *)dst->op_params; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC }); +} + static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device if (dst->backend == GGML_BACKEND_TYPE_CPU) { @@ -3414,43 +3632,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t const size_t y_ne = k * n * batch; const size_t d_ne = m * n * batch; - vk_pipeline * p; + vk_pipeline p; std::string shname; if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_s; + p = ctx->device->pipeline_matmul_f32->a_s; shname = "F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_s; + p = ctx->device->pipeline_matmul_f16_f32->a_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_s; + p = ctx->device->pipeline_matmul_f16->a_s; shname = "F16_ALIGNED_S"; } else { GGML_ASSERT(false); } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_m; + p = ctx->device->pipeline_matmul_f32->a_m; shname = "F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_m; + p = ctx->device->pipeline_matmul_f16_f32->a_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_m; + p = ctx->device->pipeline_matmul_f16->a_m; shname = "F16_ALIGNED_M"; } else { GGML_ASSERT(false); } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_aligned_l; + p = ctx->device->pipeline_matmul_f32->a_l; shname = "F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_aligned_l; + p = ctx->device->pipeline_matmul_f16_f32->a_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_aligned_l; + p = ctx->device->pipeline_matmul_f16->a_l; shname = "F16_ALIGNED_L"; } else { GGML_ASSERT(false); @@ -3464,43 +3682,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (k != kpad) { if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_s; + p = ctx->device->pipeline_matmul_f32->s; shname = "F32_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_s; + p = ctx->device->pipeline_matmul_f16_f32->s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_s; + p = ctx->device->pipeline_matmul_f16->s; shname = "F16_S"; } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_m; + p = ctx->device->pipeline_matmul_f32->m; shname = "F32_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_m; + p = ctx->device->pipeline_matmul_f16_f32->m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_m; + p = ctx->device->pipeline_matmul_f16->m; shname = "F16_M"; } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f32_l; + p = ctx->device->pipeline_matmul_f32->l; shname = "F32_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_f32_l; + p = ctx->device->pipeline_matmul_f16_f32->l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = &ctx->pipeline_matmul_f16_l; + p = ctx->device->pipeline_matmul_f16->l; shname = "F16_L"; } } } - ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it); if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer @@ -3530,9 +3748,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t } for (size_t i = 0; i < y_ne; i++) { if (std::is_same()) { - y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + y[i] = (i % k == i / k) ? 1.0f : 0.0f; } else if (std::is_same()) { - y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f); } else { GGML_ASSERT(false); } @@ -3541,17 +3761,17 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch); ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); for (size_t i = 0; i < num_it; i++) { ggml_vk_ctx_begin(ctx, subctx); - ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); ggml_vk_ctx_end(subctx); } auto begin = std::chrono::high_resolution_clock::now(); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double time = std::chrono::duration_cast(end-begin).count() / 1000.0; @@ -3630,6 +3850,8 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; std::cerr << "Actual result: " << std::endl << std::endl; ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b); std::cerr << "Expected result: " << std::endl << std::endl; ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); @@ -3655,15 +3877,15 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t free(d_chk); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue); ggml_vk_destroy_buffer(d_X); ggml_vk_destroy_buffer(d_Y); ggml_vk_destroy_buffer(d_D); - ggml_pipeline_cleanup(*p); - ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce); + ggml_pipeline_cleanup(p); + ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce); free(x); free(y); @@ -3736,7 +3958,7 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_ data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; } - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); @@ -3745,8 +3967,8 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_ ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor)); @@ -3818,7 +4040,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool x[i] = rand() / (float)RAND_MAX; } - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); auto begin = std::chrono::high_resolution_clock::now(); @@ -3832,8 +4054,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); @@ -3847,8 +4069,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); @@ -3879,89 +4101,118 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool } } -static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; -#endif - const size_t x_sz = sizeof(float) * ne; - const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; - const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); - float * x = (float *) malloc(x_sz); - void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); - ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); - - for (size_t i = 0; i < ne; i++) { - x[i] = rand() / (float)RAND_MAX; - } - +static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { std::vector hist_cur(1 << 4, 0); - vk_pipeline& p = ctx->pipeline_dequant[quant]; - switch(quant) { + case GGML_TYPE_F32: + memcpy(to, from, sizeof(float) * ne); + break; case GGML_TYPE_Q4_0: - ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q4_0(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q4_1: - ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q4_1(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q5_0: - ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q5_0(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q5_1: - ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q5_1(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q8_0: - ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q8_0(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q2_K: - ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q2_K(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q3_K: - ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q3_K(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q4_K: - ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q4_K(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q5_K: - ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q5_K(from, to, ne, ne, hist_cur.data()); break; case GGML_TYPE_Q6_K: - ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data()); + ggml_quantize_q6_K(from, to, ne, ne, hist_cur.data()); break; default: GGML_ASSERT(false); } +} + +static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; +#endif + const size_t x_sz = sizeof(float) * ne; + const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; + const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); + float * x = (float *) malloc(x_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + vk_pipeline p = ctx->device->pipeline_dequant[quant]; + + ggml_vk_quantize_data(x, qx, ne, quant); ggml_pipeline_allocate_descriptor_sets(ctx, p, 1); ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); - vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, subctx); - const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; + const std::vector pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne }; ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16); + int first_err = -1; + double avg_err = 0.0; for (size_t i = 0; i < ne; i++) { - avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + avg_err += error; + + if (first_err < 0 && error > 0.05) { + first_err = i; + } } - std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl; + avg_err /= ne; + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1) { + std::cerr << "first_error = " << first_err << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", "; + } + std::cerr << std::endl << "Expected result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << x[i] << ", "; + } + std::cerr << std::endl; + } ggml_vk_destroy_buffer(x_buf); ggml_vk_destroy_buffer(qx_buf); @@ -3970,6 +4221,190 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ free(qx); free(x_chk); } + +static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl; +#endif + const size_t x_ne = m * k * batch; + const size_t y_ne = k * n * batch; + const size_t d_ne = m * n * batch; + + vk_pipeline p; + std::string shname; + if (shader_size == 0) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; + } else if (shader_size == 1) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; + } else if (shader_size == 2) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; + } else { + GGML_ASSERT(0); + } + + const size_t kpad = ggml_vk_align_size(k, p->align); + + if (k != kpad) { + if (shader_size == 0) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s; + shname = std::string(ggml_type_name(quant)) + "_S"; + } else if (shader_size == 1) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m; + shname = std::string(ggml_type_name(quant)) + "_M"; + } else if (shader_size == 2) { + p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l; + shname = std::string(ggml_type_name(quant)) + "_L"; + } else { + GGML_ASSERT(0); + } + } + + const size_t x_sz = sizeof(float) * x_ne; + const size_t y_sz = sizeof(float) * y_ne; + const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant); + const size_t d_sz = sizeof(float) * d_ne; + float * x = (float *) malloc(x_sz); + float * y = (float *) malloc(y_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + float * d = (float *) malloc(d_sz); + float * d_chk = (float *) malloc(d_sz); + + for (size_t i = 0; i < x_ne; i++) { + x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + } + + ggml_vk_quantize_data(x, qx, x_ne, quant); + + for (size_t i = 0; i < y_ne; i++) { + // y[i] = rand() / (float)RAND_MAX; + y[i] = (i % k == i / k) ? 1.0f : 0.0f; + } + + ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it); + if (split_k > 1) { + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); + + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { + // Resize buffer + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + } + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + } + + ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz); + + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); + for (size_t i = 0; i < num_it; i++) { + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(subctx); + } + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double time_ms = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz); + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch); + ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch); + ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); + + src0_ggml->data = qx; + src1_ggml->data = y; + tensor_ggml->data = d_chk; + + ctx->disable = true; + + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph, tensor_ggml); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); + + ctx->disable = false; + + ggml_free(ggml_ctx); + + double avg_err = 0.0; + int first_err_n = -1; + int first_err_m = -1; + int first_err_b = -1; + + for (size_t i = 0; i < m*n*batch; i++) { + double err = std::fabs(d[i] - d_chk[i]); + avg_err += err; + + if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) { + first_err_b = i / (m * n); + first_err_n = (i % (m * n)) / m; + first_err_m = (i % (m * n)) % m; + } + } + + avg_err /= m * n; + + std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1 || std::isnan(avg_err)) { + std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + if (split_k > 1) { + float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + + std::cerr << "d_buf0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf2: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf3: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + free(split_k_buf); + } + } + + ggml_vk_destroy_buffer(qx_buf); + ggml_vk_destroy_buffer(y_buf); + ggml_vk_destroy_buffer(d_buf); + + free(x); + free(qx); + free(y); + free(d); + free(d_chk); +} #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { @@ -3982,18 +4417,8 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) return extra; } -static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph * graph) { - GGML_ASSERT(node != nullptr); - - for (int i = graph->n_nodes - 1; i >= 0; i--) { - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (graph->nodes[i]->src[j] == node) { - return graph->nodes[i]; - } - } - } - - return nullptr; +static bool ggml_vk_cpu_assist_op(const ggml_tensor * node) { + return node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID; } static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ @@ -4004,7 +4429,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU)); - if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node))) { return; } @@ -4035,7 +4460,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm const bool f16_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32; int split_k; - if (node->op == GGML_OP_MUL_MAT) { + if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); } else { split_k = 1; @@ -4044,11 +4469,11 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm const uint32_t y_ne = ne10 * ne11; const uint32_t d_ne = ne20 * ne21; - const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; if (extra->buffer_gpu.expired()) { @@ -4076,6 +4501,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: + case GGML_OP_ARGSORT: break; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { @@ -4088,6 +4514,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: if (ctx->prealloc_size_qx < qx_sz) { ctx->prealloc_size_qx = qx_sz; } @@ -4121,21 +4548,66 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { #endif #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); ggml_vk_test_transfer(ctx, 8192 * 1000, false); ggml_vk_test_transfer(ctx, 8192 * 1000, true); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K); + + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 0); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 1); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 2); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 0); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 1); + ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 2); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1); + + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0); + + std::cerr << std::endl; const std::vector vals { 8, 8, 8, @@ -4225,7 +4697,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU); - if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node)) || (ggml_vk_cpu_assist_op(node) && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; } @@ -4237,6 +4709,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; + const ggml_tensor * src2 = node->src[2]; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; @@ -4271,7 +4744,9 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: case GGML_OP_NONE: + case GGML_OP_ARGSORT: break; default: if (any_on_device) { @@ -4282,7 +4757,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } if (ctx->compute_ctx == nullptr) { - ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx, ctx->compute_ctx); } @@ -4353,16 +4828,25 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node); break; case GGML_OP_ROPE: ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_ARGSORT: + ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_MUL_MAT: ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_MUL_MAT_ID: + //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node); + std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl; + GGML_ASSERT(false); + break; default: return; @@ -4389,7 +4873,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); - if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(tensor))) { return false; } @@ -4415,6 +4899,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NONE: + case GGML_OP_ARGSORT: extra = (ggml_tensor_extra_gpu *) tensor->extra; break; @@ -4430,6 +4915,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: if (!any_on_device && !ggml_vk_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { return false; } @@ -4475,8 +4961,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } if (tensor == subctx.exit_tensor) { - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); // Do staging buffer copies for (auto& cpy : subctx.out_memcpys) { @@ -4504,20 +4990,25 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { } ctx->gc.temp_buffers.clear(); - for (auto * pipeline : ctx->gc.pipelines) { - ggml_pipeline_cleanup(*pipeline); + for (auto& pipeline : ctx->device->pipelines) { + if (pipeline.expired()) { + continue; + } + + vk_pipeline pl = pipeline.lock(); + ggml_pipeline_cleanup(pl); } - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); - ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue); for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { - ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s }); } ctx->gc.semaphores.clear(); for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { - ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); } ctx->gc.tl_semaphores.clear(); ctx->semaphore_idx = 0; @@ -4525,7 +5016,7 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { ctx->event_idx = 0; for (auto& event : ctx->gc.events) { - ctx->device.lock()->device.resetEvent(event); + ctx->device->device.resetEvent(event); } ctx->staging_offset = 0; @@ -4562,21 +5053,11 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ctx->staging_size = 0; for (auto& event : ctx->gc.events) { - ctx->device.lock()->device.destroyEvent(event); + ctx->device->device.destroyEvent(event); } ctx->gc.events.clear(); - for (auto* pipeline : ctx->gc.pipelines) { - ggml_vk_destroy_pipeline(ctx, pipeline); - } - ctx->gc.pipelines.clear(); - - ctx->device.lock()->device.destroyFence(ctx->fence); - - ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool); - if (!ctx->device.lock()->single_queue) { - ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool); - } + ctx->device->device.destroyFence(ctx->fence); } GGML_CALL static int ggml_vk_get_device_count() { @@ -4787,7 +5268,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_vk(src->buffer)) { - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; @@ -4799,6 +5279,8 @@ GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t bu return true; } return false; + + UNUSED(buffer); } GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -4845,12 +5327,12 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; + return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->ctx->device.lock()->max_memory_allocation_size; + return ctx->ctx->device->max_memory_allocation_size; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { @@ -4936,7 +5418,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu } GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment; + return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment; UNUSED(buft); } @@ -4981,8 +5463,7 @@ GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { ggml_vk_cleanup(ctx); - // Release device - vk_instance.devices[ctx->idx].reset(); + ctx->device.reset(); ctx->initialized = false; vk_instance.initialized[idx] = false; @@ -5011,7 +5492,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5032,7 +5513,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5052,7 +5533,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } @@ -5082,8 +5563,8 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { } ggml_vk_submit(ctx->transfer_ctx, ctx->fence); - VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - ctx->device.lock()->device.resetFences({ ctx->fence }); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); for (auto& cpy : ctx->transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); @@ -5153,6 +5634,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } break; case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: { struct ggml_tensor * a; struct ggml_tensor * b; @@ -5226,6 +5708,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + case GGML_OP_ARGSORT: return true; default: return false; @@ -5505,6 +5988,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -5516,13 +6000,16 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; + struct ggml_tensor * src2_clone = nullptr; struct ggml_tensor * tensor_clone = nullptr; size_t src0_size; size_t src1_size; + size_t src2_size; void * src0_buffer; void * src1_buffer; + void * src2_buffer; if (src0 != nullptr) { src0_clone = ggml_dup_tensor(ggml_ctx, src0); @@ -5536,12 +6023,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5552,7 +6039,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); if (offset + src0_size >= buffer_gpu->size) { src0_size = buffer_gpu->size - offset; } @@ -5581,12 +6067,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } else if (src1->backend == GGML_BACKEND_TYPE_GPU) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5597,7 +6083,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - vk_buffer buffer_gpu = extra->buffer_gpu.lock(); if (offset + src1_size >= buffer_gpu->size) { src1_size = buffer_gpu->size - offset; } @@ -5630,6 +6115,66 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); } + if (src2 != nullptr) { + src2_clone = ggml_dup_tensor(ggml_ctx, src2); + + src2_size = ggml_nbytes(src2); + + src2_buffer = malloc(src2_size); + src2_clone->data = src2_buffer; + if (src2->backend == GGML_BACKEND_TYPE_CPU) { + memcpy(src2_clone->data, src2->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (src2->backend == GGML_BACKEND_TYPE_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; + vk_buffer buf = extra->buffer_gpu.lock(); + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { + for (int i3 = 0; i3 < src2->ne[3]; i3++) { + for (int i2 = 0; i2 < src2->ne[2]; i2++) { + const int idx = i3*src2->ne[2] + i2; + ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); + } + } + + src2_clone->nb[0] = src2->nb[0]; + src2_clone->nb[1] = src2->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; + } + } else { + if (offset + src2_size >= buf->size) { + src2_size = buf->size - offset; + } + ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ASSERT(false); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(ctx, src2, "src2"); + std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; + if (src2->src[0] != nullptr) { + std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; + } + if (src2->src[1] != nullptr) { + std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(src2_clone, done); + } + + ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone); + } if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); @@ -5648,7 +6193,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { + if (src1 != nullptr) { + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); + } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5728,6 +6277,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { free(src1_buffer); } + if (src2 != nullptr) { + free(src1_buffer); + } ggml_free(ggml_ctx); } diff --git a/ggml-vulkan.h b/ggml-vulkan.h index 9645126b4f4a5..e4317c3e03481 100644 --- a/ggml-vulkan.h +++ b/ggml-vulkan.h @@ -10,6 +10,7 @@ extern "C" { #define GGML_VK_NAME "Vulkan" #define GGML_VK_MAX_DEVICES 16 +GGML_API void ggml_vk_instance_init(void); GGML_API void ggml_vk_init_cpu_assist(void); GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index b2e86e1821305..4a6f5e3231b84 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -64,6 +64,7 @@ #define A_TYPE block_q5_0 """ shader_q5_1_defines = """ +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require #define QUANT_K 32 #define QUANT_R 2 @@ -187,7 +188,8 @@ shader_q5_1_dequant_func = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ -const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ +const uint uint_qh = data_a[ib].qh; \ +const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ const uint vui = uint(data_a[ib].qs[iqs]); \ vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ v = v*d + m; @@ -206,12 +208,15 @@ #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require -#ifndef LOAD_VEC -#define LOAD_VEC 1 +#ifndef LOAD_VEC_A +#define LOAD_VEC_A 1 +#endif +#ifndef LOAD_VEC_B +#define LOAD_VEC_B 1 #endif """ -mulmat_body = """ +mulmat_body1 = """ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; @@ -240,7 +245,7 @@ layout (constant_id = 1) const uint BM = 64; layout (constant_id = 2) const uint BN = 64; -layout (constant_id = 3) const uint BK = 16; +layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant layout (constant_id = 4) const uint WM = 32; layout (constant_id = 5) const uint WN = 32; layout (constant_id = 6) const uint WMITER = 2; @@ -277,16 +282,19 @@ const uint tiwr = tiw % (WSUBM / TM); const uint tiwc = tiw / (WSUBM / TM); - const uint loadr = gl_LocalInvocationID.x % (BK / LOAD_VEC); - const uint loadc = gl_LocalInvocationID.x / (BK / LOAD_VEC); + const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A); + const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A); + const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B); + const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B); - const uint loadstride = gl_WorkGroupSize.x * LOAD_VEC / BK; + const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK; + const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK; const uint start_k = ik * p.k_split; const uint end_k = min(p.K, (ik + 1) * p.k_split); - uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC; - uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC; + uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; + uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; float sums[WMITER * TM * WNITER * TN]; FLOAT_TYPE cache_a[WMITER * TM]; @@ -297,61 +305,145 @@ } [[unroll]] for (uint block = start_k; block < end_k; block += BK) { - [[unroll]] for (uint l = 0; l < BM; l += loadstride) { -#if LOAD_VEC == 8 - const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx][0].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx][0].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx][0].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx][0].w); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_a[idx][1].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_a[idx][1].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_a[idx][1].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_a[idx][1].w); -#elif LOAD_VEC == 4 - const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx].x); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx].y); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx].z); - buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx].w); + [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {""" + +mulmat_load_scalar = """ +#if LOAD_VEC_A == 8 + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx][0].x); + buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y); + buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z); + buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w); + buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x); + buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y); + buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z); + buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w); +#elif LOAD_VEC_A == 4 + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx].x); + buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y); + buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z); + buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w); #else - if (ir * BM + loadc + l < p.M && block + loadr < end_k) { - buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_a[pos_a + (loadc + l) * p.stride_a + loadr]); + if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) { + buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]); } else { - buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f); } #endif +""" + +mulmat_load_q4_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q4_1 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q5_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q5_1 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; + + const uint ib = idx / 16; + const uint iqs = idx & 0xF; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint uint_qh = data_a[ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[ib].qs[iqs]); + const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" + +mulmat_load_q8_0 = """ + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; + + const uint ib = idx / 16; + const uint iqs = (idx & 0xF) * 2; + + const float d = float(data_a[ib].d); + const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d; + + buf_a[buf_idx ] = FLOAT_TYPE(v.x); + buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);""" + +mulmat_body2 = """ } - [[unroll]] for (uint l = 0; l < BN; l += loadstride) { -#if LOAD_VEC == 8 - const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx][0].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx][0].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx][0].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx][0].w); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_b[idx][1].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_b[idx][1].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_b[idx][1].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_b[idx][1].w); -#elif LOAD_VEC == 4 - const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx].x); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx].y); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx].z); - buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx].w); + [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { +#if LOAD_VEC_B == 8 + const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; + const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; + buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x); + buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y); + buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z); + buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w); + buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x); + buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y); + buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z); + buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w); +#elif LOAD_VEC_B == 4 + const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; + const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; + buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); + buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); + buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); + buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); #else - if (ic * BN + loadc + l < p.N && block + loadr < end_k) { - buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_b[pos_b + (loadc + l) * p.stride_b + loadr]); + if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); } else { - buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); } #endif } barrier(); - pos_a += BK / LOAD_VEC; - pos_b += BK / LOAD_VEC; + pos_a += BK / LOAD_VEC_A; + pos_b += BK / LOAD_VEC_B; for (uint i = 0; i < BK; i++) { // Load from shared into cache @@ -438,45 +530,191 @@ #extension GL_EXT_control_flow_attributes : require #extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint M; + uint K; + uint stride_a; + uint stride_b; + uint nel; +} p; """ -dequant_body = """ +dequant_f32_body = """ layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 0) readonly buffer A {float data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; +void main() { + const uint i = gl_GlobalInvocationID.x * 16; + + if (i >= p.nel) { + return; + } + + [[unroll]] for (uint l = 0; l < 16; l++) { + data_b[i + l] = D_TYPE(data_a[i + l]); + } +} +""" + +dequant_q4_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; void main() { - const int i = int(gl_GlobalInvocationID.x); + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float dm = -8.0f * d; - // Transposed - const int row = i % (p.K / QUANT_K); - const int col = i / (p.K / QUANT_K); + const uint q_idx = 8*il; - if (row * QUANT_K >= p.K || col >= p.M) { + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm); + data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + dm); + } +} +""" + +dequant_q4_1_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q4_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { return; } - const int stride_a = p.stride_a / QUANT_K; + const uint b_idx = 1024*i + 32*ir + 8*il; - const int ib = col * stride_a + row; + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); - const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - const int step = QUANT_R == 1 ? 2 : 1; + const uint q_idx = 8*il; - [[unroll]] for (int iqs = 0; iqs < QUANT_K/QUANT_R; iqs += step) { - DEQUANT_FUNC + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + m); + } +} +""" + +dequant_q5_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f)); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f)); + } +} +""" + +dequant_q5_1_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint qh = data_a[ib].qh; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10))) + m); + } +} +""" + +dequant_q8_0_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q8_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } - data_b[col * p.stride_b + row*QUANT_K + iqs + 0 ] = D_TYPE(v.x); - data_b[col * p.stride_b + row*QUANT_K + iqs + y_offset] = D_TYPE(v.y); + const uint b_idx = 1024*i + 32*ir + 16*il; + + const float d = float(data_a[ib].d); + + const uint q_idx = 16*il; + + [[unroll]] for (uint l = 0; l < 16; l += 2) { + data_b[b_idx + l ] = D_TYPE(d * data_a[ib].qs[q_idx + l ]); + data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]); } } """ @@ -488,29 +726,21 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; - const int y_idx = i * QUANT_K + 128 * ip + il; + const uint y_idx = i * QUANT_K + 128 * ip + il; - const int ql_idx = 32 * ip + il; + const uint ql_idx = 32 * ip + il; const uint8_t qs = data_a[i].qs[32 * ip + il]; FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); @@ -528,31 +758,23 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = uint(gl_WorkGroupID.x * 256 + wgy); if (i >= p.M * p.K / QUANT_K) { return; } - const int r = int(gl_LocalInvocationID.x) / 4; - const int tid = r / 2; - const int is0 = r % 2; - const int l0 = 16 * is0 + 4 * (int(gl_LocalInvocationID.x) % 4); - const int n = tid / 4; - const int j = tid - 4*n; + const uint r = gl_LocalInvocationID.x / 4; + const uint tid = r / 2; + const uint is0 = r % 2; + const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4); + const uint n = tid / 4; + const uint j = tid - 4*n; const uint8_t m = uint8_t(1 << (4*n + j)); - const int is = 8*n + 2*j + is0; - const int shift = 2*j; + const uint is = 8*n + 2*j + is0; + const uint shift = 2*j; const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : @@ -561,10 +783,10 @@ const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); - const int y_idx = i * QUANT_K + 128 * n + 32 * j; - const int qs_idx = 32*n; + const uint y_idx = i * QUANT_K + 128 * n + 32 * j; + const uint qs_idx = 32*n; - for (int l = l0; l < l0 + 4; ++l) { + for (uint l = l0; l < l0 + 4; ++l) { data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); } } @@ -576,32 +798,24 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int il = tid / 8; - const int ir = tid % 8; - const int is = 2 * il; - const int n = 4; + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 8; + const uint ir = tid % 8; + const uint is = 2 * il; + const uint n = 4; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - const int y_idx = i * QUANT_K + 64 * il + n * ir; - const int qs_idx = 32*il + n * ir; + const uint y_idx = i * QUANT_K + 64 * il + n * ir; + const uint qs_idx = 32*il + n * ir; uint8_t sc; uint8_t m; @@ -625,7 +839,7 @@ const FLOAT_TYPE d2 = dall * sc; const FLOAT_TYPE m2 = dmin * m; - [[unroll]] for (int l = 0; l < n; ++l) { + [[unroll]] for (uint l = 0; l < n; ++l) { data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1); data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >> 4) - m2); } @@ -638,32 +852,24 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int il = tid / 16; - const int ir = tid % 16; - const int is = 2 * il; + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 16; + const uint ir = tid % 16; + const uint is = 2 * il; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - const int y_idx = i * QUANT_K + 64 * il + 2 * ir; - const int qs_idx = 32*il + 2 * ir; - const int qh_idx = 2 * ir; + const uint y_idx = i * QUANT_K + 64 * il + 2 * ir; + const uint qs_idx = 32*il + 2 * ir; + const uint qh_idx = 2 * ir; uint8_t sc; uint8_t m; @@ -702,28 +908,20 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - void main() { - [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { - const int i = int(gl_WorkGroupID.x * 256 + wgy); + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; if (i >= p.M * p.K / QUANT_K) { return; } - const int tid = int(gl_LocalInvocationID.x); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; - const int y_idx = i * QUANT_K + 128 * ip + il; + const uint y_idx = i * QUANT_K + 128 * ip + il; - const int ql_idx = 64 * ip + il; + const uint ql_idx = 64 * ip + il; const uint8_t qh = data_a[i].qh[32 * ip + il]; const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); @@ -742,49 +940,50 @@ #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require #extension GL_EXT_shader_8bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ncols; + uint b_offset; + uint d_offset; +} p; """ mul_mat_vec_body = """ -layout(local_size_x = QUANT_K, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; +layout (constant_id = 0) const uint BLOCK_SIZE = 32; -shared FLOAT_TYPE tmp[QUANT_K]; +shared FLOAT_TYPE tmp[BLOCK_SIZE]; void main() { - const int block_size = int(gl_WorkGroupSize.x); - const int row = int(gl_WorkGroupID.x); - const int tid = int(gl_LocalInvocationID.x); + const uint row = gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; - const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; tmp[tid] = FLOAT_TYPE(0.0f); - [[unroll]] for (int i = 0; i < p.ncols/block_size; i += 2) { - const int col = i*block_size + 2*tid; - const int ib = (row*p.ncols + col)/QUANT_K; // block index - const int iqs = (col%QUANT_K)/QUANT_R; // quant index - const int iybs = col - col%QUANT_K; // y block start index + [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) { + const uint col = i*BLOCK_SIZE + 2*tid; + const uint ib = (row*p.ncols + col)/QUANT_K; // block index + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index DEQUANT_FUNC // matrix multiplication - tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]); - tmp[tid] += FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); + tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]) + + FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); } // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = block_size/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -804,38 +1003,31 @@ layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int s_offset = 8*v_im; - const int y_offset = 128*v_im + l0; + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint s_offset = 8*v_im; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -865,7 +1057,7 @@ // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -883,41 +1075,34 @@ layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 const uint8_t m = uint8_t(1 << (4 * v_im)); - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 128*v_im + l0; + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp const uint s_shift = 4 * v_im; - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -937,7 +1122,7 @@ // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -955,42 +1140,35 @@ layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - const int il = tid/step; // 0...3 - const int ir = tid - step*il; // 0...7 or 0...3 - const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 + const uint il = tid/step; // 0...3 + const uint ir = tid - step*il; // 0...7 or 0...3 + const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - const int v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int v_in = il % 2; + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; - const int l0 = n * (2 * ir + v_in); // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 64*v_im + l0; + const uint l0 = n * (2 * ir + v_in); // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y1_idx = i * QUANT_K + y_offset; - const int y2_idx = y1_idx + 128; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -1058,7 +1236,7 @@ // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1076,42 +1254,35 @@ layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/2; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%2; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - const int il = tid/4; // 0...3 - const int ir = tid - 4*il; // 0...7 or 0...3 + const uint il = tid/4; // 0...3 + const uint ir = tid - 4*il; // 0...7 or 0...3 - const int v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int v_in = il % 2; + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; - const int l0 = 4*ir + 2*v_in; // 0...15 - const int q_offset = 32*v_im + l0; - const int y_offset = 64*v_im + l0; + const uint l0 = 4*ir + 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; const uint8_t hm1 = uint8_t(1 << (2*v_im)); const uint8_t hm2 = uint8_t(hm1 << 4); tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += 2) { - const int y1_idx = i * QUANT_K + y_offset; - const int y2_idx = y1_idx + 128; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); @@ -1175,7 +1346,7 @@ // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1193,46 +1364,40 @@ layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; -layout (push_constant) uniform parameter -{ - int ncols; - int b_offset; - int d_offset; -} p; - shared FLOAT_TYPE tmp[32]; void main() { - const int row = int(gl_WorkGroupID.x); + const uint block_size = gl_WorkGroupSize.x; + const uint row = gl_WorkGroupID.x; - const int num_blocks_per_row = p.ncols / QUANT_K; - const int ib0 = row*num_blocks_per_row; + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = row*num_blocks_per_row; - const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 #if K_QUANTS_PER_ITERATION == 1 - const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const int is = 0; + const uint l0 = v_in; // 0...15 + const uint is = 0; #else - const int l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const int is = v_in / 4; + const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const uint is = v_in / 4; #endif - const int ql_offset = 64*v_im + l0; - const int qh_offset = 32*v_im + l0; - const int s_offset = 8*v_im + is; - const int y_offset = 128*v_im + l0; + const uint ql_offset = 64*v_im + l0; + const uint qh_offset = 32*v_im + l0; + const uint s_offset = 8*v_im + is; + const uint y_offset = 128*v_im + l0; tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const int y_idx = i * QUANT_K + y_offset; + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -1260,7 +1425,7 @@ // sum up partial sums and write back result barrier(); - [[unroll]] for (int s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } @@ -1421,34 +1586,6 @@ } """ -# F16 to F32 -f32_to_f16_src = """#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float16_t data_b[];}; - -layout (push_constant) uniform parameter -{ - int M; - int K; - int stride_a; - int stride_b; -} p; - -void main() { - const int row = int(gl_GlobalInvocationID.x % p.K); - const int col = int(gl_GlobalInvocationID.x / p.K); - - if (row < p.K && col < p.M) { - data_b[col * p.stride_b + row] = float16_t(data_a[col * p.stride_a + row]); - } -} -""" - generic_head = """ #version 450 @@ -1463,136 +1600,147 @@ } p; """ -# MUL F32 -mul_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; +generic_unary_op_head = """#version 450 - if (idx >= p.KX) { - return; - } +#extension GL_EXT_shader_16bit_storage : require - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(data_b[idx % p.KY])); -} -""" +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint d_offset; + float param1; float param2; +} p; -# ADD -add_body = """ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) + FLOAT_TYPE(data_b[idx % p.KY])); -} -""" - -# SCALE -scale_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { - return; - } - - data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); +uint src0_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; } -""" - -# SQR -sqr_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - if (idx >= p.KX) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); - data_d[idx] = D_TYPE(val * val); +uint dst_idx(uint idx) { + const uint i13 = idx / (p.ne12*p.ne11*p.ne10); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; } -""" - -# CLAMP -clamp_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.KX) { + if (gl_GlobalInvocationID.x >= p.ne) { return; } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); - data_d[idx] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); -} """ -# CPY -cpy_src = """#version 450 +generic_binary_op_head = """#version 450 #extension GL_EXT_shader_16bit_storage : require layout (push_constant) uniform parameter { uint ne; - uint ne00; uint ne01; uint nb00; uint nb01; uint nb02; - uint ne10; uint ne11; uint nb10; uint nb11; uint nb12; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; uint d_offset; + uint param1; uint param2; } p; layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 1) readonly buffer B {A_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +uint src0_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint src1_idx(uint idx) { + const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + + return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; +} + +uint dst_idx(uint idx) { + const uint i23 = idx / (p.ne22*p.ne21*p.ne20); + const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; + const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); + const uint i22_offset = i22*p.ne21*p.ne20; + const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; + const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; + return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; +} void main() { if (gl_GlobalInvocationID.x >= p.ne) { return; } +""" + +# MUL F32 +mul_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); +} +""" + +# ADD +add_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); +} +""" - const uint i02 = gl_GlobalInvocationID.x / (p.ne00*p.ne01); - const uint i01 = (gl_GlobalInvocationID.x - i02*p.ne01*p.ne00) / p.ne00; - const uint i00 = gl_GlobalInvocationID.x - i02*p.ne01*p.ne00 - i01*p.ne00; - const uint a_idx = i00*p.nb00 + i01*p.nb01 + i02*p.nb02; +# SCALE +scale_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(p.param1)); +} +""" - const uint i12 = gl_GlobalInvocationID.x / (p.ne10*p.ne11); - const uint i11 = (gl_GlobalInvocationID.x - i12*p.ne11*p.ne10) / p.ne10; - const uint i10 = gl_GlobalInvocationID.x - i12*p.ne11*p.ne10 - i11*p.ne10; - const uint d_idx = i10*p.nb10 + i11*p.nb11 + i12*p.nb12; +# SQR +sqr_body = """ + const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val); +} """ + +# CLAMP +clamp_body = """ + const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); +} +""" + +# CPY cpy_end = """ - data_d[p.d_offset + d_idx] = D_TYPE(data_a[a_idx]); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); } """ # Causes an optimization error otherwise cpy_f16_f16_end = """ - data_d[p.d_offset + d_idx] = data_a[a_idx]; + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)]; } """ @@ -1815,6 +1963,24 @@ """ # SOFT_MAX +soft_max_head = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint KZ; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; +} p; +""" + soft_max_body = """ #extension GL_EXT_control_flow_attributes : enable #define BLOCK_SIZE 512 @@ -1823,7 +1989,8 @@ layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; +layout (binding = 2) readonly buffer Z {C_TYPE data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; shared FLOAT_TYPE vals[BLOCK_SIZE]; @@ -1832,11 +1999,23 @@ const uint rowx = gl_WorkGroupID.x; const uint rowy = rowx % p.KY; + float slope = 0.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = rowx/p.KY; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + // Find max vals[tid] = uintBitsToFloat(0xFF800000); [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); + vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * data_c[col] : 0.0f)); } barrier(); @@ -1855,7 +2034,7 @@ [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); vals[tid] += val; data_d[i] = D_TYPE(val); } @@ -2028,6 +2207,65 @@ } """ +argsort_src = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) buffer D {int data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + bool ascending; +} p; + +void swap(uint idx0, uint idx1) { + int tmp = data_d[idx0]; + data_d[idx0] = data_d[idx1]; + data_d[idx1] = tmp; +} + +void main() { + // bitonic sort + const int col = int(gl_LocalInvocationID.x); + const uint row = gl_WorkGroupID.y; + + if (col >= p.ncols) { + return; + } + + const uint a_idx = row * p.ncols; + const uint d_idx = row * p.ncols; + + // initialize indices + if (col < p.ncols) { + data_d[col] = col; + } + barrier(); + + for (uint k = 2; k <= p.ncols; k *= 2) { + for (uint j = k / 2; j > 0; j /= 2) { + const uint ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) { + swap(d_idx + col, d_idx + ixj); + } + } else { + if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) { + swap(d_idx + col, d_idx + ixj); + } + } + } + barrier(); + } + } +} +""" + GLSLC = "glslc" VK_NUM_TYPES = 16 @@ -2129,6 +2367,8 @@ async def main(): tasks = [] + stream = [] + for fp16 in (False, True): # mulmat if fp16: @@ -2142,28 +2382,41 @@ async def main(): vec_type_f16 = "f16vec4" vec_type = "vec4" - stream = [] - stream.extend((mulmat_head, shader_float_type, mulmat_body)) - tasks.append(string_to_spv("matmul_f32_l", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_m", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_s", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - tasks.append(string_to_spv("matmul_f16_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - - tasks.append(string_to_spv("matmul_f16_f32_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + stream.clear() + stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) + tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2)) + tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2)) + tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + stream.clear() + stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2)) + tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) # Shaders where precision is needed, so no fp16 version @@ -2205,18 +2458,18 @@ async def main(): stream.extend((dequant_head, shader_int8_ext, shader_f32)) - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func, dequant_body)) + if i == GGML_TYPE_F32: + stream.append(dequant_f32_body) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body)) + stream.extend((shader_q4_0_defines, dequant_q4_0_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body)) + stream.extend((shader_q4_1_defines, dequant_q4_1_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body)) + stream.extend((shader_q5_0_defines, dequant_q5_0_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body)) + stream.extend((shader_q5_1_defines, dequant_q5_1_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body)) + stream.extend((shader_q8_0_defines, dequant_q8_0_body)) elif i == GGML_TYPE_Q2_K: stream.extend((shader_q2_K_defines, dequant_q2_K_body)) elif i == GGML_TYPE_Q3_K: @@ -2232,8 +2485,6 @@ async def main(): tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) - tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {})) - # get_rows for i in range(0, VK_NUM_TYPES): stream.clear() @@ -2264,20 +2515,20 @@ async def main(): tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_head}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_head}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_head}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("add_f32", f"{generic_binary_op_head}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) - tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_head}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_head}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_head}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_head}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) @@ -2285,7 +2536,7 @@ async def main(): tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) @@ -2293,6 +2544,8 @@ async def main(): tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"})) + # Helper to decorate tasks with semaphore acquisition. async def withSemaphore(sem, task): async with sem: diff --git a/llama.cpp b/llama.cpp index 76afcbc135f4c..e9192b4fa60dc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5014,8 +5014,8 @@ static struct ggml_tensor * llm_build_kqv( ggml_mul_mat_set_prec(kq, GGML_PREC_F32); } -#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) -#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, and Kompute") +#if defined(GGML_USE_KOMPUTE) +#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") #pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") if (hparams.f_max_alibi_bias > 0.0f) { From b1a4e994fde929300d4aeb1deb8320c59cb6edec Mon Sep 17 00:00:00 2001 From: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com> Date: Tue, 5 Mar 2024 14:44:29 +0100 Subject: [PATCH 181/401] grammars : don't allow to output unescaped new line in string (#5885) * Don't allow grammar json array to output unescaped new line in string * Don't allow new line in json object string --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a9537cdf9fbe4..34e014d55d685 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\n] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index ef53e77a0badd..fd5740eebc525 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\n] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From 82cb31eb93fd19b74115e0f0133225d1dfdbfdbc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 5 Mar 2024 15:56:24 +0200 Subject: [PATCH 182/401] Revert "grammars : don't allow to output unescaped new line in string (#5885)" This reverts commit b1a4e994fde929300d4aeb1deb8320c59cb6edec. --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index 34e014d55d685..a9537cdf9fbe4 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\\n] | + [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index fd5740eebc525..ef53e77a0badd 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\\n] | + [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From 3de31677d36aa4f82d4d99898902d7bcf398e666 Mon Sep 17 00:00:00 2001 From: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com> Date: Tue, 5 Mar 2024 17:33:08 +0100 Subject: [PATCH 183/401] grammars : blacklists character control set (#5888) * Prevent control characters from being served in json string * Prevent control characters from being served in json string (array) --- grammars/json.gbnf | 2 +- grammars/json_arr.gbnf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a9537cdf9fbe4..a8a80752e6562 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -15,7 +15,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\x7F\x00-\x1F] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index ef53e77a0badd..31a3202f863c1 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -24,7 +24,7 @@ array ::= string ::= "\"" ( - [^"\\] | + [^"\\\x7F\x00-\x1F] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes )* "\"" ws From bd836944f826f07e19b7edcf994a78728da49c1c Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 5 Mar 2024 11:56:37 -0500 Subject: [PATCH 184/401] quants : use MM256_SET_M128I consistently to fix gcc 7 build (#5889) --- ggml-quants.c | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 2a8881d73422d..e0c125d432300 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -51,6 +51,7 @@ #define UNUSED GGML_UNUSED +// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) @@ -9563,7 +9564,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); - const __m256i full_signs = _mm256_set_m128i(full_sign_bits, full_sign_bits); + const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits); const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); @@ -9585,8 +9586,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const __m256i sc1 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); + const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); + const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); @@ -9653,8 +9654,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); - const __m256i full_signs_1 = _mm256_set_m128i(full_signs_l, full_signs_l); - const __m256i full_signs_2 = _mm256_set_m128i(full_signs_h, full_signs_h); + const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); + const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); __m256i signs; signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); @@ -10551,10 +10552,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs); const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs); const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs); - const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); @@ -10661,10 +10662,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; From 652ca2bded3c818320d92c70d2b67f64bdbff5e5 Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 5 Mar 2024 22:27:29 +0100 Subject: [PATCH 185/401] compare-llama-bench.py : remove mul_mat_q (#5892) --- scripts/compare-llama-bench.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 39c3e52e52789..54a7771d897a2 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -18,7 +18,7 @@ KEY_PROPERTIES = [ "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", - "type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" + "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: From 8ced9f7e3225adb8501e9821ed1bbd92e3a5c7ae Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 6 Mar 2024 12:08:32 +0800 Subject: [PATCH 186/401] add wait() to make code stable (#5895) --- ci/run.sh | 3 ++- ggml-sycl.cpp | 59 +++++++++++++++++++++++++++++++++++++++------------ 2 files changed, 48 insertions(+), 14 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index 35eb3c7aa00ef..51f4c74cc2cf5 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -45,7 +45,8 @@ fi if [ ! -z ${GG_BUILD_SYCL} ]; then if [ -z ${ONEAPI_ROOT} ]; then - echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh" + echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:" + echo "source /opt/intel/oneapi/setvars.sh" exit 1 fi diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 477f5cb02db52..ddd951dd684a7 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3769,8 +3769,42 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo std::ofstream logfile; logfile.open(filename); for(size_t i=0; imemcpy(local_buf, src, total_size).wait(); + } + else { + local_buf = (sycl::half *)src; + } + + std::ofstream logfile; + logfile.open(filename); + for(size_t i=0; iwait(); const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); } @@ -14159,6 +14193,7 @@ inline void ggml_sycl_op_mul_mat_sycl( dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); + g_sycl_handles[id]->wait(); } (void) dst; (void) src1_ddq_i; @@ -15295,8 +15330,8 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, sycl_pool_alloc dst_f16; char * dst_t; - dpct::library_data_t cu_compute_type = dpct::library_data_t::real_half; - dpct::library_data_t cu_data_type = dpct::library_data_t::real_half; + dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float; + dpct::library_data_t cu_data_type = dpct::library_data_t::real_float; // dst strides size_t nbd2 = dst->nb[2]; @@ -15308,15 +15343,13 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const float alpha_f32 = 1.0f; const float beta_f32 = 0.0f; - const void * alpha = &alpha_f16; - const void * beta = &beta_f16; + const void * alpha = &alpha_f32; + const void * beta = &beta_f32; // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway - // once oneMKL open source supports half, half, float, float: datatypes - dst_t = (char *) dst_f16.alloc(ne_dst); + // oneMKL open source supports half, half, float, float: datatypes - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); + dst_t = (char *) dst_ddf; GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -15356,6 +15389,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb11 / nb10, nb12 / nb10, beta, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); + g_sycl_handles[g_main_device]->wait(); } else { const int ne23 = ne12*ne13; @@ -15386,7 +15420,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb02, nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1); }); - }); + }).wait(); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, @@ -15397,11 +15431,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); + g_sycl_handles[g_main_device]->wait(); } #endif - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ From 1e35d619a6fb0b9c5e3dc955345980ff056ddbaf Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 6 Mar 2024 09:12:25 +0200 Subject: [PATCH 187/401] convert : remove AWQ remnants (#5768) --- convert.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/convert.py b/convert.py index 6e3a0319b1e46..c15f8c47ea4f7 100755 --- a/convert.py +++ b/convert.py @@ -1377,7 +1377,6 @@ def main(args_in: list[str] | None = None) -> None: # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") - parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") @@ -1393,18 +1392,6 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") args = parser.parse_args(args_in) - if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights # type: ignore[import-not-found] - tmp_model_path = args.model / "weighted_model" - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") - else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") - args.model = tmp_model_path if args.dump_single: model_plus = lazy_load_file(args.model) From e25fb4b18fcedb9bed6be4585cf842e9a669b28b Mon Sep 17 00:00:00 2001 From: bobqianic <129547291+bobqianic@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:35:07 +0000 Subject: [PATCH 188/401] ggml : use `uint8x16_t` return type for `ggml_vqtbl1q_u8` (#5894) * use uint8x16_t * Update ggml-quants.c --- ggml-quants.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index e0c125d432300..9dcb76def46db 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -464,8 +464,8 @@ inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { } // NOTE: not tested -inline static int8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { - int8x16_t res; +inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { + uint8x16_t res; res[ 0] = a[b[ 0]]; res[ 1] = a[b[ 1]]; From e04e04f8fad549bb0b3ec1c91f0413aeb08baf29 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Wed, 6 Mar 2024 15:42:23 -0500 Subject: [PATCH 189/401] ggml : use SYS_get_cpu if SYS_getcpu is not defined (#5906) Fixes #5694 Fixes ggerganov/whisper.cpp#1894 --- ggml.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 6a10bbcb45e45..92b17ee6ed7bd 100644 --- a/ggml.c +++ b/ggml.c @@ -2154,7 +2154,10 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); #else // old glibc doesn't have a wrapper for this call. Fall back on direct syscall - getcpu_ret = syscall(SYS_getcpu,¤t_cpu,&g_state.numa.current_node); +# if !defined(SYS_getcpu) && defined(SYS_get_cpu) +# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name +# endif + getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); #endif if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { From ceca1aef0738b57951cd12c603c3477e75312dec Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Thu, 7 Mar 2024 16:34:31 +0800 Subject: [PATCH 190/401] [SYCL] fix error when set main gpu to non-zero (#5901) * fix error when set main gpu to non-zero * fix delete condition --- ggml-sycl.cpp | 154 ++++++++++++++++++++++++++++++-------------------- ggml-sycl.h | 1 + llama.cpp | 16 ++++-- 3 files changed, 107 insertions(+), 64 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ddd951dd684a7..221d67b8d7e96 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3559,12 +3559,31 @@ class sycl_gpu_mgr { int work_group_size = 0; std::string gpus_list = ""; + /* + Use all GPU with same top max compute units + */ sycl_gpu_mgr() { detect_sycl_gpu_list_with_max_cu(); get_allow_gpus(); create_context_with_gpus(); } + /* + Use the assigned GPU as only one + */ + sycl_gpu_mgr(int main_gpu_id) { + sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); + dpct::device_info prop; + dpct::get_device_info(prop, device); + gpus.push_back(main_gpu_id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + max_compute_units = prop.get_max_compute_units(); + + get_allow_gpus(); + create_context_with_gpus(); + } + void create_context_with_gpus() { sycl::context ctx = sycl::context(devices); assert(gpus.size() > 0); @@ -3580,7 +3599,7 @@ class sycl_gpu_mgr { gpus_list += std::to_string(gpus[i]); gpus_list += ","; } - if (gpus_list.length() > 2) { + if (gpus_list.length() > 1) { gpus_list.pop_back(); } } @@ -3629,8 +3648,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + printf("miss to get device index by id=%d\n", id); + GGML_ASSERT(false); } int get_next_index(int id) { @@ -3639,8 +3658,7 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + GGML_ASSERT(false); } }; @@ -3649,6 +3667,7 @@ static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; static int g_main_device_id = -1; +static bool g_ggml_backend_sycl_buffer_type_initialized = false; static std::array g_default_tensor_split = {}; @@ -13225,7 +13244,7 @@ void ggml_backend_sycl_print_sycl_devices() { } void print_gpu_device_list() { - fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", + fprintf(stderr, "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n", g_sycl_gpu_mgr->get_gpu_count(), g_sycl_gpu_mgr->gpus_list.c_str(), g_sycl_gpu_mgr->max_compute_units); @@ -13264,6 +13283,15 @@ void ggml_init_sycl() try { #else fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif + +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + if (CHECK_TRY_ERROR(g_all_sycl_device_count = dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; @@ -13272,68 +13300,61 @@ void ggml_init_sycl() try { } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); ggml_backend_sycl_print_sycl_devices(); - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + print_gpu_device_list(); + initialized = true; + g_sycl_loaded = true; + } - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); - g_work_group_size = g_sycl_gpu_mgr->work_group_size; - print_gpu_device_list(); - int64_t total_vram = 0; + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; - } + int64_t total_vram = 0; - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; + } - g_default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; - g_device_caps[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device_id)))); - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; - } + g_default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); + g_device_caps[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + } - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); - } + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } + + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); } - initialized = true; - g_sycl_loaded = true; + const dpct::queue_ptr stream = g_syclStreams[i][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } } catch (sycl::exception const &exc) { @@ -16732,22 +16753,24 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { + if (device_index>=g_device_count or device_index<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device_index, g_device_count-1); + GGML_ASSERT(device_indexgpus[i])}, }; } - ggml_backend_sycl_buffer_type_initialized = true; + g_ggml_backend_sycl_buffer_type_initialized = true; } - - return &ggml_backend_sycl_buffer_types[device]; + return &ggml_backend_sycl_buffer_types[device_index]; } // sycl split buffer type @@ -17496,6 +17519,17 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { return g_sycl_gpu_mgr->get_index(device_id); } +GGML_API GGML_CALL void ggml_backend_sycl_set_single_device(int main_gpu_id) { + GGML_ASSERT(main_gpu_idbackends.push_back(backend); } else { // LLAMA_SPLIT_LAYER requires a backend for each GPU - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - int device_id = id_list[i]; ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, id_list[i], i); llama_free(ctx); return nullptr; } From 2002bc96bf2cbf5ab981a17d7e994d817c9801f5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 7 Mar 2024 11:41:53 +0200 Subject: [PATCH 191/401] server : refactor (#5882) * server : refactoring (wip) * server : remove llava/clip objects from build * server : fix empty prompt handling + all slots idle logic * server : normalize id vars * server : code style * server : simplify model chat template validation * server : code style * server : minor * llama : llama_chat_apply_template support null buf * server : do not process embedding requests when disabled * server : reorganize structs and enums + naming fixes * server : merge oai.hpp in utils.hpp * server : refactor system prompt update at start * server : disable cached prompts with self-extend * server : do not process more than n_batch tokens per iter * server: tests: embeddings use a real embeddings model (#5908) * server, tests : bump batch to fit 1 embedding prompt * server: tests: embeddings fix build type Debug is randomly failing (#5911) * server: tests: embeddings, use different KV Cache size * server: tests: embeddings, fixed prompt do not exceed n_batch, increase embedding timeout, reduce number of concurrent embeddings * server: tests: embeddings, no need to wait for server idle as it can timout * server: refactor: clean up http code (#5912) * server : avoid n_available var ggml-ci * server: refactor: better http codes * server : simplify json parsing + add comment about t_last * server : rename server structs * server : allow to override FQDN in tests ggml-ci * server : add comments --------- Co-authored-by: Pierrick Hymbert --- .github/workflows/server.yml | 3 +- Makefile | 5 +- examples/server-embd.py | 2 +- examples/server/CMakeLists.txt | 4 +- examples/server/README.md | 2 +- examples/server/oai.hpp | 225 - examples/server/server.cpp | 3771 ++++++++--------- .../server/tests/features/embeddings.feature | 94 + .../server/tests/features/parallel.feature | 46 - examples/server/tests/features/server.feature | 28 - examples/server/tests/features/steps/steps.py | 86 +- examples/server/tests/requirements.txt | 1 + examples/server/utils.hpp | 703 ++- llama.cpp | 6 +- 14 files changed, 2265 insertions(+), 2711 deletions(-) delete mode 100644 examples/server/oai.hpp create mode 100644 examples/server/tests/features/embeddings.feature diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 04e3fc0c17cf9..f9aeefaa847ed 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -58,7 +58,8 @@ jobs: cmake \ python3-pip \ wget \ - psmisc + psmisc \ + language-pack-en - name: Build id: cmake_build diff --git a/Makefile b/Makefile index 4f26c0463fcd8..efce10bb8bd7e 100644 --- a/Makefile +++ b/Makefile @@ -724,10 +724,9 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) + $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/examples/server-embd.py b/examples/server-embd.py index c5c4ea87b09fc..118e042716c02 100644 --- a/examples/server-embd.py +++ b/examples/server-embd.py @@ -13,7 +13,7 @@ async def main(): model_url = "http://127.0.0.1:6900" responses: list[requests.Response] = await asyncio.gather(*[requests_post_async( url= f"{model_url}/embedding", - json= {"content": str(i)*1024} + json= {"content": str(0)*1024} ) for i in range(n)]) for response in responses: diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index cc13b2d630652..c21eba634310b 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,12 +1,12 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h) +add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h) install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 21da7a0a04e23..591f748f84cd7 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -436,7 +436,7 @@ Notice that each `probs` is an array of length `n_probs`. "next_token": { "has_next_token": true, "n_remain": -1, - "num_tokens_predicted": 0, + "n_decoded": 0, "stopped_eos": false, "stopped_limit": false, "stopped_word": false, diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp deleted file mode 100644 index ff4ad69943552..0000000000000 --- a/examples/server/oai.hpp +++ /dev/null @@ -1,225 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include - -#include "json.hpp" -#include "utils.hpp" - -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" - -using json = nlohmann::json; - -inline static json oaicompat_completion_params_parse( - const struct llama_model * model, - const json &body, /* openai api json semantics */ - const std::string &chat_template) -{ - json llama_params; - - llama_params["__oaicompat"] = true; - - // Map OpenAI parameters to llama.cpp parameters - // - // For parameters that are defined by the OpenAI documentation (e.g. - // temperature), we explicitly specify OpenAI's intended default; we - // need to do that because sometimes OpenAI disagrees with llama.cpp - // - // https://platform.openai.com/docs/api-reference/chat/create - llama_sampling_params default_sparams; - llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); - llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.0); - llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); - llama_params["top_p"] = json_value(body, "top_p", 1.0); - llama_params["n_predict"] = json_value(body, "max_tokens", -1); - llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); - llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); - llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); - llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); - - if (body.count("grammar") != 0) { - llama_params["grammar"] = json_value(body, "grammar", json::object()); - } - - // Handle 'stop' field - if (body.contains("stop") && body["stop"].is_string()) { - llama_params["stop"] = json::array({body["stop"].get()}); - } else { - llama_params["stop"] = json_value(body, "stop", json::array()); - } - - // Ensure there is ChatML-specific end sequence among stop words - llama_params["stop"].push_back("<|im_end|>"); - - return llama_params; -} - -inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) -{ - json result = response.result_json; - - bool stopped_word = result.count("stopped_word") != 0; - bool stopped_eos = json_value(result, "stopped_eos", false); - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason = "length"; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - - json choices = - streaming ? json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}) - : json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"message", json{{"content", content}, - {"role", "assistant"}}}}}); - - std::time_t t = std::time(0); - - json res = - json{{"choices", choices}, - {"created", t}, - {"model", - json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, - {"usage", - json{{"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, - {"id", gen_chatcmplid()}}; - - if (server_verbose) { - res["__verbose"] = result; - } - - if (result.contains("completion_probabilities")) { - res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); - } - - return res; -} - -// return value is vector as there is one case where we might need to generate two responses -inline static std::vector format_partial_response_oaicompat(const task_result &response) { - json result = response.result_json; - - if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { - return std::vector({response.result_json}); - } - - bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; - std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - - bool stopped_word = json_value(result, "stopped_word", false); - bool stopped_eos = json_value(result, "stopped_eos", false); - bool stopped_limit = json_value(result, "stopped_limit", false); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - if (stopped_limit) { - finish_reason = "length"; - } - - std::time_t t = std::time(0); - - json choices; - - if (!finish_reason.empty()) { - choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); - } else { - if (first) { - if (content.empty()) { - choices = json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{{"role", "assistant"}}}}}); - } else { - // We have to send this as two updates to conform to openai behavior - json initial_ret = json{{"choices", json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"role", "assistant"} - }}}})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - json second_ret = json{ - {"choices", json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"content", content}}} - }})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({initial_ret, second_ret}); - } - } else { - // Some idiosyncrasy in task processing logic makes several trailing calls - // with empty content, we ignore these at the calee site. - if (content.empty()) { - return std::vector({json::object()}); - } - - choices = json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", - json{ - {"content", content}, - }}, - }}); - } - } - - json ret = json{{"choices", choices}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({ret}); -} - -inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings) -{ - json res = - json{ - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", - json{{"prompt_tokens", 0}, - {"total_tokens", 0}}}, - {"data", embeddings} - }; - return res; -} - diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8fe5e0b19668f..3bdbde954b383 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,13 +1,8 @@ +#include "utils.hpp" + #include "common.h" #include "llama.h" #include "grammar-parser.h" -#include "utils.hpp" -#include "oai.hpp" - -#include "../llava/clip.h" -#include "../llava/llava.h" - -#include "stb_image.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -24,46 +19,76 @@ #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" -#include -#include +#include #include #include -#include +#include +#include +#include +#include #include using json = nlohmann::json; -struct server_params { - std::string hostname = "127.0.0.1"; - std::vector api_keys; - std::string public_path = "examples/server/public"; - std::string chat_template = ""; - int32_t port = 8080; - int32_t read_timeout = 600; - int32_t write_timeout = 600; - bool slots_endpoint = true; - bool metrics_endpoint = false; - int n_threads_http = -1; -}; - bool server_verbose = false; bool server_log_json = true; enum stop_type { - STOP_FULL, - STOP_PARTIAL, + STOP_TYPE_FULL, + STOP_TYPE_PARTIAL, }; -// TODO: can become bool if we can't find use of more states enum slot_state { - IDLE, - PROCESSING, + SLOT_STATE_IDLE, + SLOT_STATE_PROCESSING, }; enum slot_command { - NONE, - LOAD_PROMPT, - RELEASE, + SLOT_COMMAND_NONE, + SLOT_COMMAND_LOAD_PROMPT, + SLOT_COMMAND_RELEASE, +}; + +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded + SERVER_STATE_ERROR // An error occurred, load_model failed +}; + +enum server_task_type { + SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_CANCEL, + SERVER_TASK_TYPE_NEXT_RESPONSE, + SERVER_TASK_TYPE_METRICS +}; + +struct server_task { + int id = -1; // to be filled by server_queue + int id_multi = -1; + int id_target = -1; + + server_task_type type; + json data; + + bool infill = false; + bool embedding = false; +}; + +struct server_task_result { + int id = -1; + int id_multi = -1; + + json data; + + bool stop; + bool error; +}; + +struct server_task_multi { + int id = -1; + + std::set subtasks_remaining; + std::vector results; }; struct slot_params { @@ -80,26 +105,32 @@ struct slot_params { json input_suffix; }; -struct slot_image { - int32_t id; +struct server_params { + int32_t port = 8080; + int32_t read_timeout = 600; + int32_t write_timeout = 600; + int32_t n_threads_http = -1; - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; + std::string hostname = "127.0.0.1"; + std::string public_path = "examples/server/public"; + std::string chat_template = ""; + std::string system_prompt = ""; - clip_image_u8 * img_data; + std::vector api_keys; - std::string prefix_prompt; // before of this image + bool slots_endpoint = true; + bool metrics_endpoint = false; }; struct server_slot { int id; - int task_id = -1; + int id_task = -1; + int id_multi = -1; struct slot_params params; - slot_state state = IDLE; - slot_command command = NONE; + slot_state state = SLOT_STATE_IDLE; + slot_command command = SLOT_COMMAND_NONE; // used to determine the slot that has been used the longest int64_t t_last_used = -1; @@ -116,27 +147,31 @@ struct server_slot { int32_t n_prompt_tokens_processed = 0; json prompt; + + // when a task is submitted, we first tokenize the prompt and store it here + std::vector prompt_tokens; + std::string generated_text; - llama_token sampled; std::vector cache_tokens; std::vector generated_token_probs; - bool infill = false; - bool embedding = false; + bool infill = false; + bool embedding = false; bool has_next_token = true; - bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; + bool truncated = false; + bool stopped_eos = false; + bool stopped_word = false; + bool stopped_limit = false; bool oaicompat = false; - std::string oaicompat_model; + std::string oaicompat_model; std::string stopping_word; // sampling + llama_token sampled; struct llama_sampling_params sparams; - llama_sampling_context *ctx_sampling = nullptr; + llama_sampling_context * ctx_sampling = nullptr; int32_t ga_i = 0; // group-attention state int32_t ga_n = 1; // group-attention factor @@ -144,48 +179,32 @@ struct server_slot { int32_t n_past_se = 0; // self-extend - // multimodal - std::vector images; - // stats size_t n_sent_text = 0; // number of sent text character size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; - int64_t t_start_genereration; + int64_t t_start_generation; double t_prompt_processing; // ms double t_token_generation; // ms - // multitasks - int multitask_id = -1; - void reset() { - n_prompt_tokens = 0; - generated_text = ""; - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - n_past = 0; - n_sent_text = 0; - n_sent_token_probs = 0; - infill = false; - ga_i = 0; - n_past_se = 0; + n_prompt_tokens = 0; + generated_text = ""; + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + n_past = 0; + n_sent_text = 0; + n_sent_token_probs = 0; + infill = false; + ga_i = 0; + n_past_se = 0; generated_token_probs.clear(); - - for (slot_image & img : images) { - free(img.image_embedding); - if (img.img_data) { - clip_image_u8_free(img.img_data); - } - img.prefix_prompt = ""; - } - - images.clear(); } bool has_budget(gpt_params &global_params) { @@ -205,32 +224,29 @@ struct server_slot { } bool available() const { - return state == IDLE && command == NONE; + return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE; } bool is_processing() const { - return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING; } - void add_token_string(const completion_token_output &token) { - if (command == RELEASE) { + void add_token_string(const completion_token_output & token) { + if (command == SLOT_COMMAND_RELEASE) { return; } - cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); } void release() { - if (state == PROCESSING) - { - t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; - command = RELEASE; + if (state == SLOT_STATE_PROCESSING) { + t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; + command = SLOT_COMMAND_RELEASE; } } - json get_formated_timings() { - return json - { + json get_formated_timings() const { + return json { {"prompt_n", n_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, @@ -243,16 +259,47 @@ struct server_slot { }; } + size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) { + size_t stop_pos = std::string::npos; + + for (const std::string & word : params.antiprompt) { + size_t pos; + + if (type == STOP_TYPE_FULL) { + const size_t tmp = word.size() + last_token_size; + const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; + + pos = text.find(word, from_pos); + } else { + pos = find_partial_stop_string(word, text); + } + + if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { + if (type == STOP_TYPE_FULL) { + stopped_word = true; + stopping_word = word; + has_next_token = false; + } + stop_pos = pos; + } + } + + return stop_pos; + } + void print_timings() const { - char buffer[512]; + char buffer[512]; + double t_token = t_prompt_processing / n_prompt_tokens_processed; double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", + + snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"n_prompt_tokens_processed", n_prompt_tokens_processed}, {"t_token", t_token}, @@ -261,22 +308,25 @@ struct server_slot { t_token = t_token_generation / n_decoded; n_tokens_second = 1e3 / t_token_generation * n_decoded; - sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", + + snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", t_token_generation, n_decoded, t_token, n_tokens_second); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_token_generation", t_token_generation}, {"n_decoded", n_decoded}, {"t_token", t_token}, {"n_tokens_second", n_tokens_second}, }); - sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation); + snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation); + LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, + {"id_slot", id}, + {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"t_token_generation", t_token_generation}, {"t_total", t_prompt_processing + t_token_generation}, @@ -291,9 +341,8 @@ struct server_metrics { uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; - uint64_t n_tokens_predicted = 0; - uint64_t t_tokens_generation = 0; - + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; void on_prompt_eval(const server_slot &slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; @@ -315,23 +364,261 @@ struct server_metrics { } }; -struct llama_server_context -{ - llama_model *model = nullptr; - llama_context *ctx = nullptr; +struct server_queue { + int id = 0; + bool running; + + // queues + std::vector queue_tasks; + std::vector queue_tasks_deferred; + + std::vector queue_multitasks; + + std::mutex mutex_tasks; + std::condition_variable condition_tasks; + + // callback functions + std::function callback_new_task; + std::function callback_finish_multitask; + std::function callback_run_slots; + + // Add a new task to the end of the queue + int post(server_task task) { + std::unique_lock lock(mutex_tasks); + if (task.id == -1) { + task.id = id++; + LOG_VERBOSE("new task id", {{"new_id", task.id}}); + } + queue_tasks.push_back(std::move(task)); + condition_tasks.notify_one(); + return task.id; + } + + // Add a new task, but defer until one slot is available + void defer(server_task task) { + std::unique_lock lock(mutex_tasks); + queue_tasks_deferred.push_back(std::move(task)); + } + + // Get the next id for creating anew task + int get_new_id() { + std::unique_lock lock(mutex_tasks); + int new_id = id++; + LOG_VERBOSE("new task id", {{"new_id", new_id}}); + return new_id; + } + + // Register function to process a new task + void on_new_task(std::function callback) { + callback_new_task = std::move(callback); + } + + // Register function to process a multitask when it is finished + void on_finish_multitask(std::function callback) { + callback_finish_multitask = std::move(callback); + } + + // Register the function to be called when all slots data is ready to be processed + void on_run_slots(std::function callback) { + callback_run_slots = std::move(callback); + } + + // Call when the state of one slot is changed + void notify_slot_changed() { + // move deferred tasks back to main loop + std::unique_lock lock(mutex_tasks); + for (auto & task : queue_tasks_deferred) { + queue_tasks.push_back(std::move(task)); + } + queue_tasks_deferred.clear(); + } + + // end the start_loop routine + void terminate() { + std::unique_lock lock(mutex_tasks); + running = false; + condition_tasks.notify_all(); + } + + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Run all slots + */ + void start_loop() { + running = true; + + while (true) { + LOG_VERBOSE("new task may arrive", {}); + + while (true) { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + lock.unlock(); + break; + } + server_task task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + lock.unlock(); + LOG_VERBOSE("callback_new_task", {{"id_task", task.id}}); + callback_new_task(task); + } + + LOG_VERBOSE("update_multitasks", {}); + + // check if we have any finished multitasks + auto queue_iterator = queue_multitasks.begin(); + while (queue_iterator != queue_multitasks.end()) { + if (queue_iterator->subtasks_remaining.empty()) { + // all subtasks done == multitask is done + server_task_multi current_multitask = *queue_iterator; + callback_finish_multitask(current_multitask); + // remove this multitask + queue_iterator = queue_multitasks.erase(queue_iterator); + } else { + ++queue_iterator; + } + } + + // all tasks in the current loop is processed, slots data is now ready + LOG_VERBOSE("callback_run_slots", {}); + + callback_run_slots(); + + LOG_VERBOSE("wait for new task", {}); + { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + if (!running) { + LOG_VERBOSE("ending start_loop", {}); + return; + } + condition_tasks.wait(lock, [&]{ + return (!queue_tasks.empty() || !running); + }); + } + } + } + } + + // + // functions to manage multitasks + // + + // add a multitask by specifying the id of all subtask (subtask is a server_task) + void add_multitask(int id_multi, std::vector & sub_ids) { + std::lock_guard lock(mutex_tasks); + server_task_multi multi; + multi.id = id_multi; + std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); + queue_multitasks.push_back(multi); + } + + // updatethe remaining subtasks, while appending results to multitask + void update_multitask(int id_multi, int id_sub, server_task_result & result) { + std::lock_guard lock(mutex_tasks); + for (auto & multitask : queue_multitasks) { + if (multitask.id == id_multi) { + multitask.subtasks_remaining.erase(id_sub); + multitask.results.push_back(result); + } + } + } +}; + +struct server_response { + typedef std::function callback_multitask_t; + callback_multitask_t callback_update_multitask; + + // for keeping track of all tasks waiting for the result + std::set waiting_task_ids; + + // the main result queue + std::vector queue_results; + + std::mutex mutex_results; + std::condition_variable condition_results; - clip_ctx *clp_ctx = nullptr; + // add the id_task to the list of tasks waiting for response + void add_waiting_task_id(int id_task) { + LOG_VERBOSE("waiting for task id", {{"id_task", id_task}}); + + std::unique_lock lock(mutex_results); + waiting_task_ids.insert(id_task); + } + + // when the request is finished, we can remove task associated with it + void remove_waiting_task_id(int id_task) { + LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}}); + + std::unique_lock lock(mutex_results); + waiting_task_ids.erase(id_task); + } + + // This function blocks the thread until there is a response for this id_task + server_task_result recv(int id_task) { + while (true) { + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); + + for (int i = 0; i < (int) queue_results.size(); i++) { + if (queue_results[i].id == id_task) { + assert(queue_results[i].id_multi == -1); + server_task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here + } + + // Register the function to update multitask + void on_multitask_update(callback_multitask_t callback) { + callback_update_multitask = std::move(callback); + } + + // Send a new result to a waiting id_task + void send(server_task_result result) { + LOG_VERBOSE("send new result", {{"id_task", result.id}}); + + std::unique_lock lock(mutex_results); + for (const auto & id_task : waiting_task_ids) { + // LOG_TEE("waiting task id %i \n", id_task); + // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result + if (result.id_multi == id_task) { + LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}}); + callback_update_multitask(id_task, result.id, result); + continue; + } + + if (result.id == id_task) { + LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}}); + queue_results.push_back(result); + condition_results.notify_all(); + return; + } + } + } +}; + +struct server_context { + llama_model * model = nullptr; + llama_context * ctx = nullptr; gpt_params params; llama_batch batch; - bool multimodal = false; - bool clean_kv_cache = true; - bool all_slots_are_idle = false; - bool add_bos_token = true; + bool clean_kv_cache = true; + bool add_bos_token = true; - int32_t n_ctx; // total context for all clients / slots + int32_t n_ctx; // total context for all clients / slots // system prompt bool system_need_update = false; @@ -346,60 +633,32 @@ struct llama_server_context std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; - llama_server_response queue_results; + server_queue queue_tasks; + server_response queue_results; server_metrics metrics; - ~llama_server_context() - { - if (ctx) - { + ~server_context() { + if (ctx) { llama_free(ctx); ctx = nullptr; } - if (model) - { + + if (model) { llama_free_model(model); model = nullptr; } } - bool load_model(const gpt_params ¶ms_) - { + bool load_model(const gpt_params & params_) { params = params_; - if (!params.mmproj.empty()) { - multimodal = true; - LOG_INFO("Multi Modal Mode Enabled", {}); - clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); - if(clp_ctx == nullptr) { - LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); - return false; - } - - if (params.n_ctx < 2048) { // request larger context for the image embedding - params.n_ctx = 2048; - } - } std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (model == nullptr) - { + if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } - if (multimodal) { - const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); - const int n_embd_llm = llama_n_embd(model); - if (n_embd_clip != n_embd_llm) { - LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm); - llama_free(ctx); - llama_free_model(model); - return false; - } - } - n_ctx = llama_n_ctx(ctx); add_bos_token = llama_should_add_bos_token(model); @@ -407,25 +666,19 @@ struct llama_server_context return true; } - void validate_model_chat_template(server_params & sparams) { + bool validate_model_chat_template() const { llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); - if (res < 0) { - LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "chatml"; - } + + const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0); + + return res > 0; } void initialize() { - // create slots - all_slots_are_idle = true; - const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); - for (int i = 0; i < params.n_parallel; i++) - { + for (int i = 0; i < params.n_parallel; i++) { server_slot slot; slot.id = i; @@ -433,7 +686,7 @@ struct llama_server_context slot.n_predict = params.n_predict; LOG_INFO("new slot", { - {"slot_id", slot.id}, + {"id_slot", slot.id}, {"n_ctx_slot", slot.n_ctx} }); @@ -447,9 +700,9 @@ struct llama_server_context //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT LOG_INFO("slot self-extend", { - {"slot_id", slot.id}, - {"ga_n", ga_n}, - {"ga_w", ga_w} + {"id_slot", slot.id}, + {"ga_n", ga_n}, + {"ga_w", ga_w} }); } @@ -468,8 +721,7 @@ struct llama_server_context batch = llama_batch_init(n_ctx, 0, params.n_parallel); } - std::vector tokenize(const json & json_prompt, bool add_bos) const - { + std::vector tokenize(const json & json_prompt, bool add_bos) const { // TODO: currently, we tokenize using special tokens by default // this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) // but it's better compared to completely ignoring ChatML and other chat templates @@ -479,38 +731,30 @@ struct llama_server_context // or the first element of the json_prompt array is a string. std::vector prompt_tokens; - if (json_prompt.is_array()) - { + if (json_prompt.is_array()) { bool first = true; - for (const auto& p : json_prompt) - { - if (p.is_string()) - { + for (const auto & p : json_prompt) { + if (p.is_string()) { auto s = p.template get(); + std::vector p; - if (first) - { + if (first) { p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); first = false; - } - else - { + } else { p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); } + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } - else - { - if (first) - { + } else { + if (first) { first = false; } + prompt_tokens.push_back(p.template get()); } } - } - else - { + } else { auto s = json_prompt.template get(); prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); } @@ -518,19 +762,18 @@ struct llama_server_context return prompt_tokens; } - server_slot* get_slot(int id) { + server_slot * get_slot(int id) { int64_t t_last = ggml_time_us(); - server_slot *last_used = nullptr; - for (server_slot & slot : slots) - { - if (slot.id == id && slot.available()) - { + server_slot * last_used = nullptr; + + for (server_slot & slot : slots) { + if (slot.id == id && slot.available()) { return &slot; } - if (slot.available() && slot.t_last_used < t_last) - { + // among all available slots, find the one that has been least recently used + if (slot.available() && slot.t_last_used < t_last) { last_used = &slot; t_last = slot.t_last_used; } @@ -539,295 +782,204 @@ struct llama_server_context return last_used; } - bool launch_slot_with_data(server_slot* &slot, json data) { + bool launch_slot_with_data(server_slot & slot, json data) const { slot_params default_params; llama_sampling_params default_sparams; if (data.count("__oaicompat") != 0) { - slot->oaicompat = true; - slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + slot.oaicompat = true; + slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); } else { - slot->oaicompat = false; - slot->oaicompat_model = ""; - } - - slot->params.stream = json_value(data, "stream", false); - slot->params.cache_prompt = json_value(data, "cache_prompt", false); - slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); - slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); - slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); - slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(data, "seed", default_params.seed); - slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); - slot->sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); - - if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) { + slot.oaicompat = false; + slot.oaicompat_model = ""; + } + + slot.params.stream = json_value(data, "stream", false); + slot.params.cache_prompt = json_value(data, "cache_prompt", false); + slot.params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot.sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); + slot.params.seed = json_value(data, "seed", default_params.seed); + slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); + + if (slot.params.cache_prompt && slot.ga_n != 1) { + LOG_WARNING("cache_prompt is not supported with group-attention", {}); + slot.params.cache_prompt = false; + } + + if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { // Might be better to reject the request with a 400 ? LOG_WARNING("Max tokens to predict exceeds server configuration", { - {"params.n_predict", slot->params.n_predict}, - {"slot.n_predict", slot->n_predict}, + {"params.n_predict", slot.params.n_predict}, + {"slot.n_predict", slot.n_predict}, }); - slot->params.n_predict = slot->n_predict; + slot.params.n_predict = slot.n_predict; } // infill - if (data.count("input_prefix") != 0) - { - slot->params.input_prefix = data["input_prefix"]; - } - else - { - slot->params.input_prefix = ""; - } + slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); + slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); + slot.prompt = json_value(data, "prompt", std::string("")); - if (data.count("input_suffix") != 0) - { - slot->params.input_suffix = data["input_suffix"]; - } - else + // penalize user-provided tokens { - slot->params.input_suffix = ""; - } + slot.sparams.penalty_prompt_tokens.clear(); + slot.sparams.use_penalty_prompt_tokens = false; - if (data.count("prompt") != 0) - { - slot->prompt = data["prompt"]; - } - else - { - slot->prompt = ""; - } + const auto & penalty_prompt = data.find("penalty_prompt"); - slot->sparams.penalty_prompt_tokens.clear(); - slot->sparams.use_penalty_prompt_tokens = false; - const auto &penalty_prompt = data.find("penalty_prompt"); - if (penalty_prompt != data.end()) - { - if (penalty_prompt->is_string()) - { - const auto penalty_prompt_string = penalty_prompt->get(); - auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false); - slot->sparams.penalty_prompt_tokens.swap(penalty_tokens); - if (slot->params.n_predict > 0) - { - slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict); + if (penalty_prompt != data.end()) { + if (penalty_prompt->is_string()) { + const auto penalty_prompt_string = penalty_prompt->get(); + slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false); + + if (slot.params.n_predict > 0) { + slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict); + } + slot.sparams.use_penalty_prompt_tokens = true; + + LOG_VERBOSE("penalty_prompt_tokens", { + {"id_slot", slot.id}, + {"tokens", slot.sparams.penalty_prompt_tokens}, + }); } - slot->sparams.use_penalty_prompt_tokens = true; - } - else if (penalty_prompt->is_array()) - { - const auto n_tokens = penalty_prompt->size(); - slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict)); - const int n_vocab = llama_n_vocab(model); - for (const auto &penalty_token : *penalty_prompt) - { - if (penalty_token.is_number_integer()) - { - const auto tok = penalty_token.get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.penalty_prompt_tokens.push_back(tok); + else if (penalty_prompt->is_array()) { + const auto n_tokens = penalty_prompt->size(); + slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict)); + + const int n_vocab = llama_n_vocab(model); + for (const auto & penalty_token : *penalty_prompt) { + if (penalty_token.is_number_integer()) { + const auto tok = penalty_token.get(); + if (tok >= 0 && tok < n_vocab) { + slot.sparams.penalty_prompt_tokens.push_back(tok); + } } } + slot.sparams.use_penalty_prompt_tokens = true; + + LOG_VERBOSE("penalty_prompt_tokens", { + {"id_slot", slot.id}, + {"tokens", slot.sparams.penalty_prompt_tokens}, + }); } - slot->sparams.use_penalty_prompt_tokens = true; } } - slot->sparams.logit_bias.clear(); - - if (json_value(data, "ignore_eos", false)) { - slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY; - } + slot.sparams.logit_bias.clear(); - const auto &logit_bias = data.find("logit_bias"); - if (logit_bias != data.end() && logit_bias->is_array()) - { - const int n_vocab = llama_n_vocab(model); - for (const auto &el : *logit_bias) - { - if (el.is_array() && el.size() == 2) - { - float bias; - if (el[1].is_number()) - { - bias = el[1].get(); - } - else if (el[1].is_boolean() && !el[1].get()) - { - bias = -INFINITY; - } - else - { - continue; - } + if (json_value(data, "ignore_eos", false)) { + slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; + } - if (el[0].is_number_integer()) - { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.logit_bias[tok] = bias; + const auto & logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { + const int n_vocab = llama_n_vocab(model); + for (const auto & el : *logit_bias) { + if (el.is_array() && el.size() == 2) { + float bias; + if (el[1].is_number()) { + bias = el[1].get(); + } else if (el[1].is_boolean() && !el[1].get()) { + bias = -INFINITY; + } else { + continue; } - } - else if (el[0].is_string()) - { - auto toks = llama_tokenize(model, el[0].get(), false); - for (auto tok : toks) - { - slot->sparams.logit_bias[tok] = bias; + + if (el[0].is_number_integer()) { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { + slot.sparams.logit_bias[tok] = bias; + } + } else if (el[0].is_string()) { + auto toks = llama_tokenize(model, el[0].get(), false); + for (auto tok : toks) { + slot.sparams.logit_bias[tok] = bias; + } } } } } } - slot->params.antiprompt.clear(); - - const auto &stop = data.find("stop"); - if (stop != data.end() && stop->is_array()) { - for (const auto &word : *stop) - { - if (!word.empty()) - { - slot->params.antiprompt.push_back(word); - } - } - } + slot.params.antiprompt.clear(); - const auto &samplers_sequence = data.find("samplers"); - if (samplers_sequence != data.end() && samplers_sequence->is_array()) - { - std::vector sampler_names; - for (const auto &sampler_name : *samplers_sequence) - { - if (sampler_name.is_string()) - { - sampler_names.emplace_back(sampler_name); + const auto & stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) { + for (const auto & word : *stop) { + if (!word.empty()) { + slot.params.antiprompt.push_back(word); + } } } - slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); - } - else - { - slot->sparams.samplers_sequence = default_sparams.samplers_sequence; } - if (multimodal) { - const auto &images_data = data.find("image_data"); - if (images_data != data.end() && images_data->is_array()) - { - for (const auto &img : *images_data) - { - const std::vector image_buffer = base64_decode(img["data"].get()); - - slot_image img_sl; - img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - img_sl.img_data = clip_image_u8_init(); - if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) - { - LOG_ERROR("failed to load image", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} - }); - return false; - } - LOG_VERBOSE("image loaded", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} - }); - img_sl.request_encode_image = true; - slot->images.push_back(img_sl); - } - // process prompt - // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} - if (slot->images.size() > 0 && !slot->prompt.is_array()) - { - std::string prompt = slot->prompt.get(); - size_t pos = 0, begin_prefix = 0; - std::string pattern = "[img-"; - while ((pos = prompt.find(pattern, pos)) != std::string::npos) { - size_t end_prefix = pos; - pos += pattern.length(); - size_t end_pos = prompt.find(']', pos); - if (end_pos != std::string::npos) - { - std::string image_id = prompt.substr(pos, end_pos - pos); - try - { - int img_id = std::stoi(image_id); - bool found = false; - for (slot_image &img : slot->images) - { - if (img.id == img_id) { - found = true; - img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); - begin_prefix = end_pos + 1; - break; - } - } - if (!found) { - LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); - slot->images.clear(); - return false; - } - } catch (const std::invalid_argument& e) { - LOG_TEE("Invalid image number id in prompt\n"); - slot->images.clear(); - return false; - } - } + const auto & samplers_sequence = data.find("samplers"); + if (samplers_sequence != data.end() && samplers_sequence->is_array()) { + std::vector sampler_names; + for (const auto & sampler_name : *samplers_sequence) { + if (sampler_name.is_string()) { + sampler_names.emplace_back(sampler_name); } - slot->prompt = ""; - slot->params.input_suffix = prompt.substr(begin_prefix); - slot->params.cache_prompt = false; // multimodal doesn't support cache prompt } + slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + } else { + slot.sparams.samplers_sequence = default_sparams.samplers_sequence; } } - if (slot->ctx_sampling != nullptr) { - llama_sampling_free(slot->ctx_sampling); + if (slot.ctx_sampling != nullptr) { + llama_sampling_free(slot.ctx_sampling); + } + slot.ctx_sampling = llama_sampling_init(slot.sparams); + llama_set_rng_seed(ctx, slot.params.seed); } - slot->ctx_sampling = llama_sampling_init(slot->sparams); - llama_set_rng_seed(ctx, slot->params.seed); - slot->command = LOAD_PROMPT; - all_slots_are_idle = false; + slot.command = SLOT_COMMAND_LOAD_PROMPT; + slot.prompt_tokens.clear(); LOG_INFO("slot is processing task", { - {"slot_id", slot->id}, - {"task_id", slot->task_id}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, }); return true; } void kv_cache_clear() { + LOG_VERBOSE("clearing KV cache", {}); + // clear the entire KV cache llama_kv_cache_clear(ctx); clean_kv_cache = false; } void system_prompt_update() { + LOG_VERBOSE("system prompt update", { + {"system_prompt", system_prompt}, + }); + kv_cache_clear(); system_tokens.clear(); @@ -836,13 +988,11 @@ struct llama_server_context llama_batch_clear(batch); - for (int i = 0; i < (int)system_tokens.size(); ++i) - { + for (int i = 0; i < (int)system_tokens.size(); ++i) { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) - { + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) { const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i)); llama_batch batch_view = { n_tokens, @@ -854,78 +1004,42 @@ struct llama_server_context batch.logits + i, 0, 0, 0, // unused }; - if (llama_decode(ctx, batch_view) != 0) - { + + if (llama_decode(ctx, batch_view) != 0) { LOG_TEE("%s: llama_decode() failed\n", __func__); return; } } // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < params.n_parallel; ++i) - { + for (int32_t i = 1; i < params.n_parallel; ++i) { llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); } } - LOG_TEE("system prompt updated\n"); system_need_update = false; } - void system_prompt_notify() { - // release all slots - for (server_slot &slot : slots) - { - slot.release(); - } - - system_need_update = true; - } - - void system_prompt_process(const json &sys_props) { + void system_prompt_set(const json & sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); + LOG_VERBOSE("system prompt process", { + {"system_prompt", system_prompt}, + {"name_user", name_user}, + {"name_assistant", name_assistant}, + }); - system_prompt_notify(); - } - - static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, server_slot &slot) - { - size_t stop_pos = std::string::npos; - - for (const std::string &word : slot.params.antiprompt) - { - size_t pos; - if (type == STOP_FULL) - { - const size_t tmp = word.size() + last_token_size; - const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; - pos = text.find(word, from_pos); - } - else - { - pos = find_partial_stop_string(word, text); - } - if (pos != std::string::npos && - (stop_pos == std::string::npos || pos < stop_pos)) - { - if (type == STOP_FULL) - { - slot.stopped_word = true; - slot.stopping_word = word; - slot.has_next_token = false; - } - stop_pos = pos; - } + // release all slots + for (server_slot & slot : slots) { + slot.release(); } - return stop_pos; + system_need_update = true; } - bool process_token(completion_token_output &result, server_slot &slot) { + bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; @@ -934,34 +1048,26 @@ struct llama_server_context slot.generated_text += token_str; slot.has_next_token = true; - if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) - { + if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) { // we can change penalty_prompt_tokens because it is always created from scratch each request slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok); } // check if there is incomplete UTF-8 character at the end bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) - { + for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) - { + if ((c & 0xC0) == 0x80) { // continuation byte: 10xxxxxx continue; } - if ((c & 0xE0) == 0xC0) - { + if ((c & 0xE0) == 0xC0) { // 2-byte character: 110xxxxx ... incomplete = i < 2; - } - else if ((c & 0xF0) == 0xE0) - { + } else if ((c & 0xF0) == 0xE0) { // 3-byte character: 1110xxxx ... incomplete = i < 3; - } - else if ((c & 0xF8) == 0xF0) - { + } else if ((c & 0xF8) == 0xF0) { // 4-byte character: 11110xxx ... incomplete = i < 4; } @@ -969,206 +1075,181 @@ struct llama_server_context break; } - if (!incomplete) - { + if (!incomplete) { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; - size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); - if (stop_pos != std::string::npos) - { + + size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); + if (stop_pos != std::string::npos) { is_stop_full = true; slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); - } - else - { + } else { is_stop_full = false; - stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); + stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); } // check if there is any token to predict - if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) - { + if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache } + slot.add_token_string(result); - if (slot.params.stream) - { + if (slot.params.stream) { send_partial_response(slot, result); } } - if (incomplete) - { + if (incomplete) { slot.has_next_token = true; } // check the limits - if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) - { - slot.stopped_limit = true; + if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) { + slot.stopped_limit = true; slot.has_next_token = false; + + LOG_VERBOSE("stopped by limit", { + {"id_slot", slot.id}, + {"n_decoded", slot.n_decoded}, + {"n_predict", slot.params.n_predict}, + }); } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) - { - slot.stopped_eos = true; + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { + slot.stopped_eos = true; slot.has_next_token = false; + LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { - {"token", result.tok}, - {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }); + {"token", result.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"n_decoded", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }); return slot.has_next_token; // continue } - bool process_images(server_slot &slot) const - { - for (slot_image &img : slot.images) - { - if (!img.request_encode_image) - { - continue; - } - - if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) { - LOG_TEE("Error processing the given image"); - return false; - } - - - img.request_encode_image = false; - } - - return slot.images.size() > 0; - } - - void send_error(task_server& task, const std::string &error) - { - LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = false; - res.error = true; - res.result_json = { { "content", error } }; - queue_results.send(res); - } - - json get_formated_generation(server_slot &slot) - { + json get_formated_generation(const server_slot & slot) const { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); - const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); + const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); + std::vector samplers_sequence; - for (const auto &sampler_type : slot.sparams.samplers_sequence) - { + samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); + for (const auto & sampler_type : slot.sparams.samplers_sequence) { samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); } return json { - {"n_ctx", slot.n_ctx}, - {"n_predict", slot.n_predict}, - {"model", params.model_alias}, - {"seed", slot.params.seed}, - {"temperature", slot.sparams.temp}, - {"dynatemp_range", slot.sparams.dynatemp_range}, - {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, - {"top_k", slot.sparams.top_k}, - {"top_p", slot.sparams.top_p}, - {"min_p", slot.sparams.min_p}, - {"tfs_z", slot.sparams.tfs_z}, - {"typical_p", slot.sparams.typical_p}, - {"repeat_last_n", slot.sparams.penalty_last_n}, - {"repeat_penalty", slot.sparams.penalty_repeat}, - {"presence_penalty", slot.sparams.penalty_present}, - {"frequency_penalty", slot.sparams.penalty_freq}, - {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, + {"n_ctx", slot.n_ctx}, + {"n_predict", slot.n_predict}, + {"model", params.model_alias}, + {"seed", slot.params.seed}, + {"temperature", slot.sparams.temp}, + {"dynatemp_range", slot.sparams.dynatemp_range}, + {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, + {"top_k", slot.sparams.top_k}, + {"top_p", slot.sparams.top_p}, + {"min_p", slot.sparams.min_p}, + {"tfs_z", slot.sparams.tfs_z}, + {"typical_p", slot.sparams.typical_p}, + {"repeat_last_n", slot.sparams.penalty_last_n}, + {"repeat_penalty", slot.sparams.penalty_repeat}, + {"presence_penalty", slot.sparams.penalty_present}, + {"frequency_penalty", slot.sparams.penalty_freq}, + {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens}, - {"mirostat", slot.sparams.mirostat}, - {"mirostat_tau", slot.sparams.mirostat_tau}, - {"mirostat_eta", slot.sparams.mirostat_eta}, - {"penalize_nl", slot.sparams.penalize_nl}, - {"stop", slot.params.antiprompt}, - {"n_predict", slot.params.n_predict}, - {"n_keep", params.n_keep}, - {"ignore_eos", ignore_eos}, - {"stream", slot.params.stream}, - {"logit_bias", slot.sparams.logit_bias}, - {"n_probs", slot.sparams.n_probs}, - {"min_keep", slot.sparams.min_keep}, - {"grammar", slot.sparams.grammar}, - {"samplers", samplers_sequence} + {"mirostat", slot.sparams.mirostat}, + {"mirostat_tau", slot.sparams.mirostat_tau}, + {"mirostat_eta", slot.sparams.mirostat_eta}, + {"penalize_nl", slot.sparams.penalize_nl}, + {"stop", slot.params.antiprompt}, + {"n_predict", slot.params.n_predict}, + {"n_keep", params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", slot.params.stream}, + {"logit_bias", slot.sparams.logit_bias}, + {"n_probs", slot.sparams.n_probs}, + {"min_keep", slot.sparams.min_keep}, + {"grammar", slot.sparams.grammar}, + {"samplers", samplers_sequence} }; } - void send_partial_response(server_slot &slot, completion_token_output tkn) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = false; + void send_error(const server_task & task, const std::string & error) { + LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - res.result_json = json - { + server_task_result res; + res.id = task.id; + res.id_multi = task.id_multi; + res.stop = false; + res.error = true; + res.data = { { "content", error } }; + + queue_results.send(res); + } + + void send_partial_response(server_slot & slot, completion_token_output tkn) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = false; + res.data = json { {"content", tkn.text_to_send}, {"stop", false}, - {"slot_id", slot.id}, - {"multimodal", multimodal} + {"id_slot", slot.id}, + {"multimodal", false} }; - if (slot.sparams.n_probs > 0) - { - std::vector probs_output = {}; + if (slot.sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); - size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) - { - probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); + const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); + const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); + + std::vector probs_output; + if (probs_pos < probs_stop_pos) { + probs_output = std::vector( + slot.generated_token_probs.begin() + probs_pos, + slot.generated_token_probs.begin() + probs_stop_pos); } slot.n_sent_token_probs = probs_stop_pos; - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + + res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; + if (slot.oaicompat) { + res.data["oaicompat_token_ctr"] = slot.n_decoded; + res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } - void send_final_response(server_slot &slot) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; - - res.result_json = json - { + void send_final_response(const server_slot & slot) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = true; + res.data = json { {"content", !slot.params.stream ? slot.generated_text : ""}, - {"slot_id", slot.id}, + {"id_slot", slot.id}, {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, @@ -1184,96 +1265,87 @@ struct llama_server_context {"timings", slot.get_formated_timings()} }; - if (slot.sparams.n_probs > 0) - { - std::vector probs = {}; - if (!slot.params.stream && slot.stopped_word) - { + if (slot.sparams.n_probs > 0) { + std::vector probs; + if (!slot.params.stream && slot.stopped_word) { const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); - probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); - } - else - { + probs = std::vector( - slot.generated_token_probs.begin(), - slot.generated_token_probs.end()); + slot.generated_token_probs.begin(), + slot.generated_token_probs.end() - stop_word_toks.size()); + } else { + probs = std::vector( + slot.generated_token_probs.begin(), + slot.generated_token_probs.end()); } - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); + + res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs); } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; + if (slot.oaicompat) { + res.data["oaicompat_token_ctr"] = slot.n_decoded; + res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } - void send_embedding(server_slot & slot, const llama_batch & batch) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; + void send_embedding(const server_slot & slot, const llama_batch & batch) { + server_task_result res; + res.id = slot.id_task; + res.id_multi = slot.id_multi; + res.error = false; + res.stop = true; const int n_embd = llama_n_embd(model); - if (!params.embedding) - { - LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); - res.result_json = json - { - {"embedding", std::vector(n_embd, 0.0f)}, - }; - } - else - { - for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { - continue; - } + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; + } - const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); - if (embd == NULL) { - embd = llama_get_embeddings_ith(ctx, i); - if (embd == NULL) { - LOG_ERROR("failed to get embeddings for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}}); - res.result_json = json - { - {"embedding", std::vector(n_embd, 0.0f)}, - }; - continue; - } - } + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + } - res.result_json = json - { - {"embedding", std::vector(embd, embd + n_embd)}, + if (embd == NULL) { + LOG_ERROR("failed to get embeddings", { + {"token", batch.token [i]}, + {"seq_id", batch.seq_id[i][0]} + }); + + res.data = json { + {"embedding", std::vector(n_embd, 0.0f)}, }; + + continue; } + + res.data = json { + {"embedding", std::vector(embd, embd + n_embd)}, + }; } + queue_results.send(res); } - void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id) - { - task_server task; - task.id = task_id; - task.target_id = 0; - task.data = std::move(data); - task.infill_mode = infill; - task.embedding_mode = embedding; - task.type = TASK_TYPE_COMPLETION; - task.multitask_id = multitask_id; + void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) { + server_task task; + task.id = id_task; + task.id_multi = id_multi; + task.id_target = 0; + task.data = std::move(data); + task.infill = infill; + task.embedding = embedding; + task.type = SERVER_TASK_TYPE_COMPLETION; // when a completion task's prompt array is not a singleton, we split it into multiple requests // otherwise, it's a single-prompt task, we actually queue it // if there's numbers in the prompt array it will be treated as an array of tokens if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { bool numbers = false; - for (const auto& e : task.data.at("prompt")) { + for (const auto & e : task.data.at("prompt")) { if (e.is_number()) { numbers = true; break; @@ -1288,106 +1360,23 @@ struct llama_server_context if (numbers) { queue_tasks.post(task); } else { - split_multiprompt_task(task_id, task); + split_multiprompt_task(id_task, task); } } else { - // an empty prompt can make slot become buggy - if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get().empty()) { - task.data["prompt"] = " "; // add a space so that we have one token - } queue_tasks.post(task); } } - // for multiple images processing - bool ingest_images(server_slot &slot, int n_batch) - { - int image_idx = 0; - - while (image_idx < (int) slot.images.size()) - { - slot_image &img = slot.images[image_idx]; - - // process prefix prompt - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) - { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { - n_tokens, - batch.token + i, - nullptr, - batch.pos + i, - batch.n_seq_id + i, - batch.seq_id + i, - batch.logits + i, - 0, 0, 0, // unused - }; - if (llama_decode(ctx, batch_view)) - { - LOG_TEE("%s : failed to eval\n", __func__); - return false; - } - } - - // process image with llm - for (int i = 0; i < img.image_tokens; i += n_batch) - { - int n_eval = img.image_tokens - i; - if (n_eval > n_batch) - { - n_eval = n_batch; - } - - const int n_embd = llama_n_embd(model); - llama_batch batch_img = { - n_eval, - nullptr, - (img.image_embedding + i * n_embd), - nullptr, - nullptr, - nullptr, - nullptr, - slot.n_past, - 1, 0 - }; - if (llama_decode(ctx, batch_img)) - { - LOG_TEE("%s : failed to eval image\n", __func__); - return false; - } - slot.n_past += n_eval; - } - image_idx++; - - llama_batch_clear(batch); - - // append prefix of next image - const auto json_prompt = (image_idx >= (int) slot.images.size()) ? - slot.params.input_suffix : // no more images, then process suffix prompt - (json)(slot.images[image_idx].prefix_prompt); - - std::vector append_tokens = tokenize(json_prompt, false); // has next image - for (int i = 0; i < (int) append_tokens.size(); ++i) - { - llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); - slot.n_past += 1; - } - } - - return true; - } + void request_cancel(int id_task) { + server_task task; + task.type = SERVER_TASK_TYPE_CANCEL; + task.id_target = id_task; - void request_cancel(int task_id) - { - task_server task; - task.type = TASK_TYPE_CANCEL; - task.target_id = task_id; queue_tasks.post(task); } - void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) - { - int prompt_count = multiprompt_task.data.at("prompt").size(); + void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) { + const int prompt_count = multiprompt_task.data.at("prompt").size(); if (prompt_count <= 1) { send_error(multiprompt_task, "error while handling multiple prompts"); return; @@ -1395,127 +1384,121 @@ struct llama_server_context // generate all the ID for subtask std::vector subtask_ids(prompt_count); - for (int i = 0; i < prompt_count; i++) - { + for (int i = 0; i < prompt_count; i++) { subtask_ids[i] = queue_tasks.get_new_id(); } // queue up the multitask so we can track its subtask progression - queue_tasks.add_multitask(multitask_id, subtask_ids); + queue_tasks.add_multitask(id_multi, subtask_ids); // add subtasks - for (int i = 0; i < prompt_count; i++) - { + for (int i = 0; i < prompt_count; i++) { json subtask_data = multiprompt_task.data; subtask_data["prompt"] = subtask_data["prompt"][i]; // subtasks inherit everything else (infill mode, embedding mode, etc.) - request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); + request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding); } } - void process_single_task(task_server& task) - { - switch (task.type) - { - case TASK_TYPE_COMPLETION: { - server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) - { - // if no slot is available, we defer this task for processing later - LOG_VERBOSE("no slot is available", {{"task_id", task.id}}); - queue_tasks.defer(task); - break; - } - - if (task.data.contains("system_prompt")) + void process_single_task(const server_task & task) { + switch (task.type) { + case SERVER_TASK_TYPE_COMPLETION: { - if (!all_slots_are_idle) { - send_error(task, "system prompt can only be updated when all slots are idle"); + server_slot * slot = get_slot(json_value(task.data, "id_slot", -1)); + if (slot == nullptr) { + // if no slot is available, we defer this task for processing later + LOG_VERBOSE("no slot is available", {{"id_task", task.id}}); + queue_tasks.defer(task); break; } - system_prompt_process(task.data["system_prompt"]); - - // reset cache_tokens for all slots - for (server_slot &slot : slots) - { - slot.cache_tokens.clear(); - slot.n_past = 0; - slot.n_past_se = 0; + + if (task.data.contains("system_prompt")) { + system_prompt_set(task.data["system_prompt"]); + + for (server_slot & slot : slots) { + slot.n_past = 0; + slot.n_past_se = 0; + } } - } - slot->reset(); + slot->reset(); - slot->infill = task.infill_mode; - slot->embedding = task.embedding_mode; - slot->task_id = task.id; - slot->multitask_id = task.multitask_id; + slot->id_task = task.id; + slot->id_multi = task.id_multi; + slot->infill = task.infill; + slot->embedding = task.embedding; - if (!launch_slot_with_data(slot, task.data)) - { - // send error result - send_error(task, "internal_error"); - break; - } - } break; - case TASK_TYPE_CANCEL: { // release slot linked with the task id - for (auto & slot : slots) - { - if (slot.task_id == task.target_id) - { - slot.release(); + if (!launch_slot_with_data(*slot, task.data)) { + // send error result + send_error(task, "internal_error"); break; } - } - } break; - case TASK_TYPE_NEXT_RESPONSE: { - // do nothing - } break; - case TASK_TYPE_METRICS: { - json slots_data = json::array(); - int n_idle_slots = 0; - int n_processing_slots = 0; - - for (server_slot &slot: slots) { - json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["task_id"] = slot.task_id; - slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; - slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }; - if (slot_data["state"] == IDLE) { - n_idle_slots++; - } else { - n_processing_slots++; + } break; + case SERVER_TASK_TYPE_CANCEL: + { + // release slot linked with the task id + for (auto & slot : slots) { + if (slot.id_task == task.id_target) { + slot.release(); + break; + } } - slots_data.push_back(slot_data); - } - LOG_INFO("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots} - }); - LOG_VERBOSE("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots}, - {"slots", slots_data} - }); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = true; - res.error = false; - res.result_json = { + } break; + case SERVER_TASK_TYPE_NEXT_RESPONSE: + { + // do nothing + } break; + case SERVER_TASK_TYPE_METRICS: + { + json slots_data = json::array(); + + int n_idle_slots = 0; + int n_processing_slots = 0; + + for (server_slot & slot : slots) { + json slot_data = get_formated_generation(slot); + slot_data["id"] = slot.id; + slot_data["id_task"] = slot.id_task; + slot_data["state"] = slot.state; + slot_data["prompt"] = slot.prompt; + slot_data["next_token"] = { + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"n_decoded", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }; + + if (slot_data["state"] == SLOT_STATE_IDLE) { + n_idle_slots++; + } else { + n_processing_slots++; + } + + slots_data.push_back(slot_data); + } + LOG_INFO("slot data", { + {"id_task", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots} + }); + + LOG_VERBOSE("slot data", { + {"id_task", task.id}, + {"n_idle_slots", n_idle_slots}, + {"n_processing_slots", n_processing_slots}, + {"slots", slots_data} + }); + + server_task_result res; + res.id = task.id; + res.id_multi = task.id_multi; + res.stop = true; + res.error = false; + res.data = { { "idle", n_idle_slots }, { "processing", n_processing_slots }, { "deferred", queue_tasks.queue_tasks_deferred.size() }, @@ -1532,71 +1515,104 @@ struct llama_server_context { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, { "slots", slots_data }, - }; - metrics.reset_bucket(); - queue_results.send(res); - } break; + }; + + metrics.reset_bucket(); + queue_results.send(res); + } break; } } - void on_finish_multitask(task_multi& multitask) - { + void on_finish_multitask(const server_task_multi & multitask) { // all subtasks done == multitask is done - task_result result; - result.id = multitask.id; - result.stop = true; + server_task_result result; + result.id = multitask.id; + result.stop = true; result.error = false; // collect json results into one json result std::vector result_jsons; - for (auto& subres : multitask.results) - { - result_jsons.push_back(subres.result_json); + for (const auto & subres : multitask.results) { + result_jsons.push_back(subres.data); result.error = result.error && subres.error; } - result.result_json = json{ { "results", result_jsons } }; + result.data = json { + { "results", result_jsons } + }; + queue_results.send(result); } bool update_slots() { - if (system_need_update) - { - LOG_INFO("updating system prompt", {}); + if (system_need_update) { system_prompt_update(); } - llama_batch_clear(batch); + // release slots + for (auto & slot : slots) { + if (slot.command == SLOT_COMMAND_RELEASE) { + slot.state = SLOT_STATE_IDLE; + slot.command = SLOT_COMMAND_NONE; + slot.t_last_used = ggml_time_us(); - if (all_slots_are_idle) - { - if (system_prompt.empty() && clean_kv_cache) - { - LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {}); - kv_cache_clear(); + LOG_INFO("slot released", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()}, + {"truncated", slot.truncated} + }); + + queue_tasks.notify_slot_changed(); } - return true; } - LOG_VERBOSE("posting NEXT_RESPONSE", {}); - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); + // check if all slots are idle + { + bool all_idle = true; + + for (auto & slot : slots) { + if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) { + all_idle = false; + break; + } + } + + if (all_idle) { + LOG_INFO("all slots are idle", {}); + if (system_prompt.empty() && clean_kv_cache) { + kv_cache_clear(); + } + + return true; + } + } - for (server_slot &slot : slots) { - if (slot.ga_n == 1) - { - if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) - { + LOG_VERBOSE("posting NEXT_RESPONSE", {}); + + server_task task; + task.type = SERVER_TASK_TYPE_NEXT_RESPONSE; + task.id_target = -1; + + queue_tasks.post(task); + } + + // apply context-shift if needed + // TODO: simplify and improve + for (server_slot & slot : slots) { + if (slot.ga_n == 1) { + if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) { // Shift context const int n_keep = slot.params.n_keep + add_bos_token; const int n_left = (int) system_tokens.size() + slot.n_past - n_keep; const int n_discard = n_left / 2; LOG_INFO("slot context shift", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"n_keep", n_keep}, {"n_left", n_left}, {"n_discard", n_discard}, @@ -1605,15 +1621,17 @@ struct llama_server_context {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()} }); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); - for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) - { - slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; - } + if (slot.params.cache_prompt) { + for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } - slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + } slot.n_past -= n_discard; @@ -1622,33 +1640,12 @@ struct llama_server_context } } - // decode any currently ongoing sequences - LOG_VERBOSE("decoding ongoing sequences", {}); - for (auto & slot : slots) - { - // release the slot - if (slot.command == RELEASE) - { - slot.state = IDLE; - slot.command = NONE; - slot.t_last_used = ggml_time_us(); + // start populating the batch for this iteration + llama_batch_clear(batch); - LOG_INFO("slot released", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - {"n_ctx", n_ctx}, - {"n_past", slot.n_past}, - {"n_system_tokens", system_tokens.size()}, - {"n_cache_tokens", slot.cache_tokens.size()}, - {"truncated", slot.truncated} - }); - queue_tasks.notify_slot_changed(); - - continue; - } - - if (slot.state == IDLE) - { + // frist, add sampled tokens from any ongoing sequences + for (auto & slot : slots) { + if (slot.state == SLOT_STATE_IDLE) { continue; } @@ -1659,193 +1656,184 @@ struct llama_server_context // TODO: we always have to take into account the "system_tokens" // this is not great and needs to be improved somehow llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + slot.n_past += 1; + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(slot.sampled); + } + + LOG_VERBOSE("slot decode token", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", n_ctx}, + {"n_past", slot.n_past}, + {"n_system_tokens", system_tokens.size()}, + {"n_cache_tokens", slot.cache_tokens.size()}, + {"truncated", slot.truncated} + }); } // process in chunks of params.n_batch int32_t n_batch = params.n_batch; - // assign workload to the slots - if (params.cont_batching || batch.n_tokens == 0) - { - for (auto & slot : slots) - { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()) || !slot.images.empty(); + // next, batch any pending prompts without exceeding n_batch + if (params.cont_batching || batch.n_tokens == 0) { + for (auto & slot : slots) { + const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); // empty prompt passed -> release the slot and send empty response // note: infill mode allows empty prompt - if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill) - { + if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; slot.release(); slot.print_timings(); send_final_response(slot); continue; } - // need process the prompt - if (slot.state == IDLE && slot.command == LOAD_PROMPT) - { - slot.state = PROCESSING; - slot.command = NONE; - std::vector prompt_tokens; - slot.t_start_process_prompt = ggml_time_us(); - slot.t_start_genereration = 0; - - if (slot.infill) - { - bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) - { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; - } - auto prefix_tokens = tokenize(slot.params.input_prefix, false); - auto suffix_tokens = tokenize(slot.params.input_suffix, false); + // this slot still has a prompt to be processed + if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) { + auto & prompt_tokens = slot.prompt_tokens; - const int space_token = 29871; // TODO: this should not be hardcoded - if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { - suffix_tokens.erase(suffix_tokens.begin()); - } + // we haven't tokenized the prompt yet - do it now: + if (prompt_tokens.empty()) { + LOG_VERBOSE("tokenizing prompt", { + {"id_slot", slot.id}, + {"id_task", slot.id_task} + }); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(model)); - prompt_tokens = prefix_tokens; - } - else - { - prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt - } + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_generation = 0; + + if (slot.infill) { + bool suff_rm_leading_spc = true; + if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { + params.input_suffix.erase(0, 1); + suff_rm_leading_spc = false; + } - slot.n_prompt_tokens = prompt_tokens.size(); + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.n_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it, if group attention self-extend is disabled - if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - std::vector new_tokens( - prompt_tokens.begin(), - prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert( - new_tokens.end(), - prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, - prompt_tokens.end()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - slot.truncated = true; - prompt_tokens = new_tokens; + const int space_token = 29871; // TODO: this should not be hardcoded + if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { + suffix_tokens.erase(suffix_tokens.begin()); + } + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(model)); + prompt_tokens = prefix_tokens; + } else { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt + } + + slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); - } - if (!slot.params.cache_prompt) - { - llama_sampling_reset(slot.ctx_sampling); + if (slot.embedding) { + // this prompt is too large to process - discard it + if (slot.n_prompt_tokens > n_batch) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; + } + } else { + if (slot.params.n_keep < 0) { + slot.params.n_keep = slot.n_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - slot.n_past = 0; - slot.n_past_se = 0; - slot.ga_i = 0; - slot.n_prompt_tokens_processed = slot.n_prompt_tokens; - } - else - { - // push the prompt into the sampling context (do not apply grammar) - for (auto &token : prompt_tokens) - { - llama_sampling_accept(slot.ctx_sampling, ctx, token, false); - } + // if input prompt is too big, truncate it (if group attention self-extend is disabled) + if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { + const int n_left = slot.n_ctx - slot.params.n_keep; - slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - // the last token of the cache is not in the KV cache until the next call to llama_decode - // (it was sampled, pushed into the "cache_tokens", but not yet put in the context) - if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size()) - { - slot.n_past -= 1; - } + std::vector new_tokens( + prompt_tokens.begin(), + prompt_tokens.begin() + slot.params.n_keep); - slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; - - if (slot.ga_n != 1) - { - int ga_i = 0; - int32_t ga_n = slot.ga_n; - int32_t ga_w = slot.ga_w; - int32_t slot_npast = 0; - for (int k = 0; k < slot.n_past; ++k) - { - while (slot_npast >= ga_i + ga_w) { - const int bd = (ga_w/ga_n)*(ga_n - 1); - slot_npast -= bd; - ga_i += ga_w/ga_n; + new_tokens.insert( + new_tokens.end(), + prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, + prompt_tokens.end()); + + prompt_tokens = std::move(new_tokens); + + slot.truncated = true; + slot.n_prompt_tokens = prompt_tokens.size(); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + }); + + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); + } + + llama_sampling_reset(slot.ctx_sampling); + + if (!slot.params.cache_prompt) { + slot.n_past_se = 0; + slot.ga_i = 0; + } else { + GGML_ASSERT(slot.ga_n == 1); + + // reuse any previously computed tokens that are common with the new prompt + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + + // remove the non-common part from the cache + slot.cache_tokens.resize(slot.n_past); + + // push the prompt into the sampling context (do not apply grammar) + for (int i = 0; i < slot.n_past; ++i) { + llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false); } - slot_npast++; } - slot.n_past_se = slot_npast; - slot.ga_i = ga_i; } - LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, - { "n_past_se", slot.n_past_se }, - { "ga_i", slot.ga_i }, - { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } - }); - } + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { + // we have to evaluate at least 1 token to generate logits. + LOG_INFO("we have to evaluate at least 1 token to generate logits", { + { "id_slot", slot.id }, + { "id_task", slot.id_task } + }); + + slot.n_past--; + if (slot.ga_i > 0) { + slot.n_past_se--; + } + } - slot.cache_tokens = prompt_tokens; + slot.n_prompt_tokens_processed = 0; + } - if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) - { - // we have to evaluate at least 1 token to generate logits. - LOG_INFO("we have to evaluate at least 1 token to generate logits", { - { "slot_id", slot.id }, - { "task_id", slot.task_id } - }); - slot.n_past--; - if (slot.ga_i > 0) - { - slot.n_past_se--; + if (slot.embedding) { + // cannot fit the prompt in the current batch - will try next iter + if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { + continue; } } - int p0 = (int) system_tokens.size() + slot.n_past; + const int p0 = (int) system_tokens.size() + slot.n_past; + llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); + LOG_INFO("kv cache rm [p0, end)", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, + { "id_slot", slot.id }, + { "id_task", slot.id_task }, { "p0", p0 } }); - llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); - - LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, - {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, - }); - - const bool has_images = process_images(slot); - - // process the prefix of first image - std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; @@ -1853,61 +1841,82 @@ struct llama_server_context int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; - for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) - { - if (slot.ga_n != 1) - { + // add prompt tokens for processing in the current batch + // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow + for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) { + if (slot.ga_n != 1) { while (slot_npast >= ga_i + ga_w) { const int bd = (ga_w/ga_n)*(ga_n - 1); slot_npast -= bd; ga_i += ga_w/ga_n; } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + + llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); + } + + slot.n_prompt_tokens_processed++; slot_npast++; } - if (has_images && !ingest_images(slot, n_batch)) - { - LOG_ERROR("failed processing images", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - }); - // FIXME @phymbert: to be properly tested - // early returning without changing the slot state will block the slot for ever - // no one at the moment is checking the return value - return false; - } + LOG_VERBOSE("prompt processing progress", { + {"id_slot", slot.id}, + {"n_past", slot.n_past}, + {"n_ctx", n_ctx}, + {"n_tokens", batch.n_tokens}, + {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens}, + }); + + // entire prompt has been processed - start decoding new tokens + if (slot.n_past == slot.n_prompt_tokens) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; - // extract the logits only for the last token - if (batch.n_tokens > 0) - { + GGML_ASSERT(batch.n_tokens > 0); + + // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; + + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + + LOG_VERBOSE("prompt done", { + {"id_slot", slot.id}, + {"n_past", slot.n_past}, + {"n_ctx", n_ctx}, + {"n_tokens", batch.n_tokens}, + }); } + } - slot.n_decoded = 0; - slot.i_batch = batch.n_tokens - 1; + if (batch.n_tokens >= n_batch) { + break; } } } - if (batch.n_tokens == 0) - { - all_slots_are_idle = true; + if (batch.n_tokens == 0) { + LOG_VERBOSE("no tokens to decode", {}); + return true; } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) - { + LOG_VERBOSE("decoding batch", { + {"n_tokens", batch.n_tokens}, + }); + + // process the created batch of tokens + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); - for (auto & slot : slots) - { - if (slot.ga_n != 1) - { + for (auto & slot : slots) { + if (slot.ga_n != 1) { // context extension via Self-Extend - while (slot.n_past_se >= slot.ga_i + slot.ga_w) - { + // TODO: simplify and/or abstract this + while (slot.n_past_se >= slot.ga_i + slot.ga_w) { const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w; const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1); const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w; @@ -1918,8 +1927,8 @@ struct llama_server_context LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); - llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; @@ -1927,12 +1936,12 @@ struct llama_server_context LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i); } + slot.n_past_se += n_tokens; } } - llama_batch batch_view = - { + llama_batch batch_view = { n_tokens, batch.token + i, nullptr, @@ -1945,10 +1954,8 @@ struct llama_server_context const int ret = llama_decode(ctx, batch_view); - if (ret != 0) - { - if (n_batch == 1 || ret < 0) - { + if (ret != 0) { + if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); return false; @@ -1959,19 +1966,17 @@ struct llama_server_context // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; + continue; } - for (auto & slot : slots) - { - if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) - { + for (auto & slot : slots) { + if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; } // prompt evaluated for embedding - if (slot.embedding) - { + if (slot.embedding) { send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -1984,10 +1989,9 @@ struct llama_server_context llama_sampling_accept(slot.ctx_sampling, ctx, id, true); slot.n_decoded += 1; - if (slot.n_decoded == 1) - { - slot.t_start_genereration = ggml_time_us(); - slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + if (slot.n_decoded == 1) { + slot.t_start_generation = ggml_time_us(); + slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; metrics.on_prompt_eval(slot); } @@ -1995,19 +1999,19 @@ struct llama_server_context result.tok = id; const int32_t n_probs = slot.sparams.n_probs; - if (slot.sparams.temp <= 0 && n_probs > 0) - { + if (slot.sparams.temp <= 0 && n_probs > 0) { // for llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &cur_p); } - for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) - { - result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); + for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) { + result.probs.push_back({ + cur_p.data[i].id, + cur_p.data[i].p + }); } - if (!process_token(result, slot)) - { + if (!process_token(result, slot)) { slot.release(); slot.print_timings(); send_final_response(slot); @@ -2019,24 +2023,23 @@ struct llama_server_context } LOG_VERBOSE("slots updated", {}); + return true; } - json model_meta() { - return json{ - {"vocab_type", llama_vocab_type(model)}, - {"n_vocab", llama_n_vocab(model)}, - {"n_ctx_train", llama_n_ctx_train(model)}, - {"n_embd", llama_n_embd(model)}, - {"n_params", llama_model_n_params(model)}, - {"size", llama_model_size(model)}, + json model_meta() const { + return json { + {"vocab_type", llama_vocab_type (model)}, + {"n_vocab", llama_n_vocab (model)}, + {"n_ctx_train", llama_n_ctx_train (model)}, + {"n_embd", llama_n_embd (model)}, + {"n_params", llama_model_n_params(model)}, + {"size", llama_model_size (model)}, }; } }; -static void server_print_usage(const char *argv0, const gpt_params ¶ms, - const server_params &sparams) -{ +static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) { printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); @@ -2054,17 +2057,14 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls}\n"); - printf(" pooling type for embeddings, use model default if unspecified\n"); + printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_supports_mlock()) - { + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_supports_mmap()) - { + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); @@ -2096,7 +2096,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); @@ -2105,7 +2105,6 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" KV cache data type for K (default: f16)\n"); printf(" -ctv TYPE, --cache-type-v TYPE\n"); printf(" KV cache data type for V (default: f16)\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-format log output format: json or text (default: json)\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); @@ -2123,57 +2122,41 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("\n"); } -static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms, llama_server_context& llama) -{ - gpt_params default_params; +static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) { + gpt_params default_params; server_params default_sparams; + std::string arg; bool invalid_param = false; - for (int i = 1; i < argc; i++) - { + for (int i = 1; i < argc; i++) { arg = argv[i]; - if (arg == "--port") - { - if (++i >= argc) - { + if (arg == "--port") { + if (++i >= argc) { invalid_param = true; break; } sparams.port = std::stoi(argv[i]); - } - else if (arg == "--host") - { - if (++i >= argc) - { + } else if (arg == "--host") { + if (++i >= argc) { invalid_param = true; break; } sparams.hostname = argv[i]; - } - else if (arg == "--path") - { - if (++i >= argc) - { + } else if (arg == "--path") { + if (++i >= argc) { invalid_param = true; break; } sparams.public_path = argv[i]; - } - else if (arg == "--api-key") - { - if (++i >= argc) - { + } else if (arg == "--api-key") { + if (++i >= argc) { invalid_param = true; break; } sparams.api_keys.emplace_back(argv[i]); - } - else if (arg == "--api-key-file") - { - if (++i >= argc) - { + } else if (arg == "--api-key-file") { + if (++i >= argc) { invalid_param = true; break; } @@ -2190,53 +2173,36 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } key_file.close(); - } - else if (arg == "--timeout" || arg == "-to") - { - if (++i >= argc) - { + } else if (arg == "--timeout" || arg == "-to") { + if (++i >= argc) { invalid_param = true; break; } sparams.read_timeout = std::stoi(argv[i]); sparams.write_timeout = std::stoi(argv[i]); - } - else if (arg == "-m" || arg == "--model") - { - if (++i >= argc) - { + } else if (arg == "-m" || arg == "--model") { + if (++i >= argc) { invalid_param = true; break; } params.model = argv[i]; - } - else if (arg == "-a" || arg == "--alias") - { - if (++i >= argc) - { + } else if (arg == "-a" || arg == "--alias") { + if (++i >= argc) { invalid_param = true; break; } params.model_alias = argv[i]; - } - else if (arg == "-h" || arg == "--help") - { + } else if (arg == "-h" || arg == "--help") { server_print_usage(argv[0], default_params, default_sparams); exit(0); - } - else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") - { - if (++i >= argc) - { + } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { + if (++i >= argc) { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); - } - else if (arg == "--rope-scaling") - { - if (++i >= argc) - { + } else if (arg == "--rope-scaling") { + if (++i >= argc) { invalid_param = true; break; } @@ -2245,59 +2211,44 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } - } - else if (arg == "--rope-freq-base") - { - if (++i >= argc) - { + } else if (arg == "--rope-freq-base") { + if (++i >= argc) { invalid_param = true; break; } params.rope_freq_base = std::stof(argv[i]); - } - else if (arg == "--rope-freq-scale") - { - if (++i >= argc) - { + } else if (arg == "--rope-freq-scale") { + if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = std::stof(argv[i]); - } - else if (arg == "--yarn-ext-factor") - { + } else if (arg == "--yarn-ext-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_ext_factor = std::stof(argv[i]); } - else if (arg == "--yarn-attn-factor") - { + else if (arg == "--yarn-attn-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_attn_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-fast") - { + } else if (arg == "--yarn-beta-fast") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_fast = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-slow") - { + } else if (arg == "--yarn-beta-slow") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_slow = std::stof(argv[i]); - } - else if (arg == "--pooling") - { + } else if (arg == "--pooling") { if (++i >= argc) { invalid_param = true; break; @@ -2307,108 +2258,79 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } - } - else if (arg == "--threads" || arg == "-t") - { + } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) { invalid_param = true; break; } params.n_threads = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-n" || arg == "-gan") - { + } else if (arg == "--grp-attn-n" || arg == "-gan") { if (++i >= argc) { invalid_param = true; break; } params.grp_attn_n = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-w" || arg == "-gaw") - { - if (++i >= argc) - { + } else if (arg == "--grp-attn-w" || arg == "-gaw") { + if (++i >= argc) { invalid_param = true; break; } params.grp_attn_w = std::stoi(argv[i]); - } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { + } else if (arg == "--threads-batch" || arg == "-tb") { + if (++i >= argc) { invalid_param = true; break; } params.n_threads_batch = std::stoi(argv[i]); - } - else if (arg == "--threads-http") - { - if (++i >= argc) - { + } else if (arg == "--threads-http") { + if (++i >= argc) { invalid_param = true; break; } sparams.n_threads_http = std::stoi(argv[i]); - } - else if (arg == "-b" || arg == "--batch-size") - { - if (++i >= argc) - { + } else if (arg == "-b" || arg == "--batch-size") { + if (++i >= argc) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); - } - else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") - { - if (++i >= argc) - { + } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { invalid_param = true; break; } if (llama_supports_gpu_offload()) { params.n_gpu_layers = std::stoi(argv[i]); } else { - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", - {{"n_gpu_layers", params.n_gpu_layers}}); + LOG_WARNING( + "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " + "See main README.md for information on enabling GPU BLAS support", + {{"n_gpu_layers", params.n_gpu_layers}}); } - } - else if (arg == "--split-mode" || arg == "-sm") - { + } else if (arg == "--split-mode" || arg == "-sm") { if (++i >= argc) { invalid_param = true; break; } std::string arg_next = argv[i]; - if (arg_next == "none") - { + if (arg_next == "none") { params.split_mode = LLAMA_SPLIT_MODE_NONE; - } - else if (arg_next == "layer") - { + } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } - else if (arg_next == "row") - { + } else if (arg_next == "row") { params.split_mode = LLAMA_SPLIT_MODE_ROW; - } - else { + } else { invalid_param = true; break; } #ifndef GGML_USE_CUBLAS fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); #endif // GGML_USE_CUBLAS - } - else if (arg == "--tensor-split" || arg == "-ts") - { - if (++i >= argc) - { + } else if (arg == "--tensor-split" || arg == "-ts") { + if (++i >= argc) { invalid_param = true; break; } @@ -2421,25 +2343,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::vector split_arg{it, {}}; GGML_ASSERT(split_arg.size() <= llama_max_devices()); - for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) - { - if (i_device < split_arg.size()) - { + for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { + if (i_device < split_arg.size()) { params.tensor_split[i_device] = std::stof(split_arg[i_device]); - } - else - { + } else { params.tensor_split[i_device] = 0.0f; } } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUBLAS - } - else if (arg == "--main-gpu" || arg == "-mg") - { - if (++i >= argc) - { + } else if (arg == "--main-gpu" || arg == "-mg") { + if (++i >= argc) { invalid_param = true; break; } @@ -2448,98 +2363,70 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); #endif - } - else if (arg == "--lora") - { - if (++i >= argc) - { + } else if (arg == "--lora") { + if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; - } - else if (arg == "--lora-scaled") - { - if (++i >= argc) - { + } else if (arg == "--lora-scaled") { + if (++i >= argc) { invalid_param = true; break; } const char * lora_adapter = argv[i]; - if (++i >= argc) - { + if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; - } - else if (arg == "--lora-base") - { - if (++i >= argc) - { + } else if (arg == "--lora-base") { + if (++i >= argc) { invalid_param = true; break; } params.lora_base = argv[i]; - } - else if (arg == "-v" || arg == "--verbose") - { + } else if (arg == "-v" || arg == "--verbose") { #if SERVER_VERBOSE != 1 LOG_WARNING("server.cpp is not built with verbose logging.", {}); #else server_verbose = true; #endif - } - else if (arg == "--mlock") - { + } else if (arg == "--mlock") { params.use_mlock = true; - } - else if (arg == "--no-mmap") - { + } else if (arg == "--no-mmap") { params.use_mmap = false; - } - else if (arg == "--numa") { + } else if (arg == "--numa") { if (++i >= argc) { invalid_param = true; break; } else { std::string value(argv[i]); /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } else { invalid_param = true; break; } } - } - else if (arg == "--embedding") - { + } else if (arg == "--embedding" || arg == "--embeddings") { params.embedding = true; - } - else if (arg == "-cb" || arg == "--cont-batching") - { + } else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = true; - } - else if (arg == "-np" || arg == "--parallel") - { - if (++i >= argc) - { + } else if (arg == "-np" || arg == "--parallel") { + if (++i >= argc) { invalid_param = true; break; } params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-n" || arg == "--n-predict") - { - if (++i >= argc) - { + } else if (arg == "-n" || arg == "--n-predict") { + if (++i >= argc) { invalid_param = true; break; } params.n_predict = std::stoi(argv[i]); - } else if (arg == "-spf" || arg == "--system-prompt-file") - { - if (++i >= argc) - { + } else if (arg == "-spf" || arg == "--system-prompt-file") { + if (++i >= argc) { invalid_param = true; break; } @@ -2549,67 +2436,39 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - std::string systm_content; + std::string system_prompt; std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), - std::back_inserter(systm_content) + std::back_inserter(system_prompt) ); - llama.system_prompt_process(json::parse(systm_content)); - } - else if (arg == "-ctk" || arg == "--cache-type-k") { + sparams.system_prompt = system_prompt; + } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; - } - else if (arg == "-ctv" || arg == "--cache-type-v") { + } else if (arg == "-ctv" || arg == "--cache-type-v") { params.cache_type_v = argv[++i]; - } - else if(arg == "--mmproj") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.mmproj = argv[i]; - } - else if (arg == "--log-format") - { - if (++i >= argc) - { + } else if (arg == "--log-format") { + if (++i >= argc) { invalid_param = true; break; } - if (std::strcmp(argv[i], "json") == 0) - { + if (std::strcmp(argv[i], "json") == 0) { server_log_json = true; - } - else if (std::strcmp(argv[i], "text") == 0) - { + } else if (std::strcmp(argv[i], "text") == 0) { server_log_json = false; - } - else - { + } else { invalid_param = true; break; } - } - else if (arg == "--log-disable") - { + } else if (arg == "--log-disable") { log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); - } - else if (arg == "--slots-endpoint-disable") - { + } else if (arg == "--slots-endpoint-disable") { sparams.slots_endpoint = false; - } - else if (arg == "--metrics") - { + } else if (arg == "--metrics") { sparams.metrics_endpoint = true; - } - else if (arg == "--chat-template") - { - if (++i >= argc) - { + } else if (arg == "--chat-template") { + if (++i >= argc) { invalid_param = true; break; } @@ -2620,9 +2479,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } sparams.chat_template = argv[i]; - } - else if (arg == "--override-kv") - { + } else if (arg == "--override-kv") { if (++i >= argc) { invalid_param = true; break; @@ -2633,6 +2490,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } + struct llama_model_kv_override kvo; std::strncpy(kvo.key, argv[i], sep - argv[i]); kvo.key[sep - argv[i]] = 0; @@ -2663,67 +2521,28 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.kv_overrides.push_back(kvo); - } - else - { + } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } + if (!params.kv_overrides.empty()) { params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } - if (invalid_param) - { + if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } -/* llama.cpp completion api semantics */ -static json format_partial_response( - llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector &probs -) { - json res = json - { - {"content", content }, - {"stop", false}, - {"slot_id", slot->id }, - {"multimodal", llama.multimodal } - }; - - if (slot->sparams.n_probs > 0) - { - res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); - } - - return res; -} - -static json format_tokenizer_response(const std::vector &tokens) -{ - return json { - {"tokens", tokens} - }; -} - -static json format_detokenized_response(std::string content) -{ - return json { - {"content", content} - }; -} - - -static void log_server_request(const httplib::Request &req, const httplib::Response &res) -{ +static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port - if (req.path == "/v1/health" || req.path == "/v1/completions") - { + if (req.path == "/v1/health" || req.path == "/v1/completions") { return; } @@ -2742,24 +2561,9 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_slot *slot) -{ - auto & gtps = slot->generated_token_probs; - auto translator = token_translator{llama.ctx}; - auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; - const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (slot->generated_text.capacity() < slot->generated_text.size() + len) - { - slot->generated_text.reserve(slot->generated_text.size() + len); - } - for (const completion_token_output & cto : gtps) - { - slot->generated_text += translator(cto); - } -} - std::function shutdown_handler; std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; + inline void signal_handler(int signal) { if (is_terminating.test_and_set()) { // in case it hangs, we can force terminate the server by hitting Ctrl+C twice @@ -2767,40 +2571,45 @@ inline void signal_handler(int signal) { fprintf(stderr, "Received second interrupt, terminating immediately.\n"); exit(1); } + shutdown_handler(signal); } -int main(int argc, char **argv) -{ +int main(int argc, char ** argv) { #if SERVER_VERBOSE != 1 log_disable(); #endif // own arguments required by this example - gpt_params params; + gpt_params params; server_params sparams; // struct that contains llama context and inference - llama_server_context llama; + server_context ctx_server; - server_params_parse(argc, argv, sparams, params, llama); + server_params_parse(argc, argv, sparams, params); - if (params.model_alias == "unknown") - { + if (!sparams.system_prompt.empty()) { + ctx_server.system_prompt_set(json::parse(sparams.system_prompt)); + } + + if (params.model_alias == "unknown") { params.model_alias = params.model; } llama_backend_init(); llama_numa_init(params.numa); - LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, - {"commit", LLAMA_COMMIT}}); + LOG_INFO("build info", { + {"build", LLAMA_BUILD_NUMBER}, + {"commit", LLAMA_COMMIT} + }); LOG_INFO("system info", { - {"n_threads", params.n_threads}, - {"n_threads_batch", params.n_threads_batch}, - {"total_threads", std::thread::hardware_concurrency()}, - {"system_info", llama_print_system_info()}, - }); + {"n_threads", params.n_threads}, + {"n_threads_batch", params.n_threads_batch}, + {"total_threads", std::thread::hardware_concurrency()}, + {"system_info", llama_print_system_info()}, + }); httplib::Server svr; @@ -2809,152 +2618,163 @@ int main(int argc, char **argv) svr.set_default_headers({{"Server", "llama.cpp"}}); // CORS preflight - svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + svr.Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_header("Access-Control-Allow-Credentials", "true"); - res.set_header("Access-Control-Allow-Methods", "POST"); - res.set_header("Access-Control-Allow-Headers", "*"); + res.set_header("Access-Control-Allow-Methods", "POST"); + res.set_header("Access-Control-Allow-Headers", "*"); }); - svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) { + svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); - switch(current_state) { - case SERVER_STATE_READY: { - // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + switch (current_state) { + case SERVER_STATE_READY: + { + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.type = SERVER_TASK_TYPE_METRICS; + task.id_target = -1; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - int n_idle_slots = result.result_json["idle"]; - int n_processing_slots = result.result_json["processing"]; + const int n_idle_slots = result.data["idle"]; + const int n_processing_slots = result.data["processing"]; - json health = { + json health = { {"status", "ok"}, {"slots_idle", n_idle_slots}, - {"slots_processing", n_processing_slots}}; - res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { - health["slots"] = result.result_json["slots"]; - } + {"slots_processing", n_processing_slots} + }; - if (n_idle_slots == 0) { - health["status"] = "no slot available"; - if (req.has_param("fail_on_no_slot")) { - res.status = 503; // HTTP Service Unavailable + res.status = 200; // HTTP OK + if (sparams.slots_endpoint && req.has_param("include_slots")) { + health["slots"] = result.data["slots"]; } + + if (n_idle_slots == 0) { + health["status"] = "no slot available"; + if (req.has_param("fail_on_no_slot")) { + res.status = 503; // HTTP Service Unavailable + } + } + + res.set_content(health.dump(), "application/json"); + break; } - res.set_content(health.dump(), "application/json"); - break; - } case SERVER_STATE_LOADING_MODEL: - res.set_content(R"({"status": "loading model"})", "application/json"); - res.status = 503; // HTTP Service Unavailable - break; + { + res.set_content(R"({"status": "loading model"})", "application/json"); + res.status = 503; // HTTP Service Unavailable + } break; case SERVER_STATE_ERROR: - res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); - res.status = 500; // HTTP Internal Server Error - break; + { + res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); + res.status = 500; // HTTP Internal Server Error + } break; } }); if (sparams.slots_endpoint) { - svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) { + svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - res.set_content(result.result_json["slots"].dump(), "application/json"); + res.set_content(result.data["slots"].dump(), "application/json"); res.status = 200; // HTTP OK }); } if (sparams.metrics_endpoint) { - svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) { + svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - json data = result.result_json; + json data = result.data; - uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - uint64_t t_prompt_processing = data["t_prompt_processing"]; + const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; + const uint64_t t_prompt_processing = data["t_prompt_processing"]; - uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - uint64_t t_tokens_generation = data["t_tokens_generation"]; + const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; + const uint64_t t_tokens_generation = data["t_tokens_generation"]; - int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { - {"counter", {{ - {"name", "prompt_tokens_total"}, - {"help", "Number of prompt tokens processed."}, - {"value", data["n_prompt_tokens_processed_total"]} - }, { - {"name", "tokens_predicted_total"}, - {"help", "Number of generation tokens processed."}, - {"value", data["n_tokens_predicted_total"]} - }}}, - {"gauge", {{ - {"name", "prompt_tokens_seconds"}, - {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} - },{ - {"name", "predicted_tokens_seconds"}, - {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} - },{ - {"name", "kv_cache_usage_ratio"}, - {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} - },{ - {"name", "kv_cache_tokens"}, - {"help", "KV-cache tokens."}, - {"value", data["kv_cache_tokens_count"]} - },{ - {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", data["processing"]} - },{ - {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", data["deferred"]} - }}} + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", data["n_prompt_tokens_processed_total"]} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", data["n_tokens_predicted_total"]} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", data["kv_cache_tokens_count"]} + },{ + {"name", "requests_processing"}, + {"help", "Number of request processing."}, + {"value", data["processing"]} + },{ + {"name", "requests_deferred"}, + {"help", "Number of request deferred."}, + {"value", data["deferred"]} + }}} }; std::stringstream prometheus; - for (const auto& el : all_metrics_def.items()) { - const auto& type = el.key(); - const auto& metrics_def = el.value(); - for (const auto& metric_def : metrics_def) { - std::string name = metric_def["name"]; - std::string help = metric_def["help"]; + + for (const auto & el : all_metrics_def.items()) { + const auto & type = el.key(); + const auto & metrics_def = el.value(); + + for (const auto & metric_def : metrics_def) { + const std::string name = metric_def["name"]; + const std::string help = metric_def["help"]; + auto value = json_value(metric_def, "value", 0); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" << "# TYPE llamacpp:" << name << " " << type << "\n" @@ -2969,49 +2789,39 @@ int main(int argc, char **argv) svr.set_logger(log_server_request); - svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) - { - const char fmt[] = "500 Internal Server Error\n%s"; - char buf[BUFSIZ]; - try - { - std::rethrow_exception(std::move(ep)); - } - catch (std::exception &e) - { - snprintf(buf, sizeof(buf), fmt, e.what()); - } - catch (...) - { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); - } - res.set_content(buf, "text/plain; charset=utf-8"); - res.status = 500; - }); + svr.set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { + const char fmt[] = "500 Internal Server Error\n%s"; - svr.set_error_handler([](const httplib::Request &, httplib::Response &res) - { - if (res.status == 401) - { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) - { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } - else if (res.status == 404) - { - res.set_content("File Not Found", "text/plain; charset=utf-8"); - res.status = 404; - } - }); + char buf[BUFSIZ]; + try { + std::rethrow_exception(std::move(ep)); + } catch (std::exception &e) { + snprintf(buf, sizeof(buf), fmt, e.what()); + } catch (...) { + snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); + } + + res.set_content(buf, "text/plain; charset=utf-8"); + res.status = 500; + }); + + svr.set_error_handler([](const httplib::Request &, httplib::Response & res) { + if (res.status == 401) { + res.set_content("Unauthorized", "text/plain; charset=utf-8"); + } + if (res.status == 400) { + res.set_content("Invalid request", "text/plain; charset=utf-8"); + } + if (res.status == 404) { + res.set_content("File Not Found", "text/plain; charset=utf-8"); + } + }); // set timeouts and change hostname and port svr.set_read_timeout (sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); - if (!svr.bind_to_port(sparams.hostname, sparams.port)) - { + if (!svr.bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } @@ -3020,8 +2830,9 @@ int main(int argc, char **argv) svr.set_base_dir(sparams.public_path); std::unordered_map log_data; + log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); + log_data["port"] = std::to_string(sparams.port); if (sparams.api_keys.size() == 1) { log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); @@ -3030,20 +2841,23 @@ int main(int argc, char **argv) } // load the model - if (!llama.load_model(params)) - { + if (!ctx_server.load_model(params)) { state.store(SERVER_STATE_ERROR); return 1; } else { - llama.initialize(); + ctx_server.initialize(); state.store(SERVER_STATE_READY); - LOG_INFO("model loaded", {}); } - const auto model_meta = llama.model_meta(); + + LOG_INFO("model loaded", {}); + + const auto model_meta = ctx_server.model_meta(); if (sparams.chat_template.empty()) { // custom chat template is not supplied - // check if the template comes with the model is supported by us - llama.validate_model_chat_template(sparams); + if (!ctx_server.validate_model_chat_template()) { + LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); + sparams.chat_template = "chatml"; + } } // Middleware for API key validation @@ -3055,6 +2869,7 @@ int main(int argc, char **argv) // Check for API key in the header auto auth_header = req.get_header_value("Authorization"); + std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); @@ -3073,179 +2888,173 @@ int main(int argc, char **argv) }; // this is only called if no index.html is found in the public --path - svr.Get("/", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); - return false; - }); + svr.Get("/", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); + return false; + }); // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); - return false; - }); + svr.Get("/index.js", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); + return false; + }); // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); - return false; - }); + svr.Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); + return false; + }); // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); - return false; - }); + svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); + return false; + }); - svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - json data = { - { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props }, - { "total_slots", llama.params.n_parallel } - }; - res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + json data = { + { "user_name", ctx_server.name_user.c_str() }, + { "assistant_name", ctx_server.name_assistant.c_str() }, + { "default_generation_settings", ctx_server.default_generation_settings_for_props }, + { "total_slots", ctx_server.params.n_parallel } + }; - svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) - { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "data: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) { - break; - } - } else { - const std::string str = - "error: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - break; - } + res.set_content(data.dump(), "application/json; charset=utf-8"); + }); + + svr.Post("/completion", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; + } + + json data = json::parse(req.body); + + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, false, false); + + if (!json_value(data, "stream", false)) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error && result.stop) { + res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); + } else { + res.status = 500; + res.set_content(result.data["content"], "text/plain; charset=utf-8"); + } + + ctx_server.queue_results.remove_waiting_task_id(id_task); + } else { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + while (true) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + const std::string str = + "data: " + + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + + if (!sink.write(str.c_str(), str.size())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; } - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; + if (result.stop) { + break; + } + } else { + const std::string str = + "error: " + + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; - auto on_complete = [task_id, &llama] (bool) - { - // cancel - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - }; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + if (!sink.write(str.c_str(), str.size())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; + } + + break; + } } - }); - svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - std::time_t t = std::time(0); - - json models = { - {"object", "list"}, - {"data", { - { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"}, - {"meta", model_meta} - }, - }} - }; + ctx_server.queue_results.remove_waiting_task_id(id_task); + sink.done(); - res.set_content(models.dump(), "application/json; charset=utf-8"); - }); + return true; + }; - const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) - { + auto on_complete = [id_task, &ctx_server] (bool) { + // cancel + ctx_server.request_cancel(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + }; + + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); + + svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + + json models = { + {"object", "list"}, + {"data", { + { + {"id", params.model_alias}, + {"object", "model"}, + {"created", std::time(0)}, + {"owned_by", "llamacpp"}, + {"meta", model_meta} + }, + }} + }; + + res.set_content(models.dump(), "application/json; charset=utf-8"); + }); + + const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } - json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); + json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); + + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, false, false); if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); + server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { - json oaicompat_result = format_final_response_oaicompat(data, result); + json result_oai = format_final_response_oaicompat(data, result.data); - res.set_content(oaicompat_result.dump(-1, ' ', false, - json::error_handler_t::replace), - "application/json; charset=utf-8"); + res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 500; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); + res.set_content(result.data["content"], "text/plain; charset=utf-8"); } - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { while (true) { - task_result llama_result = llama.queue_results.recv(task_id); - if (!llama_result.error) { - std::vector result_array = format_partial_response_oaicompat( llama_result); + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + std::vector result_array = format_partial_response_oaicompat(result.data); - for (auto it = result_array.begin(); it != result_array.end(); ++it) - { + for (auto it = result_array.begin(); it != result_array.end(); ++it) { if (!it->empty()) { const std::string str = "data: " + @@ -3253,251 +3062,235 @@ int main(int argc, char **argv) "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } } } - if (llama_result.stop) { + if (result.stop) { break; } } else { const std::string str = "error: " + - llama_result.result_json.dump(-1, ' ', false, - json::error_handler_t::replace) + + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } break; } } sink.done(); - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.queue_results.remove_waiting_task_id(id_task); return true; }; - auto on_complete = [task_id, &llama](bool) { + auto on_complete = [id_task, &ctx_server](bool) { // cancel request - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); + ctx_server.request_cancel(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; - svr.Post("/chat/completions", chat_completions); + svr.Post("/chat/completions", chat_completions); svr.Post("/v1/chat/completions", chat_completions); - svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, true, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) - { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "data: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) - { - break; - } - } - else - { - break; - } - } + svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; + } - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; + json data = json::parse(req.body); - auto on_complete = [task_id, &llama] (bool) - { - // cancel - llama.request_cancel(task_id); - }; + const int id_task = ctx_server.queue_tasks.get_new_id(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, data, true, false); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) - { return res.set_content("", "application/json; charset=utf-8"); }); + if (!json_value(data, "stream", false)) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error && result.stop) { + res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); + } else { + res.status = 404; + res.set_content(result.data["content"], "text/plain; charset=utf-8"); + } - svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - std::vector tokens; - if (body.count("content") != 0) - { - tokens = llama.tokenize(body["content"], false); - } - const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + ctx_server.queue_results.remove_waiting_task_id(id_task); + } else { + const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + while (true) { + server_task_result result = ctx_server.queue_results.recv(id_task); + if (!result.error) { + const std::string str = + "data: " + + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; - svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - std::string content; - if (body.count("tokens") != 0) - { - const std::vector tokens = body["tokens"]; - content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); - } + LOG_VERBOSE("data stream", { + { "to_send", str } + }); - const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + if (!sink.write(str.c_str(), str.size())) { + ctx_server.queue_results.remove_waiting_task_id(id_task); + return false; + } - svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - json prompt; - if (body.count("content") != 0) - { - prompt = body["content"]; - } - else - { - prompt = ""; + if (result.stop) { + break; + } + } else { + break; + } } - json image_data; - if (body.count("image_data") != 0) { - image_data = body["image_data"]; - } - else - { - image_data = ""; - } + ctx_server.queue_results.remove_waiting_task_id(id_task); + sink.done(); - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); + return true; + }; - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); + auto on_complete = [id_task, &ctx_server] (bool) { + ctx_server.request_cancel(id_task); + }; - // send the result - return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); - }); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); - svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); + svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + return res.set_content("", "application/json; charset=utf-8"); + }); - json prompt; - if (body.count("input") != 0) - { - prompt = body["input"]; - // batch - if(prompt.is_array()) { - json data = json::array(); - int i = 0; - for (const json &elem : prompt) { - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - json embedding = json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }; - data.push_back(embedding); - } - json result = format_embeddings_response_oaicompat(body, data); - return res.set_content(result.dump(), "application/json; charset=utf-8"); - } - } - else - { - prompt = ""; - } + svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + const json body = json::parse(req.body); - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1); + std::vector tokens; + if (body.count("content") != 0) { + tokens = ctx_server.tokenize(body["content"], false); + } + const json data = format_tokenizer_response(tokens); + return res.set_content(data.dump(), "application/json; charset=utf-8"); + }); - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); + svr.Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + const json body = json::parse(req.body); - json data = json::array({json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", 0}, - {"object", "embedding"} - }} - ); + std::string content; + if (body.count("tokens") != 0) { + const std::vector tokens = body["tokens"]; + content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); + } - json root = format_embeddings_response_oaicompat(body, data); + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json; charset=utf-8"); + }); - // send the result - return res.set_content(root.dump(), "application/json; charset=utf-8"); - }); + svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!params.embedding) { + res.status = 501; + res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); + return; + } - // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? - // "Bus error: 10" - this is on macOS, it does not crash on Linux - //std::thread t2([&]() - /*{ - bool running = true; - while (running) - { - running = llama.update_slots(); + const json body = json::parse(req.body); + + json prompt; + if (body.count("content") != 0) { + prompt = body["content"]; + } else { + prompt = ""; + } + + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + // send the result + return res.set_content(result.data.dump(), "application/json; charset=utf-8"); + }); + + svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!params.embedding) { + res.status = 501; + res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); + return; + } + + const json body = json::parse(req.body); + + json prompt; + if (body.count("input") != 0) { + prompt = body["input"]; + if (prompt.is_array()) { + json data = json::array(); + + int i = 0; + for (const json & elem : prompt) { + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + json embedding = json{ + {"embedding", json_value(result.data, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + + data.push_back(embedding); + } + + json result = format_embeddings_response_oaicompat(body, data); + + return res.set_content(result.dump(), "application/json; charset=utf-8"); + } + } else { + prompt = ""; } - }*/ - //); + + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); + + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); + + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + json data = json::array({json{ + {"embedding", json_value(result.data, "embedding", json::array())}, + {"index", 0}, + {"object", "embedding"} + }} + ); + + json root = format_embeddings_response_oaicompat(body, data); + + return res.set_content(root.dump(), "application/json; charset=utf-8"); + }); if (sparams.n_threads_http < 1) { // +2 threads for monitoring endpoints @@ -3507,34 +3300,33 @@ int main(int argc, char **argv) svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); + // run the HTTP server in a thread - see comment below - std::thread t([&]() - { - if (!svr.listen_after_bind()) - { - state.store(SERVER_STATE_ERROR); - return 1; - } + std::thread t([&]() { + if (!svr.listen_after_bind()) { + state.store(SERVER_STATE_ERROR); + return 1; + } - return 0; - }); + return 0; + }); - llama.queue_tasks.on_new_task(std::bind( - &llama_server_context::process_single_task, &llama, std::placeholders::_1)); - llama.queue_tasks.on_finish_multitask(std::bind( - &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_run_slots(std::bind( - &llama_server_context::update_slots, &llama)); - llama.queue_results.on_multitask_update(std::bind( - &llama_server_queue::update_multitask, - &llama.queue_tasks, + ctx_server.queue_tasks.on_new_task(std::bind( + &server_context::process_single_task, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_finish_multitask(std::bind( + &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_run_slots(std::bind( + &server_context::update_slots, &ctx_server)); + ctx_server.queue_results.on_multitask_update(std::bind( + &server_queue::update_multitask, + &ctx_server.queue_tasks, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3 )); shutdown_handler = [&](int) { - llama.queue_tasks.terminate(); + ctx_server.queue_tasks.terminate(); }; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) @@ -3549,10 +3341,13 @@ int main(int argc, char **argv) }; SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - llama.queue_tasks.start_loop(); + + ctx_server.queue_tasks.start_loop(); + svr.stop(); t.join(); llama_backend_free(); + return 0; } diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature new file mode 100644 index 0000000000000..b47661e943ca6 --- /dev/null +++ b/examples/server/tests/features/embeddings.feature @@ -0,0 +1,94 @@ +@llama.cpp +@embeddings +Feature: llama.cpp server + + Background: Server startup + Given a server listening on localhost:8080 + And a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models + And a model alias bert-bge-small + And 42 as server seed + And 2 slots + And 1024 as batch size + And 2048 KV cache size + And embeddings extraction + Then the server is starting + Then the server is healthy + + Scenario: Embedding + When embeddings are computed for: + """ + What is the capital of Bulgaria ? + """ + Then embeddings are generated + + Scenario: OAI Embeddings compatibility + Given a model bert-bge-small + When an OAI compatible embeddings computation request for: + """ + What is the capital of Spain ? + """ + Then embeddings are generated + + Scenario: OAI Embeddings compatibility with multiple inputs + Given a model bert-bge-small + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + When an OAI compatible embeddings computation request for multiple inputs + Then embeddings are generated + + Scenario: Multi users embeddings + Given a prompt: + """ + Write a very long story about AI. + """ + And a prompt: + """ + Write another very long music lyrics. + """ + And a prompt: + """ + Write a very long poem. + """ + And a prompt: + """ + Write a very long joke. + """ + Given concurrent embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated + + Scenario: Multi users OAI compatibility embeddings + Given a prompt: + """ + In which country Paris is located ? + """ + And a prompt: + """ + Is Madrid the capital of Spain ? + """ + And a prompt: + """ + What is the biggest US city ? + """ + And a prompt: + """ + What is the capital of Bulgaria ? + """ + And a model bert-bge-small + Given concurrent OAI embedding requests + Then the server is busy + Then the server is idle + Then all embeddings are generated + + Scenario: All embeddings should be the same + Given 10 fixed prompts + And a model bert-bge-small + Given concurrent OAI embedding requests + Then all embeddings are the same diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 86cdf72829f8c..066698c8e3c1e 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -9,7 +9,6 @@ Feature: Parallel And 512 as batch size And 64 KV cache size And 2 slots - And embeddings extraction And continuous batching Then the server is starting Then the server is healthy @@ -99,48 +98,3 @@ Feature: Parallel Then the server is busy Then the server is idle Then all prompts are predicted - - Scenario: Multi users embeddings - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - Given concurrent embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: Multi users OAI compatibility embeddings - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - And a prompt: - """ - What is the biggest US city ? - """ - And a prompt: - """ - What is the capital of Bulgaria ? - """ - And a model tinyllama-2 - Given concurrent OAI embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 7c977bccecaad..f3b758c798c8d 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -49,34 +49,6 @@ Feature: llama.cpp server | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | - Scenario: Embedding - When embeddings are computed for: - """ - What is the capital of Bulgaria ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility - Given a model tinyllama-2 - When an OAI compatible embeddings computation request for: - """ - What is the capital of Spain ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility with multiple inputs - Given a model tinyllama-2 - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - When an OAI compatible embeddings computation request for multiple inputs - Then embeddings are generated - Scenario: Tokenize / Detokenize When tokenizing: """ diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 3195278022ffb..a0b2ffdfef9f0 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -10,6 +10,7 @@ from re import RegexFlag import aiohttp +import numpy as np import openai from behave import step from behave.api.async_step import async_run_until_complete @@ -24,6 +25,9 @@ def step_server_config(context, server_fqdn, server_port): if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) print(f"$PORT set, overriding server port with to {context.server_port}") + if 'FQDN' in os.environ: + context.server_fqdn = os.environ['FQDN'] + print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}") context.base_url = f'http://{context.server_fqdn}:{context.server_port}' @@ -34,6 +38,7 @@ def step_server_config(context, server_fqdn, server_port): context.n_ga_w = None context.n_gpu_layer = None context.n_predict = None + context.n_prompts = 0 context.n_server_predict = None context.n_slots = None context.prompt_prefix = None @@ -202,6 +207,7 @@ def step_n_tokens_predicted(context, predicted_n): @step(u'a user prompt {user_prompt}') def step_user_prompt(context, user_prompt): context.prompts.append(user_prompt) + context.n_prompts = len(context.prompts) @step(u'a system prompt {system_prompt}') @@ -290,6 +296,12 @@ def step_prompt_passkey(context): context.prompt_passkey = context.text +@step(u'{n_prompts:d} fixed prompts') +def step_fixed_prompts(context, n_prompts): + context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)]) + context.n_prompts = n_prompts + + @step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') def step_prompt_passkey(context, passkey, i_pos): prompt = "" @@ -301,6 +313,7 @@ def step_prompt_passkey(context, passkey, i_pos): passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) + context.n_prompts = len(context.prompts) @step(u'an OAI compatible chat completions request with {api_error} api error') @@ -341,11 +354,13 @@ async def step_oai_chat_completions(context, api_error): @step(u'a prompt') def step_a_prompt(context): context.prompts.append(context.text) + context.n_prompts = len(context.prompts) @step(u'a prompt {prompt}') def step_a_prompt_prompt(context, prompt): context.prompts.append(prompt) + context.n_prompts = len(context.prompts) @step(u'concurrent completion requests') @@ -430,25 +445,47 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None): @step(u'embeddings are computed for') @async_run_until_complete async def step_compute_embedding(context): + context.n_prompts = 1 context.embeddings = await request_embedding(context.text, base_url=context.base_url) +@step(u'all embeddings are the same') +@async_run_until_complete +async def step_all_embeddings_are_the_same(context): + n_embedding_requests = await gather_tasks_results(context) + assert n_embedding_requests > 0 + embeddings = [] + for i in range(n_embedding_requests): + embedding = context.tasks_result.pop().pop() + embeddings.append(embedding) + assert_embeddings(embedding) + n = len(embeddings) + for i in range(n-1): + for j in range(i+1, n): + embedding1 = np.array(embeddings[i]) + embedding2 = np.array(embeddings[j]) + if context.debug: + print(f"embedding1: {embedding1[-8:]}\n") + print(f"embedding2: {embedding2[-8:]}\n") + similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) + msg = f"Similarity between {i} and {j}: {similarity:.10f}" + if context.debug: + print(f"{msg}\n") + assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg + @step(u'embeddings are generated') def step_assert_embeddings(context): - if len(context.prompts) == 0: - assert_embeddings(context.embeddings) - else: - assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n" - f"context.prompts={context.prompts}\n" - f"context.embeddings={context.embeddings}") - for embedding in context.embeddings: - context.prompts.pop() - assert_embeddings(embedding) + assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n" + f"context.n_prompts={context.n_prompts}\n" + f"context.embeddings={context.embeddings}") + for embedding in context.embeddings: + assert_embeddings(embedding) @step(u'an OAI compatible embeddings computation request for') @async_run_until_complete async def step_oai_compute_embeddings(context): + context.n_prompts = 1 context.embeddings = await request_oai_embeddings(context.text, base_url=context.base_url, user_api_key=context.user_api_key, @@ -462,6 +499,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context): base_url=context.base_url, user_api_key=context.user_api_key, model=context.model) + context.prompts.clear() @step(u'concurrent embedding requests') @@ -488,9 +526,9 @@ async def step_concurrent_oai_embedding_requests(context): @async_run_until_complete() async def all_embeddings_are_generated(context): n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests > 0 + assert n_embedding_requests == context.n_prompts for i in range(n_embedding_requests): - assert_embeddings(context.tasks_result.pop()) + assert_embeddings(context.tasks_result.pop().pop()) @step(u'tokenizing') @@ -588,11 +626,11 @@ def step_supported_models(context, i_model, param, preposition, param_value): async def concurrent_requests(context, f_completion, *args, **kwargs): - n_prompts = len(context.prompts) + context.n_prompts = len(context.prompts) if context.debug: - print(f"starting {n_prompts} concurrent completion requests...") - assert n_prompts > 0 - for prompt_no in range(n_prompts): + print(f"starting {context.n_prompts} concurrent completion requests...") + assert context.n_prompts > 0 + for prompt_no in range(context.n_prompts): shifted_args = [context.prompts.pop(), *args] context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) await asyncio.sleep(0.1) @@ -765,7 +803,7 @@ async def request_embedding(content, base_url=None): }) as response: assert response.status == 200 response_json = await response.json() - return response_json['embedding'] + return [response_json['embedding']] async def request_oai_embeddings(input, @@ -775,6 +813,7 @@ async def request_oai_embeddings(input, user_api_key = user_api_key if user_api_key is not None else 'nope' if async_client: origin = 'llama.cpp' + headers=[] if user_api_key is not None: headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} async with aiohttp.ClientSession() as session: @@ -783,14 +822,21 @@ async def request_oai_embeddings(input, "input": input, "model": model, }, - headers=headers) as response: + headers=headers, + timeout=3600) as response: assert response.status == 200, f"received status code not expected: {response.status}" assert response.headers['Access-Control-Allow-Origin'] == origin assert response.headers['Content-Type'] == "application/json; charset=utf-8" response_json = await response.json() assert response_json['model'] == model, f"invalid model received: {response_json['model']}" assert response_json['object'] == 'list' - return response_json['data'] + if isinstance(input, collections.abc.Sequence): + embeddings = [] + for an_oai_embeddings in response_json['data']: + embeddings.append(an_oai_embeddings['embedding']) + else: + embeddings = [response_json['data']['embedding']] + return embeddings else: openai.api_key = user_api_key openai.api_base = f'{base_url}/v1' @@ -804,7 +850,7 @@ async def request_oai_embeddings(input, for an_oai_embeddings in oai_embeddings.data: embeddings.append(an_oai_embeddings.embedding) else: - embeddings = oai_embeddings.data.embedding + embeddings = [oai_embeddings.data.embedding] return embeddings @@ -899,6 +945,8 @@ def assert_embeddings(embeddings): assert len(embeddings) > 0 embeddings_computed = False for emb in embeddings: + if not isinstance(emb, float): + assert False, f"Bad embeddings: {embeddings}" if emb != 0: embeddings_computed = True assert embeddings_computed, f"Embeddings: {embeddings}" diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 5d4210164a50a..2e4f42ad28c23 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,5 +1,6 @@ aiohttp~=3.9.3 behave~=1.2.6 huggingface_hub~=0.20.3 +numpy~=1.24.4 openai~=0.25.0 prometheus-client~=0.20.0 diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index b6e49d8b98a2a..df0a27782e646 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -1,15 +1,16 @@ #pragma once -#include -#include -#include -#include -#include -#include +#include "llama.h" +#include "common.h" #include "json.hpp" -#include "../llava/clip.h" +#include +#include +#include +#include + +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::json; @@ -37,83 +38,35 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded - SERVER_STATE_ERROR // An error occurred, load_model failed -}; - -enum task_type { - TASK_TYPE_COMPLETION, - TASK_TYPE_CANCEL, - TASK_TYPE_NEXT_RESPONSE, - TASK_TYPE_METRICS -}; - -struct task_server { - int id = -1; // to be filled by llama_server_queue - int target_id; - task_type type; - json data; - bool infill_mode = false; - bool embedding_mode = false; - int multitask_id = -1; -}; - -struct task_result { - int id; - int multitask_id = -1; - bool stop; - bool error; - json result_json; -}; - -struct task_multi { - int id; - std::set subtasks_remaining{}; - std::vector results{}; -}; - -// completion token output with probabilities -struct completion_token_output { - struct token_prob - { - llama_token tok; - float prob; - }; - - std::vector probs; - llama_token tok; - std::string text_to_send; -}; - -struct token_translator { - llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } -}; +template +static T json_value(const json &body, const std::string &key, const T &default_value) { + // Fallback null to default value + return body.contains(key) && !body.at(key).is_null() + ? body.value(key, default_value) + : default_value; +} static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); json log = nlohmann::ordered_json{ - {"tid", ss_tid.str()}, + {"tid", ss_tid.str()}, {"timestamp", time(nullptr)}, }; if (server_log_json) { - log.merge_patch( - { - {"level", level}, - {"function", function}, - {"line", line}, - {"msg", message}, - }); + log.merge_patch( { + {"level", level}, + {"function", function}, + {"line", line}, + {"msg", message}, + }); + if (!extra.empty()) { log.merge_patch(extra); } - std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush; + printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str()); } else { char buf[1024]; snprintf(buf, 1024, "%4s [%24s] %s", level, function, message); @@ -136,22 +89,13 @@ static inline void server_log(const char *level, const char *function, int line, } // -// server utils +// chat template utils // -template -static T json_value(const json &body, const std::string &key, const T &default_value) { - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; -} - // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid inline bool verify_custom_template(const std::string & tmpl) { llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size()); + int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); return res >= 0; } @@ -163,7 +107,7 @@ inline std::string format_chat(const struct llama_model * model, const std::stri std::vector chat(messages.size()); for (size_t i = 0; i < messages.size(); ++i) { - auto &curr_msg = messages[i]; + const auto & curr_msg = messages[i]; str[i*2 + 0] = json_value(curr_msg, "role", std::string("")); str[i*2 + 1] = json_value(curr_msg, "content", std::string("")); alloc_size += str[i*2 + 1].length(); @@ -183,261 +127,13 @@ inline std::string format_chat(const struct llama_model * model, const std::stri res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); } - std::string formatted_chat(buf.data(), res); + const std::string formatted_chat(buf.data(), res); + LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}}); return formatted_chat; } -// -// work queue utils -// - -struct llama_server_queue { - int id = 0; - std::mutex mutex_tasks; - bool running; - // queues - std::vector queue_tasks; - std::vector queue_tasks_deferred; - std::vector queue_multitasks; - std::condition_variable condition_tasks; - // callback functions - std::function callback_new_task; - std::function callback_finish_multitask; - std::function callback_run_slots; - - // Add a new task to the end of the queue - int post(task_server task) { - std::unique_lock lock(mutex_tasks); - if (task.id == -1) { - task.id = id++; - LOG_VERBOSE("new task id", {{"new_id", task.id}}); - } - queue_tasks.push_back(std::move(task)); - condition_tasks.notify_one(); - return task.id; - } - - // Add a new task, but defer until one slot is available - void defer(task_server task) { - std::unique_lock lock(mutex_tasks); - queue_tasks_deferred.push_back(std::move(task)); - } - - // Get the next id for creating anew task - int get_new_id() { - std::unique_lock lock(mutex_tasks); - int new_id = id++; - LOG_VERBOSE("new task id", {{"new_id", new_id}}); - return new_id; - } - - // Register function to process a new task - void on_new_task(std::function callback) { - callback_new_task = callback; - } - - // Register function to process a multitask when it is finished - void on_finish_multitask(std::function callback) { - callback_finish_multitask = callback; - } - - // Register the function to be called when all slots data is ready to be processed - void on_run_slots(std::function callback) { - callback_run_slots = callback; - } - - // Call when the state of one slot is changed - void notify_slot_changed() { - // move deferred tasks back to main loop - std::unique_lock lock(mutex_tasks); - for (auto & task : queue_tasks_deferred) { - queue_tasks.push_back(std::move(task)); - } - queue_tasks_deferred.clear(); - } - - // end the start_loop routine - void terminate() { - { - std::unique_lock lock(mutex_tasks); - running = false; - } - condition_tasks.notify_all(); - } - - /** - * Main loop consists of these steps: - * - Wait until a new task arrives - * - Process the task (i.e. maybe copy data into slot) - * - Check if multitask is finished - * - Run all slots - */ - void start_loop() { - running = true; - while (true) { - LOG_VERBOSE("new task may arrive", {}); - { - while (true) - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - lock.unlock(); - break; - } - task_server task = queue_tasks.front(); - queue_tasks.erase(queue_tasks.begin()); - lock.unlock(); - LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); - callback_new_task(task); - } - LOG_VERBOSE("update_multitasks", {}); - // check if we have any finished multitasks - auto queue_iterator = queue_multitasks.begin(); - while (queue_iterator != queue_multitasks.end()) - { - if (queue_iterator->subtasks_remaining.empty()) - { - // all subtasks done == multitask is done - task_multi current_multitask = *queue_iterator; - callback_finish_multitask(current_multitask); - // remove this multitask - queue_iterator = queue_multitasks.erase(queue_iterator); - } - else - { - ++queue_iterator; - } - } - // all tasks in the current loop is processed, slots data is now ready - LOG_VERBOSE("callback_run_slots", {}); - callback_run_slots(); - } - LOG_VERBOSE("wait for new task", {}); - // wait for new task - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - if (!running) { - LOG_VERBOSE("ending start_loop", {}); - return; - } - condition_tasks.wait(lock, [&]{ - return (!queue_tasks.empty() || !running); - }); - } - } - } - } - - // - // functions to manage multitasks - // - - // add a multitask by specifying the id of all subtask (subtask is a task_server) - void add_multitask(int multitask_id, std::vector& sub_ids) - { - std::lock_guard lock(mutex_tasks); - task_multi multi; - multi.id = multitask_id; - std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); - queue_multitasks.push_back(multi); - } - - // updatethe remaining subtasks, while appending results to multitask - void update_multitask(int multitask_id, int subtask_id, task_result& result) - { - std::lock_guard lock(mutex_tasks); - for (auto& multitask : queue_multitasks) - { - if (multitask.id == multitask_id) - { - multitask.subtasks_remaining.erase(subtask_id); - multitask.results.push_back(result); - } - } - } -}; - -struct llama_server_response { - typedef std::function callback_multitask_t; - callback_multitask_t callback_update_multitask; - // for keeping track of all tasks waiting for the result - std::set waiting_task_ids; - // the main result queue - std::vector queue_results; - std::mutex mutex_results; - std::condition_variable condition_results; - - // add the task_id to the list of tasks waiting for response - void add_waiting_task_id(int task_id) { - LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.insert(task_id); - } - - // when the request is finished, we can remove task associated with it - void remove_waiting_task_id(int task_id) { - LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.erase(task_id); - } - - // This function blocks the thread until there is a response for this task_id - task_result recv(int task_id) { - while (true) - { - std::unique_lock lock(mutex_results); - condition_results.wait(lock, [&]{ - return !queue_results.empty(); - }); - - for (int i = 0; i < (int) queue_results.size(); i++) - { - if (queue_results[i].id == task_id) - { - assert(queue_results[i].multitask_id == -1); - task_result res = queue_results[i]; - queue_results.erase(queue_results.begin() + i); - return res; - } - } - } - - // should never reach here - } - - // Register the function to update multitask - void on_multitask_update(callback_multitask_t callback) { - callback_update_multitask = callback; - } - - // Send a new result to a waiting task_id - void send(task_result result) { - std::unique_lock lock(mutex_results); - LOG_VERBOSE("send new result", {{"task_id", result.id}}); - for (auto& task_id : waiting_task_ids) { - // LOG_TEE("waiting task id %i \n", task_id); - // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result - if (result.multitask_id == task_id) - { - LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}}); - callback_update_multitask(task_id, result.id, result); - continue; - } - - if (result.id == task_id) - { - LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}}); - queue_results.push_back(result); - condition_results.notify_all(); - return; - } - } - } -}; - // // base64 utils (TODO: move to common in the future) // @@ -447,13 +143,11 @@ static const std::string base64_chars = "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; -static inline bool is_base64(uint8_t c) -{ +static inline bool is_base64(uint8_t c) { return (isalnum(c) || (c == '+') || (c == '/')); } -static inline std::vector base64_decode(const std::string & encoded_string) -{ +static inline std::vector base64_decode(const std::string & encoded_string) { int i = 0; int j = 0; int in_ = 0; @@ -465,13 +159,10 @@ static inline std::vector base64_decode(const std::string & encoded_str std::vector ret; - while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) - { + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; - if (i == 4) - { - for (i = 0; i <4; i++) - { + if (i == 4) { + for (i = 0; i < 4; i++) { char_array_4[i] = base64_chars.find(char_array_4[i]); } @@ -479,23 +170,20 @@ static inline std::vector base64_decode(const std::string & encoded_str char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - for (i = 0; (i < 3); i++) - { + for (i = 0; (i < 3); i++) { ret.push_back(char_array_3[i]); } + i = 0; } } - if (i) - { - for (j = i; j <4; j++) - { + if (i) { + for (j = i; j < 4; j++) { char_array_4[j] = 0; } - for (j = 0; j <4; j++) - { + for (j = 0; j < 4; j++) { char_array_4[j] = base64_chars.find(char_array_4[j]); } @@ -503,8 +191,7 @@ static inline std::vector base64_decode(const std::string & encoded_str char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - for (j = 0; (j < i - 1); j++) - { + for (j = 0; j < i - 1; j++) { ret.push_back(char_array_3[j]); } } @@ -516,8 +203,7 @@ static inline std::vector base64_decode(const std::string & encoded_str // random string / id // -static std::string random_string() -{ +static std::string random_string() { static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); std::random_device rd; @@ -532,10 +218,10 @@ static std::string random_string() return result; } -static std::string gen_chatcmplid() -{ +static std::string gen_chatcmplid() { std::stringstream chatcmplid; chatcmplid << "chatcmpl-" << random_string(); + return chatcmplid.str(); } @@ -543,91 +229,316 @@ static std::string gen_chatcmplid() // other common utils // -static size_t common_part(const std::vector &a, const std::vector &b) -{ +static size_t common_part(const std::vector & a, const std::vector & b) { size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) - { - } + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + return i; } -static bool ends_with(const std::string &str, const std::string &suffix) -{ - return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); +static bool ends_with(const std::string & str, const std::string & suffix) { + return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } -static size_t find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { +static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { + if (!text.empty() && !stop.empty()) { const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { + if (stop[char_index] == text_last_char) { const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { + if (ends_with(text, current_partial)) { return text.size() - char_index - 1; } } } } + return std::string::npos; } // TODO: reuse llama_detokenize template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { std::string ret; - for (; begin != end; ++begin) - { + for (; begin != end; ++begin) { ret += llama_token_to_piece(ctx, *begin); } + return ret; } // format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ +static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { + if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); std::string res(ss.str()); out = "byte: \\x" + res; } + return out; } +struct completion_token_output { + llama_token tok; + std::string text_to_send; + + struct token_prob { + llama_token tok; + float prob; + }; + + std::vector probs; +}; + // convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) -{ +static json probs_vector_to_json(const llama_context * ctx, const std::vector & probs) { json out = json::array(); - for (const auto &prob : probs) - { + + for (const auto & prob : probs) { json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { + + for (const auto & p : prob.probs) { + const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json { {"tok_str", tok_str}, {"prob", p.prob}, }); } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ + + const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json { {"content", tok_str}, {"probs", probs_for_token}, }); } + return out; } + +// +// OAI utils +// + +static json oaicompat_completion_params_parse( + const struct llama_model * model, + const json & body, /* openai api json semantics */ + const std::string & chat_template) { + json llama_params; + + llama_params["__oaicompat"] = true; + + // Map OpenAI parameters to llama.cpp parameters + // + // For parameters that are defined by the OpenAI documentation (e.g. + // temperature), we explicitly specify OpenAI's intended default; we + // need to do that because sometimes OpenAI disagrees with llama.cpp + // + // https://platform.openai.com/docs/api-reference/chat/create + llama_sampling_params default_sparams; + llama_params["model"] = json_value(body, "model", std::string("unknown")); + llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); + llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); + llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); + llama_params["top_p"] = json_value(body, "top_p", 1.0); + llama_params["n_predict"] = json_value(body, "max_tokens", -1); + llama_params["logit_bias"] = json_value(body, "logit_bias", json::object()); + llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); + llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); + llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); + llama_params["stream"] = json_value(body, "stream", false); + llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); + llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); + llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); + llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); + llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); + llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); + + if (body.count("grammar") != 0) { + llama_params["grammar"] = json_value(body, "grammar", json::object()); + } + + // Handle 'stop' field + if (body.contains("stop") && body["stop"].is_string()) { + llama_params["stop"] = json::array({body["stop"].get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Ensure there is ChatML-specific end sequence among stop words + llama_params["stop"].push_back("<|im_end|>"); + + return llama_params; +} + +static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) { + bool stopped_word = result.count("stopped_word") != 0; + bool stopped_eos = json_value(result, "stopped_eos", false); + int num_tokens_predicted = json_value(result, "tokens_predicted", 0); + int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason = "length"; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + + json choices = + streaming ? json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}) + : json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"message", json{{"content", content}, + {"role", "assistant"}}}}}); + + std::time_t t = std::time(0); + + json res = json { + {"choices", choices}, + {"created", t}, + {"model", + json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, + {"usage", json { + {"completion_tokens", num_tokens_predicted}, + {"prompt_tokens", num_prompt_tokens}, + {"total_tokens", num_tokens_predicted + num_prompt_tokens} + }}, + {"id", gen_chatcmplid()} + }; + + if (server_verbose) { + res["__verbose"] = result; + } + + if (result.contains("completion_probabilities")) { + res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); + } + + return res; +} + +// return value is vector as there is one case where we might need to generate two responses +static std::vector format_partial_response_oaicompat(json result) { + if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { + return std::vector({result}); + } + + bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; + std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + + bool stopped_word = json_value(result, "stopped_word", false); + bool stopped_eos = json_value(result, "stopped_eos", false); + bool stopped_limit = json_value(result, "stopped_limit", false); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + if (stopped_limit) { + finish_reason = "length"; + } + + std::time_t t = std::time(0); + + json choices; + + if (!finish_reason.empty()) { + choices = json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}); + } else { + if (first) { + if (content.empty()) { + choices = json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{{"role", "assistant"}}}}}); + } else { + // We have to send this as two updates to conform to openai behavior + json initial_ret = json{{"choices", json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"role", "assistant"} + }}}})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + json second_ret = json{ + {"choices", json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"content", content}}} + }})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + return std::vector({initial_ret, second_ret}); + } + } else { + // Some idiosyncrasy in task processing logic makes several trailing calls + // with empty content, we ignore these at the calee site. + if (content.empty()) { + return std::vector({json::object()}); + } + + choices = json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", + json{ + {"content", content}, + }}, + }}); + } + } + + json ret = json { + {"choices", choices}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"} + }; + + return std::vector({ret}); +} + +static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { + json res = json { + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json { + {"prompt_tokens", 0}, + {"total_tokens", 0} + }}, + {"data", embeddings} + }; + + return res; +} + +static json format_tokenizer_response(const std::vector & tokens) { + return json { + {"tokens", tokens} + }; +} + +static json format_detokenized_response(const std::string & content) { + return json { + {"content", content} + }; +} diff --git a/llama.cpp b/llama.cpp index b27aa272833ec..47809964805c5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13541,18 +13541,22 @@ LLAMA_API int32_t llama_chat_apply_template( curr_tmpl = std::string(model_template.data(), model_template.size()); } } + // format the chat to string std::vector chat_vec; chat_vec.resize(n_msg); for (size_t i = 0; i < n_msg; i++) { chat_vec[i] = &chat[i]; } + std::string formatted_chat; int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass); if (res < 0) { return res; } - strncpy(buf, formatted_chat.c_str(), length); + if (buf && length > 0) { + strncpy(buf, formatted_chat.c_str(), length); + } return res; } From 55a2a900ff4a02fc33708ac7858d595d289a3f2a Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Thu, 7 Mar 2024 19:42:39 +0900 Subject: [PATCH 192/401] server : add `/v1/completions` endpoint (#5914) * add-`/v1/completions`-endpoint * add legacy comment to `/completion` endpoint --- examples/server/server.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3bdbde954b383..f255ad76455bf 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2923,7 +2923,7 @@ int main(int argc, char ** argv) { res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/completion", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto completions = [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; @@ -3001,7 +3001,11 @@ int main(int argc, char ** argv) { res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } - }); + }; + + svr.Post("/completion", completions); // legacy + svr.Post("/completions", completions); + svr.Post("/v1/completions", completions); svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); From 89fb735fcfd21781a8194b211cf32824beb3f71f Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Thu, 7 Mar 2024 19:14:49 +0800 Subject: [PATCH 193/401] Revert "[SYCL] fix error when set main gpu to non-zero (#5901)" (#5918) This reverts commit ceca1aef0738b57951cd12c603c3477e75312dec. --- ggml-sycl.cpp | 154 ++++++++++++++++++++------------------------------ ggml-sycl.h | 1 - llama.cpp | 16 ++---- 3 files changed, 64 insertions(+), 107 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 221d67b8d7e96..ddd951dd684a7 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3559,31 +3559,12 @@ class sycl_gpu_mgr { int work_group_size = 0; std::string gpus_list = ""; - /* - Use all GPU with same top max compute units - */ sycl_gpu_mgr() { detect_sycl_gpu_list_with_max_cu(); get_allow_gpus(); create_context_with_gpus(); } - /* - Use the assigned GPU as only one - */ - sycl_gpu_mgr(int main_gpu_id) { - sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); - dpct::device_info prop; - dpct::get_device_info(prop, device); - gpus.push_back(main_gpu_id); - devices.push_back(device); - work_group_size = prop.get_max_work_group_size(); - max_compute_units = prop.get_max_compute_units(); - - get_allow_gpus(); - create_context_with_gpus(); - } - void create_context_with_gpus() { sycl::context ctx = sycl::context(devices); assert(gpus.size() > 0); @@ -3599,7 +3580,7 @@ class sycl_gpu_mgr { gpus_list += std::to_string(gpus[i]); gpus_list += ","; } - if (gpus_list.length() > 1) { + if (gpus_list.length() > 2) { gpus_list.pop_back(); } } @@ -3648,8 +3629,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - printf("miss to get device index by id=%d\n", id); - GGML_ASSERT(false); + assert(false); + return -1; } int get_next_index(int id) { @@ -3658,7 +3639,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - GGML_ASSERT(false); + assert(false); + return -1; } }; @@ -3667,7 +3649,6 @@ static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; static int g_main_device_id = -1; -static bool g_ggml_backend_sycl_buffer_type_initialized = false; static std::array g_default_tensor_split = {}; @@ -13244,7 +13225,7 @@ void ggml_backend_sycl_print_sycl_devices() { } void print_gpu_device_list() { - fprintf(stderr, "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n", + fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", g_sycl_gpu_mgr->get_gpu_count(), g_sycl_gpu_mgr->gpus_list.c_str(), g_sycl_gpu_mgr->max_compute_units); @@ -13283,15 +13264,6 @@ void ggml_init_sycl() try { #else fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif - -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - if (CHECK_TRY_ERROR(g_all_sycl_device_count = dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; @@ -13300,61 +13272,68 @@ void ggml_init_sycl() try { } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); ggml_backend_sycl_print_sycl_devices(); - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); - print_gpu_device_list(); - initialized = true; - g_sycl_loaded = true; - } + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); - g_work_group_size = g_sycl_gpu_mgr->work_group_size; + print_gpu_device_list(); - int64_t total_vram = 0; + int64_t total_vram = 0; +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; + } - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; - } + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device_id)))); - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); + g_default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); - g_default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); + g_device_caps[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + } - g_device_caps[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; - } + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); + } - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); + const dpct::queue_ptr stream = g_syclStreams[i][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + initialized = true; + g_sycl_loaded = true; } } catch (sycl::exception const &exc) { @@ -16753,24 +16732,22 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { - if (device_index>=g_device_count or device_index<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device_index, g_device_count-1); - GGML_ASSERT(device_indexgpus[i])}, }; } - g_ggml_backend_sycl_buffer_type_initialized = true; + ggml_backend_sycl_buffer_type_initialized = true; } - return &ggml_backend_sycl_buffer_types[device_index]; + + return &ggml_backend_sycl_buffer_types[device]; } // sycl split buffer type @@ -17519,17 +17496,6 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { return g_sycl_gpu_mgr->get_index(device_id); } -GGML_API GGML_CALL void ggml_backend_sycl_set_single_device(int main_gpu_id) { - GGML_ASSERT(main_gpu_idbackends.push_back(backend); } else { // LLAMA_SPLIT_LAYER requires a backend for each GPU - + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { + int device_id = id_list[i]; ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, id_list[i], i); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); llama_free(ctx); return nullptr; } From 6cdabe652695167263c8b447520987b11856f7ca Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 7 Mar 2024 16:32:38 +0200 Subject: [PATCH 194/401] llama-bench : add embeddings option (#5924) * llama-bench : add embeddings option * llama-bench : do not hard code embd default value --------- Co-authored-by: slaren --- examples/llama-bench/llama-bench.cpp | 30 +++++++++++++++++++++++++--- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index aa79d002a0875..2ff86ef6f1146 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -173,6 +173,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector> tensor_split; std::vector use_mmap; + std::vector embeddings; int reps; bool verbose; output_formats output_format; @@ -192,6 +193,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ {false}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, + /* embeddings */ {false}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -214,6 +216,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -382,6 +385,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + } else if (arg == "-embd" || arg == "--embeddings") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -453,6 +463,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } + if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } return params; @@ -472,6 +483,7 @@ struct cmd_params_instance { bool no_kv_offload; std::vector tensor_split; bool use_mmap; + bool embeddings; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -502,6 +514,7 @@ struct cmd_params_instance { cparams.type_k = type_k; cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; + cparams.embeddings = embeddings; return cparams; } @@ -517,6 +530,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) for (const auto & mmp : params.use_mmap) + for (const auto & embd : params.embeddings) for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) @@ -540,6 +554,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; instances.push_back(instance); } @@ -562,6 +577,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; instances.push_back(instance); } @@ -597,6 +613,7 @@ struct test { bool no_kv_offload; std::vector tensor_split; bool use_mmap; + bool embeddings; int n_prompt; int n_gen; std::string test_time; @@ -619,6 +636,7 @@ struct test { no_kv_offload = inst.no_kv_offload; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; + embeddings = inst.embeddings; n_prompt = inst.n_prompt; n_gen = inst.n_gen; // RFC 3339 date-time format @@ -690,7 +708,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -710,7 +728,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "use_mmap") { + field == "use_mmap" || field == "embeddings") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -744,7 +762,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -914,6 +932,9 @@ struct markdown_printer : public printer { if (field == "use_mmap") { return "mmap"; } + if (field == "embeddings") { + return "embd"; + } if (field == "tensor_split") { return "ts"; } @@ -957,6 +978,9 @@ struct markdown_printer : public printer { if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { fields.emplace_back("use_mmap"); } + if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { + fields.emplace_back("embeddings"); + } fields.emplace_back("test"); fields.emplace_back("t/s"); From 581ed5c4fe3a8909aaa8313633ac443f471ba755 Mon Sep 17 00:00:00 2001 From: "UEXTM.com" <84163508+uextm@users.noreply.github.com> Date: Fri, 8 Mar 2024 04:35:04 -0500 Subject: [PATCH 195/401] log : fix MSVC compile errors (#5643) MSVC gives the following error with the existing macros: `Error C2059 : syntax error: ','` This patch adds `##` as a prefix to `__VA_ARGS__` to address this error. --- common/log.h | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/common/log.h b/common/log.h index e4e1b9f4f01aa..eb111e784bc9f 100644 --- a/common/log.h +++ b/common/log.h @@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #ifndef _MSC_VER #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else - #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") + #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // Main TEE macro. @@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #ifndef _MSC_VER #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else - #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") + #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // LOG macro variants with auto endline. @@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else - #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n") - #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n") + #define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n") + #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n") #endif // INTERNAL, DO NOT USE From af37fd8b30e37ccbffdd82e6f48559e2fb7ce7dd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 8 Mar 2024 12:40:02 +0200 Subject: [PATCH 196/401] server : fix EOS token detection with disabled cache (#5938) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f255ad76455bf..1434095fc4e09 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1123,7 +1123,7 @@ struct server_context { }); } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { + if (result.tok == llama_token_eos(model)) { slot.stopped_eos = true; slot.has_next_token = false; From e457fb3540e0aaec47cfde0abf784c213f9216ee Mon Sep 17 00:00:00 2001 From: Don Mahurin Date: Fri, 8 Mar 2024 02:41:50 -0800 Subject: [PATCH 197/401] llama : assume tied weights if lm_head/output weights is missing (#5824) This is to support model configurations with "tie_word_embeddings" set to true. Co-authored-by: Don Mahurin <2797413+dmahurin@users.noreply.github.com> --- llama.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 4225f955590dd..458382b21057c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3888,7 +3888,13 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); if (model.arch != LLM_ARCH_MINICPM){ - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } } } From 76e868821a94072fbc87cb1fcca291694319eae8 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 8 Mar 2024 12:25:04 +0100 Subject: [PATCH 198/401] server: metrics: add llamacpp:prompt_seconds_total and llamacpp:tokens_predicted_seconds_total, reset bucket only on /metrics. Fix values cast to int. Add Process-Start-Time-Unix header. (#5937) Closes #5850 --- examples/server/server.cpp | 47 ++++++++++++++----- examples/server/tests/features/server.feature | 1 + examples/server/tests/features/steps/steps.py | 11 ++++- 3 files changed, 46 insertions(+), 13 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1434095fc4e09..109ff717577ec 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -335,8 +335,12 @@ struct server_slot { }; struct server_metrics { + const int64_t t_start = ggml_time_us(); + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; @@ -348,12 +352,14 @@ struct server_metrics { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; + t_prompt_processing_total += slot.t_prompt_processing; } void on_prediction(const server_slot &slot) { - n_tokens_predicted_total += slot.n_decoded; - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + n_tokens_predicted_total += slot.n_decoded; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; + t_tokens_generation_total += slot.t_token_generation; } void reset_bucket() { @@ -1502,9 +1508,12 @@ struct server_context { { "idle", n_idle_slots }, { "processing", n_processing_slots }, { "deferred", queue_tasks.queue_tasks_deferred.size() }, + { "t_start", metrics.t_start}, { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, + { "t_tokens_generation_total", metrics.t_tokens_generation_total}, { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, + { "t_prompt_processing_total", metrics.t_prompt_processing_total}, { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, { "t_prompt_processing", metrics.t_prompt_processing}, @@ -1517,7 +1526,9 @@ struct server_context { { "slots", slots_data }, }; - metrics.reset_bucket(); + if (json_value(task.data, "reset_bucket", false)) { + metrics.reset_bucket(); + } queue_results.send(res); } break; } @@ -2709,6 +2720,7 @@ int main(int argc, char ** argv) { task.id_multi = -1; task.id_target = -1; task.type = SERVER_TASK_TYPE_METRICS; + task.data.push_back({{"reset_bucket", true}}); ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task); @@ -2732,20 +2744,28 @@ int main(int argc, char ** argv) { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, - {"value", data["n_prompt_tokens_processed_total"]} + {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} + }, { + {"name", "prompt_seconds_total"}, + {"help", "Prompt process time"}, + {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, - {"value", data["n_tokens_predicted_total"]} + {"value", (uint64_t) data["n_tokens_predicted_total"]} + }, { + {"name", "tokens_predicted_seconds_total"}, + {"help", "Predict process time"}, + {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} + {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} },{ {"name", "predicted_tokens_seconds"}, {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} + {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} },{ {"name", "kv_cache_usage_ratio"}, {"help", "KV-cache usage. 1 means 100 percent usage."}, @@ -2753,15 +2773,15 @@ int main(int argc, char ** argv) { },{ {"name", "kv_cache_tokens"}, {"help", "KV-cache tokens."}, - {"value", data["kv_cache_tokens_count"]} + {"value", (uint64_t) data["kv_cache_tokens_count"]} },{ {"name", "requests_processing"}, {"help", "Number of request processing."}, - {"value", data["processing"]} + {"value", (uint64_t) data["processing"]} },{ {"name", "requests_deferred"}, {"help", "Number of request deferred."}, - {"value", data["deferred"]} + {"value", (uint64_t) data["deferred"]} }}} }; @@ -2775,13 +2795,16 @@ int main(int argc, char ** argv) { const std::string name = metric_def["name"]; const std::string help = metric_def["help"]; - auto value = json_value(metric_def, "value", 0); + auto value = json_value(metric_def, "value", 0.); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" << "# TYPE llamacpp:" << name << " " << type << "\n" << "llamacpp:" << name << " " << value << "\n"; } } + const int64_t t_start = data["t_start"]; + res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); res.status = 200; // HTTP OK }); diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index f3b758c798c8d..878ac1363c419 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -29,6 +29,7 @@ Feature: llama.cpp server And a completion request with no api error Then tokens are predicted matching And prometheus metrics are exposed + And metric llamacpp:tokens_predicted is Examples: Prompts | prompt | n_predict | re_content | n_predicted | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index a0b2ffdfef9f0..d7f0058360c23 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -586,14 +586,24 @@ async def step_prometheus_metrics_exported(context): metric_exported = False if context.debug: print(f"/metrics answer:\n{metrics_raw}\n") + context.metrics = {} for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: case "llamacpp:kv_cache_usage_ratio": assert len(metric.samples) > 0 metric_exported = True + context.metrics[metric.name] = metric + assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time" assert metric_exported, "No metrics exported" +@step(u'metric {metric_name} is {metric_value:d}') +def step_assert_metric_value(context, metric_name, metric_value): + if metric_name not in context.metrics: + assert False, f"no metric {metric_name} in {context.metrics.keys()}" + assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}" + + @step(u'available models') def step_available_models(context): # openai client always expects an api_key @@ -879,7 +889,6 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re f' {n_predicted} <> {expected_predicted_n}') - async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: From 515f7d0d4fce41c752fc253acf30707c3be2531e Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Fri, 8 Mar 2024 10:53:37 -0500 Subject: [PATCH 199/401] llama : fix quantization of shared token_embd (#5944) --- llama.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 458382b21057c..4a20b79289fa5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10973,6 +10973,9 @@ struct quantize_state_internal { bool has_imatrix = false; + // used to figure out if a model shares tok_embd with the output weight + bool has_output = false; + quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params) : model(model) , params(params) @@ -11070,8 +11073,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings // with the quantization of the output tensor - if (name == tn(LLM_TENSOR_OUTPUT, "weight") || - (LLM_TENSOR_NAMES.at(arch).find(LLM_TENSOR_OUTPUT) == LLM_TENSOR_NAMES.at(arch).end() && name == "token_embd.weight")) { + if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) { int nx = tensor->ne[0]; if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; @@ -11460,6 +11462,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s else if (name.find("ffn_up") != std::string::npos) { ++qs.n_ffn_up; } + else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { + qs.has_output = true; + } } if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) { LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n", From c2101a2e909ac7c08976d414e64e96c90ee5fa9e Mon Sep 17 00:00:00 2001 From: compilade <113953597+compilade@users.noreply.github.com> Date: Fri, 8 Mar 2024 17:31:00 -0500 Subject: [PATCH 200/401] llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`. --- README.md | 2 + common/common.cpp | 1 + convert-hf-to-gguf.py | 118 ++++ examples/batched-bench/batched-bench.cpp | 13 +- examples/batched/batched.cpp | 3 +- examples/parallel/parallel.cpp | 20 +- examples/perplexity/perplexity.cpp | 9 +- examples/server/server.cpp | 51 +- ggml.c | 379 +++++++++++- ggml.h | 19 + gguf-py/gguf/constants.py | 41 ++ gguf-py/gguf/gguf_writer.py | 12 + gguf-py/gguf/tensor_mapping.py | 46 +- llama.cpp | 698 +++++++++++++++++++++-- llama.h | 4 +- 15 files changed, 1342 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index f754022de894d..d7dba73e62267 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 @@ -110,6 +111,7 @@ Typically finetunes of the base models below are supported as well. - [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) +- [x] [Mamba](https://github.com/state-spaces/mamba) **Multimodal models:** diff --git a/common/common.cpp b/common/common.cpp index c244db6443eaa..d7f650ef486e9 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1288,6 +1288,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_ctx = params.n_ctx; cparams.n_batch = params.n_batch; + cparams.n_parallel = params.n_parallel; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f6369af38081d..5eee320163d29 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1847,6 +1847,124 @@ class StarCoder2Model(Model): model_arch = gguf.MODEL_ARCH.STARCODER2 +@Model.register("MambaForCausalLM", "MambaLMHeadModel") +class MambaModel(Model): + model_arch = gguf.MODEL_ARCH.MAMBA + + def set_vocab(self): + vocab_size = self.hparams["vocab_size"] + # Round vocab size to next multiple of 8 + pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8) + # pad using ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + vocab_size = -(vocab_size // -pad_vocab) * pad_vocab + self.hparams["vocab_size"] = vocab_size + + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + # Use the GPT-NeoX tokenizer when no tokenizer files are present + tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf" + print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + neox_reader = gguf.GGUFReader(tokenizer_path, "r") + + field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1])) + field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST) + self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES) + self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID) + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID) + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID) + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + + def set_gguf_parameters(self): + d_model = self.find_hparam(["hidden_size", "d_model"]) + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 + # ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 + dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 + + # Fail early for models which don't have a block expansion factor of 2 + assert d_inner == 2 * d_model + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default + self.gguf_writer.add_embedding_length(d_model) + self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading + self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(dt_rank) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams["n_layer"] + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + tok_embd = None + tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight" + output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight" + + for name, data_torch in self.get_tensors(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + if name.endswith(".A_log"): + print("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + # assuming token_embd.weight is seen before output.weight + if tok_embd is not None and new_name == output_name: + if torch.equal(tok_embd, data_torch): + print(f"{output_name} is equivalent to {tok_embd_name}, omitting") + continue + if new_name == tok_embd_name: + tok_embd = data_torch + + data = data_torch.squeeze().numpy() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert big float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 19aff18aefde7..dff6c68ec2e69 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -105,6 +105,9 @@ int main(int argc, char ** argv) { ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + // ensure enough sequences are available + ctx_params.n_parallel = *std::max_element(n_pl.begin(), n_pl.end()); + llama_context * ctx = llama_new_context_with_model(model, ctx_params); if (ctx == NULL) { @@ -174,10 +177,10 @@ int main(int argc, char ** argv) { llama_batch_clear(batch); - const int n_tokens = is_pp_shared ? pp : pl*pp; - - for (int i = 0; i < n_tokens; ++i) { - llama_batch_add(batch, 0, i, { 0 }, false); + for (int i = 0; i < pp; ++i) { + for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { + llama_batch_add(batch, 0, i, { j }, false); + } } batch.logits[batch.n_tokens - 1] = true; @@ -192,7 +195,7 @@ int main(int argc, char ** argv) { if (is_pp_shared) { for (int32_t i = 1; i < pl; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, pp); + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 9be7eb56bcd8a..dde4d5a068e24 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -80,6 +80,7 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_len, n_parallel); + ctx_params.n_parallel = n_parallel; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -132,7 +133,7 @@ int main(int argc, char ** argv) { // assign the system KV cache to all parallel sequences // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them for (int32_t i = 1; i < n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens); + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } if (n_parallel > 1) { diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 7d11fcd593080..a2ef0fb039c3f 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -107,6 +107,9 @@ int main(int argc, char ** argv) { // number of simultaneous "clients" to simulate const int32_t n_clients = params.n_parallel; + // dedicate one sequence to the system prompt + params.n_parallel += 1; + // requests to simulate const int32_t n_seq = params.n_sequences; @@ -196,8 +199,8 @@ int main(int argc, char ** argv) { } // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < n_clients; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system); + for (int32_t i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } LOG_TEE("\n"); @@ -221,15 +224,17 @@ int main(int argc, char ** argv) { client.i_batch = batch.n_tokens; - llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true); + llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true); client.n_decoded += 1; } if (batch.n_tokens == 0) { // all sequences have ended - clear the entire KV cache - for (int i = 0; i < n_clients; ++i) { - llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1); + for (int i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_rm(ctx, i, -1, -1); + // but keep the system prompt + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } LOG_TEE("%s: clearing the KV cache\n", __func__); @@ -255,7 +260,7 @@ int main(int argc, char ** argv) { tokens_prompt = ::llama_tokenize(ctx, client.prompt, false); for (size_t i = 0; i < tokens_prompt.size(); ++i) { - llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false); + llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false); } // extract the logits only for the last token @@ -366,7 +371,8 @@ int main(int argc, char ** argv) { } // delete only the generated part of the sequence, i.e. keep the system prompt in the cache - llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1); + llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1); + llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1); const auto t_main_end = ggml_time_us(); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 9ec989389cfad..52789ee631234 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -809,7 +809,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1086,7 +1086,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 128; - const int max_seq = 2*max_tasks_per_batch; + const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1438,7 +1438,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1815,6 +1815,9 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; + // ensure there's at least enough seq_ids for HellaSwag + params.n_parallel = std::max(4, params.n_parallel); + // load the model and apply lora adapter, if any std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == NULL) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 109ff717577ec..59a59d56b6023 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -659,7 +659,11 @@ struct server_context { bool load_model(const gpt_params & params_) { params = params_; + // dedicate one sequence to the system prompt + params.n_parallel += 1; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + params.n_parallel -= 1; // but be sneaky about it if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; @@ -1018,8 +1022,8 @@ struct server_context { } // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < params.n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); + for (int32_t i = 1; i <= params.n_parallel; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } @@ -1306,7 +1310,7 @@ struct server_context { const int n_embd = llama_n_embd(model); for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { continue; } @@ -1633,8 +1637,8 @@ struct server_context { {"n_cache_tokens", slot.cache_tokens.size()} }); - llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); - llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); if (slot.params.cache_prompt) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1666,7 +1670,7 @@ struct server_context { // TODO: we always have to take into account the "system_tokens" // this is not great and needs to be improved somehow - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true); slot.n_past += 1; @@ -1804,9 +1808,6 @@ struct server_context { // reuse any previously computed tokens that are common with the new prompt slot.n_past = common_part(slot.cache_tokens, prompt_tokens); - // remove the non-common part from the cache - slot.cache_tokens.resize(slot.n_past); - // push the prompt into the sampling context (do not apply grammar) for (int i = 0; i < slot.n_past; ++i) { llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false); @@ -1837,8 +1838,28 @@ struct server_context { } } - const int p0 = (int) system_tokens.size() + slot.n_past; - llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); + // keep only the common part + int p0 = (int) system_tokens.size() + slot.n_past; + if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) { + // could not partially delete (likely using a non-Transformer model) + llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); + + p0 = (int) system_tokens.size(); + if (p0 != 0) { + // copy over the system prompt when there is one + llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1); + } + + // there is no common part left (except for the system prompt) + slot.n_past = 0; + slot.n_past_se = 0; + slot.ga_i = 0; + // TODO: is the system prompt ever in the sampling context? + llama_sampling_reset(slot.ctx_sampling); + } + + // remove the non-common part from the cache + slot.cache_tokens.resize(slot.n_past); LOG_INFO("kv cache rm [p0, end)", { { "id_slot", slot.id }, @@ -1863,7 +1884,7 @@ struct server_context { } } - llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); + llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -1937,9 +1958,9 @@ struct server_context { LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); - llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd); + llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); + llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; diff --git a/ggml.c b/ggml.c index 92b17ee6ed7bd..6eff98ab63f0c 100644 --- a/ggml.c +++ b/ggml.c @@ -1841,6 +1841,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "FLASH_ATTN", "FLASH_FF", "FLASH_ATTN_BACK", + "SSM_CONV", + "SSM_SCAN", "WIN_PART", "WIN_UNPART", "GET_REL_POS", @@ -1863,7 +1865,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1929,6 +1931,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "flash_attn(x)", "flash_ff(x)", "flash_attn_back(x)", + "ssm_conv(x)", + "ssm_scan(x)", "win_part(x)", "win_unpart(x)", "get_rel_pos(x)", @@ -1951,7 +1955,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -6154,6 +6158,108 @@ struct ggml_tensor * ggml_flash_attn_back( return result; } +// ggml_ssm_conv + +struct ggml_tensor * ggml_ssm_conv( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * c, + struct ggml_tensor * sq) { + GGML_ASSERT(ggml_is_3d(s)); + GGML_ASSERT(ggml_is_matrix(x)); + GGML_ASSERT(ggml_is_matrix(c)); + GGML_ASSERT(ggml_is_matrix(sq)); + GGML_ASSERT(sq->type == GGML_TYPE_I32); + + const int64_t d_conv = c->ne[0]; + const int64_t d_inner = c->ne[1]; + const int64_t n_tokens = x->ne[1]; + const int64_t n_kv = s->ne[2]; + + GGML_ASSERT( s->ne[0] == d_conv - 1); + GGML_ASSERT( s->ne[1] == d_inner); + GGML_ASSERT( x->ne[0] == d_inner); + GGML_ASSERT(sq->ne[0] == n_kv); + GGML_ASSERT(sq->ne[1] == n_tokens); + + bool is_node = false; + + if (s->grad || x->grad || c->grad || sq->grad) { + GGML_ASSERT(false); // TODO: implement + is_node = true; + } + + // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv} + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv)); + + result->op = GGML_OP_SSM_CONV; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = s; + result->src[1] = x; + result->src[2] = c; + result->src[3] = sq; + + return result; +} + +// ggml_ssm_scan + +struct ggml_tensor * ggml_ssm_scan( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * dt, + struct ggml_tensor * A, + struct ggml_tensor * B, + struct ggml_tensor * C, + struct ggml_tensor * sq) { + GGML_ASSERT(ggml_is_contiguous(s)); + GGML_ASSERT(ggml_is_contiguous(x)); + GGML_ASSERT(ggml_is_contiguous(dt)); + GGML_ASSERT(ggml_is_contiguous(A)); + GGML_ASSERT(sq->type == GGML_TYPE_I32); + GGML_ASSERT(B->nb[0] == ggml_type_size(B->type)); + GGML_ASSERT(C->nb[0] == ggml_type_size(C->type)); + GGML_ASSERT(ggml_are_same_shape(x, dt)); + + { + const int64_t d_state = s->ne[0]; + const int64_t d_inner = s->ne[1]; + const int64_t n_tokens = x->ne[1]; + + GGML_ASSERT(x->ne[0] == d_inner); + GGML_ASSERT(A->ne[0] == d_state); + GGML_ASSERT(A->ne[1] == d_inner); + GGML_ASSERT(B->ne[0] == d_state); + GGML_ASSERT(B->ne[1] == n_tokens); + GGML_ASSERT(C->ne[0] == d_state); + GGML_ASSERT(C->ne[1] == n_tokens); + } + + bool is_node = false; + + if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) { + GGML_ASSERT(false); // TODO: implement + is_node = true; + } + + // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv} + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s)); + + result->op = GGML_OP_SSM_SCAN; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = s; + result->src[1] = x; + result->src[2] = dt; + result->src[3] = A; + result->src[4] = B; + result->src[5] = C; + result->src[6] = sq; + + return result; +} + // ggml_win_part struct ggml_tensor * ggml_win_part( @@ -14771,6 +14877,257 @@ static void ggml_compute_forward_flash_attn_back( } } +// ggml_compute_forward_ssm_conv + +static void ggml_compute_forward_ssm_conv_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; // conv_state + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight + const struct ggml_tensor * src3 = dst->src[3]; // state_seq + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src2->ne[0]; // d_conv + const int nr = src0->ne[1]; // d_inner + const int n_t = src1->ne[1]; // n_tokens + const int n_kv = src0->ne[2]; // max number of sequences in the batch + + GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(int32_t)); + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // for use with the destination state offset between sequences + GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + if (n_kv > 1) { + // multiple sequences means it's hard to know when it's the first time a state is read, + // so copy them all over to the destination, just to be sure. + for (int i3 = 0; i3 < n_kv; ++i3) { + float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); + float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float)); + // can't use memcpy because of d_conv vs d_conv - 1 + for (int i1 = 0; i1 < ir; ++i1) { + for (int i0 = 0; i0 < nc - 1; ++i0) { + // copy s0 to last (d_conv - 1) columns of s + s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)]; + } + } + } + } + + for (int i2 = 0; i2 < n_t; ++i2) { + int32_t * sq = (int32_t *) ((char *) src3->data + i2*(src3->nb[1])); // {n_kv, n_tokens} + float * x = (float *) ((char *) dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens} + float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv} + float * s0; // {d_conv - 1, d_inner, n_kv} + float * x0 = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * c = (float *) ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner} + int ne0s0; + + GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); + + // avoid needing to copy the state for the first token + if (i2 == 0) { + s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv} + ne0s0 = src0->ne[0]; + } else { + // the source is the last (d_conv - 1) columns of the destination + s0 = s + 1; + ne0s0 = nc; + } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // shift state left + for (int i0 = 0; i0 < nc - 1; ++i0) { + s[i0 + i1*nc] = s0[i0 + i1*ne0s0]; + } + // insert x on the last column + s[(nc - 1) + i1*nc] = x0[i1]; + } + + // handle copies when there are multiple output states + for (int i3 = 1; i3 < n_kv; ++i3) { + int32_t seq = sq[i3]; + if (0 <= seq && seq < n_kv) { + float * s1 = s + (seq - sq[0])*nc*nr; + memcpy(s1, s, nc*ir*sizeof(float)); + } else { + // stop at negative or too big seq_ids + break; + } + } + + // it seems a little faster when this is separate from the state shift + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + float sumf = 0.0f; + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + sumf += s[i] * c[i]; + } + x[i1] = sumf; + } + } +} + +static void ggml_compute_forward_ssm_conv( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_conv_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_ssm_scan + +static void ggml_compute_forward_ssm_scan_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; // s + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // dt + const struct ggml_tensor * src3 = dst->src[3]; // A + const struct ggml_tensor * src4 = dst->src[4]; // B + const struct ggml_tensor * src5 = dst->src[5]; // C + const struct ggml_tensor * src6 = dst->src[6]; // sq + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens in the batch + const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch + + GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(float)); + GGML_ASSERT(src4->nb[0] == sizeof(float)); + GGML_ASSERT(src5->nb[0] == sizeof(float)); + // required for the dot product between s and C, and when copying the states + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // required for per-sequence offsets for states + GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[2]) + GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + if (n_kv > 1) { + // it's hard to know if the source states have already been copied + // when there are multiple, so copy them already. + for (int i3 = 0; i3 < n_kv; ++i3) { + float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); + float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]); + memcpy(s, s0, nc*ir*sizeof(float)); + } + } + + for (int i2 = 0; i2 < n_t; ++i2) { + int32_t * sq = (int32_t *) ((char *) src6->data + i2*(src6->nb[1])); // {n_kv, n_tokens} + float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv} + float * s0; + float * x = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} + float * dt = (float *) ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens} + float * A = (float *) ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + float * B = (float *) ((char *) src4->data + i2*(src4->nb[1])); // {d_state, n_tokens} + float * C = (float *) ((char *) src5->data + i2*(src5->nb[1])); // {d_state, n_tokens} + + GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); + + // avoid needing to copy the state for the first token + if (i2 == 0) { + s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv} + } else { + // otherwise the source is the same as the destination + s0 = s; + } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; + } + + // handle copies when there are multiple output states + for (int i3 = 1; i3 < n_kv; ++i3) { + int32_t seq = sq[i3]; + if (0 <= seq && seq < n_kv) { + float * s1 = s + (seq - sq[0])*nc*nr; + memcpy(s1, s, nc*ir*sizeof(float)); + } else { + // stop at negative or too big seq_ids + break; + } + } + } +} + +static void ggml_compute_forward_ssm_scan( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_scan_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_win_part static void ggml_compute_forward_win_part_f32( @@ -15830,6 +16187,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm bool masked = t != 0; ggml_compute_forward_flash_attn_back(params, masked, tensor); } break; + case GGML_OP_SSM_CONV: + { + ggml_compute_forward_ssm_conv(params, tensor); + } break; + case GGML_OP_SSM_SCAN: + { + ggml_compute_forward_ssm_scan(params, tensor); + } break; case GGML_OP_WIN_PART: { ggml_compute_forward_win_part(params, tensor); @@ -16884,6 +17249,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // not supported } break; + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_UNARY: @@ -17590,6 +17960,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + n_tasks = n_threads; + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_GET_REL_POS: diff --git a/ggml.h b/ggml.h index 0ea4f8847795c..a13b0cec4edcf 100644 --- a/ggml.h +++ b/ggml.h @@ -472,6 +472,8 @@ extern "C" { GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, + GGML_OP_SSM_CONV, + GGML_OP_SSM_SCAN, GGML_OP_WIN_PART, GGML_OP_WIN_UNPART, GGML_OP_GET_REL_POS, @@ -1728,6 +1730,23 @@ extern "C" { struct ggml_tensor * c0, struct ggml_tensor * c1); + GGML_API struct ggml_tensor * ggml_ssm_conv( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * c, + struct ggml_tensor * sq); + + GGML_API struct ggml_tensor * ggml_ssm_scan( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * dt, + struct ggml_tensor * A, + struct ggml_tensor * B, + struct ggml_tensor * C, + struct ggml_tensor * sq); + // partition into non-overlapping windows with padding if needed // example: // a: 768 64 64 1 diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a62139811ef36..b23badb1019c1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -61,6 +61,12 @@ class Rope: SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + class SSM: + CONV_KERNEL = "{arch}.ssm.conv_kernel" + INNER_SIZE = "{arch}.ssm.inner_size" + STATE_SIZE = "{arch}.ssm.state_size" + TIME_STEP_RANK = "{arch}.ssm.time_step_rank" + class Tokenizer: MODEL = "tokenizer.ggml.model" LIST = "tokenizer.ggml.tokens" @@ -113,6 +119,7 @@ class MODEL_ARCH(IntEnum): MINICPM = auto() GEMMA = auto() STARCODER2 = auto() + MAMBA = auto() class MODEL_TENSOR(IntEnum): @@ -144,6 +151,13 @@ class MODEL_TENSOR(IntEnum): ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() + SSM_IN = auto() + SSM_CONV1D = auto() + SSM_X = auto() + SSM_DT = auto() + SSM_A = auto() + SSM_D = auto() + SSM_OUT = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -171,6 +185,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", MODEL_ARCH.STARCODER2: "starcoder2", + MODEL_ARCH.MAMBA: "mamba", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -202,6 +217,13 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}", MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", + MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", + MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", + MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", + MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", + MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", + MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", + MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -543,6 +565,19 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.MAMBA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + ], # TODO } @@ -734,6 +769,12 @@ def get_type(val: Any) -> GGUFValueType: KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED +# SSM +KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL +KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE +KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE +KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK + # tokenization KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 8011608323c45..e49c5db6866a2 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -382,6 +382,18 @@ def add_rope_scaling_orig_ctx_len(self, value: int) -> None: def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + def add_ssm_conv_kernel(self, value: int) -> None: + self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) + + def add_ssm_inner_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value) + + def add_ssm_state_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value) + + def add_ssm_time_step_rank(self, value: int) -> None: + self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value) + def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index db2ec9704a441..ed89955d8970f 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -20,6 +20,9 @@ class TensorNameMap: "wte", # gpt2 "transformer.embd.wte", # phi2 "model.tok_embeddings", # internlm2 + "model.embedding", # mamba-qbert + "backbone.embedding", # mamba + "backbone.embeddings", # mamba-hf ), # Token type embeddings @@ -44,7 +47,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -61,6 +64,8 @@ class TensorNameMap: "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon "lm_head.ln", # phi2 + "model.norm_f", # mamba-qbert + "backbone.norm_f", # mamba ), # Rope frequencies @@ -86,6 +91,8 @@ class TensorNameMap: "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo "model.layers.{bid}.attention_norm", # internlm2 + "model.layers.{bid}.norm", # mamba-qbert + "backbone.layers.{bid}.norm", # mamba ), # Attention norm 2 @@ -282,7 +289,42 @@ class TensorNameMap: MODEL_TENSOR.LAYER_OUT_NORM: ( "encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layers.{bid}.norm2", # nomic-bert - ) + ), + + MODEL_TENSOR.SSM_IN: ( + "model.layers.{bid}.in_proj", + "backbone.layers.{bid}.mixer.in_proj", + ), + + MODEL_TENSOR.SSM_CONV1D: ( + "model.layers.{bid}.conv1d", + "backbone.layers.{bid}.mixer.conv1d", + ), + + MODEL_TENSOR.SSM_X: ( + "model.layers.{bid}.x_proj", + "backbone.layers.{bid}.mixer.x_proj", + ), + + MODEL_TENSOR.SSM_DT: ( + "model.layers.{bid}.dt_proj", + "backbone.layers.{bid}.mixer.dt_proj", + ), + + MODEL_TENSOR.SSM_A: ( + "model.layers.{bid}.A_log", + "backbone.layers.{bid}.mixer.A_log", + ), + + MODEL_TENSOR.SSM_D: ( + "model.layers.{bid}.D", + "backbone.layers.{bid}.mixer.D", + ), + + MODEL_TENSOR.SSM_OUT: ( + "model.layers.{bid}.out_proj", + "backbone.layers.{bid}.mixer.out_proj", + ), } mapping: dict[str, tuple[MODEL_TENSOR, str]] diff --git a/llama.cpp b/llama.cpp index 4a20b79289fa5..8c147a42b1e53 100644 --- a/llama.cpp +++ b/llama.cpp @@ -213,6 +213,7 @@ enum llm_arch { LLM_ARCH_MINICPM, LLM_ARCH_GEMMA, LLM_ARCH_STARCODER2, + LLM_ARCH_MAMBA, LLM_ARCH_UNKNOWN, }; @@ -241,6 +242,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -284,6 +286,11 @@ enum llm_kv { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_SSM_INNER_SIZE, + LLM_KV_SSM_CONV_KERNEL, + LLM_KV_SSM_STATE_SIZE, + LLM_KV_SSM_TIME_STEP_RANK, + LLM_KV_TOKENIZER_MODEL, LLM_KV_TOKENIZER_LIST, LLM_KV_TOKENIZER_TOKEN_TYPE, @@ -342,6 +349,11 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" }, + { LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" }, + { LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" }, + { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" }, + { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" }, @@ -399,6 +411,13 @@ enum llm_tensor { LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, + LLM_TENSOR_SSM_IN, + LLM_TENSOR_SSM_CONV1D, + LLM_TENSOR_SSM_X, + LLM_TENSOR_SSM_DT, + LLM_TENSOR_SSM_A, + LLM_TENSOR_SSM_D, + LLM_TENSOR_SSM_OUT, }; static const std::map> LLM_TENSOR_NAMES = { @@ -801,6 +820,22 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MAMBA, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" }, + { LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" }, + { LLM_TENSOR_SSM_X, "blk.%d.ssm_x" }, + { LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" }, + { LLM_TENSOR_SSM_A, "blk.%d.ssm_a" }, + { LLM_TENSOR_SSM_D, "blk.%d.ssm_d" }, + { LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1613,6 +1648,12 @@ struct llama_hparams { float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; + // for State Space Models + uint32_t ssm_d_conv = 0; + uint32_t ssm_d_inner = 0; + uint32_t ssm_d_state = 0; + uint32_t ssm_dt_rank = 0; + float f_clamp_kqv = 0.0f; float f_max_alibi_bias = 0.0f; @@ -1641,6 +1682,11 @@ struct llama_hparams { if (this->rope_finetuned != other.rope_finetuned) return true; if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; + if (this->ssm_d_conv != other.ssm_d_conv) return true; + if (this->ssm_d_inner != other.ssm_d_inner) return true; + if (this->ssm_d_state != other.ssm_d_state) return true; + if (this->ssm_dt_rank != other.ssm_dt_rank) return true; + const float EPSILON = 1e-9f; if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; @@ -1652,6 +1698,9 @@ struct llama_hparams { } uint32_t n_gqa() const { + if (n_head_kv == 0) { + return 0; + } return n_head/n_head_kv; } @@ -1662,6 +1711,18 @@ struct llama_hparams { uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads return n_embd_head_v * n_head_kv; } + + uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings + // corresponds to Mamba's conv_states size + // TODO: maybe support other convolution strides than 1 + // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed + return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner; + } + + uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings + // corresponds to Mamba's ssm_states size + return ssm_d_state * ssm_d_inner; + } }; struct llama_cparams { @@ -1739,11 +1800,27 @@ struct llama_layer { struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 struct ggml_tensor * ffn_act; + + // mamba proj + struct ggml_tensor * ssm_in; + struct ggml_tensor * ssm_x; + struct ggml_tensor * ssm_dt; + struct ggml_tensor * ssm_out; + + // mamba + struct ggml_tensor * ssm_conv1d; + struct ggml_tensor * ssm_a; + struct ggml_tensor * ssm_d; + + // mamba bias + struct ggml_tensor * ssm_conv1d_b; + struct ggml_tensor * ssm_dt_b; }; struct llama_kv_cell { llama_pos pos = -1; llama_pos delta = 0; + int32_t src = 0; // used by recurrent state models to copy states std::set seq_id; @@ -1764,6 +1841,9 @@ struct llama_kv_cell { struct llama_kv_cache { bool has_shift = false; bool do_defrag = false; + bool do_copy = false; + // with recurrent state models, a cell can hold the state for more than one past token + bool recurrent = false; // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_internal also uses it, so it @@ -2003,11 +2083,14 @@ struct llama_context { struct ggml_tensor * inp_tokens; // I32 [n_batch] struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] struct ggml_tensor * inp_pos; // I32 [n_batch] - struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] - struct ggml_tensor * inp_KQ_pos; // F32 [n_ctx] - struct ggml_tensor * inp_K_shift; // I32 [n_ctx] + struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] + struct ggml_tensor * inp_KQ_pos; // F32 [kv_size] + struct ggml_tensor * inp_K_shift; // I32 [kv_size] struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] + struct ggml_tensor * inp_s_copy; // I32 [kv_size] + struct ggml_tensor * inp_s_mask; // F32 [kv_size] + struct ggml_tensor * inp_s_seq; // I32 [kv_size, n_batch] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -2023,25 +2106,42 @@ static bool llama_kv_cache_init( const llama_model & model, ggml_type type_k, ggml_type type_v, - uint32_t n_ctx, + uint32_t kv_size, bool offload) { const struct llama_hparams & hparams = model.hparams; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); const int64_t n_layer = hparams.n_layer; cache.has_shift = false; + // TODO: find a nicer way to add other recurrent model architectures + cache.recurrent = model.arch == LLM_ARCH_MAMBA; + + // TODO: support mixed reccurent Transformer architectues + // NOTE: (!a || b) is a logical implication (a -> b) + GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s()); + GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s()); + GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa()); + GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa()); + cache.head = 0; - cache.size = n_ctx; + cache.size = kv_size; cache.used = 0; cache.type_k = type_k; cache.type_v = type_v; cache.cells.clear(); - cache.cells.resize(n_ctx); + cache.cells.resize(kv_size); + + if (cache.recurrent) { + // init state copy sources + for (uint32_t i = 0; i < cache.size; ++i) { + cache.cells[i].src = i; + } + } #ifdef GGML_USE_CLBLAST offload = false; @@ -2080,8 +2180,8 @@ static bool llama_kv_cache_init( for (int i = 0; i < (int) n_layer; i++) { struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); - ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*n_ctx); + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); + ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); @@ -2115,6 +2215,54 @@ static bool llama_kv_cache_find_slot( const uint32_t n_ctx = cache.size; const uint32_t n_tokens = batch.n_tokens; + if (cache.recurrent) { + // For recurrent state architectures (like Mamba), + // each KV cache cell can store the state for a whole sequence. + + llama_seq_id min = cache.size - 1; + llama_seq_id max = 0; + + for (uint32_t i = 0; i < n_tokens; ++i) { + for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) { + llama_seq_id seq_id = batch.seq_id[i][j]; + // make sure it's a valid seq_id + if ((uint32_t) seq_id < cache.size) { + if (seq_id > max) { + max = seq_id; + } + if (seq_id < min) { + min = seq_id; + } + // Assuming the tokens are in-order + if (batch.pos[i] != cache.cells[seq_id].pos + 1) { + // What should happen when the pos backtracks or skips a value? + // Clearing the state mid-batch would require special-casing which isn't done. + LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n", + __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id); + } + if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) { + cache.used += 1; + } + cache.cells[seq_id].pos = batch.pos[i]; + // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set + } else { + // too big seq_id + // TODO: would it be possible to resize the KV cache size instead? + LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size); + return false; + } + } + } + + // allow getting the range of used cells, from head to head + n + cache.head = min; + cache.n = max - min + 1; + + // sanity check + return max >= min; + } + // otherwise, one cell per token. + if (n_tokens > n_ctx) { LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx); return false; @@ -2184,7 +2332,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { cache.used = 0; } -static void llama_kv_cache_seq_rm( +static bool llama_kv_cache_seq_rm( struct llama_kv_cache & cache, llama_seq_id seq_id, llama_pos p0, @@ -2194,6 +2342,25 @@ static void llama_kv_cache_seq_rm( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + // models like Mamba can't have a state partially erased + if (cache.recurrent) { + if (seq_id >= (int64_t) cache.size) { + // could be fatal + return false; + } + if (0 <= seq_id) { + // partial intersection is invalid + if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) { + return false; + } + } else { + // seq_id is negative, then the range should include everything or nothing + if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { + return false; + } + } + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { if (seq_id < 0) { @@ -2215,6 +2382,8 @@ static void llama_kv_cache_seq_rm( // If we freed up a slot, set head to it so searching can start there. if (new_head != cache.size && new_head < cache.head) cache.head = new_head; + + return true; } static void llama_kv_cache_seq_cp( @@ -2226,6 +2395,29 @@ static void llama_kv_cache_seq_cp( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) { + seq_id_src = cache.cells[seq_id_src].src; + GGML_ASSERT((uint32_t) seq_id_src < cache.size); + // intent to "copy from" + // supports copy chains thanks to taking the source of the source + cache.cells[seq_id_dst].src = seq_id_src; + + // preserve the "keep or clear" status of the copied sequence + if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) { + cache.cells[seq_id_dst].seq_id.insert(seq_id_dst); + } else { + cache.cells[seq_id_dst].seq_id.erase(seq_id_dst); + } + + cache.do_copy = true; + + cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos; + } + return; + } + // otherwise, this is the KV cache of a Transformer-like model + cache.head = 0; for (uint32_t i = 0; i < cache.size; ++i) { @@ -2265,6 +2457,17 @@ static void llama_kv_cache_seq_add( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + // for Mamba-like models, only the pos needs to be shifted + if (0 <= seq_id && seq_id < (int64_t) cache.size) { + llama_kv_cell & cell = cache.cells[seq_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos += delta; + } + } + return; + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { cache.has_shift = true; @@ -2298,6 +2501,17 @@ static void llama_kv_cache_seq_div( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + if (cache.recurrent) { + // for Mamba-like models, only the pos needs to be changed + if (0 <= seq_id && seq_id < (int64_t) cache.size) { + llama_kv_cell & cell = cache.cells[seq_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos /= d; + } + } + return; + } + for (uint32_t i = 0; i < cache.size; ++i) { if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) { cache.has_shift = true; @@ -3117,7 +3331,7 @@ static void llm_load_hparams( // sanity check for n_rot (optional) { - hparams.n_rot = hparams.n_embd / hparams.n_head; + hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); @@ -3130,10 +3344,10 @@ static void llm_load_hparams( // gpt-j n_rot = rotary_dim } - hparams.n_embd_head_k = hparams.n_embd / hparams.n_head; + hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); - hparams.n_embd_head_v = hparams.n_embd / hparams.n_head; + hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); // arch-specific KVs @@ -3383,6 +3597,36 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MAMBA: + { + ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); + ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner); + ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state); + ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank); + + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 24: + switch (hparams.n_embd) { + case 768: model.type = e_model::MODEL_SMALL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 48: + switch (hparams.n_embd) { + case 1024: model.type = e_model::MODEL_MEDIUM; break; + case 1536: model.type = e_model::MODEL_LARGE; break; + case 2048: model.type = e_model::MODEL_XL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 64: + switch (hparams.n_embd) { + case 2560: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3702,6 +3946,10 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); + LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv); + LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner); + LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state); + LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank); LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); if (ml.n_elements >= 1e12) { @@ -4609,6 +4857,57 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); } } break; + case LLM_ARCH_MAMBA: + { + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = hparams.ssm_d_inner; + const int64_t d_state = hparams.ssm_d_state; + const int64_t dt_rank = hparams.ssm_dt_rank; + // only an expansion factor of 2 is supported for now + GGML_ASSERT(2 * n_embd == d_inner); + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed, duplicated to allow offloading + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + // norm + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}); + + layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}); + layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}); + + layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}); + + layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}); + layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}); + + // no "weight" suffix for these + layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}); + layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner}); + + // out_proj + layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -4834,6 +5133,8 @@ static void llm_build_kv_store( const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(kv.size == n_ctx); + // compute the transposed [n_tokens, n_embd] V matrix struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens)); //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed @@ -5043,6 +5344,8 @@ static struct ggml_tensor * llm_build_kqv( cb(kq, "kq_soft_max_ext", il); } + GGML_ASSERT(kv.size == n_ctx); + // split cached v into n_head heads struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], @@ -5190,8 +5493,8 @@ struct llm_build_context { norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), n_tokens (batch.n_tokens), - n_kv (worst_case ? n_ctx : kv_self.n), - kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + n_kv (worst_case ? kv_self.size : kv_self.n), + kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), pooling_type (cparams.pooling_type), rope_type (hparams.rope_type), @@ -5220,6 +5523,8 @@ struct llm_build_context { struct ggml_cgraph * build_k_shift() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + GGML_ASSERT(kv_self.size == n_ctx); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions @@ -5238,6 +5543,27 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_s_copy() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + GGML_ASSERT(kv_self.recurrent); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); + struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); + + conv_states = ggml_get_rows(ctx0, conv_states, lctx.inp_s_copy); + ssm_states = ggml_get_rows(ctx0, ssm_states, lctx.inp_s_copy); + + // TODO: name the intermediate tensors with cb() + + ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il])); + ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il])); + } + + return gf; + } + struct ggml_cgraph * build_defrag(const std::vector & ids) { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -7835,6 +8161,145 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_mamba() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t d_model = n_embd; + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = hparams.ssm_d_inner; + GGML_ASSERT(2 * d_model == d_inner); + const int64_t d_state = hparams.ssm_d_state; + const int64_t dt_rank = hparams.ssm_dt_rank; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + struct ggml_tensor * state_mask = ggml_view_2d(ctx0, lctx.inp_s_mask, 1, n_kv, lctx.inp_s_mask->nb[0], 0); + struct ggml_tensor * state_seq = ggml_view_2d(ctx0, lctx.inp_s_seq, n_kv, n_tokens, n_kv*ggml_element_size(lctx.inp_s_seq), 0); + + for (int il = 0; il < n_layer; ++il) { + // (ab)using the KV cache to store the states + struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); + struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); + + // clear states of sequences which are starting at the beginning of this batch + { + conv_states = ggml_mul(ctx0, + ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]), + state_mask); + ssm_states = ggml_mul(ctx0, + ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]), + state_mask); + } + + conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv); + ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv); + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens} + struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur); + // split the above in two + // => {d_inner, n_tokens} + struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0); + struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner); + + // conv + { + // Custom operator which is needed only to ease simultaneous sequence processing. + // For a single sequence, the equivalent is to concatenate the columns of conv_states and x, + // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension, + // then element-wise multiply that with the conv1d weigth, + // then sum the elements of each row, + // (the last two steps are a dot product over rows (also doable with mul_mat)) + // then permute away the ne[0] dimension, + // and then you're left with the resulting x tensor. + // The new conv_states is the last (d_conv - 1) columns + // of the last 3rd dimensional "layer" of the self-overlapping view. + // For simultaneous sequences, it's more complicated. + struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq); + + // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, + ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)), + ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_self.head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv)))); + + // extract x from x_conv + x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0); + + // bias + x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b); + + x = ggml_silu(ctx0, x); + } + + // ssm + { + // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens} + struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x); + // split + struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0); + struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank); + struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state)); + + // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens} + dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt); + dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b); + + // Custom operator to optimize the parallel associative scan + // as described in the Annex D of the Mamba paper. + // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined, + // because only a single tensor can be returned. + struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq); + + // store last states (the second part of y_ssm_states) + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, + ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)), + ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_self.head*d_state*d_inner*ggml_element_size(ssm_states)))); + + struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0); + + // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens} + y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d)); + y = ggml_mul(ctx0, y, ggml_silu(ctx0, z)); + + // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens} + cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y); + } + + // residual + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + // final rmsnorm + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -7871,6 +8336,23 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { return result; } +static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) { + llama_batch dummy; + dummy.n_tokens = 0; + + llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; + + struct llm_build_context llm(lctx, dummy, cb, false); + + llm.init(); + + struct ggml_cgraph * result = llm.build_s_copy(); + + llm.free(); + + return result; +} + static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch, @@ -7985,6 +8467,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_starcoder2(); } break; + case LLM_ARCH_MAMBA: + { + result = llm.build_mamba(); + } break; default: GGML_ASSERT(false); } @@ -7995,19 +8481,29 @@ static struct ggml_cgraph * llama_build_graph( } static void llama_set_k_shift(llama_context & lctx) { - const auto & cparams = lctx.cparams; - - const int64_t n_ctx = cparams.n_ctx; + const int64_t kv_size = lctx.kv_self.size; assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); int32_t * data = (int32_t *) lctx.inp_K_shift->data; - for (int i = 0; i < n_ctx; ++i) { + for (int i = 0; i < kv_size; ++i) { data[i] = lctx.kv_self.cells[i].delta; } } +static void llama_set_s_copy(llama_context & lctx) { + const int64_t kv_size = lctx.kv_self.size; + + assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer)); + + int32_t * data = (int32_t *) lctx.inp_s_copy->data; + + for (int i = 0; i < kv_size; ++i) { + data[i] = lctx.kv_self.cells[i].src; + } +} + static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // // set input data @@ -8044,6 +8540,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { float * data = (float *) lctx.inp_KQ_mask->data; + // For causal attention, use only the previous KV cells + // of the correct sequence for each token of the batch. + // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { const llama_pos pos = batch.pos[j]; @@ -8149,6 +8648,53 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } } + + if (kv_self.recurrent) { + const int64_t n_kv = kv_self.n; + + { + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer)); + float * data = (float *) lctx.inp_s_mask->data; + + // states which are not affected by the current batch are left untouched + for (int i = 0; i < n_kv; ++i) { + llama_seq_id seq_id = i + lctx.kv_self.head; + llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id]; + bool has_self_seq = kv_cell.has_seq_id(seq_id); + + data[i] = (float) has_self_seq; + + // ensure current sequences will be kept + if (!has_self_seq && kv_cell.pos >= 0) { + kv_cell.seq_id.insert(seq_id); + } + } + } + // For Mamba (and other recurrent architectures), + // update the correct state(s)/sequence(s) for each token of the batch. + // Like with the KQ_mask, if a token in the batch has multiple sequences, + // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv). + { + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer)); + int32_t * data = (int32_t *) lctx.inp_s_seq->data; + + for (int j = 0; j < n_tokens; ++j) { + const int32_t n_seq = batch.n_seq_id[j]; + GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence + + for (int i = 0; i < n_kv; ++i) { + if (i < n_seq) { + // for this type of model, the head is the minimum seq_id of the batch + data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head; + } else { + data[j*n_kv + i] = -1; + } + } + } + } + } } static void llama_graph_compute( @@ -8271,11 +8817,13 @@ static int llama_decode_internal( return 1; } - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); - //kv_self.n = llama_kv_cache_cell_max(kv_self); + if (!kv_self.recurrent) { + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + //kv_self.n = llama_kv_cache_cell_max(kv_self); + } } //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); @@ -8701,6 +9249,26 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { } } + if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) { + llama_set_s_copy(lctx); + + { + ggml_cgraph * gf = llama_build_graph_s_copy(lctx); + + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); + } + + { + auto & kv_self = lctx.kv_self; + + kv_self.do_copy = false; + + for (uint32_t i = 0; i < kv_self.size; ++i) { + kv_self.cells[i].src = i; + } + } + } + // defragment the KV cache if needed if (lctx.kv_self.do_defrag) { llama_kv_cache_defrag_internal(lctx); @@ -11535,6 +12103,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight"); quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight"); + // do not quantize Mamba's small yet 2D weights + // NOTE: can't use LLM_TN here because the layer number is not known + quantize &= name.find("ssm_conv1d.weight") == std::string::npos; + quantize &= name.find("ssm_x.weight") == std::string::npos; + quantize &= name.find("ssm_dt.weight") == std::string::npos; + enum ggml_type new_type; void * new_data; size_t new_size; @@ -11985,6 +12559,7 @@ struct llama_context_params llama_context_default_params() { /*.seed =*/ LLAMA_DEFAULT_SEED, /*.n_ctx =*/ 512, /*.n_batch =*/ 512, + /*.n_parallel =*/ 1, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, @@ -12146,6 +12721,7 @@ struct llama_context * llama_new_context_with_model( auto & cparams = ctx->cparams; cparams.n_batch = params.n_batch; + // TODO: maybe add n_parallel here too cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch; cparams.yarn_ext_factor = params.yarn_ext_factor; @@ -12203,8 +12779,18 @@ struct llama_context * llama_new_context_with_model( ctx->rng = std::mt19937(params.seed); ctx->logits_all = params.logits_all; - const ggml_type type_k = params.type_k; - const ggml_type type_v = params.type_v; + uint32_t kv_size = cparams.n_ctx; + ggml_type type_k = params.type_k; + ggml_type type_v = params.type_v; + + // Mamba only needs a constant number of KV cache cells per sequence + if (model->arch == LLM_ARCH_MAMBA) { + // Mamba needs at least as many KV cells as there are sequences kept at any time + kv_size = std::max((uint32_t) 1, params.n_parallel); + // it's probably best to keep as much precision as possible for the states + type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states + type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states + } GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); @@ -12304,7 +12890,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, cparams.n_ctx, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -12338,7 +12924,7 @@ struct llama_context * llama_new_context_with_model( // graph inputs { ggml_init_params init_params = { - /* .mem_size */ ggml_tensor_overhead()*8, + /* .mem_size */ ggml_tensor_overhead()*(8 + 3*(ctx->kv_self.recurrent)), /* .mem_buffer */ nullptr, /* .no_alloc */ true, }; @@ -12347,11 +12933,16 @@ struct llama_context * llama_new_context_with_model( ctx->inp_tokens = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_embd = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch); ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); - ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); - ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx); - ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); + ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, kv_size, cparams.n_batch); + ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); + ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); + if (ctx->kv_self.recurrent) { + ctx->inp_s_copy = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); + ctx->inp_s_mask = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); + ctx->inp_s_seq = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_I32, kv_size, cparams.n_batch); + } ggml_set_name(ctx->inp_tokens, "inp_tokens"); ggml_set_name(ctx->inp_embd, "inp_embd"); @@ -12361,6 +12952,11 @@ struct llama_context * llama_new_context_with_model( ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); ggml_set_name(ctx->inp_mean, "inp_mean"); ggml_set_name(ctx->inp_cls, "inp_cls"); + if (ctx->kv_self.recurrent) { + ggml_set_name(ctx->inp_s_copy, "inp_s_copy"); + ggml_set_name(ctx->inp_s_mask, "inp_s_mask"); + ggml_set_name(ctx->inp_s_seq, "inp_s_seq"); + } ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, @@ -12447,6 +13043,10 @@ uint32_t llama_n_batch(const struct llama_context * ctx) { return ctx->cparams.n_batch; } +uint32_t llama_n_max_seq(const struct llama_context * ctx) { + return ctx->kv_self.size; +} + enum llama_vocab_type llama_vocab_type(const struct llama_model * model) { return model->vocab.type; } @@ -12460,6 +13060,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_MPT: case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: + case LLM_ARCH_MAMBA: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -12713,8 +13314,8 @@ void llama_kv_cache_clear(struct llama_context * ctx) { llama_kv_cache_clear(ctx->kv_self); } -void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { - llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1); +bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { + return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1); } void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { @@ -12891,8 +13492,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); const size_t kv_buf_size = kv_self.total_size(); const uint32_t kv_head = llama_kv_cache_cell_max(kv_self); @@ -12913,6 +13514,17 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); + if (kv_self.recurrent) { + // v is contiguous for recurrent models + // TODO: use other tensors for state models than k and v + const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); + + tmp_buf.resize(v_size); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + continue; + } + // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); @@ -13005,8 +13617,8 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); size_t kv_buf_size; uint32_t kv_head; @@ -13027,6 +13639,16 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); inp += k_size; + if (kv_self.recurrent) { + // v is contiguous for recurrent models + // TODO: use other tensors for state models than k and v + const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); + + ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size); + inp += v_size; + continue; + } + // v is not contiguous, copy row by row const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head); const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size); diff --git a/llama.h b/llama.h index 3dc162b078d30..7a107c7f335d5 100644 --- a/llama.h +++ b/llama.h @@ -235,6 +235,7 @@ extern "C" { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model uint32_t n_batch; // prompt processing maximum batch size + uint32_t n_parallel; // number of parallel sequences (i.e. distinct states for recurrent models) uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -376,6 +377,7 @@ extern "C" { LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_max_seq (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); @@ -502,7 +504,7 @@ extern "C" { // seq_id < 0 : match any sequence // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) - LLAMA_API void llama_kv_cache_seq_rm( + LLAMA_API bool llama_kv_cache_seq_rm( struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, From fd72d2d2a5e79d61ccef6af3d15f16e5e5cbc352 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 9 Mar 2024 10:30:04 +0100 Subject: [PATCH 201/401] server: tests: add truncated prompt tests, better kv cache size (#5933) * server: tests: add truncated prompt tests, better size * server, tests : update regex --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 23 +++++++++-- .../server/tests/features/parallel.feature | 5 ++- examples/server/tests/features/server.feature | 41 ++++++++++++++----- examples/server/tests/features/steps/steps.py | 35 +++++++++++++--- 4 files changed, 81 insertions(+), 23 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 59a59d56b6023..6f44499843a63 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1128,6 +1128,7 @@ struct server_context { LOG_VERBOSE("stopped by limit", { {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"n_decoded", slot.n_decoded}, {"n_predict", slot.params.n_predict}, }); @@ -1141,6 +1142,8 @@ struct server_context { } LOG_VERBOSE("next token", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", slot.has_next_token}, @@ -1750,6 +1753,15 @@ struct server_context { slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); + LOG_VERBOSE("prompt tokenized", { + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_prompt_tokens", slot.n_prompt_tokens}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + }); + if (slot.embedding) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_batch) { @@ -1788,10 +1800,13 @@ struct server_context { slot.n_prompt_tokens = prompt_tokens.size(); LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, + {"id_slot", slot.id}, + {"id_task", slot.id_task}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"n_prompt_tokens", slot.n_prompt_tokens}, + {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index 066698c8e3c1e..a66fed626619d 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -6,8 +6,8 @@ Feature: Parallel Given a server listening on localhost:8080 And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And 42 as server seed - And 512 as batch size - And 64 KV cache size + And 128 as batch size + And 256 KV cache size And 2 slots And continuous batching Then the server is starting @@ -76,6 +76,7 @@ Feature: Parallel | disabled | 128 | | enabled | 64 | + Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 Given a prompt: """ diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 878ac1363c419..aa132fa3472ef 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -10,11 +10,10 @@ Feature: llama.cpp server # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 # see --ctx-size and #5568 - And 32 KV cache size - And 512 as batch size - And 1 slots - And embeddings extraction - And 32 server max tokens to predict + And 256 KV cache size + And 32 as batch size + And 2 slots + And 64 server max tokens to predict And prometheus compatible metrics exposed Then the server is starting Then the server is healthy @@ -23,18 +22,35 @@ Feature: llama.cpp server Then the server is ready And all slots are idle + Scenario Outline: Completion Given a prompt And max tokens to predict And a completion request with no api error Then tokens are predicted matching + And the completion is truncated + And prompt tokens are processed And prometheus metrics are exposed And metric llamacpp:tokens_predicted is Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (read\|going)+ | 8 | - | Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 | + | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | + | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | + | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not | + + Scenario: Completion prompt truncated + Given a prompt: + """ + Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. + Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. + Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. + Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. + """ + And a completion request with no api error + Then 64 tokens are predicted matching fun|Annaks|popcorns + And the completion is truncated + And 109 prompt tokens are processed + Scenario Outline: OAI Compatibility Given a model @@ -44,11 +60,14 @@ Feature: llama.cpp server And streaming is Given an OAI compatible chat completions request with no api error Then tokens are predicted matching + And prompt tokens are processed + And the completion is truncated Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | + | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | + | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | | + Scenario: Tokenize / Detokenize When tokenizing: diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index d7f0058360c23..0076f805be4d3 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -196,12 +196,30 @@ async def step_request_completion(context, api_error): @step(u'{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content) + context.completion = context.tasks_result.pop() + assert_n_tokens_predicted(context.completion, predicted_n, re_content) @step(u'{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n) + context.completion = context.tasks_result.pop() + assert_n_tokens_predicted(context.completion, predicted_n) + + +@step(u'the completion is truncated') +def step_assert_completion_truncated(context): + step_assert_completion_truncated(context, '') + + +@step(u'the completion is {truncated} truncated') +def step_assert_completion_truncated(context, truncated): + truncated = truncated != "not" + assert context.completion['truncated'] == truncated, f'{context.completion}' + + +@step(u'{n_prompt:d} prompt tokens are processed') +def step_impl(context, n_prompt): + assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}" @step(u'a user prompt {user_prompt}') @@ -722,7 +740,8 @@ async def oai_chat_completions(user_prompt, completion_response = { 'content': '', 'timings': { - 'predicted_n': 0 + 'predicted_n': 0, + 'prompt_n': 0 } } if async_client: @@ -763,7 +782,8 @@ async def oai_chat_completions(user_prompt, completion_response = { 'content': chat_completion_raw['choices'][0]['message'], 'timings': { - 'predicted_n': chat_completion_raw['usage']['completion_tokens'] + 'predicted_n': chat_completion_raw['usage']['completion_tokens'], + 'prompt_n': chat_completion_raw['usage']['prompt_tokens'] } } else: @@ -792,13 +812,16 @@ async def oai_chat_completions(user_prompt, if 'content' in delta: completion_response['content'] += delta['content'] completion_response['timings']['predicted_n'] += 1 + completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop' else: assert len(chat_completion.choices) == 1 completion_response = { 'content': chat_completion.choices[0].message.content, 'timings': { - 'predicted_n': chat_completion.usage.completion_tokens - } + 'predicted_n': chat_completion.usage.completion_tokens, + 'prompt_n': chat_completion.usage.prompt_tokens + }, + 'truncated': chat_completion.choices[0].finish_reason != 'stop' } if debug: print("OAI response formatted to llama.cpp:", completion_response) From e1fa9569ba8ce276bc7801a3cebdcf8b1aa116ea Mon Sep 17 00:00:00 2001 From: Gabe Goodhart Date: Sat, 9 Mar 2024 02:57:09 -0700 Subject: [PATCH 202/401] server : add SSL support (#5926) * add cmake build toggle to enable ssl support in server Signed-off-by: Gabe Goodhart * add flags for ssl key/cert files and use SSLServer if set All SSL setup is hidden behind CPPHTTPLIB_OPENSSL_SUPPORT in the same way that the base httlib hides the SSL support Signed-off-by: Gabe Goodhart * Update readme for SSL support in server Signed-off-by: Gabe Goodhart * Add LLAMA_SERVER_SSL variable setup to top-level Makefile Signed-off-by: Gabe Goodhart --------- Signed-off-by: Gabe Goodhart --- Makefile | 4 ++ examples/server/CMakeLists.txt | 6 ++ examples/server/README.md | 26 ++++++++ examples/server/server.cpp | 108 ++++++++++++++++++++++----------- 4 files changed, 110 insertions(+), 34 deletions(-) diff --git a/Makefile b/Makefile index efce10bb8bd7e..aea9692227298 100644 --- a/Makefile +++ b/Makefile @@ -201,6 +201,10 @@ ifdef LLAMA_SERVER_VERBOSE MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif +ifdef LLAMA_SERVER_SSL + MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT + MK_LDFLAGS += -lssl -lcrypto +endif ifdef LLAMA_CODE_COVERAGE MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index c21eba634310b..f94de1e99b7e9 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) +option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h) install(TARGETS ${TARGET} RUNTIME) @@ -7,6 +8,11 @@ target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) +if (LLAMA_SERVER_SSL) + find_package(OpenSSL REQUIRED) + target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) + target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT) +endif() if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 591f748f84cd7..bf8c450b60223 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -59,6 +59,10 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--log-disable`: Output logs to stdout only, default: enabled. - `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json) +**If compiled with `LLAMA_SERVER_SSL=ON`** +- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key +- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate + ## Build server is build alongside everything else from the root of the project @@ -75,6 +79,28 @@ server is build alongside everything else from the root of the project cmake --build . --config Release ``` +## Build with SSL + +server can also be built with SSL support using OpenSSL 3 + +- Using `make`: + + ```bash + # NOTE: For non-system openssl, use the following: + # CXXFLAGS="-I /path/to/openssl/include" + # LDFLAGS="-L /path/to/openssl/lib" + make LLAMA_SERVER_SSL=true server + ``` + +- Using `CMake`: + + ```bash + mkdir build + cd build + cmake .. -DLLAMA_SERVER_SSL=ON + make server + ``` + ## Quick Start To get started right away, run the following command, making sure to use the correct path for the model you have: diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6f44499843a63..c3b87c846a6e2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -27,6 +27,7 @@ #include #include #include +#include using json = nlohmann::json; @@ -118,6 +119,11 @@ struct server_params { std::vector api_keys; +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + std::string ssl_key_file = ""; + std::string ssl_cert_file = ""; +#endif + bool slots_endpoint = true; bool metrics_endpoint = false; }; @@ -2142,6 +2148,10 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n"); + printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n"); +#endif printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); @@ -2220,7 +2230,24 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, } } key_file.close(); - } else if (arg == "--timeout" || arg == "-to") { + + } +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + else if (arg == "--ssl-key-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.ssl_key_file = argv[i]; + } else if (arg == "--ssl-cert-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.ssl_cert_file = argv[i]; + } +#endif + else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; break; @@ -2658,21 +2685,34 @@ int main(int argc, char ** argv) { {"system_info", llama_print_system_info()}, }); - httplib::Server svr; + std::unique_ptr svr; +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") { + LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}}); + svr.reset( + new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str()) + ); + } else { + LOG_INFO("Running without SSL", {}); + svr.reset(new httplib::Server()); + } +#else + svr.reset(new httplib::Server()); +#endif std::atomic state{SERVER_STATE_LOADING_MODEL}; - svr.set_default_headers({{"Server", "llama.cpp"}}); + svr->set_default_headers({{"Server", "llama.cpp"}}); // CORS preflight - svr.Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { + svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_header("Access-Control-Allow-Credentials", "true"); res.set_header("Access-Control-Allow-Methods", "POST"); res.set_header("Access-Control-Allow-Headers", "*"); }); - svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) { + svr->Get("/health", [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); switch (current_state) { case SERVER_STATE_READY: @@ -2728,7 +2768,7 @@ int main(int argc, char ** argv) { }); if (sparams.slots_endpoint) { - svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) { + svr->Get("/slots", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); @@ -2749,7 +2789,7 @@ int main(int argc, char ** argv) { } if (sparams.metrics_endpoint) { - svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { + svr->Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); @@ -2846,9 +2886,9 @@ int main(int argc, char ** argv) { }); } - svr.set_logger(log_server_request); + svr->set_logger(log_server_request); - svr.set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { + svr->set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; @@ -2864,7 +2904,7 @@ int main(int argc, char ** argv) { res.status = 500; }); - svr.set_error_handler([](const httplib::Request &, httplib::Response & res) { + svr->set_error_handler([](const httplib::Request &, httplib::Response & res) { if (res.status == 401) { res.set_content("Unauthorized", "text/plain; charset=utf-8"); } @@ -2877,16 +2917,16 @@ int main(int argc, char ** argv) { }); // set timeouts and change hostname and port - svr.set_read_timeout (sparams.read_timeout); - svr.set_write_timeout(sparams.write_timeout); + svr->set_read_timeout (sparams.read_timeout); + svr->set_write_timeout(sparams.write_timeout); - if (!svr.bind_to_port(sparams.hostname, sparams.port)) { + if (!svr->bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } // Set the base directory for serving static files - svr.set_base_dir(sparams.public_path); + svr->set_base_dir(sparams.public_path); std::unordered_map log_data; @@ -2947,30 +2987,30 @@ int main(int argc, char ** argv) { }; // this is only called if no index.html is found in the public --path - svr.Get("/", [](const httplib::Request &, httplib::Response & res) { + svr->Get("/", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); return false; }); // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const httplib::Request &, httplib::Response & res) { + svr->Get("/index.js", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { + svr->Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response & res) { + svr->Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); return false; }); - svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", ctx_server.name_user.c_str() }, @@ -3062,11 +3102,11 @@ int main(int argc, char ** argv) { } }; - svr.Post("/completion", completions); // legacy - svr.Post("/completions", completions); - svr.Post("/v1/completions", completions); + svr->Post("/completion", completions); // legacy + svr->Post("/completions", completions); + svr->Post("/v1/completions", completions); - svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + svr->Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json models = { @@ -3161,10 +3201,10 @@ int main(int argc, char ** argv) { } }; - svr.Post("/chat/completions", chat_completions); - svr.Post("/v1/chat/completions", chat_completions); + svr->Post("/chat/completions", chat_completions); + svr->Post("/v1/chat/completions", chat_completions); - svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + svr->Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; @@ -3228,11 +3268,11 @@ int main(int argc, char ** argv) { } }); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { return res.set_content("", "application/json; charset=utf-8"); }); - svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3244,7 +3284,7 @@ int main(int argc, char ** argv) { return res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3258,7 +3298,7 @@ int main(int argc, char ** argv) { return res.set_content(data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3289,7 +3329,7 @@ int main(int argc, char ** argv) { return res.set_content(result.data.dump(), "application/json; charset=utf-8"); }); - svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + svr->Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3360,13 +3400,13 @@ int main(int argc, char ** argv) { sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() { - if (!svr.listen_after_bind()) { + if (!svr->listen_after_bind()) { state.store(SERVER_STATE_ERROR); return 1; } @@ -3407,7 +3447,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.start_loop(); - svr.stop(); + svr->stop(); t.join(); llama_backend_free(); From 950ba1ab84db199f0bbdecdb2bb911f35261b321 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 9 Mar 2024 11:27:53 +0100 Subject: [PATCH 203/401] Server: reorganize some http logic (#5939) * refactor static file handler * use set_pre_routing_handler for validate_api_key * merge embedding handlers * correct http verb for endpoints * fix embedding response * fix test case CORS Options * fix code style --- examples/server/README.md | 4 +- examples/server/server.cpp | 637 +++++++++--------- .../server/tests/features/security.feature | 5 +- examples/server/tests/features/steps/steps.py | 3 +- 4 files changed, 335 insertions(+), 314 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index bf8c450b60223..3abb1abe3b92b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -42,7 +42,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. -- `--path`: path from which to serve static files (default examples/server/public) +- `--path`: path from which to serve static files (default: disabled) - `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. - `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. - `--embedding`: Enable embedding extraction, Default: disabled. @@ -558,7 +558,7 @@ The HTTP server supports OAI-like API ### Extending or building alternative Web Front End -The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. +You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. Read the documentation in `/completion.js` to see convenient ways to access llama. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c3b87c846a6e2..6e0f8328cdf5a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -113,7 +113,7 @@ struct server_params { int32_t n_threads_http = -1; std::string hostname = "127.0.0.1"; - std::string public_path = "examples/server/public"; + std::string public_path = ""; std::string chat_template = ""; std::string system_prompt = ""; @@ -2145,7 +2145,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); #ifdef CPPHTTPLIB_OPENSSL_SUPPORT @@ -2211,7 +2211,7 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } - sparams.api_keys.emplace_back(argv[i]); + sparams.api_keys.push_back(argv[i]); } else if (arg == "--api-key-file") { if (++i >= argc) { invalid_param = true; @@ -2712,180 +2712,6 @@ int main(int argc, char ** argv) { res.set_header("Access-Control-Allow-Headers", "*"); }); - svr->Get("/health", [&](const httplib::Request & req, httplib::Response & res) { - server_state current_state = state.load(); - switch (current_state) { - case SERVER_STATE_READY: - { - // request slots data using task queue - server_task task; - task.id = ctx_server.queue_tasks.get_new_id(); - task.type = SERVER_TASK_TYPE_METRICS; - task.id_target = -1; - - ctx_server.queue_results.add_waiting_task_id(task.id); - ctx_server.queue_tasks.post(task); - - // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); - ctx_server.queue_results.remove_waiting_task_id(task.id); - - const int n_idle_slots = result.data["idle"]; - const int n_processing_slots = result.data["processing"]; - - json health = { - {"status", "ok"}, - {"slots_idle", n_idle_slots}, - {"slots_processing", n_processing_slots} - }; - - res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { - health["slots"] = result.data["slots"]; - } - - if (n_idle_slots == 0) { - health["status"] = "no slot available"; - if (req.has_param("fail_on_no_slot")) { - res.status = 503; // HTTP Service Unavailable - } - } - - res.set_content(health.dump(), "application/json"); - break; - } - case SERVER_STATE_LOADING_MODEL: - { - res.set_content(R"({"status": "loading model"})", "application/json"); - res.status = 503; // HTTP Service Unavailable - } break; - case SERVER_STATE_ERROR: - { - res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); - res.status = 500; // HTTP Internal Server Error - } break; - } - }); - - if (sparams.slots_endpoint) { - svr->Get("/slots", [&](const httplib::Request &, httplib::Response & res) { - // request slots data using task queue - server_task task; - task.id = ctx_server.queue_tasks.get_new_id(); - task.id_multi = -1; - task.id_target = -1; - task.type = SERVER_TASK_TYPE_METRICS; - - ctx_server.queue_results.add_waiting_task_id(task.id); - ctx_server.queue_tasks.post(task); - - // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); - ctx_server.queue_results.remove_waiting_task_id(task.id); - - res.set_content(result.data["slots"].dump(), "application/json"); - res.status = 200; // HTTP OK - }); - } - - if (sparams.metrics_endpoint) { - svr->Get("/metrics", [&](const httplib::Request &, httplib::Response & res) { - // request slots data using task queue - server_task task; - task.id = ctx_server.queue_tasks.get_new_id(); - task.id_multi = -1; - task.id_target = -1; - task.type = SERVER_TASK_TYPE_METRICS; - task.data.push_back({{"reset_bucket", true}}); - - ctx_server.queue_results.add_waiting_task_id(task.id); - ctx_server.queue_tasks.post(task); - - // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); - ctx_server.queue_results.remove_waiting_task_id(task.id); - - json data = result.data; - - const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - const uint64_t t_prompt_processing = data["t_prompt_processing"]; - - const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - const uint64_t t_tokens_generation = data["t_tokens_generation"]; - - const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; - - // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names - json all_metrics_def = json { - {"counter", {{ - {"name", "prompt_tokens_total"}, - {"help", "Number of prompt tokens processed."}, - {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} - }, { - {"name", "prompt_seconds_total"}, - {"help", "Prompt process time"}, - {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} - }, { - {"name", "tokens_predicted_total"}, - {"help", "Number of generation tokens processed."}, - {"value", (uint64_t) data["n_tokens_predicted_total"]} - }, { - {"name", "tokens_predicted_seconds_total"}, - {"help", "Predict process time"}, - {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} - }}}, - {"gauge", {{ - {"name", "prompt_tokens_seconds"}, - {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} - },{ - {"name", "predicted_tokens_seconds"}, - {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} - },{ - {"name", "kv_cache_usage_ratio"}, - {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} - },{ - {"name", "kv_cache_tokens"}, - {"help", "KV-cache tokens."}, - {"value", (uint64_t) data["kv_cache_tokens_count"]} - },{ - {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", (uint64_t) data["processing"]} - },{ - {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", (uint64_t) data["deferred"]} - }}} - }; - - std::stringstream prometheus; - - for (const auto & el : all_metrics_def.items()) { - const auto & type = el.key(); - const auto & metrics_def = el.value(); - - for (const auto & metric_def : metrics_def) { - const std::string name = metric_def["name"]; - const std::string help = metric_def["help"]; - - auto value = json_value(metric_def, "value", 0.); - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << value << "\n"; - } - } - - const int64_t t_start = data["t_start"]; - res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); - - res.set_content(prometheus.str(), "text/plain; version=0.0.4"); - res.status = 200; // HTTP OK - }); - } - svr->set_logger(log_server_request); svr->set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { @@ -2925,16 +2751,14 @@ int main(int argc, char ** argv) { return 1; } - // Set the base directory for serving static files - svr->set_base_dir(sparams.public_path); - std::unordered_map log_data; log_data["hostname"] = sparams.hostname; log_data["port"] = std::to_string(sparams.port); if (sparams.api_keys.size() == 1) { - log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); + auto key = sparams.api_keys[0]; + log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); } else if (sparams.api_keys.size() > 1) { log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; } @@ -2959,13 +2783,37 @@ int main(int argc, char ** argv) { } } - // Middleware for API key validation - auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { + // + // Middlewares + // + + auto middleware_validate_api_key = [&sparams](const httplib::Request & req, httplib::Response & res) { + // TODO: should we apply API key to all endpoints, including "/health" and "/models"? + static const std::set protected_endpoints = { + "/props", + "/completion", + "/completions", + "/v1/completions", + "/chat/completions", + "/v1/chat/completions", + "/infill", + "/tokenize", + "/detokenize", + "/embedding", + "/embeddings", + "/v1/embeddings", + }; + // If API key is not set, skip validation if (sparams.api_keys.empty()) { return true; } + // If path is not in protected_endpoints list, skip validation + if (protected_endpoints.find(req.path) == protected_endpoints.end()) { + return true; + } + // Check for API key in the header auto auth_header = req.get_header_value("Authorization"); @@ -2978,6 +2826,8 @@ int main(int argc, char ** argv) { } // API key is invalid or not provided + // TODO: make another middleware for CORS related logic + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); res.status = 401; // Unauthorized @@ -2986,31 +2836,201 @@ int main(int argc, char ** argv) { return false; }; - // this is only called if no index.html is found in the public --path - svr->Get("/", [](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); - return false; + // register server middlewares + svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) { + if (!middleware_validate_api_key(req, res)) { + return httplib::Server::HandlerResponse::Handled; + } + return httplib::Server::HandlerResponse::Unhandled; }); - // this is only called if no index.js is found in the public --path - svr->Get("/index.js", [](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); - return false; - }); + // + // Route handlers (or controllers) + // - // this is only called if no index.html is found in the public --path - svr->Get("/completion.js", [](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); - return false; - }); + const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) { + server_state current_state = state.load(); + switch (current_state) { + case SERVER_STATE_READY: + { + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.type = SERVER_TASK_TYPE_METRICS; + task.id_target = -1; - // this is only called if no index.html is found in the public --path - svr->Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); - return false; - }); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + const int n_idle_slots = result.data["idle"]; + const int n_processing_slots = result.data["processing"]; + + json health = { + {"status", "ok"}, + {"slots_idle", n_idle_slots}, + {"slots_processing", n_processing_slots} + }; + + res.status = 200; // HTTP OK + if (sparams.slots_endpoint && req.has_param("include_slots")) { + health["slots"] = result.data["slots"]; + } - svr->Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + if (n_idle_slots == 0) { + health["status"] = "no slot available"; + if (req.has_param("fail_on_no_slot")) { + res.status = 503; // HTTP Service Unavailable + } + } + + res.set_content(health.dump(), "application/json"); + break; + } + case SERVER_STATE_LOADING_MODEL: + { + res.set_content(R"({"status": "loading model"})", "application/json"); + res.status = 503; // HTTP Service Unavailable + } break; + case SERVER_STATE_ERROR: + { + res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); + res.status = 500; // HTTP Internal Server Error + } break; + } + }; + + const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { + if (!sparams.slots_endpoint) { + res.status = 501; + res.set_content("This server does not support slots endpoint.", "text/plain; charset=utf-8"); + return; + } + + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + res.set_content(result.data["slots"].dump(), "application/json"); + res.status = 200; // HTTP OK + }; + + const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { + if (!sparams.metrics_endpoint) { + res.status = 501; + res.set_content("This server does not support metrics endpoint.", "text/plain; charset=utf-8"); + return; + } + + // request slots data using task queue + server_task task; + task.id = ctx_server.queue_tasks.get_new_id(); + task.id_multi = -1; + task.id_target = -1; + task.type = SERVER_TASK_TYPE_METRICS; + task.data.push_back({{"reset_bucket", true}}); + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + // get the result + server_task_result result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + json data = result.data; + + const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; + const uint64_t t_prompt_processing = data["t_prompt_processing"]; + + const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; + const uint64_t t_tokens_generation = data["t_tokens_generation"]; + + const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + + // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names + json all_metrics_def = json { + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} + }, { + {"name", "prompt_seconds_total"}, + {"help", "Prompt process time"}, + {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", (uint64_t) data["n_tokens_predicted_total"]} + }, { + {"name", "tokens_predicted_seconds_total"}, + {"help", "Predict process time"}, + {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", (uint64_t) data["kv_cache_tokens_count"]} + },{ + {"name", "requests_processing"}, + {"help", "Number of request processing."}, + {"value", (uint64_t) data["processing"]} + },{ + {"name", "requests_deferred"}, + {"help", "Number of request deferred."}, + {"value", (uint64_t) data["deferred"]} + }}} + }; + + std::stringstream prometheus; + + for (const auto & el : all_metrics_def.items()) { + const auto & type = el.key(); + const auto & metrics_def = el.value(); + + for (const auto & metric_def : metrics_def) { + const std::string name = metric_def["name"]; + const std::string help = metric_def["help"]; + + auto value = json_value(metric_def, "value", 0.); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; + } + } + + const int64_t t_start = data["t_start"]; + res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); + + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); + res.status = 200; // HTTP OK + }; + + const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", ctx_server.name_user.c_str() }, @@ -3020,13 +3040,10 @@ int main(int argc, char ** argv) { }; res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - const auto completions = [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } json data = json::parse(req.body); @@ -3102,11 +3119,7 @@ int main(int argc, char ** argv) { } }; - svr->Post("/completion", completions); // legacy - svr->Post("/completions", completions); - svr->Post("/v1/completions", completions); - - svr->Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { + const auto handle_models = [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json models = { @@ -3123,14 +3136,10 @@ int main(int argc, char ** argv) { }; res.set_content(models.dump(), "application/json; charset=utf-8"); - }); + }; - const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, &sparams](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); const int id_task = ctx_server.queue_tasks.get_new_id(); @@ -3201,14 +3210,8 @@ int main(int argc, char ** argv) { } }; - svr->Post("/chat/completions", chat_completions); - svr->Post("/v1/chat/completions", chat_completions); - - svr->Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) { + const auto handle_infill = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } json data = json::parse(req.body); @@ -3266,13 +3269,9 @@ int main(int argc, char ** argv) { res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } - }); - - svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { - return res.set_content("", "application/json; charset=utf-8"); - }); + }; - svr->Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3282,9 +3281,9 @@ int main(int argc, char ** argv) { } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - svr->Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); @@ -3296,9 +3295,9 @@ int main(int argc, char ** argv) { const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); + }; - svr->Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_embeddings = [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3307,94 +3306,114 @@ int main(int argc, char ** argv) { } const json body = json::parse(req.body); + bool is_openai = false; - json prompt; - if (body.count("content") != 0) { - prompt = body["content"]; + // an input prompt can string or a list of tokens (integer) + std::vector prompts; + if (body.count("input") != 0) { + is_openai = true; + if (body["input"].is_array()) { + // support multiple prompts + for (const json & elem : body["input"]) { + prompts.push_back(elem); + } + } else { + // single input prompt + prompts.push_back(body["input"]); + } + } else if (body.count("content") != 0) { + // only support single prompt here + std::string content = body["content"]; + prompts.push_back(content); } else { - prompt = ""; - } - - // create and queue the task - const int id_task = ctx_server.queue_tasks.get_new_id(); - - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true); - - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - // send the result - return res.set_content(result.data.dump(), "application/json; charset=utf-8"); - }); - - svr->Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!params.embedding) { - res.status = 501; - res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); - return; + // TODO @ngxson : should return an error here + prompts.push_back(""); } - const json body = json::parse(req.body); - - json prompt; - if (body.count("input") != 0) { - prompt = body["input"]; - if (prompt.is_array()) { - json data = json::array(); - - int i = 0; - for (const json & elem : prompt) { - const int id_task = ctx_server.queue_tasks.get_new_id(); - - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true); - - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - json embedding = json{ - {"embedding", json_value(result.data, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }; + // process all prompts + json responses = json::array(); + for (auto & prompt : prompts) { + // TODO @ngxson : maybe support multitask for this endpoint? + // create and queue the task + const int id_task = ctx_server.queue_tasks.get_new_id(); - data.push_back(embedding); - } - - json result = format_embeddings_response_oaicompat(body, data); + ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); - return res.set_content(result.dump(), "application/json; charset=utf-8"); + // get the result + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + responses.push_back(result.data); + } + + // write JSON response + json root; + if (is_openai) { + json res_oai = json::array(); + int i = 0; + for (auto & elem : responses) { + res_oai.push_back(json{ + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }); } + root = format_embeddings_response_oaicompat(body, res_oai); } else { - prompt = ""; + root = responses[0]; } + return res.set_content(root.dump(), "application/json; charset=utf-8"); + }; - // create and queue the task - const int id_task = ctx_server.queue_tasks.get_new_id(); - - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); + // + // Router + // - // get the result - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); - - json data = json::array({json{ - {"embedding", json_value(result.data, "embedding", json::array())}, - {"index", 0}, - {"object", "embedding"} - }} - ); + // register static assets routes + if (!sparams.public_path.empty()) { + // Set the base directory for serving static files + svr->set_base_dir(sparams.public_path); + } - json root = format_embeddings_response_oaicompat(body, data); + // using embedded static files + auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { + return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(content), len, mime_type); + return false; + }; + }; - return res.set_content(root.dump(), "application/json; charset=utf-8"); + svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { + // TODO @ngxson : I have no idea what it is... maybe this is redundant? + return res.set_content("", "application/json; charset=utf-8"); }); + svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); + svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); + svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); + svr->Get("/json-schema-to-grammar.mjs", handle_static_file( + json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + + // register API routes + svr->Get ("/health", handle_health); + svr->Get ("/slots", handle_slots); + svr->Get ("/metrics", handle_metrics); + svr->Get ("/props", handle_props); + svr->Get ("/v1/models", handle_models); + svr->Post("/completion", handle_completions); // legacy + svr->Post("/completions", handle_completions); + svr->Post("/v1/completions", handle_completions); + svr->Post("/chat/completions", handle_chat_completions); + svr->Post("/v1/chat/completions", handle_chat_completions); + svr->Post("/infill", handle_infill); + svr->Post("/embedding", handle_embeddings); // legacy + svr->Post("/embeddings", handle_embeddings); + svr->Post("/v1/embeddings", handle_embeddings); + svr->Post("/tokenize", handle_tokenize); + svr->Post("/detokenize", handle_detokenize); + // + // Start the server + // if (sparams.n_threads_http < 1) { // +2 threads for monitoring endpoints sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index 42a6709a53380..1d6aa40ea6985 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -39,8 +39,9 @@ Feature: Security Scenario Outline: CORS Options - When an OPTIONS request is sent from - Then CORS header is set to + Given a user api key llama.cpp + When an OPTIONS request is sent from + Then CORS header is set to Examples: Headers | origin | cors_header | cors_header_value | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 0076f805be4d3..14204850960c9 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -582,8 +582,9 @@ async def step_detokenize(context): @async_run_until_complete async def step_options_request(context, origin): async with aiohttp.ClientSession() as session: + headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin} async with session.options(f'{context.base_url}/v1/chat/completions', - headers={"Origin": origin}) as response: + headers=headers) as response: assert response.status == 200 context.options_response = response From 9674aaf35cb81478eb38c3f3ebde713ec72fbb79 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 12:34:18 +0200 Subject: [PATCH 204/401] server : simplify logic for empty prompts (#5953) --- examples/server/server.cpp | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6e0f8328cdf5a..aedf0afc603c3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1704,19 +1704,6 @@ struct server_context { // next, batch any pending prompts without exceeding n_batch if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()); - - // empty prompt passed -> release the slot and send empty response - // note: infill mode allows empty prompt - if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) { - slot.state = SLOT_STATE_PROCESSING; - slot.command = SLOT_COMMAND_NONE; - slot.release(); - slot.print_timings(); - send_final_response(slot); - continue; - } - // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) { auto & prompt_tokens = slot.prompt_tokens; @@ -1768,6 +1755,21 @@ struct server_context { {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); + // empty prompt passed -> release the slot and send empty response + if (prompt_tokens.empty()) { + LOG_INFO("empty prompt - releasing slot", { + {"id_slot", slot.id}, + {"id_task", slot.id_task} + }); + + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; + } + if (slot.embedding) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_batch) { From 8a3012a4ad08112bb3dc3f1399afec4e93780c44 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 12:47:57 +0200 Subject: [PATCH 205/401] ggml : add ggml-common.h to deduplicate shared code (#5940) * ggml : add ggml-common.h to shared code ggml-ci * scripts : update sync scripts * sycl : reuse quantum tables ggml-ci * ggml : minor * ggml : minor * sycl : try to fix build --- CMakeLists.txt | 3 +- Makefile | 4 +- ggml-common.h | 779 ++++++++++++++++++++++++++++++++++++++++ ggml-cuda.cu | 743 +------------------------------------- ggml-metal.metal | 707 +----------------------------------- ggml-quants.c | 708 +----------------------------------- ggml-quants.h | 4 +- ggml-sycl.cpp | 384 +------------------- scripts/sync-ggml-am.sh | 2 + scripts/sync-ggml.sh | 1 + 10 files changed, 799 insertions(+), 2536 deletions(-) create mode 100644 ggml-common.h diff --git a/CMakeLists.txt b/CMakeLists.txt index 48880f7204bf5..9309ca6bbe4d7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -199,7 +199,8 @@ if (LLAMA_METAL) # get full path to the file #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - # copy ggml-metal.metal to bin directory + # copy ggml-common.h and ggml-metal.metal to bin directory + configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) if (LLAMA_METAL_EMBED_LIBRARY) diff --git a/Makefile b/Makefile index aea9692227298..d809b8b3bbfc1 100644 --- a/Makefile +++ b/Makefile @@ -453,7 +453,7 @@ endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) endif -ggml-cuda.o: ggml-cuda.cu ggml-cuda.h +ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml-common.h ifdef JETSON_EOL_MODULE_DETECT $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ else @@ -630,7 +630,7 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h $(CC) $(CFLAGS) -c $< -o $@ -ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h +ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h $(CC) $(CFLAGS) -c $< -o $@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o diff --git a/ggml-common.h b/ggml-common.h new file mode 100644 index 0000000000000..4b6d248b6db90 --- /dev/null +++ b/ggml-common.h @@ -0,0 +1,779 @@ +#pragma once + +#if defined(GGML_COMMON_IMPL_C) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_METAL) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const constant type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_CUDA) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static const __device__ __constant__ type name[size] = { +#define GGML_TABLE_END() }; + +#define GGML_COMMON_IMPL +#elif defined(GGML_COMMON_IMPL_SYCL) +#include + +#define GGML_TABLE_BEGIN(type, name, size) static dpct::global_memory name(sycl::range<1>(size), { +#define GGML_TABLE_END() }); + +#define GGML_COMMON_IMPL +#endif + +#if defined(GGML_COMMON_IMPL) + +GGML_TABLE_BEGIN(uint8_t, kmask_iq2xs, 8) + 1, 2, 4, 8, 16, 32, 64, 128 +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint8_t, ksigns_iq2xs, 128) + 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, + 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, + 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, + 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, + 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, + 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, + 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, + 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, +GGML_TABLE_END() + +//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +GGML_TABLE_BEGIN(uint64_t, ksigns64, 128) + 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff, + 0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff, + 0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, + 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff, + 0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff, + 0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, + 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff, + 0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff, + 0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, + 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff, + 0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, + 0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, + 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff, + 0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff, + 0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, + 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff, + 0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff, + 0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, + 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff, + 0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff, + 0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, + 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff, + 0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff, + 0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, + 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff, + 0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff, + 0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, + 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff, + 0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff, + 0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, + 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff, + 0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff, +GGML_TABLE_END() +//#endif + + +GGML_TABLE_BEGIN(uint64_t, iq2xxs_grid, 256) + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, + 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, + 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, + 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, + 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, + 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, + 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, + 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, + 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, + 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, + 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, + 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, + 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, + 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, + 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, + 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, + 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, + 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, + 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, + 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, + 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, + 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, + 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, + 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, + 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, + 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, + 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, + 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, + 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, + 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, + 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, + 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, + 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, + 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, + 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, + 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, + 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, + 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, + 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, + 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, + 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, + 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, + 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, + 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, + 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, + 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, + 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, + 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, + 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, + 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, + 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, + 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, + 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, + 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, + 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, + 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, + 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, + 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, + 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, + 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint64_t, iq2xs_grid, 512) + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, + 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, + 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, + 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, + 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, + 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, + 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, + 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, + 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, + 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, + 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, + 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, + 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, + 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, + 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, + 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, + 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, + 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, + 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, + 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, + 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, + 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, + 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, + 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, + 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, + 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, + 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, + 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, + 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, + 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, + 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 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0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint32_t, iq3xxs_grid, 256) + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, +GGML_TABLE_END() + +GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512) + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, +GGML_TABLE_END() + +#define NGRID_IQ2XXS 512 +GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ2XXS) + 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, + 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, + 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, + 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, + 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, + 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, + 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, + 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, + 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, + 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, + 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, + 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, + 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, + 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, + 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, + 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, + 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, + 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, + 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, + 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, + 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, + 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, + 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, + 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, + 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, + 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, + 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, + 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, + 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, + 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, + 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, + 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, + 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, + 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, + 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, + 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, + 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, + 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, + 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, + 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, + 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, + 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, + 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, + 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, + 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, + 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, + 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, + 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, + 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, + 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, + 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, + 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, + 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, + 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, + 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, + 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, + 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, + 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, + 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, + 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, + 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, + 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, + 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, + 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, + 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, + 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, + 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, + 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, + 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, + 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, + 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, + 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, + 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, + 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, + 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, + 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, + 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, + 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, + 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, + 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, + 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, + 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, + 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, + 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, + 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, + 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, + 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, + 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, + 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, + 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, + 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, + 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, + 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, + 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, + 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, + 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, + 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, + 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, + 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, + 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, + 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, + 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, + 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, + 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, + 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, + 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, + 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, + 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, + 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, + 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, + 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, + 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, + 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, + 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, + 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, + 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, + 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, + 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, + 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, + 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, + 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, + 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, + 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, + 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, + 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, + 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, + 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, +GGML_TABLE_END() + +#endif // GGML_COMMON_IMPL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 72bcec8cdb17a..c207ff87a281a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2,6 +2,9 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#define GGML_COMMON_IMPL_CUDA +#include "ggml-common.h" + #include #include #include @@ -1569,746 +1572,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t #endif } -static const __device__ uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -static const __device__ uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -static const __device__ uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, - 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, - 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, - 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, - 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, - 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, - 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, - 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, - 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, - 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, - 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, - 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, - 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, - 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, - 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, - 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, - 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, - 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, - 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, - 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, - 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, - 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, - 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, - 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, - 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, - 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, - 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, - 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, - 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, - 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, - 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, - 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, - 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, - 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, - 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, - 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, - 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, - 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, - 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, - 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, - 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, - 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, - 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, - 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, - 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, - 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, - 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, - 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, - 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, - 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, - 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, - 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, - 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, - 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, - 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, - 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, - 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, - 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, - 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, - 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, - 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, - 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, - 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, - 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, - 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, - 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, - 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, - 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, - 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, - 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, - 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, - 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, - 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, - 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, - 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, - 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, - 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, - 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, - 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, - 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, - 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, - 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, - 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, - 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, - 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, - 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, - 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, - 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, - 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, - 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, - 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, - 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, - 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, - 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, - 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, - 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, - 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, - 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, - 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, - 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, - 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, - 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, - 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, - 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, - 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, - 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, - 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, - 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, - 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, - 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, - 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, - 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, - 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, - 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, - 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, - 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, - 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, - 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, - 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, - 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, - 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, - 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, - 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, - 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, - 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, - 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, - 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, - 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, - 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, - 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, - 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, - 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, - 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, - 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, - 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, - 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, - 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, - 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, - 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, - 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, - 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, - 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, - 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, - 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, - 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, - 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, - 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, - 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, - 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, - 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, - 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, - 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, - 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, - 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, - 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, - 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, - 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, - 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, - 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, - 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, - 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, - 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, - 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, - 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, - 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, - 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, - 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, - 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, - 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, - 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, - 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, - 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, - 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, - 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, - 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, - 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, - 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, - 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, - 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, - 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, - 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, - 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, - 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, - 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, - 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, - 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, - 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, - 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, - 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, - 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, - 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, - 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, - 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, - 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, - 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, - 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, - 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, - 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, - 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, - 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, - 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, - 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, - 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -static const __device__ uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -static const __device__ uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -static const __device__ uint64_t iq1s_grid[512] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, -}; - -static const __device__ uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -static const __device__ uint64_t ksigns64[128] = { - 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff, - 0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff, - 0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, - 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff, - 0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff, - 0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, - 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff, - 0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff, - 0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, - 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff, - 0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, - 0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, - 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff, - 0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff, - 0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, - 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff, - 0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff, - 0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, - 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff, - 0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff, - 0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, - 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff, - 0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff, - 0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, - 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff, - 0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff, - 0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, - 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff, - 0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff, - 0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, - 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff, - 0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff, -}; -//#endif - -static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - inline bool ggml_cuda_supports_mmq(enum ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: diff --git a/ggml-metal.metal b/ggml-metal.metal index a65d126412bfb..6ebbbd195e7ce 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1,5 +1,8 @@ #include +#define GGML_COMMON_IMPL_METAL +#include "ggml-common.h" + using namespace metal; #define MAX(x, y) ((x) > (y) ? (x) : (y)) @@ -3638,710 +3641,6 @@ kernel void kernel_mul_mv_q6_K_f32( // ======================= "True" 2-bit -constexpr constant static uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -constexpr constant static uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -constexpr constant static uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, - 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, - 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, - 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, - 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, - 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, - 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, - 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, - 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, - 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, - 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, - 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, - 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, - 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, - 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, - 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, - 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, - 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, - 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, - 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, - 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, - 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, - 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, - 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, - 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, - 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, - 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, - 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, - 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, - 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, - 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, - 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, - 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0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -constexpr constant static uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -constexpr constant static uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -#define NGRID_IQ1S 512 -constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, -}; - -constexpr constant static uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -constexpr constant static uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - void kernel_mul_mv_iq2_xxs_f32_impl( device const void * src0, device const float * src1, diff --git a/ggml-quants.c b/ggml-quants.c index 9dcb76def46db..5bb46def3d2b8 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1,6 +1,9 @@ #include "ggml-quants.h" #include "ggml-impl.h" +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + #include #include #include @@ -3327,711 +3330,6 @@ size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int // ====================== "True" 2-bit (de)-quantization -static const uint64_t iq2xxs_grid[256] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808, - 0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819, - 0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b, - 0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08, - 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b, - 0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08, - 0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808, - 0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808, - 0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08, - 0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819, - 0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808, - 0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908, - 0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808, - 0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908, - 0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19, - 0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b, - 0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808, - 0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08, - 0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08, - 0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819, - 0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808, - 0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19, - 0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819, - 0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b, - 0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08, - 0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908, - 0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b, - 0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08, - 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08, - 0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b, - 0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808, - 0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b, - 0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808, - 0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b, - 0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808, - 0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908, -}; - -static const uint64_t iq2xs_grid[512] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819, - 0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819, - 0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b, - 0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b, - 0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908, - 0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919, - 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908, - 0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, - 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08, - 0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808, - 0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819, - 0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808, - 0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819, - 0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819, - 0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908, - 0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19, - 0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b, - 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919, - 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819, - 0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819, - 0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908, - 0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808, - 0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808, - 0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, - 0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808, - 0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908, - 0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b, - 0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, - 0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908, - 0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908, - 0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919, - 0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19, - 0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b, - 0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808, - 0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08, - 0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908, - 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b, - 0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, - 0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808, - 0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08, - 0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819, - 0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808, - 0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808, - 0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819, - 0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908, - 0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b, - 0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908, - 0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808, - 0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819, - 0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819, - 0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b, - 0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819, - 0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08, - 0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808, - 0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919, - 0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808, - 0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b, - 0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808, - 0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, - 0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808, - 0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819, - 0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808, - 0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19, - 0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08, - 0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08, - 0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08, - 0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908, - 0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b, - 0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808, - 0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b, - 0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808, - 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19, - 0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808, - 0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, - 0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b, - 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, -}; - -static const uint64_t iq2s_grid[1024] = { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, - 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, - 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, - 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, - 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, - 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, - 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, - 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, - 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, - 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, - 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, - 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, - 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, - 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, - 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, - 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, - 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, - 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, - 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, - 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, - 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, - 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, - 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, - 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, - 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, - 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, - 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, - 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, - 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, - 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, - 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, - 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, - 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, - 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, - 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, - 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, - 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, - 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, - 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, - 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, - 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, - 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, - 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, - 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, - 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, - 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, - 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, - 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, - 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, - 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, - 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, - 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, - 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, - 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, - 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, - 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, - 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, - 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, - 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, - 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, - 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, - 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, - 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, - 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, - 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, - 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, - 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, - 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, - 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, - 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, - 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, - 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, - 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, - 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, - 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, - 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, - 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, - 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, - 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, - 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, - 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, - 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, - 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, - 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, - 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, - 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, - 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, - 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, - 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, - 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, - 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, - 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, - 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, - 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, - 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, - 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, - 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, - 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, - 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, - 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, - 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, - 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, - 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, - 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, - 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, - 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, - 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, - 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, - 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, - 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, - 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, - 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, - 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, - 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, - 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, - 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, - 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, - 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, - 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, - 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, - 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, - 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, - 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, - 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, - 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, - 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, - 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, - 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, - 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, - 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, - 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, - 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, - 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, - 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, - 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, - 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, - 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, - 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, - 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, - 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, - 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, - 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, - 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, - 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, - 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, - 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, - 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, - 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, - 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, - 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, - 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, - 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, - 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, - 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, - 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, - 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, - 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, - 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, - 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, - 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, - 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, - 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, - 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, - 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, - 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, - 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, - 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, - 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, - 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, - 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, - 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, - 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, - 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, - 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, - 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, - 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, - 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, - 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, - 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, - 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, - 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, - 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, - 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, - 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, - 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, - 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, - 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, - 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, - 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, - 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, - 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, - 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, - 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, - 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, - 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, - 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, - 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, - 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, - 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, - 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, - 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, - 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, - 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, - 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, - 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, - 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, - 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, - 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, - 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, - 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, - 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, - 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, - 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, - 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, - 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, - 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, - 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, - 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, - 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, - 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, - 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, - 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, - 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, - 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, - 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, - 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, - 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, - 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, - 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, - 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, - 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, - 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, - 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, - 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, - 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, - 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, - 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, -}; - -static const uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, - 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, - 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, - 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, - 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, - 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, - 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, - 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, - 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, -}; - -static const uint32_t iq3s_grid[512] = { - 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, - 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, - 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, - 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, - 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, - 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, - 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, - 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, - 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, - 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, - 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, - 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, - 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, - 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, - 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, - 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, - 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, - 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, - 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, - 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, - 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, - 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, - 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, - 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, - 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, - 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, - 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, - 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, - 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, - 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, - 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, - 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, - 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, - 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, - 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, - 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, - 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, - 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, - 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, - 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, - 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, - 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, - 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, - 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, - 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, - 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, - 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, - 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, - 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, - 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, - 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, - 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, - 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, - 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, - 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, - 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, - 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, - 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, - 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, - 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, - 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, - 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, - 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, - 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, -}; - -#define NGRID_IQ2XXS 512 -static const uint64_t iq1s_grid[NGRID_IQ2XXS] = { - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, - 0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01, - 0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100, - 0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000, - 0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00, - 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100, - 0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000, - 0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, - 0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00, - 0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff, - 0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100, - 0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff, - 0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000, - 0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff, - 0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff, - 0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00, - 0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001, - 0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001, - 0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01, - 0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000, - 0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101, - 0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00, - 0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100, - 0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101, - 0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101, - 0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000, - 0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff, - 0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff, - 0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101, - 0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, - 0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101, - 0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001, - 0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff, - 0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff, - 0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01, - 0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff, - 0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100, - 0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001, - 0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00, - 0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff, - 0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff, - 0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000, - 0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000, - 0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101, - 0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001, - 0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000, - 0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101, - 0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000, - 0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, - 0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000, - 0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100, - 0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000, - 0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000, - 0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100, - 0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff, - 0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff, - 0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00, - 0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101, - 0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000, - 0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00, - 0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000, - 0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff, - 0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101, - 0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff, - 0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00, - 0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff, - -}; - -static const uint8_t ksigns_iq2xs[128] = { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, - 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, - 160, 33, 34, 163, 36, 165, 166, 39, 40, 169, 170, 43, 172, 45, 46, 175, - 48, 177, 178, 51, 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, - 192, 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, 78, 207, - 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, 219, 92, 221, 222, 95, - 96, 225, 226, 99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111, - 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, -}; - -static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; - void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; diff --git a/ggml-quants.h b/ggml-quants.h index 316e356876037..d2d25eb4441d2 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -1,9 +1,9 @@ #pragma once -#include "ggml-impl.h" - // GGML internal header +#include "ggml-impl.h" + #include #include diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ddd951dd684a7..6fafa5282c8f9 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3144,6 +3144,8 @@ namespace dpct } // COPY from DPCT head files +#define GGML_COMMON_IMPL_SYCL +#include "ggml-common.h" static int g_ggml_sycl_debug=0; #define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0) @@ -4745,388 +4747,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri #endif } -static dpct::global_memory - iq2xxs_grid(sycl::range<1>(256), - { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, - 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, - 0x0808080808191908, 0x08080808082b0808, 0x08080808082b082b, - 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819, - 0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, - 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b082b2b, 0x080808082b2b082b, - 0x0808081908080819, 0x0808081908081908, 0x0808081908190808, - 0x0808081908191919, 0x0808081919080808, 0x080808192b081908, - 0x080808192b192b08, 0x0808082b08080808, 0x0808082b0808082b, - 0x0808082b082b082b, 0x0808082b2b08082b, 0x0808190808080819, - 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, - 0x0808190819082b08, 0x08081908192b0808, 0x080819082b080819, - 0x080819082b081908, 0x080819082b190808, 0x080819082b2b1908, - 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08, - 0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, - 0x080819192b080808, 0x080819192b190819, 0x0808192b08082b19, - 0x0808192b08190808, 0x0808192b19080808, 0x0808192b2b081908, - 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919, - 0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, - 0x08082b0819080819, 0x08082b0819081908, 0x08082b0819190808, - 0x08082b081919082b, 0x08082b082b082b08, 0x08082b1908081908, - 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908, - 0x0819080808080819, 0x0819080808081908, 0x0819080808190808, - 0x08190808082b0819, 0x0819080819080808, 0x08190808192b0808, - 0x081908082b081908, 0x081908082b190808, 0x081908082b191919, - 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808, - 0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, - 0x0819082b082b1908, 0x0819082b19081919, 0x0819190808080808, - 0x0819190808082b08, 0x08191908082b0808, 0x08191908082b1919, - 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08, - 0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, - 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808, - 0x08192b0819080808, 0x08192b082b080819, 0x08192b1908080808, - 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819, - 0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, - 0x082b080819081908, 0x082b0808192b0819, 0x082b08082b080808, - 0x082b08082b08082b, 0x082b0819082b2b19, 0x082b081919082b08, - 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819, - 0x082b190808081908, 0x082b190808190808, 0x082b190819080808, - 0x082b19081919192b, 0x082b191908080808, 0x082b191919080819, - 0x082b1919192b1908, 0x082b192b2b190808, 0x082b2b0808082b08, - 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908, - 0x1908080808080819, 0x1908080808081908, 0x1908080808190808, - 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x1908080819082b08, 0x190808081919192b, - 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908, - 0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, - 0x19080819192b0819, 0x190808192b080808, 0x190808192b081919, - 0x1908082b08080819, 0x1908082b08190808, 0x1908082b19082b08, - 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808, - 0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, - 0x190819082b192b19, 0x190819190819082b, 0x19081919082b1908, - 0x1908192b08080808, 0x19082b0808080819, 0x19082b0808081908, - 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, - 0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, - 0x19082b192b08082b, 0x19082b2b19081919, 0x19082b2b2b190808, - 0x1919080808080808, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808, - 0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, - 0x191908192b2b1908, 0x1919082b2b190819, 0x191919082b190808, - 0x191919082b19082b, 0x1919191908082b2b, 0x1919192b08080819, - 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819, - 0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, - 0x19192b2b08082b08, 0x192b080808081908, 0x192b080808190808, - 0x192b080819080808, 0x192b0808192b2b08, 0x192b081908080808, - 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808, - 0x192b190808080808, 0x192b190808081919, 0x192b191908190808, - 0x192b19190819082b, 0x192b19192b081908, 0x192b2b081908082b, - 0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808082b2b, - 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908, - 0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, - 0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, - 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908, - 0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, - 0x2b082b080808082b, 0x2b082b1908081908, 0x2b082b2b08190819, - 0x2b19080808081908, 0x2b19080808190808, 0x2b190808082b1908, - 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b, - 0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, - 0x2b191908082b082b, 0x2b19190819081908, 0x2b19191919190819, - 0x2b192b082b080819, 0x2b192b19082b0808, 0x2b2b08080808082b, - 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19, - 0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, - 0x2b2b2b1908081908, - }); - -static dpct::global_memory - iq2xs_grid(sycl::range<1>(512), - { - 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, - 0x0808080808082b08, 0x0808080808082b2b, 0x0808080808190819, - 0x0808080808191908, 0x080808080819192b, 0x0808080808192b19, - 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, - 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, - 0x080808081908192b, 0x0808080819082b19, 0x0808080819190808, - 0x080808081919082b, 0x0808080819191919, 0x0808080819192b08, - 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808, - 0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, - 0x080808082b190819, 0x080808082b191908, 0x080808082b192b19, - 0x080808082b2b0808, 0x0808081908080819, 0x0808081908081908, - 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, - 0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, - 0x0808081908192b2b, 0x08080819082b0819, 0x08080819082b1908, - 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, - 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908, - 0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, - 0x080808192b081908, 0x080808192b190808, 0x0808082b08080808, - 0x0808082b0808082b, 0x0808082b08081919, 0x0808082b08082b08, - 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808, - 0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, - 0x0808082b19191919, 0x0808082b2b080808, 0x0808082b2b082b2b, - 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, - 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, - 0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, - 0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, - 0x0808190819081919, 0x0808190819082b08, 0x0808190819190819, - 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808, - 0x080819082b080819, 0x080819082b081908, 0x080819082b190808, - 0x0808191908080808, 0x080819190808082b, 0x0808191908081919, - 0x0808191908082b08, 0x0808191908190819, 0x0808191908191908, - 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908, - 0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, - 0x0808192b08080819, 0x0808192b08081908, 0x0808192b08190808, - 0x0808192b082b192b, 0x0808192b19080808, 0x0808192b1908082b, - 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b, - 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, - 0x08082b0808190819, 0x08082b0808191908, 0x08082b08082b0808, - 0x08082b08082b1919, 0x08082b0819080819, 0x08082b0819081908, - 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808, - 0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, - 0x08082b1908081908, 0x08082b1908190808, 0x08082b1919080808, - 0x08082b192b080819, 0x08082b192b082b19, 0x08082b2b08080808, - 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b, - 0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, - 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, - 0x081908080819082b, 0x0819080808191919, 0x0819080808192b08, - 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808, - 0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, - 0x0819080819190819, 0x0819080819191908, 0x08190808192b0808, - 0x08190808192b2b2b, 0x081908082b080819, 0x081908082b081908, - 0x081908082b190808, 0x0819081908080808, 0x081908190808082b, - 0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, - 0x0819081908191908, 0x08190819082b0808, 0x0819081919080819, - 0x0819081919081908, 0x0819081919190808, 0x081908192b080808, - 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819, - 0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, - 0x0819082b19080808, 0x0819082b192b0808, 0x0819190808080808, - 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, - 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808, - 0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, - 0x0819190819190808, 0x08191908192b1908, 0x081919082b080808, - 0x0819191908080819, 0x0819191908081908, 0x0819191908190808, - 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908, - 0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, - 0x08192b0808190808, 0x08192b080819082b, 0x08192b0819080808, - 0x08192b0819191908, 0x08192b082b08192b, 0x08192b1908080808, - 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819, - 0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, - 0x082b080808081919, 0x082b080808082b08, 0x082b080808082b2b, - 0x082b080808190819, 0x082b080808191908, 0x082b0808082b0808, - 0x082b080819080819, 0x082b080819081908, 0x082b080819190808, - 0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, - 0x082b081908081908, 0x082b081908190808, 0x082b081919080808, - 0x082b081919082b08, 0x082b0819192b1919, 0x082b082b08080808, - 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08, - 0x082b190808080819, 0x082b190808081908, 0x082b190808190808, - 0x082b1908082b2b19, 0x082b190819080808, 0x082b191908080808, - 0x082b191919080819, 0x082b19191919082b, 0x082b19192b192b19, - 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b, - 0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, - 0x082b2b08082b0808, 0x082b2b0819191919, 0x082b2b082b082b08, - 0x082b2b082b2b082b, 0x082b2b19192b2b08, 0x082b2b192b190808, - 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b, - 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, - 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, - 0x1908080808190808, 0x190808080819082b, 0x1908080808191919, - 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908, - 0x1908080819080808, 0x190808081908082b, 0x1908080819081919, - 0x1908080819082b08, 0x1908080819082b2b, 0x1908080819190819, - 0x1908080819191908, 0x19080808192b0808, 0x19080808192b1919, - 0x190808082b080819, 0x190808082b081908, 0x190808082b190808, - 0x1908081908080808, 0x190808190808082b, 0x1908081908081919, - 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, - 0x19080819082b0808, 0x1908081919080819, 0x1908081919081908, - 0x1908081919190808, 0x190808192b080808, 0x190808192b081919, - 0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, - 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b082b2b19, - 0x1908082b19080808, 0x1908190808080808, 0x190819080808082b, - 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819, - 0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, - 0x1908190819080819, 0x1908190819081908, 0x1908190819190808, - 0x190819082b080808, 0x190819082b191908, 0x1908191908080819, - 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908, - 0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, - 0x1908192b08082b2b, 0x1908192b19081908, 0x1908192b19190808, - 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808190808, - 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908, - 0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, - 0x19082b1919081908, 0x19082b1919190808, 0x19082b19192b2b19, - 0x19082b2b08081908, 0x1919080808080808, 0x191908080808082b, - 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, - 0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, - 0x1919080819080819, 0x1919080819081908, 0x1919080819190808, - 0x191908082b080808, 0x1919081908080819, 0x1919081908081908, - 0x1919081908190808, 0x1919081908191919, 0x1919081919080808, - 0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, - 0x1919082b2b2b2b2b, 0x1919190808080819, 0x1919190808081908, - 0x1919190808190808, 0x19191908082b0819, 0x1919190819080808, - 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819, - 0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, - 0x191919192b082b08, 0x1919192b082b0819, 0x1919192b192b2b08, - 0x1919192b2b2b0819, 0x19192b0808080808, 0x19192b0808191908, - 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19, - 0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, - 0x19192b2b2b081919, 0x192b080808080819, 0x192b080808081908, - 0x192b080808190808, 0x192b080819080808, 0x192b080819191908, - 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19, - 0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, - 0x192b082b2b19082b, 0x192b190808080808, 0x192b19080819192b, - 0x192b191908190808, 0x192b191919080808, 0x192b191919081919, - 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b, - 0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, - 0x192b2b2b192b082b, 0x2b08080808080808, 0x2b0808080808082b, - 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, - 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b, - 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, - 0x2b0808082b080808, 0x2b0808082b08082b, 0x2b0808082b2b2b08, - 0x2b0808082b2b2b2b, 0x2b08081908080819, 0x2b08081908081908, - 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808, - 0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, - 0x2b08082b082b0808, 0x2b08082b2b080808, 0x2b08082b2b08082b, - 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08, 0x2b08190808080819, - 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b, - 0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, - 0x2b0819082b082b19, 0x2b08191908080808, 0x2b08191919081908, - 0x2b0819192b2b1919, 0x2b08192b08192b08, 0x2b08192b192b2b2b, - 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919, - 0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, - 0x2b082b082b2b2b08, 0x2b082b190808192b, 0x2b082b2b082b082b, - 0x2b082b2b2b080808, 0x2b082b2b2b082b08, 0x2b082b2b2b19192b, - 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908, - 0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, - 0x2b1908082b081908, 0x2b19081908080808, 0x2b190819082b082b, - 0x2b190819192b1908, 0x2b19082b1919192b, 0x2b19082b2b082b19, - 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908, - 0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, - 0x2b1919192b190808, 0x2b1919192b19082b, 0x2b19192b19080819, - 0x2b192b0819190819, 0x2b192b082b2b192b, 0x2b192b1919082b19, - 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808, - 0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, - 0x2b2b0808082b0808, 0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, - 0x2b2b081919190819, 0x2b2b081919192b19, 0x2b2b08192b2b192b, - 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08, - 0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, - 0x2b2b190819080808, 0x2b2b19082b191919, 0x2b2b192b192b1919, - 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b, 0x2b2b2b08082b0808, - 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808, - 0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, - 0x2b2b2b192b08192b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, - 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, - }); - -static dpct::global_memory iq3xxs_grid( - sycl::range<1>(256), - { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, - 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, - 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, 0x040c140c, 0x040c142c, - 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, - 0x04141c1c, 0x04141c3e, 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, - 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, 0x041c3e04, 0x04240c1c, - 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, - 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, - 0x043e0c24, 0x043e0c34, 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, - 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, 0x0c041c04, 0x0c041c14, - 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, - 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, - 0x0c14140c, 0x0c141c04, 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, - 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, 0x0c24042c, 0x0c242c04, - 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, - 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, - 0x14041414, 0x14041434, 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, - 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, 0x140c1c04, 0x140c341c, - 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, - 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, - 0x141c0c04, 0x141c0c24, 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, - 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, 0x143e040c, 0x143e041c, - 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, - 0x1c0c1404, 0x1c0c1c0c, 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, - 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, 0x1c1c0c0c, 0x1c1c1c1c, - 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, - 0x24040424, 0x24040c3e, 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, - 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, 0x24143404, 0x24143434, - 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, - 0x2c040c14, 0x2c04240c, 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, - 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, 0x2c1c0414, 0x2c1c2c1c, - 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, - 0x34043424, 0x340c140c, 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, - 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, 0x34341c1c, 0x343e041c, - 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, - 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, - 0x3e1c0404, 0x3e1c0c2c, 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, - 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, - }); - -static dpct::global_memory ksigns_iq2xs( - sycl::range<1>(128), - { - 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, - 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, - 154, 27, 156, 29, 30, 159, 160, 33, 34, 163, 36, 165, 166, - 39, 40, 169, 170, 43, 172, 45, 46, 175, 48, 177, 178, 51, - 180, 53, 54, 183, 184, 57, 58, 187, 60, 189, 190, 63, 192, - 65, 66, 195, 68, 197, 198, 71, 72, 201, 202, 75, 204, 77, - 78, 207, 80, 209, 210, 83, 212, 85, 86, 215, 216, 89, 90, - 219, 92, 221, 222, 95, 96, 225, 226, 99, 228, 101, 102, 231, - 232, 105, 106, 235, 108, 237, 238, 111, 240, 113, 114, 243, 116, - 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, - }); - -static dpct::global_memory - ksigns64(sycl::range<1>(128), - { - 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, - 0x000000000000ffff, 0xff00000000ff0000, 0x0000000000ff00ff, - 0x0000000000ffff00, 0xff00000000ffffff, 0xff000000ff000000, - 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, - 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, - 0x00000000ffffffff, 0xff0000ff00000000, 0x000000ff000000ff, - 0x000000ff0000ff00, 0xff0000ff0000ffff, 0x000000ff00ff0000, - 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, - 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, - 0x000000ffff00ffff, 0xff0000ffffff0000, 0x000000ffffff00ff, - 0x000000ffffffff00, 0xff0000ffffffffff, 0xff00ff0000000000, - 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, - 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, - 0x0000ff0000ffffff, 0x0000ff00ff000000, 0xff00ff00ff0000ff, - 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, 0xff00ff00ffff0000, - 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, - 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, - 0x0000ffff0000ffff, 0xff00ffff00ff0000, 0x0000ffff00ff00ff, - 0x0000ffff00ffff00, 0xff00ffff00ffffff, 0xff00ffffff000000, - 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, - 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, - 0x0000ffffffffffff, 0xffff000000000000, 0x00ff0000000000ff, - 0x00ff00000000ff00, 0xffff00000000ffff, 0x00ff000000ff0000, - 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, - 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, - 0x00ff0000ff00ffff, 0xffff0000ffff0000, 0x00ff0000ffff00ff, - 0x00ff0000ffffff00, 0xffff0000ffffffff, 0x00ff00ff00000000, - 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, - 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, - 0xffff00ff00ffffff, 0xffff00ffff000000, 0x00ff00ffff0000ff, - 0x00ff00ffff00ff00, 0xffff00ffff00ffff, 0x00ff00ffffff0000, - 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, - 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, - 0x00ffff000000ffff, 0xffffff0000ff0000, 0x00ffff0000ff00ff, - 0x00ffff0000ffff00, 0xffffff0000ffffff, 0xffffff00ff000000, - 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, - 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, - 0x00ffff00ffffffff, 0xffffffff00000000, 0x00ffffff000000ff, - 0x00ffffff0000ff00, 0xffffffff0000ffff, 0x00ffffff00ff0000, - 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, - 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, - 0x00ffffffff00ffff, 0xffffffffffff0000, 0x00ffffffffff00ff, - 0x00ffffffffffff00, 0xffffffffffffffff, - }); -//#endif - -static dpct::global_memory - kmask_iq2xs(sycl::range<1>(8), {1, 2, 4, 8, 16, 32, 64, 128}); - template static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy, const sycl::nd_item<3> &item_ct1, diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 2c391e6415ca1..83e6359b1dcef 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -94,6 +94,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-alloc.c -> ggml-alloc.c # src/ggml-backend-impl.h -> ggml-backend-impl.h # src/ggml-backend.c -> ggml-backend.c + # src/ggml-common.h -> ggml-common.h # src/ggml-cuda.cu -> ggml-cuda.cu # src/ggml-cuda.h -> ggml-cuda.h # src/ggml-impl.h -> ggml-impl.h @@ -126,6 +127,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \ -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \ -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \ + -e 's/src\/ggml-common\.h/ggml-common.h/g' \ -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \ -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \ -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \ diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index feb34bbc8bb8d..f1e6f0e57ce0d 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -4,6 +4,7 @@ cp -rpv ../ggml/src/ggml.c ./ggml.c cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml-backend-impl.h cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c +cp -rpv ../ggml/src/ggml-common.h ./ggml-common.h cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h From 0db32beaf09d90b8959d3d0cc493ed1e45685353 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 9 Mar 2024 11:16:53 +0000 Subject: [PATCH 206/401] server : fix passing prompt as tokens (#5955) * server: fix passing prompt as tokens * Update examples/server/server.cpp --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index aedf0afc603c3..8cff514f2a98a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -852,7 +852,16 @@ struct server_context { // infill slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); - slot.prompt = json_value(data, "prompt", std::string("")); + + // get prompt + { + const auto & prompt = data.find("prompt"); + if (prompt == data.end()) { + slot.prompt = ""; + } else { + slot.prompt = *prompt; + } + } // penalize user-provided tokens { From 2c4f566c88322ebf2f9bd11b01b5ebdaa0130b89 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 14:17:11 +0200 Subject: [PATCH 207/401] tests : gitignore ggml-common.h --- tests/.gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/.gitignore b/tests/.gitignore index 9427cf13d036f..620a48ee4449b 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1,3 +1,4 @@ * !*.* *.o +ggml-common.h From fb215c3832236fec7380c4fb618bd7154cb196ef Mon Sep 17 00:00:00 2001 From: SeungWon Jeong <65549245+redlion0929@users.noreply.github.com> Date: Sat, 9 Mar 2024 21:27:58 +0900 Subject: [PATCH 208/401] server : normalize embeddings (#5956) * output normalize embedding in '/v1/embeddings' * common : reuse llama_embd_normalize * common : better normalize impl --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 15 +++++++++++++++ common/common.h | 7 +++++++ examples/embedding/embedding.cpp | 14 +------------- examples/server/server.cpp | 8 +++++++- 4 files changed, 30 insertions(+), 14 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index d7f650ef486e9..16ef4d7f74dd9 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1852,3 +1852,18 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } + +void llama_embd_normalize(const float * inp, float * out, int n) { + double sum = 0.0; + for (int i = 0; i < n; i++) { + sum += inp[i] * inp[i]; + } + sum = sqrt(sum); + + const float norm = sum > 0.0 ? 1.0f / sum : 0.0f; + + for (int i = 0; i < n; i++) { + out[i] = inp[i] * norm; + } +} + diff --git a/common/common.h b/common/common.h index 977ce419ff93b..f8d82b8713c87 100644 --- a/common/common.h +++ b/common/common.h @@ -260,3 +260,10 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); + +// +// Embedding utils +// + +void llama_embd_normalize(const float * inp, float * out, int n); + diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index ff5883da6ba27..a553ae1c3f35d 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -23,17 +23,6 @@ static void batch_add_seq(llama_batch & batch, const std::vector & toke } } -static void normalize(const float * vec, float * out, int n) { - float norm = 0; - for (int i = 0; i < n; i++) { - norm += vec[i] * vec[i]; - } - norm = sqrt(norm); - for (int i = 0; i < n; i++) { - out[i] = vec[i] / norm; - } -} - static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_clear(ctx); @@ -44,7 +33,6 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu fprintf(stderr, "%s : failed to decode\n", __func__); } - // normalize on copy for (int i = 0; i < batch.n_tokens; i++) { if (!batch.logits[i]) { continue; @@ -61,7 +49,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; - normalize(embd, out, n_embd); + llama_embd_normalize(embd, out, n_embd); } } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8cff514f2a98a..796f3499c9877 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1327,6 +1327,8 @@ struct server_context { const int n_embd = llama_n_embd(model); + std::vector embd_res(n_embd, 0.0f); + for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { continue; @@ -1350,8 +1352,10 @@ struct server_context { continue; } + llama_embd_normalize(embd, embd_res.data(), n_embd); + res.data = json { - {"embedding", std::vector(embd, embd + n_embd)}, + {"embedding", embd_res}, }; } @@ -3354,6 +3358,8 @@ int main(int argc, char ** argv) { // get the result server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); + + // append to the responses responses.push_back(result.data); } From 97c09585d65a95864773b4d25d66d0f708baf38d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 15:47:47 +0200 Subject: [PATCH 209/401] server : clarify some items in the readme (#5957) * server : clarify some items in the readme * server : fix typo --- examples/server/README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 3abb1abe3b92b..23606b32a2c81 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -195,7 +195,11 @@ node index.js *Options:* - `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does. + `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: + + - The prompt is a string or an array with the first element given as a string + - The model's `tokenizer.ggml.add_bos_token` metadata is `true` + - The system prompt is empty `temperature`: Adjust the randomness of the generated text (default: 0.8). @@ -308,7 +312,7 @@ Notice that each `probs` is an array of length `n_probs`. `content`: Set the text to tokenize. - Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`. + Note that a special `BOS` token is never inserted. - **POST** `/detokenize`: Convert tokens to text. From 5b09797321430f08caf0473143a962916ab2ea89 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 15:53:59 +0200 Subject: [PATCH 210/401] ggml : remove old quantization functions (#5942) * ggml : remove old quantization functions ggml-ci * ggml : simplify ggml_quantize_chunk ggml-ci * ggml : restrict correctness ggml-ci * ggml : remove hist data from the quantization API ggml-ci * tests : remove hist usage in test-backend-ops ggml-ci * vulkan : remove hist and fix typo --- examples/benchmark/benchmark-matmult.cpp | 6 +- examples/llava/clip.cpp | 55 +--- ggml-quants.c | 143 +++------- ggml-quants.h | 42 +-- ggml-vulkan.cpp | 40 +-- ggml.c | 339 +++-------------------- ggml.h | 23 +- llama.cpp | 48 +--- tests/test-backend-ops.cpp | 3 +- 9 files changed, 131 insertions(+), 568 deletions(-) diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index e89f3de2fd397..47cb16c69d536 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -189,12 +189,10 @@ int main(int argc, char ** argv) { int32_t nelements = sizex*sizey; - std::vector hist_cur(1 << 4, 0); - // Set up a the benchmark matrices // printf("Creating new tensor q11 & Running quantize\n"); struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr); + ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr); // Set up a the compute graph // printf("Creating new tensor q31\n"); @@ -207,7 +205,7 @@ int main(int argc, char ** argv) { // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr); + ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr); // printf("Creating new tensor q32\n"); struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ef9e4ba7a6b5a..6653b815d93a1 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1862,7 +1862,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i std::vector work(512); std::vector conv_buf(512); - std::vector hist_all(1 << 4, 0); size_t total_size_org = 0; size_t total_size_new = 0; @@ -1917,48 +1916,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i } new_data = work.data(); - std::vector hist_cur(1 << 4, 0); - - switch (new_type) { - case GGML_TYPE_Q4_0: { - new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_1: { - new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_0: { - new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_1: { - new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q8_0: { - new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q2_K: { - new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q3_K: { - new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_K: { - new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_K: { - new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q6_K: { - new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - default: { - fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type); - return false; - } - } - - for (size_t j = 0; j < hist_cur.size(); ++j) { - hist_all[j] += hist_cur[j]; - } + new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr); } else { new_type = cur->type; new_data = cur->data; @@ -1993,17 +1951,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i { printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); - - int64_t sum_all = 0; - for (size_t i = 0; i < hist_all.size(); ++i) { - sum_all += hist_all[i]; - } - - printf("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); ++i) { - printf("%5.3f ", hist_all[i] / (float)sum_all); - } - printf("\n"); } return true; diff --git a/ggml-quants.c b/ggml-quants.c index 5bb46def3d2b8..4ee4e0604b02c 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1704,16 +1704,6 @@ void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q2_K_reference(x, vy, k); } -size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K; - quantize_row_q2_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q2_K)); -} - static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, float rmin, float rdelta, int nstep, bool use_mad) { @@ -1966,8 +1956,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q2_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row); if (!quant_weights) { quantize_row_q2_K_reference(src, dst, nrow*n_per_row); @@ -2186,16 +2175,6 @@ void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q3_K_reference(x, vy, k); } -size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K; - quantize_row_q3_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q3_K)); -} - static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2285,8 +2264,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri #endif } -size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q3_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row); if (!quant_weights) { quantize_row_q3_K_reference(src, dst, nrow*n_per_row); @@ -2456,17 +2434,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q4_K_reference(x, y, k); } -size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K; - quantize_row_q4_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q4_K)); -} - static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2545,8 +2512,7 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri #endif } -size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q4_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row); if (!quant_weights) { quantize_row_q4_K_reference(src, dst, nrow*n_per_row); @@ -2757,17 +2723,6 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q5_K_reference(x, y, k); } -size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K; - quantize_row_q5_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q5_K)); -} - static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -2866,8 +2821,7 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri #endif } -size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q5_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row); if (!quant_weights) { quantize_row_q5_K_reference(src, dst, nrow*n_per_row); @@ -3020,17 +2974,6 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q6_K_reference(x, y, k); } -size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K; - quantize_row_q6_K_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_q6_K)); -} - static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; @@ -3120,8 +3063,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri #endif } -size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_q6_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row); if (!quant_weights) { quantize_row_q6_K_reference(src, dst, nrow*n_per_row); @@ -3165,9 +3107,10 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri } } -size_t quantize_q4_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q4_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q4_0(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q4_0_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row); char * qrow = (char *)dst; @@ -3209,9 +3152,10 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri } } -size_t quantize_q4_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q4_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q4_1(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q4_1_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row); char * qrow = (char *)dst; @@ -3262,9 +3206,10 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri } } -size_t quantize_q5_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q5_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q5_0(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q5_0_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row); char * qrow = (char *)dst; @@ -3314,9 +3259,10 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri } } -size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_q5_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { if (!quant_weights) { - return ggml_quantize_q5_1(src, dst, nrow*n_per_row, n_per_row, hist); + quantize_row_q5_1_reference(src, dst, nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row); char * qrow = (char *)dst; @@ -3328,6 +3274,13 @@ size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int return nrow * row_size; } +size_t quantize_q8_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { + (void)quant_weights; // not used + const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row); + quantize_row_q8_0_reference(src, dst, nrow*n_per_row); + return nrow * row_size; +} + // ====================== "True" 2-bit (de)-quantization void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { @@ -9373,7 +9326,7 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void #endif } -void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); @@ -9620,7 +9573,7 @@ static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { } #endif -void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); @@ -10220,7 +10173,7 @@ void iq2xs_init_impl(enum ggml_type type) { int * kmap_q2xs; uint16_t * kneighbors_q2xs; - printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t)); for (int k = 0; k < grid_size; ++k) { int8_t * pos = (int8_t *)(the_grid + k); @@ -10275,7 +10228,7 @@ void iq2xs_init_impl(enum ggml_type type) { } num_neighbors += n; } - printf("%s: %d neighbours in total\n", __func__, num_neighbors); + //printf("%s: %d neighbours in total\n", __func__, num_neighbors); kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); iq2_data[gindex].neighbours = kneighbors_q2xs; int counter = 0; @@ -10698,8 +10651,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } } -size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -10711,8 +10663,7 @@ size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, return nrow * nblock * sizeof(block_iq2_xxs); } -size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -10816,7 +10767,7 @@ void iq3xs_init_impl(int grid_size) { int * kmap_q3xs; uint16_t * kneighbors_q3xs; - printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t)); for (int k = 0; k < grid_size; ++k) { int8_t * pos = (int8_t *)(the_grid + k); @@ -10871,7 +10822,7 @@ void iq3xs_init_impl(int grid_size) { } num_neighbors += n; } - printf("%s: %d neighbours in total\n", __func__, num_neighbors); + //printf("%s: %d neighbours in total\n", __func__, num_neighbors); kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); iq3_data[gindex].neighbours = kneighbors_q3xs; int counter = 0; @@ -11154,8 +11105,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } } -size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11361,8 +11311,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } #define IQ3S_BLOCK_SIZE 32 -size_t quantize_iq3_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; float scales[QK_K/IQ3S_BLOCK_SIZE]; @@ -11392,7 +11341,7 @@ void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq3_s(x, y, 1, k, NULL, NULL); + quantize_iq3_s(x, y, 1, k, NULL); } @@ -11587,8 +11536,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq1_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11613,7 +11561,7 @@ static inline int best_index_int8(int n, const int8_t * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x, +static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * restrict x, ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l, float * scales, float * weight, uint8_t * L, const int8_t * values, @@ -11721,8 +11669,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block } } -size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK4_NL == 0); int nblock = n_per_row/QK4_NL; char * qrow = (char *)dst; @@ -11752,14 +11699,13 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) { assert(k % QK4_NL == 0); - quantize_iq4_nl(x, y, 1, k, NULL, NULL); + quantize_iq4_nl(x, y, 1, k, NULL); } -size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { #if QK_K == 64 - return quantize_iq4_nl(src, dst, nrow, n_per_row, hist, quant_weights); + return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); #else - (void)hist; GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11788,7 +11734,7 @@ void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq4_xs(x, y, 1, k, NULL, NULL); + quantize_iq4_xs(x, y, 1, k, NULL); } // =============================== 2.5625 bpw @@ -11961,8 +11907,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { - (void)hist; +size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -11976,7 +11921,7 @@ size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, in void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) { assert(k % QK_K == 0); - quantize_iq2_s(x, y, 1, k, NULL, NULL); + quantize_iq2_s(x, y, 1, k, NULL); } void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) { diff --git a/ggml-quants.h b/ggml-quants.h index d2d25eb4441d2..47dd52856422a 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -261,6 +261,7 @@ void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGM void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); + void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k); @@ -280,6 +281,7 @@ void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); + void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -300,6 +302,7 @@ void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRI void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); + void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -321,6 +324,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -330,26 +334,26 @@ void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -// // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") -// -size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q4_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); -size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); + +size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); void iq2xs_init_impl(enum ggml_type type); void iq2xs_free_impl(enum ggml_type type); diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 5a1b3f4776181..d41aa7d22f096 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -4102,45 +4102,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool } static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { - std::vector hist_cur(1 << 4, 0); - - switch(quant) { - case GGML_TYPE_F32: - memcpy(to, from, sizeof(float) * ne); - break; - case GGML_TYPE_Q4_0: - ggml_quantize_q4_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_1: - ggml_quantize_q4_1(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_0: - ggml_quantize_q5_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_1: - ggml_quantize_q5_1(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q8_0: - ggml_quantize_q8_0(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q2_K: - ggml_quantize_q2_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q3_K: - ggml_quantize_q3_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q4_K: - ggml_quantize_q4_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q5_K: - ggml_quantize_q5_K(from, to, ne, ne, hist_cur.data()); - break; - case GGML_TYPE_Q6_K: - ggml_quantize_q6_K(from, to, ne, ne, hist_cur.data()); - break; - default: - GGML_ASSERT(false); - } + ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr); } static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { diff --git a/ggml.c b/ggml.c index 6eff98ab63f0c..80efa6f2ac900 100644 --- a/ggml.c +++ b/ggml.c @@ -20159,133 +20159,6 @@ void ggml_quantize_free(void) { ggml_critical_section_end(); } -size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK4_0 == 0); - const int nb = k / QK4_0; - - for (int b = 0; b < n; b += k) { - block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0; - - quantize_row_q4_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK4_0; j += 2) { - const uint8_t vi0 = y[i].qs[j/2] & 0x0F; - const uint8_t vi1 = y[i].qs[j/2] >> 4; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK4_0*sizeof(block_q4_0)); -} - -size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK4_1 == 0); - const int nb = k / QK4_1; - - for (int b = 0; b < n; b += k) { - block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1; - - quantize_row_q4_1_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK4_1; j += 2) { - const uint8_t vi0 = y[i].qs[j/2] & 0x0F; - const uint8_t vi1 = y[i].qs[j/2] >> 4; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK4_1*sizeof(block_q4_1)); -} - -size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK5_0 == 0); - const int nb = k / QK5_0; - - for (int b = 0; b < n; b += k) { - block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0; - - quantize_row_q5_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, &y[i].qh, sizeof(qh)); - - for (int j = 0; j < QK5_0; j += 2) { - const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4; - const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12)); - - // cast to 16 bins - const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; - const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK5_0*sizeof(block_q5_0)); -} - -size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK5_1 == 0); - const int nb = k / QK5_1; - - for (int b = 0; b < n; b += k) { - block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1; - - quantize_row_q5_1_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, &y[i].qh, sizeof(qh)); - - for (int j = 0; j < QK5_1; j += 2) { - const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4; - const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12)); - - // cast to 16 bins - const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; - const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; - - hist[vi0]++; - hist[vi1]++; - } - } - } - - return (n/QK5_1*sizeof(block_q5_1)); -} - -size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - for (int b = 0; b < n; b += k) { - block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0; - - quantize_row_q8_0_reference(src + b, y, k); - - for (int i = 0; i < nb; i++) { - for (int j = 0; j < QK8_0; ++j) { - const int8_t vi = y[i].qs[j]; - - hist[vi/16 + 8]++; - } - } - } - - return (n/QK8_0*sizeof(block_q8_0)); -} - bool ggml_quantize_requires_imatrix(enum ggml_type type) { return type == GGML_TYPE_IQ2_XXS || @@ -20293,177 +20166,52 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) { type == GGML_TYPE_IQ1_S; } -size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, - int nrows, int n_per_row, int64_t * hist, const float * imatrix) { +size_t ggml_quantize_chunk( + enum ggml_type type, + const float * src, + void * dst, + int start, + int nrows, + int n_per_row, + const float * imatrix) { + const int n = nrows * n_per_row; + + if (ggml_quantize_requires_imatrix(type)) { + GGML_ASSERT(imatrix != NULL); + } + + GGML_ASSERT(start % type_traits[type].blck_size == 0); + GGML_ASSERT(start % n_per_row == 0); + ggml_quantize_init(type); // this is noop if already initialized + + const size_t start_row = start / n_per_row; + const size_t row_size = ggml_row_size(type, n_per_row); + size_t result = 0; - int n = nrows * n_per_row; + switch (type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(start % QK4_0 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(start % QK4_1 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_0: - { - GGML_ASSERT(start % QK5_0 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_1: - { - GGML_ASSERT(start % QK5_1 == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q8_0: - { - GGML_ASSERT(start % QK8_0 == 0); - block_q8_0 * block = (block_q8_0*)dst + start / QK8_0; - result = ggml_quantize_q8_0(src + start, block, n, n, hist); - } break; - case GGML_TYPE_Q2_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q3_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q4_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q5_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_Q6_K: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_XXS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - GGML_ASSERT(imatrix); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_XS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - GGML_ASSERT(imatrix); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ3_XXS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ3_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ2_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq2_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ1_S: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq1_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; - case GGML_TYPE_IQ4_NL: + case GGML_TYPE_Q4_0: result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_1: result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_0: result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_1: result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q8_0: result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q2_K: result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q3_K: result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_K: result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_K: result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q6_K: result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; #if QK_K == 64 - case GGML_TYPE_IQ4_XS: -#endif - { - GGML_ASSERT(start % QK4_NL == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; -#if QK_K != 64 - case GGML_TYPE_IQ4_XS: - { - GGML_ASSERT(start % QK_K == 0); - GGML_ASSERT(start % n_per_row == 0); - size_t start_row = start / n_per_row; - size_t row_size = ggml_row_size(type, n_per_row); - result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); - GGML_ASSERT(result == row_size * nrows); - } break; + case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; +#else + case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; #endif case GGML_TYPE_F16: { @@ -20480,6 +20228,9 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i default: assert(false); } + + GGML_ASSERT(result == nrows * row_size); + return result; } diff --git a/ggml.h b/ggml.h index a13b0cec4edcf..1171088a96526 100644 --- a/ggml.h +++ b/ggml.h @@ -2194,25 +2194,18 @@ extern "C" { GGML_API void ggml_quantize_init(enum ggml_type type); GGML_API void ggml_quantize_free(void); - // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk - GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist); - - GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); - // some quantization type cannot be used without an importance matrix GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type); // calls ggml_quantize_init internally (i.e. can allocate memory) - GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, - int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + GGML_API size_t ggml_quantize_chunk( + enum ggml_type type, + const float * src, + void * dst, + int start, + int nrows, + int n_per_row, + const float * imatrix); // // gguf diff --git a/llama.cpp b/llama.cpp index 8c147a42b1e53..c58a029f74faf 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11890,17 +11890,16 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty return new_type; } -static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, int64_t * hist_cur, const float * imatrix, std::vector & workers, const int nthread) { +static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector & workers, const int nthread) { std::mutex mutex; int counter = 0; size_t new_size = 0; if (nthread < 2) { // single-thread - return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur, imatrix); + return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix); } - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix]() { - std::array local_hist = {}; const int nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; while (true) { @@ -11908,17 +11907,13 @@ static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const flo int first_row = counter; counter += nrows_per_chunk; if (first_row >= nrows) { if (local_size > 0) { - for (int j=0; j hist_all(1 << 4, 0); std::vector workers; workers.reserve(nthread); @@ -12175,7 +12169,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s work.resize(nelements * 4); // upper bound on size } new_data = work.data(); - std::array hist_cur = {}; const int n_per_row = tensor->ne[0]; const int nrows = nelements / n_per_row; @@ -12185,22 +12178,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; - new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, hist_cur.data(), imatrix, workers, nthread_use); - - LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); - int64_t tot_count = 0; - for (size_t i = 0; i < hist_cur.size(); i++) { - hist_all[i] += hist_cur[i]; - tot_count += hist_cur[i]; - } + new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix, workers, nthread_use); - if (tot_count > 0) { - LLAMA_LOG_INFO(" | hist: "); - for (size_t i = 0; i < hist_cur.size(); i++) { - LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements)); - } - } - LLAMA_LOG_INFO("\n"); + LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); } total_size_org += ggml_nbytes(tensor); total_size_new += new_size; @@ -12229,22 +12209,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); - // print histogram for all tensors - { - int64_t sum_all = 0; - for (size_t i = 0; i < hist_all.size(); i++) { - sum_all += hist_all[i]; - } - - if (sum_all > 0) { - LLAMA_LOG_INFO("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); i++) { - LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all)); - } - LLAMA_LOG_INFO("\n"); - } - } - if (qs.n_fallback > 0) { LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n", __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 8a6999f21f1f6..fc5edcc4bf6fc 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -53,7 +53,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) { GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); std::vector dataq(ggml_row_size(tensor->type, size)); - int64_t hist[16]; std::vector imatrix(tensor->ne[0], 1.0f); // dummy importance matrix const float * im = imatrix.data(); if (!ggml_quantize_requires_imatrix(tensor->type)) { @@ -63,7 +62,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m im = nullptr; } } - ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, im); + ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. From 58308a0ecce7cc261b802f4803c38d420063db21 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 17:34:15 +0200 Subject: [PATCH 211/401] server : fix metrics init (#5964) --- examples/server/server.cpp | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 796f3499c9877..2374b7e4ab232 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -341,7 +341,7 @@ struct server_slot { }; struct server_metrics { - const int64_t t_start = ggml_time_us(); + int64_t t_start = 0; uint64_t n_prompt_tokens_processed_total = 0; uint64_t t_prompt_processing_total = 0; @@ -354,14 +354,18 @@ struct server_metrics { uint64_t n_tokens_predicted = 0; uint64_t t_tokens_generation = 0; - void on_prompt_eval(const server_slot &slot) { + void init() { + t_start = ggml_time_us(); + } + + void on_prompt_eval(const server_slot & slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; t_prompt_processing_total += slot.t_prompt_processing; } - void on_prediction(const server_slot &slot) { + void on_prediction(const server_slot & slot) { n_tokens_predicted_total += slot.n_decoded; n_tokens_predicted += slot.n_decoded; t_tokens_generation += slot.t_token_generation; @@ -690,10 +694,11 @@ struct server_context { return res > 0; } - void initialize() { + void init() { const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); + for (int i = 0; i < params.n_parallel; i++) { server_slot slot; @@ -735,6 +740,8 @@ struct server_context { default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); + + metrics.init(); } std::vector tokenize(const json & json_prompt, bool add_bos) const { @@ -2783,7 +2790,7 @@ int main(int argc, char ** argv) { state.store(SERVER_STATE_ERROR); return 1; } else { - ctx_server.initialize(); + ctx_server.init(); state.store(SERVER_STATE_READY); } From 8380ecfb219c2e73cc706fcc83935b7c806cc7c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 17:36:20 +0200 Subject: [PATCH 212/401] ggml : fix unnecessary f32 -> f16 -> f32 casts (mmla) (#5951) --- ggml-quants.c | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 4ee4e0604b02c..807c5e391069c 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -4059,10 +4059,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); From 098dbaab449f5309a54871ba7e5acef72ae696de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 18:14:13 +0200 Subject: [PATCH 213/401] readme : update hot topics --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d7dba73e62267..e3ec0817ae0e5 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,11 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +> [!IMPORTANT] +> **Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962** +> +> Vote for which quantization type provides better responses, all other parameters being the same. + ### Recent API changes - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 @@ -16,11 +21,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) -- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) -- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 -- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 -- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 +- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328 ---- From d894f352bf433157232dc8dc54eacd50014e898e Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 9 Mar 2024 19:55:54 +0100 Subject: [PATCH 214/401] perplexity : support using multiple sequences to allow larger batch sizes (#5946) * perplexity : support using multiple sequences to allow larger batch sizes ggml-ci * set cparams.n_parallel to the number of sequences * print tested n_ctx, add assert --- examples/perplexity/perplexity.cpp | 139 +++++++++++++++++++---------- llama.cpp | 22 +++-- 2 files changed, 108 insertions(+), 53 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 52789ee631234..293eb52c33653 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -442,7 +442,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & return {tokens, std::exp(nll / count), logit_history, prob_history}; } -static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { +static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) { if (params.ppl_stride > 0) { return perplexity_v2(ctx, params); } @@ -453,7 +453,6 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // BOS tokens will be added for each chunk before eval const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - const int n_ctx = llama_n_ctx(ctx); std::ofstream logits_stream; if (!params.logits_file.empty()) { @@ -499,13 +498,19 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par double nll2 = 0.0; const int num_batches = (n_ctx + n_batch - 1) / n_batch; + const int n_seq = std::max(1, n_batch / n_ctx); + + GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0); + GGML_ASSERT(params.n_ctx == n_seq * n_ctx); + + llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1); std::vector logits; if (num_batches > 1) { logits.reserve((size_t)n_ctx * n_vocab); } - fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq); std::vector workers(std::thread::hardware_concurrency() - 1); @@ -518,10 +523,26 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par log_probs.resize(n_ctx * nv); } - for (int i = 0; i < n_chunk; ++i) { + // We get the logits for all the tokens in the context window (params.n_ctx) + // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, + // calculate the perplexity over the last half of the window (so the model always has + // some context to predict the token). + // + // We rely on the fact that attention in the forward pass only looks at previous + // tokens here, so the logits returned for each token are an accurate representation + // of what the model would have predicted at that point. + // + // Example, we have a context window of 512, we will compute perplexity for each of the + // last 256 tokens. Then, we split the input up into context window size chunks to + // process the entire prompt. + const int first = n_ctx/2; + + for (int i = 0; i < n_chunk; i += n_seq) { const int start = i * n_ctx; const int end = start + n_ctx; + const int n_seq_batch = std::min(n_seq, n_chunk - i); + const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache @@ -531,22 +552,37 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); - // save original token and restore it after eval - const auto token_org = tokens[batch_start]; + batch.n_tokens = 0; + for (int seq = 0; seq < n_seq_batch; seq++) { + int seq_start = batch_start + seq*n_ctx; - // add BOS token for the first batch of each chunk - if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + // save original token and restore it after eval + const auto token_org = tokens[seq_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[seq_start] = llama_token_bos(llama_get_model(ctx)); + } + + for (int k = 0; k < batch_size; ++k) { + const int idx = seq*n_ctx + k; + batch.token[idx] = tokens[seq_start + k]; + batch.pos[idx] = j*n_batch + k; + batch.n_seq_id[idx] = 1; + batch.seq_id[idx][0] = seq; + batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0; + } + batch.n_tokens += batch_size; + + // restore the original token in case it was set to BOS + tokens[seq_start] = token_org; } - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + if (llama_decode(ctx, batch)) { fprintf(stderr, "%s : failed to eval\n", __func__); return {tokens, -1, logit_history, prob_history}; } - // restore the original token in case it was set to BOS - tokens[batch_start] = token_org; - if (num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); @@ -558,7 +594,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par if (i == 0) { const float t_total = std::chrono::duration(t_end - t_start).count(); fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); - int total_seconds = (int)(t_total * n_chunk); + int total_seconds = (int)(t_total*n_chunk/n_seq); if (total_seconds >= 60*60) { fprintf(stderr, "%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); @@ -566,37 +602,31 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); } - // We get the logits for all the tokens in the context window (params.n_ctx) - // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, - // calculate the perplexity over the last half of the window (so the model always has - // some context to predict the token). - // - // We rely on the fact that attention in the forward pass only looks at previous - // tokens here, so the logits returned for each token are an accurate representation - // of what the model would have predicted at that point. - // - // Example, we have a context window of 512, we will compute perplexity for each of the - // last 256 tokens. Then, we split the input up into context window size chunks to - // process the entire prompt. - const int first = n_ctx/2; - const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); - if (!params.logits_file.empty()) { - process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs, nll, nll2); - } else { - process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); - } - count += n_ctx - first - 1; - - // perplexity is e^(average negative log-likelihood) - if (params.ppl_output_type == 0) { - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); - } else { - double av = nll/count; - double av2 = nll2/count - av*av; - if (av2 > 0) av2 = sqrt(av2/(count-1)); - printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + for (int seq = 0; seq < n_seq_batch; seq++) { + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx); + llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first; + if (!params.logits_file.empty()) { + process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, + tokens_data, n_ctx - 1 - first, + workers, log_probs, nll, nll2); + } else { + process_logits(n_vocab, all_logits + first*n_vocab, + tokens_data, n_ctx - 1 - first, + workers, nll, nll2, + logit_history.data() + start + seq*n_ctx + first, + prob_history.data() + start + seq*n_ctx + first); + } + count += n_ctx - first - 1; + + // perplexity is e^(average negative log-likelihood) + if (params.ppl_output_type == 0) { + printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count)); + } else { + double av = nll/count; + double av2 = nll2/count - av*av; + if (av2 > 0) av2 = sqrt(av2/(count-1)); + printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + } } fflush(stdout); @@ -615,6 +645,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par printf("Unexpected negative standard deviation of log(prob)\n"); } + llama_batch_free(batch); + return {tokens, ppl, logit_history, prob_history}; } @@ -1782,13 +1814,24 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { int main(int argc, char ** argv) { gpt_params params; - params.n_batch = 512; if (!gpt_params_parse(argc, argv, params)) { return 1; } params.logits_all = true; - params.n_batch = std::min(params.n_batch, params.n_ctx); + + const int32_t n_ctx = params.n_ctx; + + const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence; + if (ppl) { + int n_seq = std::max(1, params.n_batch / n_ctx); + int32_t n_kv = n_seq * n_ctx; + params.n_parallel = n_seq; + params.n_ctx = n_kv; + params.n_batch = std::min(params.n_batch, n_kv); + } else { + params.n_batch = std::min(params.n_batch, params.n_ctx); + } if (params.ppl_stride > 0) { fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n", @@ -1847,7 +1890,7 @@ int main(int argc, char ** argv) { } else if (params.kl_divergence) { kl_divergence(ctx, params); } else { - results = perplexity(ctx, params); + results = perplexity(ctx, params, n_ctx); } llama_print_timings(ctx); diff --git a/llama.cpp b/llama.cpp index c58a029f74faf..b19616e8f9a5f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8925,17 +8925,29 @@ static int llama_decode_internal( if (batch.logits) { logits_out.resize(n_vocab * n_tokens); + int32_t i_first = -1; for (uint32_t i = 0; i < n_tokens; i++) { - if (batch.logits[i] == 0) { - continue; + if (batch.logits[i] && i_first == -1) { + i_first = (int32_t) i; + } + if (batch.logits[i] == 0 || i == n_tokens - 1) { + if (i_first != -1) { + int i_last = batch.logits[i] == 0 ? i : i + 1; + // extract logits for the range [i_first, i_last) + // group the requests to minimize the number of calls to the backend + ggml_backend_tensor_get_async(backend_res, res, + logits_out.data() + (n_vocab*i_first), + (n_vocab*i_first)*sizeof(float), + (i_last - i_first)*n_vocab*sizeof(float)); + i_first = -1; + } } - ggml_backend_tensor_get_async(backend_res, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG - logits_valid[i] = true; + logits_valid[i] = batch.logits[i] != 0; #endif } } else if (lctx.logits_all) { - logits_out.resize(n_vocab * n_tokens); + logits_out.resize(n_vocab*n_tokens); ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); From 77d1ac7e00bf049b9f2bba1b5a310a78318c49c4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 9 Mar 2024 22:04:00 +0200 Subject: [PATCH 215/401] server : print chat template info --- examples/server/server.cpp | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2374b7e4ab232..b14cca61b1530 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2197,7 +2197,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); - printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); + printf(" only commonly used templates are accepted:\n"); + printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n"); printf("\n"); } @@ -2798,13 +2799,30 @@ int main(int argc, char ** argv) { const auto model_meta = ctx_server.model_meta(); - if (sparams.chat_template.empty()) { // custom chat template is not supplied + // if a custom chat template is not supplied, we will use the one that comes with the model (if any) + if (sparams.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); sparams.chat_template = "chatml"; } } + // print sample chat example to make it clear which template is used + { + json chat; + chat.push_back({{"role", "system"}, {"content", "You are a helpful assistant"}}); + chat.push_back({{"role", "user"}, {"content", "Hello"}}); + chat.push_back({{"role", "assistant"}, {"content", "Hi there"}}); + chat.push_back({{"role", "user"}, {"content", "How are you?"}}); + + const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat); + + LOG_INFO("chat template", { + {"chat_example", chat_example}, + {"built_in", sparams.chat_template.empty()}, + }); + } + // // Middlewares // From 621e86b331f8b0e71f79fd82a4ae1cd54c3e4396 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 9 Mar 2024 23:41:49 +0100 Subject: [PATCH 216/401] server: benchmark: chat/completions scenario and other llm servers comparison (#5941) * server: bench: Init a bench scenario with K6 See #5827 * server: bench: EOL EOF * server: bench: PR feedback and improved k6 script configuration * server: bench: remove llamacpp_completions_tokens_seconds as it include prompt processing time and it's misleading server: bench: add max_tokens from SERVER_BENCH_MAX_TOKENS server: bench: increase truncated rate to 80% before failing * server: bench: fix doc * server: bench: change gauge custom metrics to trend * server: bench: change gauge custom metrics to trend server: bench: add trend custom metrics for total tokens per second average * server: bench: doc add an option to debug http request * server: bench: filter dataset too short and too long sequences * server: bench: allow to filter out conversation in the dataset based on env variable * server: bench: fix assistant message sent instead of user message * server: bench: fix assistant message sent instead of user message * server : add defrag thold parameter * server: bench: select prompts based on the current iteration id not randomly to make the bench more reproducible --------- Co-authored-by: Georgi Gerganov --- examples/server/bench/README.md | 88 +++++++++++++++++++++++ examples/server/bench/script.js | 120 ++++++++++++++++++++++++++++++++ examples/server/server.cpp | 8 +++ 3 files changed, 216 insertions(+) create mode 100644 examples/server/bench/README.md create mode 100644 examples/server/bench/script.js diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md new file mode 100644 index 0000000000000..a53ad64d7359b --- /dev/null +++ b/examples/server/bench/README.md @@ -0,0 +1,88 @@ +### Server benchmark tools + +Benchmark is using [k6](https://k6.io/). + +##### Install k6 + +Follow instruction from: https://k6.io/docs/get-started/installation/ + +Example for ubuntu: +```shell +snap install k6 +``` + +#### Download a dataset + +This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md). + +```shell +wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json +``` + +#### Download a model +Example for PHI-2 + +```shell +../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf +``` + +#### Start the server +The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`. + +Example: +```shell +server --host localhost --port 8080 \ + --model ggml-model-q4_0.gguf \ + --cont-batching \ + --metrics \ + --parallel 8 \ + --batch-size 512 \ + --ctx-size 4096 \ + --log-format text \ + -ngl 33 +``` + +#### Run the benchmark + +For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run: +```shell +k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +The benchmark values can be overridden with: +- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1` +- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480` +- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model` +- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512` +- `SERVER_BENCH_DATASET` path to the benchmark dataset file +- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024` +- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048` + +Note: the local tokenizer is just a string space split, real number of tokens will differ. + +Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/): + +```shell +SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`. + +#### Metrics + +Following metrics are available computed from the OAI chat completions response `usage`: +- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration` +- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens` +- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens` +- `llamacpp_completion_tokens` Trend of `usage.completion_tokens` +- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens` +- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'` +- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'` + +The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`. + +K6 metrics might be compared against [server metrics](../README.md), with: + +```shell +curl http://localhost:8080/metrics +``` diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js new file mode 100644 index 0000000000000..a4f5ac5ab22ad --- /dev/null +++ b/examples/server/bench/script.js @@ -0,0 +1,120 @@ +import http from 'k6/http' +import {check, sleep} from 'k6' +import {SharedArray} from 'k6/data' +import {Counter, Rate, Trend} from 'k6/metrics' +import exec from 'k6/execution'; + +// Server chat completions prefix +const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1' + +// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users +const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8 + +// Model name to request +const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model' + +// Dataset path +const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json' + +// Max tokens to predict +const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512 + +// Max prompt tokens +const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024 + +// Max slot context +const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048 + +export function setup() { + console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`) +} + +const data = new SharedArray('conversations', function () { + const tokenizer = (message) => message.split(/[\s,'".?]/) + + return JSON.parse(open(dataset_path)) + // Filter out the conversations with less than 2 turns. + .filter(data => data["conversations"].length >= 2) + .filter(data => data["conversations"][0]["from"] === "human") + .map(data => { + return { + prompt: data["conversations"][0]["value"], + n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length, + n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length, + } + }) + // Filter out too short sequences + .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4) + // Filter out too long sequences. + .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot) + // Keep only first n prompts + .slice(0, n_prompt) +}) + +const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens') +const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens') +const llamacpp_tokens_second = new Trend('llamacpp_tokens_second') + +const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter') +const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter') + +const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate') +const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate') + +export const options = { + thresholds: { + llamacpp_completions_truncated_rate: [ + // more than 80% of truncated input will abort the test + {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'}, + ], + }, + duration: '10m', + vus: 8, +} + +export default function () { + const conversation = data[exec.scenario.iterationInInstance % data.length] + const payload = { + "messages": [ + { + "role": "system", + "content": "You are ChatGPT, an AI assistant.", + }, + { + "role": "user", + "content": conversation.prompt, + } + ], + "model": model, + "stream": false, + "max_tokens": max_tokens + } + + const body = JSON.stringify(payload) + + let res = http.post(`${server_url}/chat/completions`, body, { + headers: {'Content-Type': 'application/json'}, + timeout: '300s' + }) + + check(res, {'success completion': (r) => r.status === 200}) + + if (res.status === 200) { + const completions = res.json() + + llamacpp_prompt_tokens.add(completions.usage.prompt_tokens) + llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens) + + llamacpp_completion_tokens.add(completions.usage.completion_tokens) + llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens) + + llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length') + llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop') + + llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3) + } else { + console.error(`response: ${res.body} request=${payload}`) + } + + sleep(0.3) +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b14cca61b1530..c7d3ed01b6347 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2133,6 +2133,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); + printf(" -dt N, --defrag-thold N\n"); + printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); @@ -2355,6 +2357,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } + } else if (arg == "--defrag-thold" || arg == "-dt") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.defrag_thold = std::stof(argv[i]); } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) { From c78541479cf835dd9eb568ccd9a2083198a7203d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 10 Mar 2024 16:43:08 +0200 Subject: [PATCH 217/401] nix: update flake.lock (#5969) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29) → 'github:NixOS/nixpkgs/9df3e30ce24fd28c7b3e2de0d986769db5d6225d' (2024-03-06) Co-authored-by: github-actions[bot] --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index b1b0916566453..f9865d5e464cd 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1709237383, - "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", + "lastModified": 1709703039, + "narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", + "rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d", "type": "github" }, "original": { From 2960eae847f8dbde23be6d170a61bcf44ebf32de Mon Sep 17 00:00:00 2001 From: Clint Herron Date: Sun, 10 Mar 2024 11:17:43 -0400 Subject: [PATCH 218/401] grammar : verify parsed state (#5950) --- common/grammar-parser.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index bf89a96f3617f..2a1301569793a 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -278,6 +278,22 @@ namespace grammar_parser { while (*pos) { pos = parse_rule(state, pos); } + // Validate the state to ensure that all rules are defined + for (const auto & rule : state.rules) { + for (const auto & elem : rule) { + if (elem.type == LLAMA_GRETYPE_RULE_REF) { + // Ensure that the rule at that location exists + if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) { + // Get the name of the rule that is missing + for (const auto & kv : state.symbol_ids) { + if (kv.second == elem.value) { + throw std::runtime_error("Undefined rule identifier '" + kv.first + "'"); + } + } + } + } + } + } return state; } catch (const std::exception & err) { fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what()); From bcebd7dbf62fd7b293d5ed089023e4e733269c71 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Sun, 10 Mar 2024 11:56:30 -0400 Subject: [PATCH 219/401] llama : add support for GritLM (#5959) * add gritlm example * gritlm results match * tabs to spaces * comment out debug printing * rebase to new embed * gritlm embeddings are back babeee * add to gitignore * allow to toggle embedding mode * Clean-up GritLM sample code. * Fix types. * Flush stdout and output ending newline if streaming. * mostly style fixes; correct KQ_mask comment * add causal_attn flag to llama_cparams * gritml : minor * llama : minor --------- Co-authored-by: Douglas Hanley Co-authored-by: Georgi Gerganov --- .gitignore | 1 + Makefile | 6 +- examples/CMakeLists.txt | 1 + examples/gritlm/CMakeLists.txt | 5 + examples/gritlm/gritlm.cpp | 229 +++++++++++++++++++++++++++++++++ llama.cpp | 25 +++- llama.h | 4 + 7 files changed, 267 insertions(+), 4 deletions(-) create mode 100644 examples/gritlm/CMakeLists.txt create mode 100644 examples/gritlm/gritlm.cpp diff --git a/.gitignore b/.gitignore index 62b6b8b1ab250..d28f4d1b801ea 100644 --- a/.gitignore +++ b/.gitignore @@ -45,6 +45,7 @@ models-mnt /embedding /gguf /gguf-llama-simple +/gritlm /imatrix /infill /libllama.so diff --git a/Makefile b/Makefile index d809b8b3bbfc1..6492da8a02fb7 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ BUILD_TARGETS = \ main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ - speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o + speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o # Binaries only useful for tests TEST_TARGETS = \ @@ -724,6 +724,10 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +gritlm: examples/gritlm/gritlm.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 653abc73ac98f..e762cf8b9238b 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -20,6 +20,7 @@ else() add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) add_subdirectory(finetune) + add_subdirectory(gritlm) add_subdirectory(infill) add_subdirectory(llama-bench) add_subdirectory(llava) diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt new file mode 100644 index 0000000000000..ac4a5ae7937ea --- /dev/null +++ b/examples/gritlm/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET gritlm) +add_executable(${TARGET} gritlm.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp new file mode 100644 index 0000000000000..3d4b085d69b6f --- /dev/null +++ b/examples/gritlm/gritlm.cpp @@ -0,0 +1,229 @@ +#include "common.h" +#include "llama.h" + +#include +#include + +// #define GRIT_DEBUG + +static float dot_product(const std::vector & v1, const std::vector & v2) { + float dot = 0.0f; + for (uint64_t i = 0; i < v1.size(); ++i) { + dot += v1[i] * v2[i]; + } + return dot; +} + +static float norm(const std::vector & v) { + return std::sqrt(dot_product(v, v)); +} + +static float cosine_similarity(const std::vector & v1, const std::vector & v2) { + return dot_product(v1, v2) / (norm(v1) * norm(v2)); +} + +static std::vector> encode(llama_context * ctx, const std::vector & sentences, const std::string & instruction) { + std::vector> result; + + const llama_model * mdl = llama_get_model(ctx); + + llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1); + + for (uint64_t i = 0; i < sentences.size(); i++) { + llama_batch_clear(batch); + + const std::string input_string = instruction + sentences[i]; + + std::vector inputs = llama_tokenize(mdl, input_string, true, false); + + const int32_t n_toks = inputs.size(); + + // GritLM seems to have EOS = "" + // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18 + // inputs.push_back(llama_token_eos(mdl)); + + // we want to ignore instruction tokens for mean pooling + const int32_t n_inst = llama_tokenize(mdl, instruction, true, false).size(); + +#ifdef GRIT_DEBUG + // debug tokens - should be matching as referenced in the GritLM sample + std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) { + std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str()); + }); + std::printf("\n"); +#endif + + // add input to batch (this increments n_tokens) + for (int32_t j = 0; j < n_toks; j++) { + llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst); + } + + // clear previous kv_cache values (irrelevant for embeddings) + llama_kv_cache_clear(ctx); + llama_set_causal_attn(ctx, false); + + // run model + llama_decode(ctx, batch); + + // get embedding dimensions + uint64_t n_embd = llama_n_embd(mdl); + + // allocate embedding output + std::vector emb_unorm(n_embd, 0.0f); + + // sum up all token embeddings + for (int32_t k = n_inst; k < n_toks; k++) { + float * emb = llama_get_embeddings_ith(ctx, k); + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] += emb[j]; + } + } + + // divide by number of tokens (mean pooling) + { + const uint64_t n_sent = n_toks - n_inst; + + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] /= n_sent; + } + } + + std::vector emb_norm(emb_unorm.size()); + llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd); + result.push_back(emb_norm); + +#ifdef GRIT_DEBUG + // print out emb_norm + std::printf("embedding %ld: ", i); + for (uint64_t j = 0; j < n_embd; j++) { + std::printf("%.5f ", emb_norm[j]); + } + std::printf("\n\n"); +#endif + } + + llama_batch_free(batch); + + return result; +} + +static std::string generate(llama_context * ctx, const std::string & prompt, bool stream) { + std::string result; + + const llama_model * mdl = llama_get_model(ctx); + llama_token eos_token = llama_token_eos(mdl); + + llama_kv_cache_clear(ctx); + llama_set_causal_attn(ctx, true); + llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1); + + std::vector inputs = llama_tokenize(mdl, prompt, false, true); + int32_t i_current_token = 0; + + while (true) { + llama_batch_clear(bat); + auto n_inputs = (int32_t)inputs.size(); + for (int32_t i = 0; i < n_inputs; i++) { + llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1); + } + inputs.clear(); + + llama_decode(ctx, bat); + auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1); + + auto candidates = std::vector(llama_n_vocab(mdl)); + auto n_candidates = (int32_t)candidates.size(); + for (int32_t token = 0; token < n_candidates; token++) { + candidates[token] = llama_token_data{ token, logits[token], 0.0f }; + } + auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false }; + + llama_token token = llama_sample_token_greedy(ctx, &candidates_p); + if (token == eos_token) { + break; + } + + std::string piece = llama_token_to_piece(ctx, token); + if (stream) { + std::printf("%s", piece.c_str()); + std::fflush(stdout); + } + + inputs.push_back(token); + + result += piece; + } + + if (stream) { + std::printf("\n"); + } + + llama_batch_free(bat); + + return result; +} + +static std::string gritlm_instruction(const std::string & instruction) { + return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n"; +} + +int main(int argc, char * argv[]) { + gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + + llama_model_params mparams = llama_model_params_from_gpt_params(params); + llama_context_params cparams = llama_context_params_from_gpt_params(params); + + llama_backend_init(); + + llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams); + + // create new context - set to embedding mode + cparams.embeddings = true; + llama_context * ctx = llama_new_context_with_model(mdl, cparams); + + // ### Embedding/Representation ### + // samples taken from: https://github.com/ContextualAI/gritlm#basic + { + const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract"; + + const std::vector queries = { + "Bitcoin: A Peer-to-Peer Electronic Cash System", + "Generative Representational Instruction Tuning", + }; + + const std::vector documents = { + "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.", + "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.", + }; + + // No need to add instruction for retrieval documents + const std::vector> d_rep = encode(ctx, documents, gritlm_instruction("")); + const std::vector> q_rep = encode(ctx, queries, gritlm_instruction(instruction)); + + const float cosine_sim_q0_d0 = cosine_similarity(q_rep[0], d_rep[0]); + const float cosine_sim_q0_d1 = cosine_similarity(q_rep[0], d_rep[1]); + const float cosine_sim_q1_d0 = cosine_similarity(q_rep[1], d_rep[0]); + const float cosine_sim_q1_d1 = cosine_similarity(q_rep[1], d_rep[1]); + + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1); + } + + // ### Generation ### + // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction + { + const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n"; + std::string response = generate(ctx, prompt, true); + } + + llama_free(ctx); + llama_free_model(mdl); + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp b/llama.cpp index b19616e8f9a5f..249442166d24e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1744,6 +1744,7 @@ struct llama_cparams { float defrag_thold; bool embeddings; + bool causal_attn; bool offload_kqv; enum llama_pooling_type pooling_type; @@ -3939,6 +3940,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); + LLAMA_LOG_INFO("%s: causal attm = %d\n", __func__, hparams.causal_attn); LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); @@ -8532,7 +8534,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } - if (hparams.causal_attn) { + GGML_ASSERT( + (hparams.causal_attn || !cparams.causal_attn) && + "non-causal attention with generative models is not supported" + ); + + // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. + if (cparams.causal_attn) { const int64_t n_kv = kv_self.n; const int64_t n_tokens = batch.n_tokens; @@ -8560,8 +8568,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } } else { - // non-causal attention attends only the tokens within the batch (i.e. the KV cache is not used) + // when using kv cache, the mask needs to match the kv cache size const int64_t n_tokens = batch.n_tokens; + const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens; assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); @@ -8580,7 +8589,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - data[h*(n_tokens*n_tokens) + j*n_tokens + i] = f; + data[h*(n_tokens*n_tokens) + j*n_stride + i] = f; + } + + for (int i = n_tokens; i < n_stride; ++i) { + data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY; } } } @@ -12733,6 +12746,8 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + cparams.causal_attn = hparams.causal_attn; + if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { cparams.pooling_type = LLAMA_POOLING_TYPE_NONE; @@ -13767,6 +13782,10 @@ void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback) ctx->abort_callback_data = abort_callback_data; } +void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) { + ctx->cparams.causal_attn = causal_attn; +} + struct llama_batch llama_batch_get_one( llama_token * tokens, int32_t n_tokens, diff --git a/llama.h b/llama.h index 7a107c7f335d5..c8e05aadd9913 100644 --- a/llama.h +++ b/llama.h @@ -643,6 +643,10 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); + // Set whether to use causal attention or not + // If set to true, the model will only attend to the past tokens + LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); + // Set abort callback LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); From fa8a809a9119411bc9c3f00026550d0343f4d9b7 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 10 Mar 2024 18:17:47 +0100 Subject: [PATCH 220/401] server: ci: windows build and tests (#5968) * server: ci: windows build and tests * server: ci: remove tmp push branch * server: ci: EOF EOL * Use builti Co-authored-by: Jared Van Bortel * server: tests: server graceful shutdown, then kill, then hard kill * server: tests: remove python2 unicode string * server: tests: remove wrong comment on server starting, close_fds is always true * server: tests: server kill, if pid exists * server: tests: remove dependency to killall * server: tests: ci windows: pid exists better handling --------- Co-authored-by: Jared Van Bortel --- .github/workflows/server.yml | 46 ++++- examples/server/tests/features/environment.py | 66 +++++-- examples/server/tests/features/server.feature | 2 +- examples/server/tests/features/steps/steps.py | 176 ++++++++++-------- 4 files changed, 191 insertions(+), 99 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index f9aeefaa847ed..e385e03f3a245 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -47,6 +47,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v3 + with: + fetch-depth: 0 - name: Dependencies id: depends @@ -58,7 +60,6 @@ jobs: cmake \ python3-pip \ wget \ - psmisc \ language-pack-en - name: Build @@ -90,3 +91,46 @@ jobs: run: | cd examples/server/tests PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow + + + server-windows: + runs-on: windows-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake .. -DLLAMA_BUILD_SERVER=ON -DCMAKE_BUILD_TYPE=Release ; + cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server + + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r examples/server/tests/requirements.txt + + - name: Tests + id: server_integration_tests + run: | + cd examples/server/tests + behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp + + - name: Slow tests + id: server_integration_tests_slow + if: ${{ github.event.schedule != '' || github.event.inputs.slow_tests == 'true' }} + run: | + cd examples/server/tests + behave.exe --stop --no-skipped --no-capture --tags slow diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 9fd330db6ddc9..8ad987e1bb618 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -1,9 +1,10 @@ +import errno import os import socket import subprocess import time from contextlib import closing -from signal import SIGKILL +import signal def before_scenario(context, scenario): @@ -29,44 +30,71 @@ def after_scenario(context, scenario): for line in f: print(line) if not is_server_listening(context.server_fqdn, context.server_port): - print("\x1b[33;101mERROR: Server stopped listening\x1b[0m") + print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n") if not pid_exists(context.server_process.pid): assert False, f"Server not running pid={context.server_process.pid} ..." - print(f"stopping server pid={context.server_process.pid} ...") - context.server_process.kill() + server_graceful_shutdown(context) + # Wait few for socket to free up time.sleep(0.05) attempts = 0 - while is_server_listening(context.server_fqdn, context.server_port): - print(f"stopping server pid={context.server_process.pid} ...") - os.kill(context.server_process.pid, SIGKILL) + while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port): + server_kill(context) time.sleep(0.1) attempts += 1 if attempts > 5: - print(f"Server dangling exits, killing all {context.server_path} ...") - process = subprocess.run(['killall', '-9', context.server_path], - stderr=subprocess.PIPE, - universal_newlines=True) - print(process) + server_kill_hard(context) + + +def server_graceful_shutdown(context): + print(f"shutting down server pid={context.server_process.pid} ...\n") + if os.name == 'nt': + os.kill(context.server_process.pid, signal.CTRL_C_EVENT) + else: + os.kill(context.server_process.pid, signal.SIGINT) + + +def server_kill(context): + print(f"killing server pid={context.server_process.pid} ...\n") + context.server_process.kill() + + +def server_kill_hard(context): + pid = context.server_process.pid + path = context.server_path + + print(f"Server dangling exits, hard killing force {pid}={path}...\n") + if os.name == 'nt': + process = subprocess.check_output(['taskkill', '/F', '/pid', str(pid)]).decode() + print(process) + else: + os.kill(-pid, signal.SIGKILL) def is_server_listening(server_fqdn, server_port): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: result = sock.connect_ex((server_fqdn, server_port)) - return result == 0 + _is_server_listening = result == 0 + if _is_server_listening: + print(f"server is listening on {server_fqdn}:{server_port}...\n") + return _is_server_listening def pid_exists(pid): """Check whether pid exists in the current process table.""" - import errno if pid < 0: return False - try: - os.kill(pid, 0) - except OSError as e: - return e.errno == errno.EPERM + if os.name == 'nt': + output = subprocess.check_output(['TASKLIST', '/FI', f'pid eq {pid}']).decode() + print(output) + return "No tasks are running" not in output else: - return True + try: + os.kill(pid, 0) + except OSError as e: + return e.errno == errno.EPERM + else: + return True diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index aa132fa3472ef..5014f326dc050 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -47,7 +47,7 @@ Feature: llama.cpp server Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. """ And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns + Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry And the completion is truncated And 109 prompt tokens are processed diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 14204850960c9..4e81a255a08d9 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -18,7 +18,7 @@ from prometheus_client import parser -@step(u"a server listening on {server_fqdn}:{server_port}") +@step("a server listening on {server_fqdn}:{server_port}") def step_server_config(context, server_fqdn, server_port): context.server_fqdn = server_fqdn context.server_port = int(server_port) @@ -57,24 +57,24 @@ def step_server_config(context, server_fqdn, server_port): context.prompts = [] -@step(u'a model file {hf_file} from HF repo {hf_repo}') +@step('a model file {hf_file} from HF repo {hf_repo}') def step_download_hf_model(context, hf_file, hf_repo): context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) if context.debug: print(f"model file: {context.model_file}\n") -@step(u'a model alias {model_alias}') +@step('a model alias {model_alias}') def step_model_alias(context, model_alias): context.model_alias = model_alias -@step(u'{seed:d} as server seed') +@step('{seed:d} as server seed') def step_seed(context, seed): context.server_seed = seed -@step(u'{ngl:d} GPU offloaded layers') +@step('{ngl:d} GPU offloaded layers') def step_n_gpu_layer(context, ngl): if 'N_GPU_LAYERS' in os.environ: new_ngl = int(os.environ['N_GPU_LAYERS']) @@ -84,37 +84,37 @@ def step_n_gpu_layer(context, ngl): context.n_gpu_layer = ngl -@step(u'{n_ctx:d} KV cache size') +@step('{n_ctx:d} KV cache size') def step_n_ctx(context, n_ctx): context.n_ctx = n_ctx -@step(u'{n_slots:d} slots') +@step('{n_slots:d} slots') def step_n_slots(context, n_slots): context.n_slots = n_slots -@step(u'{n_predict:d} server max tokens to predict') +@step('{n_predict:d} server max tokens to predict') def step_server_n_predict(context, n_predict): context.n_server_predict = n_predict -@step(u'continuous batching') +@step('continuous batching') def step_server_continuous_batching(context): context.server_continuous_batching = True -@step(u'embeddings extraction') +@step('embeddings extraction') def step_server_embeddings(context): context.server_embeddings = True -@step(u'prometheus compatible metrics exposed') +@step('prometheus compatible metrics exposed') def step_server_metrics(context): context.server_metrics = True -@step(u"the server is starting") +@step("the server is starting") def step_start_server(context): start_server_background(context) attempts = 0 @@ -131,7 +131,7 @@ def step_start_server(context): time.sleep(0.1) -@step(u"the server is {expecting_status}") +@step("the server is {expecting_status}") @async_run_until_complete async def step_wait_for_the_server_to_be_started(context, expecting_status): match expecting_status: @@ -160,7 +160,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): assert False, "unknown status" -@step(u'all slots are {expected_slot_status_string}') +@step('all slots are {expected_slot_status_string}') @async_run_until_complete async def step_all_slots_status(context, expected_slot_status_string): match expected_slot_status_string: @@ -176,7 +176,7 @@ async def step_all_slots_status(context, expected_slot_status_string): await request_slots_status(context, expected_slots) -@step(u'a completion request with {api_error} api error') +@step('a completion request with {api_error} api error') @async_run_until_complete async def step_request_completion(context, api_error): expect_api_error = api_error == 'raised' @@ -194,133 +194,133 @@ async def step_request_completion(context, api_error): assert completion == 401, f"completion must be an 401 status code: {completion}" -@step(u'{predicted_n:d} tokens are predicted matching {re_content}') +@step('{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): context.completion = context.tasks_result.pop() assert_n_tokens_predicted(context.completion, predicted_n, re_content) -@step(u'{predicted_n:d} tokens are predicted') +@step('{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): context.completion = context.tasks_result.pop() assert_n_tokens_predicted(context.completion, predicted_n) -@step(u'the completion is truncated') +@step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') -@step(u'the completion is {truncated} truncated') +@step('the completion is {truncated} truncated') def step_assert_completion_truncated(context, truncated): truncated = truncated != "not" assert context.completion['truncated'] == truncated, f'{context.completion}' -@step(u'{n_prompt:d} prompt tokens are processed') +@step('{n_prompt:d} prompt tokens are processed') def step_impl(context, n_prompt): assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}" -@step(u'a user prompt {user_prompt}') +@step('a user prompt {user_prompt}') def step_user_prompt(context, user_prompt): context.prompts.append(user_prompt) context.n_prompts = len(context.prompts) -@step(u'a system prompt {system_prompt}') +@step('a system prompt {system_prompt}') def step_system_prompt(context, system_prompt): context.system_prompt = system_prompt -@step(u'a model {model}') +@step('a model {model}') def step_model(context, model): context.model = model -@step(u'{max_tokens:d} max tokens to predict') +@step('{max_tokens:d} max tokens to predict') def step_max_tokens(context, max_tokens): context.n_predict = max_tokens -@step(u'streaming is {enable_streaming}') +@step('streaming is {enable_streaming}') def step_streaming(context, enable_streaming): context.enable_streaming = enable_streaming == 'enabled' -@step(u'a user api key {user_api_key}') +@step('a user api key {user_api_key}') def step_user_api_key(context, user_api_key): context.user_api_key = user_api_key -@step(u'no user api key') +@step('no user api key') def step_no_user_api_key(context): context.user_api_key = None -@step(u'a user api key ') +@step('a user api key ') def step_no_user_api_key_space(context): context.user_api_key = None -@step(u'a server api key {server_api_key}') +@step('a server api key {server_api_key}') def step_server_api_key(context, server_api_key): context.server_api_key = server_api_key -@step(u'{n_junk:d} as number of junk') +@step('{n_junk:d} as number of junk') def step_n_junk(context, n_junk): context.n_junk = n_junk -@step(u'{n_batch:d} as batch size') +@step('{n_batch:d} as batch size') def step_n_batch(context, n_batch): context.n_batch = n_batch -@step(u'{seed:d} as seed') +@step('{seed:d} as seed') def step_seed(context, seed): context.seed = seed -@step(u'a prefix prompt') +@step('a prefix prompt') def step_prompt_prefix(context): - context.prompt_prefix = context.text + context.prompt_prefix = context_text(context) -@step(u'a junk suffix prompt') +@step('a junk suffix prompt') def step_prompt_junk_suffix(context): - context.prompt_junk_suffix = context.text + context.prompt_junk_suffix = context_text(context) -@step(u'a suffix prompt') +@step('a suffix prompt') def step_prompt_suffix(context): - context.prompt_suffix = context.text + context.prompt_suffix = context_text(context) -@step(u'{n_ga:d} group attention factor' - u' to extend context size through self-extend') +@step('{n_ga:d} group attention factor' + ' to extend context size through self-extend') def step_impl(context, n_ga): context.n_ga = n_ga -@step(u'{n_ga_w:d} group attention width to extend context size through self-extend') +@step('{n_ga_w:d} group attention width to extend context size through self-extend') def step_impl(context, n_ga_w): context.n_ga_w = n_ga_w -@step(u'a passkey prompt template') +@step('a passkey prompt template') def step_prompt_passkey(context): - context.prompt_passkey = context.text + context.prompt_passkey = context_text(context) -@step(u'{n_prompts:d} fixed prompts') +@step('{n_prompts:d} fixed prompts') def step_fixed_prompts(context, n_prompts): context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)]) context.n_prompts = n_prompts -@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') +@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') def step_prompt_passkey(context, passkey, i_pos): prompt = "" for i in range(context.n_junk): @@ -334,7 +334,7 @@ def step_prompt_passkey(context, passkey, i_pos): context.n_prompts = len(context.prompts) -@step(u'an OAI compatible chat completions request with {api_error} api error') +@step('an OAI compatible chat completions request with {api_error} api error') @async_run_until_complete async def step_oai_chat_completions(context, api_error): if context.debug: @@ -369,19 +369,19 @@ async def step_oai_chat_completions(context, api_error): print(f"Completion response: {completion}") -@step(u'a prompt') +@step('a prompt') def step_a_prompt(context): - context.prompts.append(context.text) + context.prompts.append(context_text(context)) context.n_prompts = len(context.prompts) -@step(u'a prompt {prompt}') +@step('a prompt {prompt}') def step_a_prompt_prompt(context, prompt): context.prompts.append(prompt) context.n_prompts = len(context.prompts) -@step(u'concurrent completion requests') +@step('concurrent completion requests') @async_run_until_complete() async def step_concurrent_completion_requests(context): await concurrent_requests(context, @@ -397,7 +397,7 @@ async def step_concurrent_completion_requests(context): 'user_api_key') else None) -@step(u'concurrent OAI completions requests') +@step('concurrent OAI completions requests') @async_run_until_complete async def step_oai_chat_completions(context): await concurrent_requests(context, oai_chat_completions, @@ -417,7 +417,7 @@ async def step_oai_chat_completions(context): if hasattr(context, 'user_api_key') else None) -@step(u'concurrent OAI completions requests no v1') +@step('concurrent OAI completions requests no v1') @async_run_until_complete async def step_oai_chat_completions(context): await concurrent_requests(context, oai_chat_completions, @@ -440,13 +440,13 @@ async def step_oai_chat_completions(context): if hasattr(context, 'user_api_key') else None) -@step(u'all prompts are predicted') +@step('all prompts are predicted') @async_run_until_complete async def step_all_prompts_are_predicted(context): await all_prompts_are_predicted(context) -@step(u'all prompts are predicted with {n_expected_predicted:d} tokens') +@step('all prompts are predicted with {n_expected_predicted:d} tokens') @async_run_until_complete async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): await all_prompts_are_predicted(context, n_expected_predicted) @@ -460,14 +460,14 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None): assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" -@step(u'embeddings are computed for') +@step('embeddings are computed for') @async_run_until_complete async def step_compute_embedding(context): context.n_prompts = 1 - context.embeddings = await request_embedding(context.text, base_url=context.base_url) + context.embeddings = await request_embedding(context_text(context), base_url=context.base_url) -@step(u'all embeddings are the same') +@step('all embeddings are the same') @async_run_until_complete async def step_all_embeddings_are_the_same(context): n_embedding_requests = await gather_tasks_results(context) @@ -491,7 +491,8 @@ async def step_all_embeddings_are_the_same(context): print(f"{msg}\n") assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg -@step(u'embeddings are generated') + +@step('embeddings are generated') def step_assert_embeddings(context): assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n" f"context.n_prompts={context.n_prompts}\n" @@ -500,17 +501,17 @@ def step_assert_embeddings(context): assert_embeddings(embedding) -@step(u'an OAI compatible embeddings computation request for') +@step('an OAI compatible embeddings computation request for') @async_run_until_complete async def step_oai_compute_embeddings(context): context.n_prompts = 1 - context.embeddings = await request_oai_embeddings(context.text, + context.embeddings = await request_oai_embeddings(context_text(context), base_url=context.base_url, user_api_key=context.user_api_key, model=context.model) -@step(u'an OAI compatible embeddings computation request for multiple inputs') +@step('an OAI compatible embeddings computation request for multiple inputs') @async_run_until_complete async def step_oai_compute_embeddings_multiple_inputs(context): context.embeddings = await request_oai_embeddings(context.prompts, @@ -520,7 +521,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context): context.prompts.clear() -@step(u'concurrent embedding requests') +@step('concurrent embedding requests') @async_run_until_complete() async def step_concurrent_embedding_requests(context): await concurrent_requests(context, @@ -529,7 +530,7 @@ async def step_concurrent_embedding_requests(context): base_url=context.base_url) -@step(u'concurrent OAI embedding requests') +@step('concurrent OAI embedding requests') @async_run_until_complete() async def step_concurrent_oai_embedding_requests(context): await concurrent_requests(context, @@ -540,7 +541,7 @@ async def step_concurrent_oai_embedding_requests(context): model=context.model) -@step(u'all embeddings are generated') +@step('all embeddings are generated') @async_run_until_complete() async def all_embeddings_are_generated(context): n_embedding_requests = await gather_tasks_results(context) @@ -549,10 +550,10 @@ async def all_embeddings_are_generated(context): assert_embeddings(context.tasks_result.pop().pop()) -@step(u'tokenizing') +@step('tokenizing') @async_run_until_complete async def step_tokenize(context): - context.tokenized_text = context.text + context.tokenized_text = context_text(context) async with aiohttp.ClientSession() as session: async with session.post(f'{context.base_url}/tokenize', json={ @@ -563,7 +564,7 @@ async def step_tokenize(context): context.tokens = tokenize_json['tokens'] -@step(u'tokens can be detokenize') +@step('tokens can be detokenize') @async_run_until_complete async def step_detokenize(context): assert len(context.tokens) > 0 @@ -578,7 +579,7 @@ async def step_detokenize(context): assert context.tokenized_text == detokenize_json['content'].strip() -@step(u'an OPTIONS request is sent from {origin}') +@step('an OPTIONS request is sent from {origin}') @async_run_until_complete async def step_options_request(context, origin): async with aiohttp.ClientSession() as session: @@ -589,12 +590,12 @@ async def step_options_request(context, origin): context.options_response = response -@step(u'CORS header {cors_header} is set to {cors_header_value}') +@step('CORS header {cors_header} is set to {cors_header_value}') def step_check_options_header_value(context, cors_header, cors_header_value): assert context.options_response.headers[cors_header] == cors_header_value -@step(u'prometheus metrics are exposed') +@step('prometheus metrics are exposed') @async_run_until_complete async def step_prometheus_metrics_exported(context): async with aiohttp.ClientSession() as session: @@ -616,14 +617,14 @@ async def step_prometheus_metrics_exported(context): assert metric_exported, "No metrics exported" -@step(u'metric {metric_name} is {metric_value:d}') +@step('metric {metric_name} is {metric_value:d}') def step_assert_metric_value(context, metric_name, metric_value): if metric_name not in context.metrics: assert False, f"no metric {metric_name} in {context.metrics.keys()}" assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}" -@step(u'available models') +@step('available models') def step_available_models(context): # openai client always expects an api_key openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' @@ -631,14 +632,14 @@ def step_available_models(context): context.models = openai.Model.list().data -@step(u'{n_model:d} models are supported') +@step('{n_model:d} models are supported') def step_supported_models(context, n_model): if context.debug: print("server models available:", context.models) assert len(context.models) == n_model -@step(u'model {i_model:d} is {param} {preposition} {param_value}') +@step('model {i_model:d} is {param} {preposition} {param_value}') def step_supported_models(context, i_model, param, preposition, param_value): assert i_model < len(context.models) model = context.models[i_model] @@ -1007,12 +1008,22 @@ async def completions_seed(context): else context.server_seed if hasattr(context, 'server_seed') else None +def context_text(context): + return context.text.replace('\r', '') + + def start_server_background(context): - context.server_path = '../../../build/bin/server' + if os.name == 'nt': + context.server_path = '../../../build/bin/Release/server.exe' + else: + context.server_path = '../../../build/bin/server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] + server_listen_addr = context.server_fqdn + if os.name == 'nt': + server_listen_addr = '0.0.0.0' server_args = [ - '--host', context.server_fqdn, + '--host', server_listen_addr, '--port', context.server_port, '--model', context.model_file ] @@ -1045,7 +1056,16 @@ def start_server_background(context): if 'SERVER_LOG_FORMAT_JSON' not in os.environ: server_args.extend(['--log-format', "text"]) print(f"starting server with: {context.server_path} {server_args}\n") + flags = 0 + if 'nt' == os.name: + flags |= subprocess.DETACHED_PROCESS + flags |= subprocess.CREATE_NEW_PROCESS_GROUP + flags |= subprocess.CREATE_NO_WINDOW + + pkwargs = { + 'creationflags': flags, + } context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], - close_fds=True) - print(f"server pid={context.server_process.pid}") + **pkwargs) + print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}") From bf47a5eefc669fdba71af096942af999bc1167d4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 10 Mar 2024 20:09:24 +0200 Subject: [PATCH 221/401] ggml : remove __constant__ specifier for CUDA tables (#5940) --- ggml-common.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-common.h b/ggml-common.h index 4b6d248b6db90..84981212cfc46 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -17,7 +17,7 @@ #elif defined(GGML_COMMON_IMPL_CUDA) #include -#define GGML_TABLE_BEGIN(type, name, size) static const __device__ __constant__ type name[size] = { +#define GGML_TABLE_BEGIN(type, name, size) static const __device__ type name[size] = { #define GGML_TABLE_END() }; #define GGML_COMMON_IMPL From df4dc3e7cb43162862f339525638ba4febcb6158 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 8 Mar 2024 23:45:07 +0200 Subject: [PATCH 222/401] ggml : try fix 32-bit arm compat (whisper/1938) * ggml : try fix 32-bit arm compat * ggml : fix cont --- ggml-quants.c | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 807c5e391069c..42d8a5d805144 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9009,8 +9009,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); const uint8x16_t m1 = vdupq_n_u8(1); const int32x4_t vzero = vdupq_n_s32(0); @@ -9354,11 +9354,12 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; - const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); - const int16x8_t hshift = vld1q_s16(k_shift); - const uint16x8_t m256 = vdupq_n_u16(256); - const uint8x16_t m1 = vdupq_n_u8(1); + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); uint8x16x2_t vs; ggml_int8x16x4_t q3s; From b838b53ad6de2e53f23ddf8f3ad5e6891cc3dd05 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 10 Mar 2024 20:10:46 +0200 Subject: [PATCH 223/401] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index afe68077d0ec9..7c30162e2c570 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -8695910a39102609073d0e099aa7c97d6bcb3bf9 +43a6d4af1971ee2912ff7bc2404011ff327b6a60 From d9f65c97c3dc3aa6fa27470b8c6e69b437ec1a27 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 10 Mar 2024 20:58:26 +0200 Subject: [PATCH 224/401] readme : update hot topics --- README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index e3ec0817ae0e5..98fdc6808ffe3 100644 --- a/README.md +++ b/README.md @@ -8,11 +8,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ -> [!IMPORTANT] -> **Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962** -> -> Vote for which quantization type provides better responses, all other parameters being the same. - ### Recent API changes - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 @@ -21,6 +16,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981 +- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962 - Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328 ---- From 7ab7b733bb48250b2df26c12b00256ef42c76932 Mon Sep 17 00:00:00 2001 From: Dean Date: Mon, 11 Mar 2024 04:03:17 +0800 Subject: [PATCH 225/401] android : fix utf8 decoding error (#5935) * examples: fix utf8 decoding error some models have a tokenizer that decodes an id into an incomplete utf8 sequence, need to validate and wait for next token one example would be: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat-GGUF/resolve/main/qwen1_5-1_8b-chat-q4_0.gguf and and an example of the token is 18137 * android : minor --------- Co-authored-by: zhangfuwen Co-authored-by: Georgi Gerganov --- .../app/src/main/cpp/llama-android.cpp | 53 ++++++++++++++++++- .../src/main/java/com/example/llama/Llm.kt | 4 +- 2 files changed, 53 insertions(+), 4 deletions(-) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp index 2beb1e0d5321d..ce8ab3b709407 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp @@ -33,6 +33,45 @@ jclass la_int_var; jmethodID la_int_var_value; jmethodID la_int_var_inc; +std::string cached_token_chars; + +bool is_valid_utf8(const char * string) { + if (!string) { + return true; + } + + const unsigned char * bytes = (const unsigned char *)string; + int num; + + while (*bytes != 0x00) { + if ((*bytes & 0x80) == 0x00) { + // U+0000 to U+007F + num = 1; + } else if ((*bytes & 0xE0) == 0xC0) { + // U+0080 to U+07FF + num = 2; + } else if ((*bytes & 0xF0) == 0xE0) { + // U+0800 to U+FFFF + num = 3; + } else if ((*bytes & 0xF8) == 0xF0) { + // U+10000 to U+10FFFF + num = 4; + } else { + return false; + } + + bytes += 1; + for (int i = 1; i < num; ++i) { + if ((*bytes & 0xC0) != 0x80) { + return false; + } + bytes += 1; + } + } + + return true; +} + static void log_callback(ggml_log_level level, const char * fmt, void * data) { if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data); else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data); @@ -295,6 +334,8 @@ Java_com_example_llama_Llm_completion_1init( jint n_len ) { + cached_token_chars.clear(); + const auto text = env->GetStringUTFChars(jtext, 0); const auto context = reinterpret_cast(context_pointer); const auto batch = reinterpret_cast(batch_pointer); @@ -372,8 +413,16 @@ Java_com_example_llama_Llm_completion_1loop( } auto new_token_chars = llama_token_to_piece(context, new_token_id); - LOGi("new_token_chars: `%s`", new_token_chars.c_str()); - auto new_token = env->NewStringUTF(new_token_chars.c_str()); + cached_token_chars += new_token_chars; + + jstring new_token = nullptr; + if (is_valid_utf8(cached_token_chars.c_str())) { + new_token = env->NewStringUTF(cached_token_chars.c_str()); + LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id); + cached_token_chars.clear(); + } else { + new_token = env->NewStringUTF(""); + } llama_batch_clear(*batch); llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true); diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt index 5f32703724a49..d86afee379083 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt +++ b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt @@ -71,7 +71,7 @@ class Llm { batch: Long, nLen: Int, ncur: IntVar - ): String + ): String? private external fun kv_cache_clear(context: Long) @@ -115,7 +115,7 @@ class Llm { val ncur = IntVar(completion_init(state.context, state.batch, message, nlen)) while (ncur.value <= nlen) { val str = completion_loop(state.context, state.batch, nlen, ncur) - if (str.isEmpty()) { + if (str == null) { break } emit(str) From bb6d00bbf98476215596b9df3870b5504ef5a29a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 10 Mar 2024 23:12:48 +0200 Subject: [PATCH 226/401] metal : move mm_id indices to shared mem (#5982) --- ggml-metal.m | 6 +++--- ggml-metal.metal | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 00df2283821b2..3cf80de7bf2e0 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1642,8 +1642,8 @@ static enum ggml_status ggml_metal_graph_compute( // TODO: make this more general GGML_ASSERT(n_as <= 8); - // max size of the src1ids array in the kernel stack - GGML_ASSERT(ne11 <= 512); + // max size of the src1ids array in the kernel shared buffer + GGML_ASSERT(ne11 <= 4096); const int64_t ne20 = src2 ? src2->ne[0] : 0; const int64_t ne21 = src2 ? src2->ne[1] : 0; @@ -1741,7 +1741,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; } - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; } else { diff --git a/ggml-metal.metal b/ggml-metal.metal index 6ebbbd195e7ce..50185ae4dea09 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -5386,7 +5386,7 @@ template Date: Mon, 11 Mar 2024 01:13:57 +0000 Subject: [PATCH 227/401] [SYCL] Add support for SYCL Nvidia target (#5738) * Add support for nvidia target in CMake * Update sycl read-me for Nvidia target * Fix errors --- CMakeLists.txt | 14 +++++++++++++- README-sycl.md | 26 ++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 9309ca6bbe4d7..00a26391a6626 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -116,6 +116,7 @@ option(LLAMA_MPI "llama: use MPI" option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) +set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device") option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) @@ -534,6 +535,10 @@ if (LLAMA_HIPBLAS) endif() if (LLAMA_SYCL) + if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$") + message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA") + endif() + if ( NOT DEFINED ENV{ONEAPI_ROOT}) message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh") endif() @@ -555,6 +560,9 @@ if (LLAMA_SYCL) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib") + if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") + endif() set(GGML_HEADERS_SYCL ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) @@ -562,7 +570,11 @@ if (LLAMA_SYCL) if (WIN32) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) else() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + if (LLAMA_SYCL_TARGET STREQUAL "INTEL") + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA") + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl) + endif() endif() endif() diff --git a/README-sycl.md b/README-sycl.md index 85eb16f2be340..9359a94901677 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -73,6 +73,29 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. +## Nvidia GPU + +### Verified + +|Intel GPU| Status | Verified Model| +|-|-|-| +|Ampere Series| Support| A100| + +### oneMKL + +The current oneMKL release does not contain the oneMKL cuBlas backend. +As a result for Nvidia GPU's oneMKL must be built from source. + +``` +git clone https://github.com/oneapi-src/oneMKL +cd oneMKL +mkdir build +cd build +cmake -G Ninja .. -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON +ninja +// Add paths as necessary +``` + ## Docker Note: @@ -186,6 +209,9 @@ source /opt/intel/oneapi/setvars.sh # Or, for FP32: cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +# For Nvidia GPUs +cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + # Build example/main only #cmake --build . --config Release --target main From ef3ced26a3817d92890b97b83acaeb018ade02d0 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Mon, 11 Mar 2024 10:27:56 +0530 Subject: [PATCH 228/401] [SYCL] Add q3_s and q1_s (#5886) * Add q3_s and q1_s * fix compilation * fix build * fix build * fix build * enable ops * rm macro * increase grid space --- ggml-sycl.cpp | 378 +++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 375 insertions(+), 3 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 6fafa5282c8f9..6d56845821f69 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3494,6 +3494,31 @@ typedef struct dpct_type_block_iq3_xxs { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#define QR3_XS 8 +#define QI3_XS (QK_K / (4*QR3_XS)) +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif +typedef struct { + sycl::half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t signs[QK_K/8]; + uint8_t scales[IQ3S_N_SCALE]; +} block_iq3_s; +static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); + +#define QR1_S 8 +#define QI1_S (QK_K / (4*QR1_S)) +typedef struct { + sycl::half d; + uint8_t qs[QK_K/8]; + uint8_t scales[QK_K/16]; +} block_iq1_s; +static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -4833,6 +4858,62 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res } +template +static void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3s_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq3_s * x = (const block_iq3_s *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * qs = x[i].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + qs[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + qs[2*il+1]); + const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); + const uint8_t signs = x[i].signs[4*ib + il]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } +#else + assert(false); +#endif + +} + +template +static void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq1s_grid, + const uint8_t *ksigns_iq2xs, + const uint8_t *kmask_iq2xs) { + + const int i = item_ct1.get_group(2); + const block_iq1_s * x = (const block_iq1_s *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const int i8 = 4*ib+il; + uint8_t h = x[i].scales[i8/2] >> 4*(i8%2); + const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5))); + const float d = (float)x[i].d * (2*(h & 7) + 1); + for (int j = 0; j < 8; ++j) y[j] = d * grid[j]; +#else + assert(false); +#endif + +} + /* DPCT1110:4: The total declared local variable size in device function dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register @@ -7679,6 +7760,76 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, #endif } +static __dpct_inline__ float +vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint32_t *iq3s_grid, const uint64_t *ksigns64) { +#if DPCT_COMPATIBILITY_TEMP >= \ + MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_s * bq2 = (const block_iq3_s *) vbq; + + const int ib32 = iqs; + const uint8_t * qs = bq2->qs + 8*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + uint32_t signs0 = dpct::vectorized_binary( + ((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201, std::equal_to<>()); + uint32_t signs1 = dpct::vectorized_binary( + ((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201, std::equal_to<>()); + const int grid_l = dpct::vectorized_binary( + grid1[0] ^ signs0, signs0, std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid2[0] ^ signs1, signs1, std::minus<>()); + sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + q8 += 8; + } + const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * bq8_1[ib32].ds[0]; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __dpct_inline__ float +vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs, + const uint64_t *iq1s_grid, const uint64_t *ksigns64) { +#if QK_K == 256 + const block_iq1_s * bq1 = (const block_iq1_s *) vbq; + + const int ib32 = iqs; + int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; + const uint8_t h1 = bq1->scales[2*ib32+0]; + const uint8_t h2 = bq1->scales[2*ib32+1]; + const int * q8 = (const int *)bq8_1[ib32].qs; + const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); + const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); + const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); + const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); + for (int j = 0; j < 2; ++j) { + sumi1 = dpct::dp4a(q8[j+0], grid1[j], sumi1); + sumi2 = dpct::dp4a(q8[j+2], grid2[j], sumi2); + sumi3 = dpct::dp4a(q8[j+4], grid3[j], sumi3); + sumi4 = dpct::dp4a(q8[j+6], grid4[j], sumi4); + } + const float d = (float)bq1->d * bq8_1[ib32].ds[0]; + return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) + + sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1)); +#else + assert(false); + return 0.f; +#endif +} template +static void mul_mat_vec_q_iq3_s_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint32_t *iq3s_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void mul_mat_vec_q_iq1_s_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1, + const uint64_t *iq1s_grid_ptr, const uint64_t *ksigns64_ptr ) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i; // x block index + + const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_ptr, ksigns64_ptr); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + template static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows, const sycl::nd_item<3> &item_ct1) { @@ -10129,6 +10372,64 @@ static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k, } } +template +static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq3s_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq3s_grid_ptr_ct1 = iq3s_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq3_s( + vx, y, item_ct1, iq3s_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + +template +static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + iq1s_grid.init(*stream); + ksigns_iq2xs.init(*stream); + kmask_iq2xs.init(*stream); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + auto iq1s_grid_ptr_ct1 = iq1s_grid.get_ptr(); + auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); + auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); + + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq1_s( + vx, y, item_ct1, iq1s_grid_ptr_ct1, + ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1); + }); + }); + } +} + template static void convert_unary_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, @@ -10179,6 +10480,10 @@ static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try { return dequantize_row_iq2_xs_sycl; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_sycl; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_sycl; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_sycl; case GGML_TYPE_F32: return convert_unary_sycl; default: @@ -10219,6 +10524,10 @@ static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { return dequantize_row_iq2_xs_sycl; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_sycl; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_sycl; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_sycl; case GGML_TYPE_F16: return convert_unary_sycl; default: @@ -10808,6 +11117,61 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, } } +static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq3s_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq3s_grid_ptr_ct1 = iq3s_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq3_s_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq3s_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} + +static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + iq1s_grid.init(*stream); + ksigns64.init(*stream); + + stream->submit([&](sycl::handler &cgh) { + auto iq1s_grid_ptr_ct1 = iq1s_grid.get_ptr(); + auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q_iq1_s_q8_1( + vx, vy, dst, ncols, nrows, item_ct1, + iq1s_grid_ptr_ct1, ksigns64_ptr_ct1); + }); + }); + } +} static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, @@ -13556,8 +13920,11 @@ static int64_t get_row_rounding(ggml_type type, const std::array= VER_GEN9 ? 128 : 64; + case GGML_TYPE_IQ3_S: + return max_compute_capability >= VER_GEN9 ? 128 : 64; case GGML_TYPE_Q6_K: return 64; default: @@ -13618,6 +13985,12 @@ inline void ggml_sycl_op_mul_mat_vec_q( case GGML_TYPE_IQ3_XXS: mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); break; + case GGML_TYPE_IQ3_S: + mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_IQ1_S: + mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -16963,9 +17336,8 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons return false; } ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || - a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { + if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ2_S || + a_type == GGML_TYPE_IQ4_XS) { return false; } return true; From be858f620508385ad12d0e5e862010e666ca729c Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 11 Mar 2024 07:51:49 +0100 Subject: [PATCH 229/401] Better 1.5 bit quantization (#5971) * Trying blocvks of 16 for IQ1_S - seems slightly better * iq1s_blocks16: Adjust scale fudge factor to 1.125 * iq1s_blocks16: going to blocks of 32 with 2048 lattice points, so same bpw. This is even better than blocks of 16. Should I try blocks of 64? But to keep the same bpw, when I go to 4096 lattice points, I need to remove blocks alltogether and just have superblocks of 256 weights. * iq1s_blocks16: Use 2* as sigma2 in weight adjustment * iq1s_blocks16: scalar and AVX2 dot products * iq1s_blocks16: CUDA dot product * iq1s_blocks16: Metal works, Neon does not Metal works but TG is dog slow (35 t/s). PP is OKish (493 t/s). Not seeing the bug in the Neon implementation for now. * iq1s_blocks16: fixed Neon * iq1s_blocks16: very slightly faster TG on Metal Still pathetic at 37 t/s * iq1s_blocks16: speedup Metal by packing codebook into uint32_t's * Formatting * iq1s_blocks16: uint32_t codebook is also better in CUDA TG-128 is now 204 t/s up from 194 t/s. PP-512 is 5890 t/s, so significantly better than other quants * iq1s_blocks16: slightly faster Neon dot product * iq1s_blocks16: faster AVX2 dot product * iq1s_blocks16: adjust to ggml-common.h --------- Co-authored-by: Iwan Kawrakow --- ggml-common.h | 903 ++++++++++++++++++++++++++++++++++++++++------- ggml-cuda.cu | 62 ++-- ggml-metal.metal | 66 ++-- ggml-quants.c | 512 ++++++++++++++++----------- ggml-quants.h | 4 +- 5 files changed, 1153 insertions(+), 394 deletions(-) diff --git a/ggml-common.h b/ggml-common.h index 84981212cfc46..5dd918081c8dd 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -644,136 +644,781 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512) 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, GGML_TABLE_END() -#define NGRID_IQ2XXS 512 -GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ2XXS) - 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, - 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, - 0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100, - 0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00, - 0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101, - 0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100, - 0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00, - 0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff, - 0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000, - 0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000, - 0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001, - 0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff, - 0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01, - 0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001, - 0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00, - 0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001, - 0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100, - 0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000, - 0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000, - 0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000, - 0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff, - 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff, - 0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01, - 0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100, - 0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff, - 0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000, - 0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101, - 0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff, - 0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff, - 0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001, - 0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01, - 0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101, - 0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100, - 0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00, - 0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001, - 0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff, - 0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000, - 0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000, - 0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100, - 0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100, - 0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01, - 0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff, - 0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101, - 0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000, - 0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff, - 0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000, - 0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff, - 0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00, - 0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101, - 0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000, - 0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000, - 0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000, - 0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100, - 0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000, - 0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001, - 0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff, - 0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000, - 0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000, - 0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000, - 0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000, - 0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff, - 0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000, - 0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, +#define NGRID_IQ1S 2048 +#if defined(GGML_COMMON_IMPL_C) +GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S) + 0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff, + 0xffffffffffff0101, 0xffffffffff00ff00, 0xffffffffff000000, 0xffffffffff01ffff, + 0xffffffffff01ff01, 0xffffffffff0101ff, 0xffffffffff010101, 0xffffffff00ff0000, + 0xffffffff0000ff00, 0xffffffff000000ff, 0xffffffff00000001, 0xffffffff00010000, + 0xffffffff01ffffff, 0xffffffff01ffff01, 0xffffffff01ff01ff, 0xffffffff01ff0101, + 0xffffffff01000000, 0xffffffff0101ffff, 0xffffffff0101ff01, 0xffffffff010101ff, + 0xffffffff01010101, 0xffffff00ffff00ff, 0xffffff00ffff0000, 0xffffff00ff00ff00, + 0xffffff00ff0000ff, 0xffffff00ff000001, 0xffffff00ff000100, 0xffffff00ff000101, + 0xffffff00ff010000, 0xffffff0000ffff00, 0xffffff0000ff0001, 0xffffff0000ff0100, + 0xffffff000000ff01, 0xffffff0000000000, 0xffffff0000000101, 0xffffff000001ff00, + 0xffffff00000100ff, 0xffffff0000010001, 0xffffff00000101ff, 0xffffff0001ff0000, + 0xffffff000100ff00, 0xffffff00010000ff, 0xffffff0001000001, 0xffffff0001010000, + 0xffffff01ffffffff, 0xffffff01ffffff01, 0xffffff01ffff01ff, 0xffffff01ffff0101, + 0xffffff01ff000000, 0xffffff01ff01ffff, 0xffffff01ff01ff01, 0xffffff01ff0101ff, + 0xffffff01ff010101, 0xffffff0100ff0000, 0xffffff010000ff00, 0xffffff0100000100, + 0xffffff01000100ff, 0xffffff0100010100, 0xffffff0101ffffff, 0xffffff0101ffff01, + 0xffffff0101ff01ff, 0xffffff0101ff0101, 0xffffff010100ff00, 0xffffff0101000000, + 0xffffff0101000100, 0xffffff010101ffff, 0xffffff010101ff01, 0xffffff01010101ff, + 0xffffff0101010101, 0xffff00ffff00ff00, 0xffff00ffff0000ff, 0xffff00ffff000001, + 0xffff00ffff010000, 0xffff00ff00ffff00, 0xffff00ff00ff0100, 0xffff00ff00000000, + 0xffff00ff00000101, 0xffff00ff000100ff, 0xffff00ff00010000, 0xffff00ff0100ff00, + 0xffff00ff01000100, 0xffff00ff01010000, 0xffff0000ffffff00, 0xffff0000ffff00ff, + 0xffff0000ffff0000, 0xffff0000ffff0001, 0xffff0000ff000000, 0xffff0000ff0001ff, + 0xffff0000ff000101, 0xffff0000ff010100, 0xffff000000ffffff, 0xffff000000ff0000, + 0xffff000000ff0101, 0xffff00000000ffff, 0xffff00000000ff00, 0xffff0000000000ff, + 0xffff000000000000, 0xffff000000000001, 0xffff000000000100, 0xffff00000001ffff, + 0xffff00000001ff01, 0xffff000000010000, 0xffff0000000101ff, 0xffff000000010101, + 0xffff000001ffff00, 0xffff00000100ff00, 0xffff000001000000, 0xffff0000010001ff, + 0xffff000001000101, 0xffff00000101ff00, 0xffff0000010100ff, 0xffff000001010000, + 0xffff000001010001, 0xffff000001010100, 0xffff0001ff0000ff, 0xffff0001ff000100, + 0xffff000100ffff00, 0xffff000100ff00ff, 0xffff00010000ffff, 0xffff00010000ff01, + 0xffff000100000000, 0xffff0001000001ff, 0xffff00010001ffff, 0xffff00010001ff00, + 0xffff000100010001, 0xffff000100010100, 0xffff000101ff0000, 0xffff00010100ff00, + 0xffff0001010000ff, 0xffff000101000100, 0xffff01ffffffffff, 0xffff01ffffffff01, + 0xffff01ffffff01ff, 0xffff01ffffff0101, 0xffff01ffff000000, 0xffff01ffff01ffff, + 0xffff01ffff01ff01, 0xffff01ffff0101ff, 0xffff01ffff010101, 0xffff01ff00ff0000, + 0xffff01ff0000ff00, 0xffff01ff00000001, 0xffff01ff00010000, 0xffff01ff01ffffff, + 0xffff01ff01ffff01, 0xffff01ff01ff01ff, 0xffff01ff01ff0101, 0xffff01ff01000000, + 0xffff01ff0101ffff, 0xffff01ff0101ff01, 0xffff01ff010101ff, 0xffff01ff01010101, + 0xffff0100ffff0000, 0xffff0100ff00ff00, 0xffff0100ff0000ff, 0xffff0100ff000100, + 0xffff0100ff0100ff, 0xffff0100ff010000, 0xffff010000ffff00, 0xffff01000000ffff, + 0xffff01000000ff00, 0xffff010000000000, 0xffff01000001ff00, 0xffff0100000100ff, + 0xffff010000010100, 0xffff01000100ff00, 0xffff0100010000ff, 0xffff010001000001, + 0xffff010001000100, 0xffff010001010000, 0xffff0101ffffffff, 0xffff0101ffffff01, + 0xffff0101ffff01ff, 0xffff0101ffff0101, 0xffff0101ff000000, 0xffff0101ff01ffff, + 0xffff0101ff01ff01, 0xffff0101ff0101ff, 0xffff0101ff010101, 0xffff010100ff0000, + 0xffff01010000ff00, 0xffff010100000100, 0xffff01010001ff00, 0xffff010100010000, + 0xffff010101ffffff, 0xffff010101ffff01, 0xffff010101ff0000, 0xffff010101ff01ff, + 0xffff010101ff0101, 0xffff010101000000, 0xffff01010101ffff, 0xffff01010101ff01, + 0xffff0101010101ff, 0xffff010101010101, 0xff00ffffff00ffff, 0xff00ffffff00ff00, + 0xff00ffffff0000ff, 0xff00ffffff000100, 0xff00ffffff0100ff, 0xff00ffffff010000, + 0xff00ffff00ffff00, 0xff00ffff00ff00ff, 0xff00ffff0000ffff, 0xff00ffff00000000, + 0xff00ffff000001ff, 0xff00ffff0001ff00, 0xff00ffff000100ff, 0xff00ffff00010000, + 0xff00ffff00010100, 0xff00ffff0100ff00, 0xff00ffff010000ff, 0xff00ffff01000001, + 0xff00ffff0101ff00, 0xff00ffff01010000, 0xff00ff00ffffff00, 0xff00ff00ffff00ff, + 0xff00ff00ffff0001, 0xff00ff00ffff0100, 0xff00ff00ff00ffff, 0xff00ff00ff00ff01, + 0xff00ff00ff000000, 0xff00ff00ff0001ff, 0xff00ff00ff01ff00, 0xff00ff00ff0100ff, + 0xff00ff00ff010100, 0xff00ff0000ff0000, 0xff00ff0000ff0101, 0xff00ff000000ffff, + 0xff00ff000000ff00, 0xff00ff000000ff01, 0xff00ff00000000ff, 0xff00ff0000000000, + 0xff00ff0000000001, 0xff00ff0000000100, 0xff00ff000001ffff, 0xff00ff0000010000, + 0xff00ff0001ff00ff, 0xff00ff000100ff01, 0xff00ff0001000000, 0xff00ff000101ff00, + 0xff00ff00010100ff, 0xff00ff01ff00ff00, 0xff00ff01ff0000ff, 0xff00ff01ff000001, + 0xff00ff01ff010000, 0xff00ff0100ffffff, 0xff00ff0100ff0001, 0xff00ff0100ff0100, + 0xff00ff010000ff01, 0xff00ff0100000000, 0xff00ff01000001ff, 0xff00ff0100000101, + 0xff00ff01000100ff, 0xff00ff0100010001, 0xff00ff0101ff0000, 0xff00ff010100ff00, + 0xff00ff01010000ff, 0xff00ff0101000001, 0xff00ff0101010000, 0xff0000ffffffff00, + 0xff0000ffffff0001, 0xff0000ffffff0100, 0xff0000ffff0000ff, 0xff0000ffff000000, + 0xff0000ffff0001ff, 0xff0000ffff000100, 0xff0000ffff01ff00, 0xff0000ffff010001, + 0xff0000ff00ffff00, 0xff0000ff00ff0000, 0xff0000ff00ff0001, 0xff0000ff00ff01ff, + 0xff0000ff00ff0101, 0xff0000ff0000ff00, 0xff0000ff000000ff, 0xff0000ff00000000, + 0xff0000ff00000001, 0xff0000ff00000100, 0xff0000ff0001ff01, 0xff0000ff00010000, + 0xff0000ff000101ff, 0xff0000ff01ff00ff, 0xff0000ff01ff0100, 0xff0000ff0100ffff, + 0xff0000ff010000ff, 0xff0000ff01000000, 0xff0000ff010001ff, 0xff0000ff01000100, + 0xff0000ff01000101, 0xff0000ff0101ff00, 0xff0000ff010100ff, 0xff0000ff01010000, + 0xff0000ff01010100, 0xff000000ffffff01, 0xff000000ffff0000, 0xff000000ffff0101, + 0xff000000ff00ff00, 0xff000000ff0000ff, 0xff000000ff000000, 0xff000000ff000001, + 0xff000000ff000100, 0xff000000ff01ffff, 0xff000000ff01ff01, 0xff000000ff010000, + 0xff000000ff0101ff, 0xff000000ff010101, 0xff00000000ffff00, 0xff00000000ff00ff, + 0xff00000000ff0000, 0xff00000000ff0001, 0xff0000000000ff00, 0xff0000000000ff01, + 0xff000000000000ff, 0xff00000000000000, 0xff00000000000001, 0xff00000000000100, + 0xff00000000000101, 0xff0000000001ff00, 0xff000000000100ff, 0xff00000000010000, + 0xff00000000010001, 0xff00000000010100, 0xff00000001ffffff, 0xff00000001ffff01, + 0xff00000001ff00ff, 0xff00000001ff0000, 0xff00000001ff01ff, 0xff00000001ff0101, + 0xff0000000100ffff, 0xff0000000100ff00, 0xff000000010000ff, 0xff00000001000000, + 0xff00000001000001, 0xff00000001000100, 0xff00000001000101, 0xff0000000101ffff, + 0xff0000000101ff01, 0xff00000001010000, 0xff000001ffffff00, 0xff000001ffff00ff, + 0xff000001ffff0000, 0xff000001ffff0001, 0xff000001ff000000, 0xff000001ff000001, + 0xff000001ff0001ff, 0xff000001ff000101, 0xff000001ff01ff00, 0xff000001ff010001, + 0xff00000100ffffff, 0xff00000100ffff01, 0xff00000100ff00ff, 0xff00000100ff0000, + 0xff00000100ff01ff, 0xff00000100ff0101, 0xff0000010000ff00, 0xff00000100000000, + 0xff00000100000001, 0xff000001000001ff, 0xff00000100000100, 0xff0000010001ff00, + 0xff000001000100ff, 0xff00000100010000, 0xff000001000101ff, 0xff00000100010100, + 0xff00000100010101, 0xff00000101ff0001, 0xff00000101ff0101, 0xff0000010100ff01, + 0xff00000101000000, 0xff000001010100ff, 0xff00000101010100, 0xff0001ffff00ff00, + 0xff0001ffff000001, 0xff0001ffff010000, 0xff0001ff00ffff00, 0xff0001ff00ff00ff, + 0xff0001ff00ff0001, 0xff0001ff00ff0100, 0xff0001ff0000ffff, 0xff0001ff00000000, + 0xff0001ff000001ff, 0xff0001ff00000101, 0xff0001ff0001ffff, 0xff0001ff0001ff00, + 0xff0001ff000100ff, 0xff0001ff00010001, 0xff0001ff00010100, 0xff0001ff01ff0000, + 0xff0001ff0100ff00, 0xff0001ff010000ff, 0xff0001ff01010000, 0xff000100ff00ffff, + 0xff000100ff00ff01, 0xff000100ff000000, 0xff000100ff000101, 0xff000100ff01ff00, + 0xff000100ff010000, 0xff00010000ffff01, 0xff00010000ff00ff, 0xff00010000ff0000, + 0xff00010000ff01ff, 0xff0001000000ff00, 0xff000100000000ff, 0xff00010000000000, + 0xff00010000000001, 0xff00010000000100, 0xff00010000000101, 0xff0001000001ffff, + 0xff00010000010000, 0xff00010000010101, 0xff00010001ff0100, 0xff0001000100ff00, + 0xff0001000100ff01, 0xff00010001000000, 0xff000100010001ff, 0xff0001000101ff00, + 0xff00010001010001, 0xff00010001010100, 0xff000101ffff0100, 0xff000101ff000001, + 0xff000101ff0100ff, 0xff000101ff010001, 0xff00010100ff00ff, 0xff00010100ff0001, + 0xff00010100ff0100, 0xff0001010000ffff, 0xff0001010000ff01, 0xff00010100000000, + 0xff000101000001ff, 0xff0001010001ff00, 0xff00010100010001, 0xff00010100010100, + 0xff00010101ff0000, 0xff0001010100ff00, 0xff00010101000001, 0xff00010101000101, + 0xff01ffffffffffff, 0xff01ffffffffff01, 0xff01ffffffff01ff, 0xff01ffffffff0101, + 0xff01ffffff000000, 0xff01ffffff01ffff, 0xff01ffffff01ff01, 0xff01ffffff010000, + 0xff01ffffff0101ff, 0xff01ffffff010101, 0xff01ffff00ff0000, 0xff01ffff0000ff00, + 0xff01ffff00000100, 0xff01ffff0001ff00, 0xff01ffff00010000, 0xff01ffff01ffffff, + 0xff01ffff01ffff01, 0xff01ffff01ff01ff, 0xff01ffff01ff0101, 0xff01ffff01000000, + 0xff01ffff0101ffff, 0xff01ffff0101ff01, 0xff01ffff01010000, 0xff01ffff010101ff, + 0xff01ffff01010101, 0xff01ff00ffff0000, 0xff01ff00ff00ff00, 0xff01ff00ff0000ff, + 0xff01ff00ff000100, 0xff01ff00ff010000, 0xff01ff0000ffff01, 0xff01ff0000ff00ff, + 0xff01ff0000ff0100, 0xff01ff0000000000, 0xff01ff00000001ff, 0xff01ff0000000101, + 0xff01ff000001ff00, 0xff01ff00000100ff, 0xff01ff0000010000, 0xff01ff0000010001, + 0xff01ff0001ff0000, 0xff01ff000100ffff, 0xff01ff0001000001, 0xff01ff0001000100, + 0xff01ff0001010000, 0xff01ff01ffffff00, 0xff01ff01ffff01ff, 0xff01ff01ffff0101, + 0xff01ff01ff00ff00, 0xff01ff01ff000000, 0xff01ff01ff01ffff, 0xff01ff01ff01ff01, + 0xff01ff01ff0101ff, 0xff01ff01ff010101, 0xff01ff0100ff0000, 0xff01ff010000ff00, + 0xff01ff0100000001, 0xff01ff0100000100, 0xff01ff0100010000, 0xff01ff0101ffff00, + 0xff01ff0101ff01ff, 0xff01ff0101ff0101, 0xff01ff010100ff00, 0xff01ff0101000000, + 0xff01ff010101ffff, 0xff01ff010101ff01, 0xff01ff01010101ff, 0xff01ff0101010101, + 0xff0100ffffff0000, 0xff0100ffff0000ff, 0xff0100ffff000001, 0xff0100ffff000100, + 0xff0100ffff010000, 0xff0100ff00ff00ff, 0xff0100ff00ff0000, 0xff0100ff00ff0001, + 0xff0100ff00ff0100, 0xff0100ff0000ff01, 0xff0100ff00000000, 0xff0100ff000001ff, + 0xff0100ff00000101, 0xff0100ff00010001, 0xff0100ff01ff0000, 0xff0100ff0100ff00, + 0xff0100ff010000ff, 0xff0100ff01000100, 0xff0100ff0101ff00, 0xff0100ff01010000, + 0xff010000ffff0100, 0xff010000ff000000, 0xff010000ff01ff00, 0xff010000ff010100, + 0xff01000000ffffff, 0xff01000000ff0000, 0xff01000000ff01ff, 0xff0100000000ff00, + 0xff010000000000ff, 0xff01000000000000, 0xff01000000000100, 0xff0100000001ff01, + 0xff01000000010000, 0xff010000000101ff, 0xff01000001ff0100, 0xff0100000100ffff, + 0xff010000010000ff, 0xff01000001000000, 0xff010000010001ff, 0xff01000001000101, + 0xff0100000101ff00, 0xff010000010100ff, 0xff01000001010001, 0xff01000001010100, + 0xff010001ffff0000, 0xff010001ff00ffff, 0xff010001ff00ff01, 0xff010001ff000100, + 0xff010001ff010000, 0xff01000100ffff00, 0xff01000100ff0100, 0xff01000100000000, + 0xff0100010001ffff, 0xff0100010001ff00, 0xff01000100010100, 0xff01000101ff00ff, + 0xff01000101ff0001, 0xff0100010100ffff, 0xff01000101000101, 0xff0101ffffffffff, + 0xff0101ffffffff01, 0xff0101ffffff01ff, 0xff0101ffffff0101, 0xff0101ffff000000, + 0xff0101ffff01ffff, 0xff0101ffff01ff01, 0xff0101ffff0101ff, 0xff0101ffff010101, + 0xff0101ff00ff0000, 0xff0101ff0000ff00, 0xff0101ff000000ff, 0xff0101ff00010000, + 0xff0101ff01ffffff, 0xff0101ff01ffff01, 0xff0101ff01ff01ff, 0xff0101ff01ff0101, + 0xff0101ff0101ffff, 0xff0101ff0101ff01, 0xff0101ff010101ff, 0xff0101ff01010101, + 0xff010100ffff0100, 0xff010100ff00ff00, 0xff010100ff0000ff, 0xff010100ff000100, + 0xff010100ff010000, 0xff01010000ff0001, 0xff01010000ff0100, 0xff0101000000ff01, + 0xff01010000000000, 0xff0101000001ff00, 0xff010100000100ff, 0xff01010000010001, + 0xff01010000010100, 0xff01010001ff0000, 0xff0101000100ffff, 0xff01010001000001, + 0xff01010001000100, 0xff010100010100ff, 0xff01010001010000, 0xff010101ffffffff, + 0xff010101ffffff01, 0xff010101ffff01ff, 0xff010101ffff0101, 0xff010101ff01ffff, + 0xff010101ff01ff01, 0xff010101ff0101ff, 0xff010101ff010101, 0xff01010100ff0000, + 0xff0101010000ff00, 0xff01010100000001, 0xff01010100000100, 0xff01010100010000, + 0xff01010101ffffff, 0xff01010101ffff01, 0xff01010101ff01ff, 0xff01010101ff0101, + 0xff01010101000000, 0xff0101010101ffff, 0xff0101010101ff01, 0xff010101010101ff, + 0xff01010101010101, 0x00ffffffffff0000, 0x00ffffffff00ff00, 0x00ffffffff000001, + 0x00ffffffff010000, 0x00ffffff00ff0100, 0x00ffffff0000ff01, 0x00ffffff00000000, + 0x00ffffff000001ff, 0x00ffffff00000101, 0x00ffffff0001ff00, 0x00ffffff000100ff, + 0x00ffffff00010001, 0x00ffffff010000ff, 0x00ffffff01000100, 0x00ffffff0101ff00, + 0x00ffffff01010001, 0x00ffff00ffffffff, 0x00ffff00ffffff00, 0x00ffff00ffff00ff, + 0x00ffff00ffff0001, 0x00ffff00ffff0100, 0x00ffff00ff00ff01, 0x00ffff00ff000000, + 0x00ffff00ff000001, 0x00ffff00ff0001ff, 0x00ffff00ff000101, 0x00ffff00ff01ff00, + 0x00ffff00ff010001, 0x00ffff00ff010100, 0x00ffff0000ff0000, 0x00ffff0000ff01ff, + 0x00ffff0000ff0101, 0x00ffff000000ff00, 0x00ffff00000000ff, 0x00ffff0000000000, + 0x00ffff0000000001, 0x00ffff0000000100, 0x00ffff0000000101, 0x00ffff0000010000, + 0x00ffff00000101ff, 0x00ffff0000010101, 0x00ffff0001ffff00, 0x00ffff0001ff00ff, + 0x00ffff0001ff0001, 0x00ffff000100ffff, 0x00ffff000100ff01, 0x00ffff0001000000, + 0x00ffff000101ffff, 0x00ffff000101ff00, 0x00ffff000101ff01, 0x00ffff01ffff0000, + 0x00ffff01ff00ff00, 0x00ffff01ff0000ff, 0x00ffff01ff000001, 0x00ffff01ff010000, + 0x00ffff0100ffff00, 0x00ffff010000ff01, 0x00ffff0100000000, 0x00ffff0100000101, + 0x00ffff01000100ff, 0x00ffff0100010100, 0x00ffff0101ff0100, 0x00ffff01010000ff, + 0x00ffff0101010000, 0x00ff00ffffffff00, 0x00ff00ffff000000, 0x00ff00ffff000100, + 0x00ff00ffff010100, 0x00ff00ff00ff0000, 0x00ff00ff00ff01ff, 0x00ff00ff00ff0101, + 0x00ff00ff0000ff00, 0x00ff00ff000000ff, 0x00ff00ff00000000, 0x00ff00ff00000001, + 0x00ff00ff0001ff00, 0x00ff00ff0001ff01, 0x00ff00ff00010000, 0x00ff00ff000101ff, + 0x00ff00ff00010101, 0x00ff00ff01ffff00, 0x00ff00ff01ff0001, 0x00ff00ff01ff0100, + 0x00ff00ff0100ffff, 0x00ff00ff0100ff01, 0x00ff00ff01000000, 0x00ff00ff0101ffff, + 0x00ff00ff0101ff00, 0x00ff00ff01010100, 0x00ff0000ffffff00, 0x00ff0000ffffff01, + 0x00ff0000ffff0000, 0x00ff0000ffff0101, 0x00ff0000ff00ff00, 0x00ff0000ff0000ff, + 0x00ff0000ff000000, 0x00ff0000ff000001, 0x00ff0000ff000100, 0x00ff0000ff01ffff, + 0x00ff0000ff010000, 0x00ff0000ff010101, 0x00ff000000ffff00, 0x00ff000000ff00ff, + 0x00ff000000ff0000, 0x00ff000000ff0001, 0x00ff000000ff0100, 0x00ff00000000ffff, + 0x00ff00000000ff00, 0x00ff0000000000ff, 0x00ff000000000000, 0x00ff000000000001, + 0x00ff0000000001ff, 0x00ff000000000100, 0x00ff00000001ff00, 0x00ff0000000100ff, + 0x00ff000000010000, 0x00ff000000010001, 0x00ff000000010100, 0x00ff000001ffff01, + 0x00ff000001ff00ff, 0x00ff000001ff0000, 0x00ff000001ff01ff, 0x00ff00000100ff00, + 0x00ff0000010000ff, 0x00ff000001000000, 0x00ff000001000001, 0x00ff000001000100, + 0x00ff000001000101, 0x00ff000001010000, 0x00ff0000010101ff, 0x00ff000001010101, + 0x00ff0001ffffff00, 0x00ff0001ffff0000, 0x00ff0001ffff0100, 0x00ff0001ff0000ff, + 0x00ff0001ff000000, 0x00ff0001ff0001ff, 0x00ff0001ff000101, 0x00ff0001ff01ff00, + 0x00ff0001ff0100ff, 0x00ff0001ff010100, 0x00ff000100ffffff, 0x00ff000100ffff01, + 0x00ff000100ff0000, 0x00ff000100ff01ff, 0x00ff00010000ffff, 0x00ff00010000ff00, + 0x00ff00010000ff01, 0x00ff000100000000, 0x00ff000100000001, 0x00ff000100000100, + 0x00ff00010001ff01, 0x00ff000100010000, 0x00ff0001000101ff, 0x00ff000101ffff00, + 0x00ff000101ff0000, 0x00ff000101ff0101, 0x00ff0001010000ff, 0x00ff000101000000, + 0x00ff00010101ff00, 0x00ff0001010100ff, 0x00ff000101010001, 0x00ff01ffffff0000, + 0x00ff01ffff00ff00, 0x00ff01ffff000000, 0x00ff01ffff000101, 0x00ff01ffff010000, + 0x00ff01ff00ffff01, 0x00ff01ff00ff0100, 0x00ff01ff0000ffff, 0x00ff01ff00000000, + 0x00ff01ff000001ff, 0x00ff01ff0001ff00, 0x00ff01ff000100ff, 0x00ff01ff00010001, + 0x00ff01ff00010100, 0x00ff01ff01ff0000, 0x00ff01ff0100ff00, 0x00ff01ff010000ff, + 0x00ff01ff01000001, 0x00ff01ff01000100, 0x00ff01ff01010000, 0x00ff0100ffffff00, + 0x00ff0100ffff0000, 0x00ff0100ffff0001, 0x00ff0100ffff0101, 0x00ff0100ff00ffff, + 0x00ff0100ff0000ff, 0x00ff0100ff000000, 0x00ff0100ff0001ff, 0x00ff0100ff01ff00, + 0x00ff0100ff0100ff, 0x00ff0100ff010001, 0x00ff010000ffffff, 0x00ff010000ff0000, + 0x00ff010000ff0101, 0x00ff01000000ff00, 0x00ff01000000ff01, 0x00ff0100000000ff, + 0x00ff010000000000, 0x00ff010000000001, 0x00ff010000000100, 0x00ff01000001ffff, + 0x00ff01000001ff01, 0x00ff010000010000, 0x00ff010000010001, 0x00ff010000010101, + 0x00ff010001ff0001, 0x00ff010001ff0100, 0x00ff01000100ff01, 0x00ff010001000000, + 0x00ff010001000001, 0x00ff0100010001ff, 0x00ff01000101ff00, 0x00ff0100010100ff, + 0x00ff010001010001, 0x00ff010001010100, 0x00ff0101ff000001, 0x00ff010100ff00ff, + 0x00ff010100ff0001, 0x00ff010100ff0100, 0x00ff010100000000, 0x00ff0101000001ff, + 0x00ff010100000101, 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0x0001010100ffff00, 0x0001010100ff00ff, 0x0001010100ff0100, + 0x000101010000ffff, 0x0001010100000000, 0x00010101000001ff, 0x0001010100000101, + 0x00010101000100ff, 0x0001010100010000, 0x0001010100010100, 0x0001010101ff0001, + 0x00010101010000ff, 0x00010101010001ff, 0x0001010101000101, 0x0001010101010001, + 0x01ffffffffffffff, 0x01ffffffffffff01, 0x01ffffffffff01ff, 0x01ffffffffff0101, + 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, 0x01ffffffff010101, + 0x01ffffff00ff0000, 0x01ffffff0000ffff, 0x01ffffff0000ff00, 0x01ffffff000000ff, + 0x01ffffff00000001, 0x01ffffff00000100, 0x01ffffff00010000, 0x01ffffff01ffffff, + 0x01ffffff01ffff01, 0x01ffffff01ff01ff, 0x01ffffff01ff0101, 0x01ffffff01000000, + 0x01ffffff0101ffff, 0x01ffffff0101ff01, 0x01ffffff010101ff, 0x01ffffff01010101, + 0x01ffff00ffff0000, 0x01ffff00ff00ff00, 0x01ffff00ff0000ff, 0x01ffff00ff000001, + 0x01ffff00ff000100, 0x01ffff00ff010000, 0x01ffff0000ffff00, 0x01ffff0000ff00ff, + 0x01ffff0000ff0100, 0x01ffff000000ffff, 0x01ffff000000ff01, 0x01ffff0000000000, + 0x01ffff0000000001, 0x01ffff00000001ff, 0x01ffff0000000100, 0x01ffff00000100ff, + 0x01ffff0000010001, 0x01ffff0000010100, 0x01ffff0001ff0000, 0x01ffff0001ff0100, + 0x01ffff00010000ff, 0x01ffff0001000001, 0x01ffff0001000100, 0x01ffff0001010000, + 0x01ffff01ffffffff, 0x01ffff01ffffff01, 0x01ffff01ffff01ff, 0x01ffff01ffff0101, + 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff01ff01, 0x01ffff01ff0101ff, + 0x01ffff01ff010101, 0x01ffff010000ff00, 0x01ffff01000000ff, 0x01ffff0100000100, + 0x01ffff0100010000, 0x01ffff0101ffffff, 0x01ffff0101ffff01, 0x01ffff0101ff01ff, + 0x01ffff0101ff0101, 0x01ffff0101000000, 0x01ffff010101ffff, 0x01ffff010101ff01, + 0x01ffff01010101ff, 0x01ffff0101010101, 0x01ff00ffff0000ff, 0x01ff00ffff000100, + 0x01ff00ff00ffff00, 0x01ff00ff00ff00ff, 0x01ff00ff0000ff00, 0x01ff00ff00000000, + 0x01ff00ff00000101, 0x01ff00ff0001ff00, 0x01ff00ff000100ff, 0x01ff00ff00010100, + 0x01ff00ff010000ff, 0x01ff00ff01000100, 0x01ff0000ffffff00, 0x01ff0000ffff0100, + 0x01ff0000ff00ff01, 0x01ff0000ff000000, 0x01ff0000ff000101, 0x01ff0000ff010001, + 0x01ff0000ff010100, 0x01ff000000ffffff, 0x01ff000000ffff00, 0x01ff000000ff0000, + 0x01ff000000ff01ff, 0x01ff00000000ff00, 0x01ff0000000000ff, 0x01ff000000000000, + 0x01ff000000000001, 0x01ff000000000100, 0x01ff000000000101, 0x01ff000000010000, + 0x01ff000000010001, 0x01ff0000000101ff, 0x01ff000000010101, 0x01ff000001ffff00, + 0x01ff000001ff00ff, 0x01ff000001ff0001, 0x01ff000001ff0100, 0x01ff00000100ffff, + 0x01ff00000100ff01, 0x01ff000001000000, 0x01ff0000010001ff, 0x01ff000001010001, + 0x01ff0001ff00ff00, 0x01ff0001ff000001, 0x01ff0001ff000100, 0x01ff0001ff010000, + 0x01ff000100ffff00, 0x01ff000100ff00ff, 0x01ff000100ff0100, 0x01ff000100ff0101, + 0x01ff00010000ffff, 0x01ff000100000000, 0x01ff000100000100, 0x01ff000100000101, + 0x01ff00010001ff00, 0x01ff000100010001, 0x01ff000100010101, 0x01ff000101ff0000, + 0x01ff00010100ff00, 0x01ff000101000101, 0x01ff0001010100ff, 0x01ff01ffffffffff, + 0x01ff01ffffffff01, 0x01ff01ffffff01ff, 0x01ff01ffffff0101, 0x01ff01ffff000000, + 0x01ff01ffff01ffff, 0x01ff01ffff01ff01, 0x01ff01ffff0101ff, 0x01ff01ffff010101, + 0x01ff01ff00ffff00, 0x01ff01ff00ff0000, 0x01ff01ff0000ff00, 0x01ff01ff000000ff, + 0x01ff01ff00000100, 0x01ff01ff00010000, 0x01ff01ff00010100, 0x01ff01ff01ffffff, + 0x01ff01ff01ffff01, 0x01ff01ff01ff01ff, 0x01ff01ff01ff0101, 0x01ff01ff01000000, + 0x01ff01ff0101ffff, 0x01ff01ff0101ff01, 0x01ff01ff010101ff, 0x01ff01ff01010101, + 0x01ff0100ffff0000, 0x01ff0100ffff0001, 0x01ff0100ff00ff00, 0x01ff0100ff0000ff, + 0x01ff0100ff000001, 0x01ff0100ff010000, 0x01ff010000ffff00, 0x01ff010000ff00ff, + 0x01ff010000ff0001, 0x01ff010000ff0100, 0x01ff01000000ffff, 0x01ff01000000ff01, + 0x01ff010000000000, 0x01ff010000000101, 0x01ff01000001ff00, 0x01ff0100000100ff, + 0x01ff010001ff0000, 0x01ff010001000001, 0x01ff010001000100, 0x01ff010001010000, + 0x01ff0101ffffffff, 0x01ff0101ffffff01, 0x01ff0101ffff01ff, 0x01ff0101ffff0101, + 0x01ff0101ff000000, 0x01ff0101ff01ffff, 0x01ff0101ff01ff01, 0x01ff0101ff0101ff, + 0x01ff0101ff010101, 0x01ff010100ff0000, 0x01ff01010000ff00, 0x01ff0101000000ff, + 0x01ff010100000001, 0x01ff010101ffffff, 0x01ff010101ffff01, 0x01ff010101ff01ff, + 0x01ff010101ff0101, 0x01ff010101000000, 0x01ff01010101ffff, 0x01ff01010101ff01, + 0x01ff0101010101ff, 0x01ff010101010101, 0x0100ffffffff0000, 0x0100ffffff00ff00, + 0x0100ffffff000001, 0x0100ffffff0001ff, 0x0100ffffff000100, 0x0100ffffff010000, + 0x0100ffff00ffff00, 0x0100ffff00ff0001, 0x0100ffff00ff0100, 0x0100ffff00000000, + 0x0100ffff000001ff, 0x0100ffff00000101, 0x0100ffff00010100, 0x0100ffff00010101, + 0x0100ffff01ff0000, 0x0100ffff0100ff00, 0x0100ffff010000ff, 0x0100ffff01000001, + 0x0100ffff01000100, 0x0100ffff01010000, 0x0100ff00ffffff00, 0x0100ff00ffff00ff, + 0x0100ff00ffff0001, 0x0100ff00ffff0100, 0x0100ff00ff00ffff, 0x0100ff00ff000000, + 0x0100ff00ff0001ff, 0x0100ff00ff000101, 0x0100ff00ff01ff00, 0x0100ff00ff0100ff, + 0x0100ff00ff010001, 0x0100ff00ff010100, 0x0100ff0000ffffff, 0x0100ff0000ff0000, + 0x0100ff000000ffff, 0x0100ff000000ff00, 0x0100ff00000000ff, 0x0100ff0000000000, + 0x0100ff0000000001, 0x0100ff0000000100, 0x0100ff000001ff01, 0x0100ff0000010000, + 0x0100ff0001ff00ff, 0x0100ff0001ff0001, 0x0100ff000100ff01, 0x0100ff0001000000, + 0x0100ff00010001ff, 0x0100ff000101ff00, 0x0100ff00010100ff, 0x0100ff0001010001, + 0x0100ff0001010100, 0x0100ff01ffff0000, 0x0100ff01ff00ff00, 0x0100ff01ff0000ff, + 0x0100ff01ff000100, 0x0100ff01ff010000, 0x0100ff0100ff00ff, 0x0100ff0100ff0001, + 0x0100ff0100ff0100, 0x0100ff010000ffff, 0x0100ff010000ff01, 0x0100ff0100000000, + 0x0100ff01000001ff, 0x0100ff0100010001, 0x0100ff0100010100, 0x0100ff0101ff0000, + 0x0100ff01010000ff, 0x0100ff0101000001, 0x0100ff0101010100, 0x010000ffffffff00, + 0x010000ffffff00ff, 0x010000ffffff0001, 0x010000ffff00ffff, 0x010000ffff000000, + 0x010000ffff0001ff, 0x010000ffff010001, 0x010000ff00ffffff, 0x010000ff00ff0101, + 0x010000ff0000ff00, 0x010000ff000000ff, 0x010000ff00000000, 0x010000ff00000001, + 0x010000ff000001ff, 0x010000ff00000100, 0x010000ff0001ffff, 0x010000ff0001ff00, + 0x010000ff0001ff01, 0x010000ff00010000, 0x010000ff01ff00ff, 0x010000ff01ff0001, + 0x010000ff0100ff01, 0x010000ff010000ff, 0x010000ff01000000, 0x010000ff010001ff, + 0x010000ff0101ff00, 0x010000ff01010100, 0x01000000ffffffff, 0x01000000ffff0000, + 0x01000000ffff01ff, 0x01000000ffff0101, 0x01000000ff00ffff, 0x01000000ff00ff00, + 0x01000000ff0000ff, 0x01000000ff000000, 0x01000000ff000001, 0x01000000ff000100, + 0x01000000ff01ff00, 0x01000000ff010000, 0x01000000ff010100, 0x01000000ff010101, + 0x0100000000ffff00, 0x0100000000ff00ff, 0x0100000000ff0000, 0x0100000000ff0001, + 0x0100000000ff0100, 0x010000000000ffff, 0x010000000000ff00, 0x010000000000ff01, + 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, 0x01000000000001ff, + 0x0100000000000100, 0x0100000000000101, 0x010000000001ff00, 0x01000000000100ff, + 0x0100000000010000, 0x0100000000010001, 0x0100000000010100, 0x0100000001ffff00, + 0x0100000001ff0000, 0x0100000001ff01ff, 0x010000000100ff00, 0x010000000100ff01, + 0x01000000010000ff, 0x0100000001000000, 0x0100000001000001, 0x0100000001000100, + 0x0100000001000101, 0x010000000101ffff, 0x010000000101ff01, 0x0100000001010000, + 0x01000000010101ff, 0x0100000001010101, 0x01000001ffffff00, 0x01000001ffff00ff, + 0x01000001ff00ffff, 0x01000001ff000000, 0x01000001ff000100, 0x01000001ff01ffff, + 0x01000001ff010001, 0x01000001ff010100, 0x0100000100ff0000, 0x0100000100ff01ff, + 0x0100000100ff0100, 0x010000010000ff00, 0x010000010000ff01, 0x0100000100000000, + 0x0100000100000001, 0x0100000100000100, 0x0100000100010000, 0x01000001000101ff, + 0x0100000101ffff01, 0x0100000101ff00ff, 0x0100000101ff0100, 0x0100000101ff0101, + 0x010000010100ff01, 0x01000001010000ff, 0x0100000101000000, 0x01000001010100ff, + 0x0100000101010001, 0x0100000101010100, 0x010001ffffff0000, 0x010001ffff000001, + 0x010001ffff000100, 0x010001ffff010000, 0x010001ff00ffff00, 0x010001ff00ff0001, + 0x010001ff0000ffff, 0x010001ff0000ff01, 0x010001ff00000000, 0x010001ff00000001, + 0x010001ff00000101, 0x010001ff000100ff, 0x010001ff00010000, 0x010001ff01ff0000, + 0x010001ff0100ff00, 0x010001ff01000001, 0x010001ff01000100, 0x010001ff01010000, + 0x01000100ffff00ff, 0x01000100ffff0001, 0x01000100ffff0100, 0x01000100ff00ffff, + 0x01000100ff00ff01, 0x01000100ff000000, 0x01000100ff0001ff, 0x01000100ff000101, + 0x01000100ff01ffff, 0x01000100ff01ff00, 0x01000100ff0100ff, 0x01000100ff010001, + 0x0100010000ffffff, 0x0100010000ffff01, 0x0100010000ff0000, 0x0100010000ff01ff, + 0x0100010000ff0101, 0x010001000000ff00, 0x01000100000000ff, 0x0100010000000000, + 0x0100010000000001, 0x0100010000000100, 0x010001000001ff01, 0x0100010000010000, + 0x0100010000010001, 0x0100010000010101, 0x0100010001ffff00, 0x0100010001ff00ff, + 0x010001000100ffff, 0x010001000100ff01, 0x0100010001000000, 0x0100010001000101, + 0x010001000101ff00, 0x0100010001010001, 0x01000101ffff0000, 0x01000101ff000000, + 0x01000101ff010000, 0x0100010100ff00ff, 0x0100010100ff0001, 0x0100010100ff0100, + 0x010001010000ffff, 0x0100010100000000, 0x01000101000001ff, 0x010001010001ff00, + 0x0100010101ff0000, 0x010001010100ff00, 0x01000101010000ff, 0x0100010101000000, + 0x0100010101000001, 0x0101ffffffffffff, 0x0101ffffffffff01, 0x0101ffffffff01ff, + 0x0101ffffffff0101, 0x0101ffffff000000, 0x0101ffffff01ffff, 0x0101ffffff01ff01, + 0x0101ffffff0101ff, 0x0101ffffff010101, 0x0101ffff00ff0000, 0x0101ffff0000ff00, + 0x0101ffff000000ff, 0x0101ffff00000001, 0x0101ffff00000100, 0x0101ffff01ffffff, + 0x0101ffff01ffff01, 0x0101ffff01ff01ff, 0x0101ffff01ff0101, 0x0101ffff01000000, + 0x0101ffff0101ffff, 0x0101ffff0101ff01, 0x0101ffff010101ff, 0x0101ffff01010101, + 0x0101ff00ffff0000, 0x0101ff00ffff0100, 0x0101ff00ff00ff00, 0x0101ff00ff0000ff, + 0x0101ff00ff000001, 0x0101ff00ff000100, 0x0101ff00ff000101, 0x0101ff0000ff0001, + 0x0101ff0000ff0100, 0x0101ff000000ff00, 0x0101ff0000000000, 0x0101ff00000001ff, + 0x0101ff0000000101, 0x0101ff000001ff00, 0x0101ff00000100ff, 0x0101ff0001ff0000, + 0x0101ff000100ffff, 0x0101ff000100ff01, 0x0101ff0001000001, 0x0101ff0001000100, + 0x0101ff01ffffff01, 0x0101ff01ffff01ff, 0x0101ff01ffff0101, 0x0101ff01ff00ffff, + 0x0101ff01ff000100, 0x0101ff01ff01ff01, 0x0101ff01ff0101ff, 0x0101ff01ff010101, + 0x0101ff0100ff0000, 0x0101ff010000ff00, 0x0101ff0100000001, 0x0101ff0100000100, + 0x0101ff0100010000, 0x0101ff0101ffffff, 0x0101ff0101ffff01, 0x0101ff0101ff01ff, + 0x0101ff0101ff0101, 0x0101ff0101000000, 0x0101ff010101ffff, 0x0101ff010101ff01, + 0x0101ff01010101ff, 0x0101ff0101010101, 0x010100ffff000100, 0x010100ffff010000, + 0x010100ff00ffff00, 0x010100ff00ff00ff, 0x010100ff0000ffff, 0x010100ff000000ff, + 0x010100ff00000000, 0x010100ff000001ff, 0x010100ff00000101, 0x010100ff0001ff00, + 0x010100ff00010000, 0x010100ff00010001, 0x010100ff000101ff, 0x010100ff00010100, + 0x010100ff01ff0000, 0x01010000ffff0001, 0x01010000ffff0100, 0x01010000ff00ffff, + 0x01010000ff00ff01, 0x01010000ff000000, 0x01010000ff0001ff, 0x01010000ff010001, + 0x01010000ff010100, 0x0101000000ffff01, 0x0101000000ff0000, 0x010100000000ff00, + 0x01010000000000ff, 0x0101000000000000, 0x0101000000000001, 0x0101000000000100, + 0x0101000000010000, 0x0101000000010101, 0x0101000001ffff00, 0x0101000001ff00ff, + 0x0101000001ff0000, 0x0101000001ff0001, 0x0101000001ff0100, 0x010100000100ff01, + 0x0101000001000000, 0x01010000010001ff, 0x01010001ffff0000, 0x01010001ff00ff00, + 0x01010001ff000001, 0x01010001ff000101, 0x01010001ff01ff00, 0x01010001ff010000, + 0x0101000100ff00ff, 0x0101000100ff0001, 0x0101000100ff0101, 0x010100010000ff01, + 0x0101000100000000, 0x0101000100000001, 0x01010001000001ff, 0x010100010001ffff, + 0x010100010001ff01, 0x0101000101ff0001, 0x010100010100ffff, 0x0101000101000000, + 0x0101000101000001, 0x0101000101000100, 0x010100010101ff00, 0x01010001010100ff, + 0x0101000101010001, 0x010101ffffffffff, 0x010101ffffffff01, 0x010101ffffff01ff, + 0x010101ffffff0101, 0x010101ffff01ffff, 0x010101ffff01ff01, 0x010101ffff0101ff, + 0x010101ffff010101, 0x010101ff0000ff00, 0x010101ff000000ff, 0x010101ff00000001, + 0x010101ff00000100, 0x010101ff01ffffff, 0x010101ff01ffff01, 0x010101ff01ff01ff, + 0x010101ff01ff0101, 0x010101ff01000000, 0x010101ff0101ffff, 0x010101ff0101ff01, + 0x010101ff010101ff, 0x010101ff01010101, 0x01010100ffff0000, 0x01010100ff0000ff, + 0x01010100ff000100, 0x01010100ff01ff00, 0x01010100ff010000, 0x0101010000ffff00, + 0x010101000000ffff, 0x0101010000000000, 0x0101010000000101, 0x010101000001ff00, + 0x0101010000010001, 0x0101010000010100, 0x010101000100ffff, 0x0101010001000001, + 0x01010101ffffffff, 0x01010101ffffff01, 0x01010101ffff01ff, 0x01010101ffff0101, + 0x01010101ff01ffff, 0x01010101ff01ff01, 0x01010101ff0101ff, 0x01010101ff010101, + 0x010101010000ff00, 0x01010101000000ff, 0x0101010100000001, 0x0101010101ffffff, + 0x0101010101ffff01, 0x0101010101ff01ff, 0x0101010101ff0101, 0x0101010101000000, + 0x010101010101ffff, 0x010101010101ff01, 0x01010101010101ff, 0x0101010101010101, +GGML_TABLE_END() +#else +GGML_TABLE_BEGIN(uint32_t, iq1s_grid_gpu, NGRID_IQ1S) + 0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000, + 0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101, + 0x02000000, 0x02000002, 0x02000200, 0x02000202, 0x02010101, 0x02020000, 0x02020002, 0x02020200, + 0x02020202, 0x00000110, 0x00000111, 0x00010011, 0x00010110, 0x00010112, 0x00010211, 0x00010212, + 0x00020111, 0x01000011, 0x01000112, 0x01000211, 0x01010012, 0x01010111, 0x01010212, 0x01020011, + 0x01020110, 0x01020112, 0x01020210, 0x02000111, 0x02010011, 0x02010110, 0x02010112, 0x02020111, + 0x00000020, 0x00000022, 0x00000220, 0x00000222, 0x00010121, 0x00020020, 0x00020022, 0x00020220, + 0x00020222, 0x01000121, 0x01010021, 0x01010221, 0x01020120, 0x01020221, 0x02000020, 0x02000022, + 0x02000220, 0x02000222, 0x02010021, 0x02010121, 0x02010221, 0x02020020, 0x02020022, 0x02020220, + 0x02020222, 0x00011001, 0x00011100, 0x00011102, 0x00021101, 0x01001001, 0x01001201, 0x01011101, + 0x01011202, 0x01021100, 0x01021101, 0x02011001, 0x02011201, 0x02021101, 0x00001011, 0x00001110, + 0x00001111, 0x00001112, 0x00011111, 0x00011210, 0x00011212, 0x00021211, 0x01001010, 0x01001111, + 0x01001212, 0x01011010, 0x01011011, 0x01011110, 0x01011111, 0x01011112, 0x01011211, 0x01021010, + 0x01021012, 0x01021111, 0x01021210, 0x01021212, 0x02001011, 0x02011011, 0x02011111, 0x02011210, + 0x02011212, 0x02021011, 0x02021110, 0x02021111, 0x02021112, 0x02021211, 0x00011120, 0x00011221, + 0x01001021, 0x01001120, 0x01011020, 0x01011022, 0x01011121, 0x01011220, 0x01021020, 0x01021021, + 0x01021122, 0x01021221, 0x02001121, 0x02011021, 0x02011120, 0x02011221, 0x00002000, 0x00002002, + 0x00002200, 0x00002202, 0x00012101, 0x00022000, 0x00022002, 0x00022200, 0x00022202, 0x01002101, + 0x01012001, 0x01012102, 0x01022101, 0x02002000, 0x02002002, 0x02002200, 0x02002202, 0x02012101, + 0x02022000, 0x02022002, 0x02022200, 0x02022202, 0x00002111, 0x00012011, 0x00012110, 0x00012211, + 0x00022110, 0x00022111, 0x01002011, 0x01012010, 0x01012011, 0x01012111, 0x01022011, 0x01022110, + 0x01022211, 0x02012011, 0x02012110, 0x02012112, 0x02012211, 0x02022111, 0x00002020, 0x00002022, + 0x00002220, 0x00002222, 0x00012121, 0x00022020, 0x00022022, 0x00022220, 0x00022222, 0x01002121, + 0x01012021, 0x01012221, 0x01022021, 0x01022121, 0x02002020, 0x02002022, 0x02002121, 0x02002220, + 0x02002222, 0x02012121, 0x02022020, 0x02022022, 0x02022220, 0x02022222, 0x00110000, 0x00110001, + 0x00110100, 0x00110201, 0x00120100, 0x00120101, 0x01100001, 0x01100100, 0x01110000, 0x01110101, + 0x01110200, 0x01120001, 0x01120100, 0x01120101, 0x01120201, 0x02110001, 0x02110100, 0x02110102, + 0x02120001, 0x02120101, 0x00100011, 0x00100110, 0x00100112, 0x00100211, 0x00110010, 0x00110012, + 0x00110111, 0x00110210, 0x00120011, 0x00120110, 0x00120211, 0x01100111, 0x01100212, 0x01110010, + 0x01110011, 0x01110012, 0x01110110, 0x01110111, 0x01110112, 0x01110211, 0x01120010, 0x01120111, + 0x02100110, 0x02110012, 0x02110111, 0x02120011, 0x02120110, 0x00110021, 0x00110120, 0x00110122, + 0x00120121, 0x01100020, 0x01100122, 0x01100221, 0x01110022, 0x01110121, 0x01110220, 0x01110222, + 0x01120120, 0x01120122, 0x02100121, 0x02110021, 0x02110120, 0x02110122, 0x02120121, 0x00101001, + 0x00101102, 0x00101201, 0x00111100, 0x00111101, 0x00111200, 0x00111201, 0x00121001, 0x00121102, + 0x01101001, 0x01101101, 0x01101102, 0x01101200, 0x01101202, 0x01111001, 0x01111100, 0x01111101, + 0x01111102, 0x01111201, 0x01121002, 0x01121101, 0x01121200, 0x02101100, 0x02101201, 0x02111000, + 0x02111100, 0x02111101, 0x02111200, 0x02111201, 0x02111202, 0x02121001, 0x02121100, 0x02121101, + 0x02121201, 0x00101012, 0x00101111, 0x00101212, 0x00111011, 0x00111110, 0x00111111, 0x00111112, + 0x00111211, 0x00121010, 0x00121012, 0x00121111, 0x00121210, 0x00121212, 0x01101011, 0x01101110, + 0x01101111, 0x01101112, 0x01111011, 0x01111012, 0x01111110, 0x01111111, 0x01111112, 0x01111211, + 0x01111212, 0x01121011, 0x01121110, 0x01121111, 0x01121112, 0x01121211, 0x02101010, 0x02101012, + 0x02101110, 0x02101111, 0x02101210, 0x02101212, 0x02111010, 0x02111011, 0x02111110, 0x02111111, + 0x02111112, 0x02111211, 0x02111212, 0x02121010, 0x02121012, 0x02121111, 0x00101021, 0x00101120, + 0x00101121, 0x00101122, 0x00111121, 0x00111122, 0x00111220, 0x00111222, 0x00121021, 0x00121122, + 0x01101020, 0x01101022, 0x01101120, 0x01101121, 0x01101220, 0x01101222, 0x01111021, 0x01111121, + 0x01111122, 0x01111220, 0x01111221, 0x01121021, 0x01121120, 0x01121121, 0x01121220, 0x01121221, + 0x01121222, 0x02101122, 0x02101222, 0x02111022, 0x02111121, 0x02121120, 0x02121221, 0x00112001, + 0x00112102, 0x00122101, 0x01102001, 0x01102100, 0x01102102, 0x01102201, 0x01112000, 0x01112101, + 0x01112200, 0x01112202, 0x01122000, 0x01122001, 0x01122100, 0x01122102, 0x01122201, 0x02102101, + 0x02112001, 0x02112100, 0x02122101, 0x00112010, 0x00112012, 0x00112111, 0x00112212, 0x00122011, + 0x00122111, 0x01102012, 0x01102110, 0x01102111, 0x01102210, 0x01112011, 0x01112110, 0x01112111, + 0x01112112, 0x01112211, 0x01112212, 0x01122010, 0x01122111, 0x01122212, 0x02102211, 0x02112011, + 0x02112012, 0x02112111, 0x02112210, 0x02122011, 0x02122112, 0x02122211, 0x00102221, 0x00112122, + 0x00122120, 0x00122122, 0x01102120, 0x01102122, 0x01102221, 0x01112020, 0x01112022, 0x01112121, + 0x01112220, 0x01122021, 0x01122122, 0x01122221, 0x02102121, 0x02112021, 0x02112122, 0x02112222, + 0x00200000, 0x00200002, 0x00200200, 0x00200202, 0x00210101, 0x00220000, 0x00220002, 0x00220101, + 0x00220200, 0x00220202, 0x01200101, 0x01210001, 0x01210201, 0x01220001, 0x01220101, 0x02200000, + 0x02200002, 0x02200200, 0x02200202, 0x02210101, 0x02220000, 0x02220002, 0x02220101, 0x02220200, + 0x02220202, 0x00200111, 0x00210011, 0x00210110, 0x00210211, 0x00220111, 0x01200012, 0x01200110, + 0x01200211, 0x01210111, 0x01210210, 0x01210212, 0x01220011, 0x01220110, 0x01220111, 0x01220112, + 0x02200111, 0x02210010, 0x02210112, 0x02210211, 0x02220111, 0x00200021, 0x00200220, 0x00200222, + 0x00210021, 0x00210121, 0x00220020, 0x00220022, 0x00220220, 0x00220222, 0x01200121, 0x01210021, + 0x01210122, 0x01210221, 0x01220121, 0x02200021, 0x02200220, 0x02200222, 0x02210021, 0x02210121, + 0x02220020, 0x02220022, 0x02220220, 0x02220222, 0x00201101, 0x00211100, 0x00211102, 0x00211201, + 0x00221101, 0x01201100, 0x01201101, 0x01201102, 0x01201201, 0x01211002, 0x01211101, 0x01211200, + 0x01211202, 0x01221102, 0x02201101, 0x02211001, 0x02211100, 0x02211201, 0x02221001, 0x02221101, + 0x00201211, 0x00211111, 0x00221011, 0x00221211, 0x01201010, 0x01201111, 0x01201210, 0x01211011, + 0x01211110, 0x01211111, 0x01211211, 0x01221012, 0x01221111, 0x01221210, 0x02201211, 0x02211010, + 0x02211110, 0x02211111, 0x02211210, 0x02211212, 0x02221011, 0x02221110, 0x02221112, 0x02221211, + 0x00201121, 0x00211020, 0x00211022, 0x00211221, 0x00221121, 0x01201021, 0x01201221, 0x01211121, + 0x01221020, 0x01221021, 0x01221221, 0x02201120, 0x02201122, 0x02211020, 0x02211222, 0x00202000, + 0x00202002, 0x00202200, 0x00202202, 0x00212101, 0x00222000, 0x00222002, 0x00222200, 0x00222202, + 0x01202101, 0x01212001, 0x01212100, 0x01222101, 0x02202000, 0x02202002, 0x02202200, 0x02202202, + 0x02222000, 0x02222002, 0x02222200, 0x02222202, 0x00202211, 0x00212011, 0x00212110, 0x00212211, + 0x00222111, 0x01202112, 0x01202211, 0x01212012, 0x01212111, 0x01222011, 0x01222110, 0x01222112, + 0x01222211, 0x02202111, 0x02212010, 0x02212112, 0x02212211, 0x02222110, 0x02222111, 0x00202020, + 0x00202022, 0x00202220, 0x00202222, 0x00222020, 0x00222022, 0x00222220, 0x00222222, 0x01202121, + 0x01212021, 0x01212122, 0x01212221, 0x01222121, 0x02202020, 0x02202022, 0x02202220, 0x02202222, + 0x02212121, 0x02222020, 0x02222022, 0x02222220, 0x02222222, 0x10000101, 0x10010001, 0x10010102, + 0x10020101, 0x11000201, 0x11010002, 0x11010101, 0x11010200, 0x11010202, 0x11020001, 0x11020100, + 0x11020102, 0x12010100, 0x12010201, 0x12020001, 0x12020102, 0x10000010, 0x10000011, 0x10000110, + 0x10000112, 0x10000211, 0x10010012, 0x10010111, 0x10010112, 0x10010210, 0x10010212, 0x10020011, + 0x10020112, 0x10020211, 0x11000111, 0x11000210, 0x11000212, 0x11010011, 0x11010110, 0x11010111, + 0x11010112, 0x11010211, 0x11010212, 0x11020111, 0x11020210, 0x11020212, 0x12000011, 0x12000110, + 0x12000112, 0x12010010, 0x12010012, 0x12010111, 0x12020010, 0x12020011, 0x12020012, 0x10000121, + 0x10010021, 0x10010120, 0x10010122, 0x10020121, 0x11000021, 0x11010022, 0x11010121, 0x11010222, + 0x11020120, 0x11020221, 0x12000221, 0x12010120, 0x12020121, 0x10001001, 0x10011101, 0x10011201, + 0x10021201, 0x11001101, 0x11001200, 0x11001202, 0x11011001, 0x11011100, 0x11011101, 0x11011102, + 0x11021001, 0x11021002, 0x11021101, 0x11021200, 0x11021202, 0x12001001, 0x12001102, 0x12001201, + 0x12011000, 0x12011002, 0x12011101, 0x12021000, 0x12021001, 0x12021201, 0x10001011, 0x10001012, + 0x10001111, 0x10001212, 0x10011011, 0x10011110, 0x10011111, 0x10011112, 0x10011211, 0x10021010, + 0x10021111, 0x10021212, 0x11001011, 0x11001110, 0x11001111, 0x11001112, 0x11001211, 0x11011010, + 0x11011011, 0x11011110, 0x11011111, 0x11011112, 0x11011210, 0x11011211, 0x11021011, 0x11021110, + 0x11021111, 0x11021112, 0x11021211, 0x12001012, 0x12001110, 0x12001111, 0x12001210, 0x12011011, + 0x12011110, 0x12011111, 0x12011112, 0x12011211, 0x12011212, 0x12021111, 0x12021210, 0x12021212, + 0x10001021, 0x10001121, 0x10001221, 0x10011120, 0x10011121, 0x10011220, 0x10011222, 0x10021021, + 0x10021120, 0x10021221, 0x11001020, 0x11001022, 0x11001121, 0x11001220, 0x11011020, 0x11011021, + 0x11011022, 0x11011121, 0x11011122, 0x11011221, 0x11021022, 0x11021121, 0x11021220, 0x12001021, + 0x12001121, 0x12001222, 0x12011120, 0x12011121, 0x12021021, 0x12021120, 0x12021122, 0x10002101, + 0x10012001, 0x10012101, 0x10012202, 0x10022101, 0x11002002, 0x11002201, 0x11012000, 0x11012101, + 0x11012200, 0x11022001, 0x11022100, 0x11022102, 0x11022201, 0x12002101, 0x12012001, 0x12012100, + 0x12012102, 0x12012201, 0x12022101, 0x10002011, 0x10002111, 0x10002112, 0x10002212, 0x10012010, + 0x10012110, 0x10012111, 0x10012210, 0x10022011, 0x10022110, 0x10022112, 0x11002010, 0x11002111, + 0x11002212, 0x11012011, 0x11012012, 0x11012110, 0x11012111, 0x11012112, 0x11012211, 0x11022010, + 0x11022012, 0x11022111, 0x11022112, 0x11022212, 0x12002112, 0x12002211, 0x12012012, 0x12012111, + 0x12012112, 0x12012210, 0x12022011, 0x12022110, 0x12022112, 0x12022211, 0x10012122, 0x11002120, + 0x11002122, 0x11002221, 0x11012121, 0x11012220, 0x11012222, 0x11022120, 0x11022221, 0x12012120, + 0x12022121, 0x10100001, 0x10100100, 0x10100101, 0x10100102, 0x10100201, 0x10110002, 0x10110101, + 0x10110202, 0x10120001, 0x10120100, 0x10120201, 0x11100000, 0x11100101, 0x11100200, 0x11110001, + 0x11110100, 0x11110101, 0x11110102, 0x11110201, 0x11120101, 0x11120200, 0x12100102, 0x12100201, + 0x12110101, 0x12110200, 0x12120000, 0x12120001, 0x12120102, 0x12120201, 0x10100111, 0x10100210, + 0x10100211, 0x10100212, 0x10110011, 0x10110110, 0x10110111, 0x10110112, 0x10110210, 0x10110211, + 0x10120010, 0x10120111, 0x10120112, 0x10120210, 0x10120212, 0x11100011, 0x11100110, 0x11100111, + 0x11100112, 0x11100211, 0x11110010, 0x11110011, 0x11110012, 0x11110110, 0x11110111, 0x11110112, + 0x11110210, 0x11110211, 0x11110212, 0x11120011, 0x11120110, 0x11120111, 0x11120112, 0x11120211, + 0x12100012, 0x12100111, 0x12110011, 0x12110110, 0x12110111, 0x12110112, 0x12110211, 0x12120010, + 0x12120111, 0x12120212, 0x10100021, 0x10100122, 0x10110022, 0x10110121, 0x10110222, 0x10120021, + 0x10120120, 0x11100022, 0x11100121, 0x11100222, 0x11110021, 0x11110120, 0x11110121, 0x11110122, + 0x11110221, 0x11120022, 0x11120121, 0x12100121, 0x12110020, 0x12110022, 0x12110121, 0x12110221, + 0x12110222, 0x12120120, 0x10101100, 0x10101101, 0x10111001, 0x10111100, 0x10111101, 0x10111102, + 0x10111200, 0x10111201, 0x10121001, 0x10121101, 0x10121200, 0x10121202, 0x11101001, 0x11101100, + 0x11101101, 0x11101102, 0x11101201, 0x11101202, 0x11111000, 0x11111001, 0x11111100, 0x11111101, + 0x11111102, 0x11111200, 0x11111201, 0x11111202, 0x11121001, 0x11121002, 0x11121100, 0x11121101, + 0x11121102, 0x11121201, 0x12101000, 0x12101200, 0x12101202, 0x12111001, 0x12111100, 0x12111101, + 0x12111102, 0x12111201, 0x12121001, 0x12121100, 0x12121101, 0x12121202, 0x10101011, 0x10101012, + 0x10101110, 0x10101111, 0x10101112, 0x10101211, 0x10111010, 0x10111011, 0x10111012, 0x10111110, + 0x10111111, 0x10111112, 0x10111211, 0x10111212, 0x10121011, 0x10121110, 0x10121111, 0x10121112, + 0x10121211, 0x11101010, 0x11101011, 0x11101012, 0x11101110, 0x11101111, 0x11101112, 0x11101210, + 0x11101211, 0x11111010, 0x11111011, 0x11111012, 0x11111110, 0x11111111, 0x11111112, 0x11111210, + 0x11111211, 0x11111212, 0x11121010, 0x11121011, 0x11121110, 0x11121111, 0x11121112, 0x11121210, + 0x11121211, 0x11121212, 0x12101011, 0x12101110, 0x12101111, 0x12101211, 0x12101212, 0x12111010, + 0x12111011, 0x12111110, 0x12111111, 0x12111112, 0x12111210, 0x12111211, 0x12121011, 0x12121110, + 0x12121111, 0x12121112, 0x12121211, 0x10101020, 0x10101021, 0x10101022, 0x10101120, 0x10101122, + 0x10101220, 0x10101221, 0x10111021, 0x10111120, 0x10111121, 0x10111220, 0x10111221, 0x10121020, + 0x10121021, 0x10121022, 0x10121120, 0x10121121, 0x10121122, 0x10121220, 0x10121221, 0x11101021, + 0x11101121, 0x11101122, 0x11101220, 0x11101221, 0x11101222, 0x11111020, 0x11111021, 0x11111022, + 0x11111120, 0x11111121, 0x11111122, 0x11111220, 0x11111221, 0x11111222, 0x11121021, 0x11121120, + 0x11121121, 0x11121221, 0x12101022, 0x12101121, 0x12101122, 0x12101220, 0x12101221, 0x12101222, + 0x12111021, 0x12111121, 0x12111222, 0x12121022, 0x12121121, 0x12121122, 0x12121220, 0x12121221, + 0x10102100, 0x10102101, 0x10102102, 0x10102201, 0x10112000, 0x10112101, 0x10112200, 0x10122001, + 0x10122202, 0x11102101, 0x11102200, 0x11102202, 0x11112001, 0x11112100, 0x11112101, 0x11112102, + 0x11112200, 0x11112201, 0x11122000, 0x11122002, 0x11122100, 0x11122101, 0x12102002, 0x12102201, + 0x12112000, 0x12112002, 0x12112101, 0x12112200, 0x12122001, 0x12122201, 0x10102011, 0x10102012, + 0x10102111, 0x10102212, 0x10112011, 0x10112110, 0x10112111, 0x10112112, 0x10112211, 0x10122111, + 0x11102011, 0x11102110, 0x11102111, 0x11102112, 0x11102211, 0x11112010, 0x11112011, 0x11112012, + 0x11112110, 0x11112111, 0x11112112, 0x11112210, 0x11112211, 0x11112212, 0x11122011, 0x11122110, + 0x11122111, 0x11122112, 0x11122211, 0x12102011, 0x12102111, 0x12102211, 0x12112011, 0x12112110, + 0x12112111, 0x12112112, 0x12112210, 0x12112211, 0x12122111, 0x10102120, 0x10102220, 0x10112121, + 0x10112222, 0x10122020, 0x10122121, 0x10122122, 0x10122221, 0x11102121, 0x11102220, 0x11102221, + 0x11112021, 0x11112121, 0x11112122, 0x11112220, 0x11112221, 0x11122022, 0x11122121, 0x11122220, + 0x11122222, 0x12102021, 0x12102222, 0x12112022, 0x12112121, 0x12112122, 0x12112220, 0x12112222, + 0x12122021, 0x10200101, 0x10210100, 0x10210102, 0x10210201, 0x10220101, 0x11200100, 0x11210000, + 0x11210101, 0x11210102, 0x11210200, 0x11210202, 0x11220001, 0x11220100, 0x11220102, 0x11220201, + 0x12200001, 0x12210102, 0x12220101, 0x10200011, 0x10200110, 0x10200112, 0x10200211, 0x10210012, + 0x10210111, 0x10220011, 0x10220012, 0x10220112, 0x10220211, 0x11200111, 0x11200211, 0x11210011, + 0x11210111, 0x11210112, 0x11210211, 0x11220111, 0x11220112, 0x11220212, 0x12200110, 0x12200212, + 0x12210012, 0x12210111, 0x12220011, 0x12220112, 0x12220211, 0x10210021, 0x10210122, 0x10210221, + 0x11200020, 0x11200021, 0x11200122, 0x11210121, 0x11210122, 0x11210220, 0x11220020, 0x12200121, + 0x12210021, 0x12210122, 0x12220121, 0x10211001, 0x10211002, 0x10211101, 0x10211102, 0x10211202, + 0x10221001, 0x10221102, 0x10221201, 0x11201000, 0x11201002, 0x11201101, 0x11201200, 0x11201202, + 0x11211001, 0x11211100, 0x11211101, 0x11211102, 0x11211201, 0x11211202, 0x11221000, 0x11221002, + 0x11221101, 0x12201100, 0x12201101, 0x12201201, 0x12211000, 0x12211002, 0x12211100, 0x12211101, + 0x12211102, 0x12211200, 0x12211202, 0x12221001, 0x12221100, 0x12221201, 0x10201111, 0x10201210, + 0x10201212, 0x10211011, 0x10211111, 0x10211112, 0x10211211, 0x11201110, 0x11201111, 0x11201112, + 0x11201211, 0x11211010, 0x11211011, 0x11211110, 0x11211111, 0x11211112, 0x11211211, 0x11221011, + 0x11221110, 0x11221111, 0x11221112, 0x11221211, 0x12201112, 0x12201211, 0x12201212, 0x12211011, + 0x12211111, 0x12211112, 0x12211211, 0x12211212, 0x12221012, 0x12221111, 0x12221112, 0x12221210, + 0x10201022, 0x10201221, 0x10211121, 0x10221020, 0x10221122, 0x10221220, 0x10221221, 0x11201020, + 0x11201121, 0x11201220, 0x11201222, 0x11211021, 0x11211120, 0x11211121, 0x11211122, 0x11211220, + 0x11211222, 0x11221020, 0x11221121, 0x11221220, 0x12201020, 0x12201022, 0x12201121, 0x12201222, + 0x12211120, 0x12211122, 0x12211220, 0x12211221, 0x12221020, 0x12221120, 0x12221122, 0x12221222, + 0x10212102, 0x10212201, 0x10222101, 0x11202001, 0x11212002, 0x11212101, 0x11212202, 0x11222001, + 0x11222201, 0x12202101, 0x12212001, 0x12212200, 0x12222102, 0x10202011, 0x10202110, 0x10212010, + 0x10212111, 0x10222011, 0x10222110, 0x10222112, 0x10222211, 0x11202010, 0x11202011, 0x11202111, + 0x11202112, 0x11202210, 0x11212011, 0x11212110, 0x11212111, 0x11212112, 0x11212211, 0x11222010, + 0x11222111, 0x11222212, 0x12202012, 0x12202110, 0x12202212, 0x12212111, 0x12222011, 0x12222110, + 0x12222111, 0x12222211, 0x10212021, 0x10212122, 0x10212220, 0x11202021, 0x11202120, 0x11202221, + 0x11212020, 0x11212121, 0x11212220, 0x11212222, 0x11222120, 0x11222121, 0x11222221, 0x12202122, + 0x12212120, 0x12212220, 0x12212222, 0x12222122, 0x20000000, 0x20000002, 0x20000200, 0x20000202, + 0x20020000, 0x20020002, 0x20020200, 0x20020202, 0x21000101, 0x21010000, 0x21010001, 0x21010100, + 0x21010102, 0x21010201, 0x21020101, 0x22000000, 0x22000002, 0x22000200, 0x22000202, 0x22010101, + 0x22020000, 0x22020002, 0x22020200, 0x22020202, 0x20000111, 0x20010011, 0x20010110, 0x20010112, + 0x20010211, 0x20020111, 0x21000011, 0x21000110, 0x21000211, 0x21010010, 0x21010012, 0x21010111, + 0x21010112, 0x21010210, 0x21010211, 0x21020110, 0x21020112, 0x21020211, 0x22000111, 0x22000211, + 0x22010110, 0x22010112, 0x22010211, 0x22020111, 0x20000020, 0x20000022, 0x20000220, 0x20000222, + 0x20010121, 0x20020020, 0x20020022, 0x20020220, 0x20020222, 0x21010021, 0x21010120, 0x21010221, + 0x21020121, 0x22000020, 0x22000022, 0x22000220, 0x22000222, 0x22010121, 0x22020020, 0x22020022, + 0x22020220, 0x22020222, 0x20011100, 0x20011201, 0x21001001, 0x21001100, 0x21011001, 0x21011101, + 0x21011202, 0x21021001, 0x21021100, 0x21021201, 0x22011100, 0x22011201, 0x20001011, 0x20001211, + 0x20011012, 0x20011111, 0x20011212, 0x20021112, 0x20021211, 0x21001010, 0x21001011, 0x21001111, + 0x21001210, 0x21011011, 0x21011110, 0x21011111, 0x21011112, 0x21011211, 0x21011212, 0x21021111, + 0x21021112, 0x21021210, 0x21021212, 0x22001011, 0x22001110, 0x22001112, 0x22001211, 0x22011010, + 0x22011012, 0x22011111, 0x22011210, 0x22021112, 0x20011021, 0x20011122, 0x20011221, 0x20021121, + 0x21001021, 0x21001120, 0x21001221, 0x21001222, 0x21011020, 0x21011121, 0x21011221, 0x21011222, + 0x21021021, 0x21021122, 0x21021222, 0x22001121, 0x22011021, 0x22011222, 0x22021120, 0x20002000, + 0x20002002, 0x20002200, 0x20002202, 0x20012101, 0x20022000, 0x20022002, 0x20022200, 0x20022202, + 0x21002001, 0x21002101, 0x21012001, 0x21012100, 0x21012201, 0x21022101, 0x21022201, 0x22002000, + 0x22002002, 0x22002200, 0x22002202, 0x22012101, 0x22022000, 0x22022002, 0x22022200, 0x22022202, + 0x20002111, 0x20002112, 0x20012011, 0x20012110, 0x20012112, 0x20022111, 0x21002011, 0x21002110, + 0x21002112, 0x21002211, 0x21012010, 0x21012012, 0x21012111, 0x21012212, 0x21022011, 0x21022110, + 0x22002111, 0x22012112, 0x22012211, 0x22022111, 0x20002020, 0x20002022, 0x20002220, 0x20002222, + 0x20012121, 0x20022020, 0x20022022, 0x20022220, 0x20022222, 0x21002121, 0x21012021, 0x21012120, + 0x21012122, 0x22002020, 0x22002022, 0x22002220, 0x22002222, 0x22012121, 0x22022020, 0x22022022, + 0x22022220, 0x22022222, 0x20100101, 0x20110001, 0x20110102, 0x20110200, 0x20110201, 0x20120101, + 0x21100001, 0x21100102, 0x21100201, 0x21110101, 0x21110200, 0x21110202, 0x21120201, 0x21120202, + 0x22100101, 0x22110001, 0x22110100, 0x22110102, 0x22110201, 0x22120101, 0x20100011, 0x20100110, + 0x20100112, 0x20100211, 0x20110010, 0x20110111, 0x20110210, 0x20110212, 0x20120011, 0x20120110, + 0x20120112, 0x20120211, 0x21100010, 0x21100111, 0x21110010, 0x21110011, 0x21110110, 0x21110111, + 0x21110112, 0x21110211, 0x21120012, 0x21120111, 0x22100110, 0x22100112, 0x22110012, 0x22110111, + 0x22110210, 0x22120011, 0x22120110, 0x22120112, 0x22120211, 0x20100121, 0x20110021, 0x20110120, + 0x20110221, 0x20120121, 0x21100120, 0x21100122, 0x21100221, 0x21110020, 0x21110022, 0x21110121, + 0x21110220, 0x21120122, 0x21120221, 0x22100121, 0x22110120, 0x22110122, 0x22120221, 0x20101001, + 0x20101100, 0x20101102, 0x20111000, 0x20111101, 0x20111200, 0x20121102, 0x21101000, 0x21101202, + 0x21111001, 0x21111100, 0x21111101, 0x21111102, 0x21111200, 0x21111201, 0x21121000, 0x21121001, + 0x21121002, 0x21121101, 0x22101100, 0x22101102, 0x22111002, 0x22111100, 0x22111101, 0x22111200, + 0x22121001, 0x22121201, 0x20101010, 0x20101111, 0x20101210, 0x20101212, 0x20111010, 0x20111011, + 0x20111110, 0x20111111, 0x20111112, 0x20111211, 0x20121011, 0x20121111, 0x20121211, 0x20121212, + 0x21101011, 0x21101110, 0x21101111, 0x21101112, 0x21101211, 0x21111010, 0x21111011, 0x21111012, + 0x21111110, 0x21111111, 0x21111112, 0x21111210, 0x21111211, 0x21111212, 0x21121011, 0x21121110, + 0x21121111, 0x21121112, 0x21121211, 0x22101011, 0x22101111, 0x22101210, 0x22111011, 0x22111012, + 0x22111110, 0x22111111, 0x22111112, 0x22111211, 0x22111212, 0x22121010, 0x22121012, 0x22121111, + 0x22121210, 0x22121212, 0x20101021, 0x20101120, 0x20111020, 0x20111121, 0x20111221, 0x20121020, + 0x20121122, 0x20121221, 0x21101121, 0x21101220, 0x21101221, 0x21111021, 0x21111022, 0x21111121, + 0x21111122, 0x21111221, 0x21121121, 0x21121220, 0x22101022, 0x22101120, 0x22101221, 0x22101222, + 0x22111022, 0x22111120, 0x22111121, 0x22121120, 0x22121122, 0x22121221, 0x20102101, 0x20112102, + 0x20112201, 0x20122101, 0x21102001, 0x21102102, 0x21112000, 0x21112002, 0x21112101, 0x21112102, + 0x21112202, 0x21122100, 0x21122101, 0x22102101, 0x22112001, 0x22112102, 0x22112201, 0x22122101, + 0x20102110, 0x20102112, 0x20102211, 0x20112010, 0x20112012, 0x20112111, 0x20112210, 0x20112212, + 0x20122010, 0x20122011, 0x20122110, 0x20122112, 0x21102010, 0x21102012, 0x21102111, 0x21102210, + 0x21102212, 0x21112011, 0x21112110, 0x21112111, 0x21112112, 0x21112211, 0x21122012, 0x21122111, + 0x21122112, 0x21122212, 0x22102011, 0x22102110, 0x22112010, 0x22112012, 0x22112111, 0x22112212, + 0x22122011, 0x22122112, 0x20102121, 0x20112121, 0x20122121, 0x21102120, 0x21102122, 0x21102221, + 0x21112020, 0x21112121, 0x21112220, 0x21122021, 0x22102121, 0x22112021, 0x22112120, 0x22112121, + 0x22112122, 0x20200000, 0x20200002, 0x20200200, 0x20200202, 0x20210101, 0x20220000, 0x20220002, + 0x20220200, 0x20220202, 0x21200101, 0x21210001, 0x21210100, 0x21210102, 0x21210201, 0x22200000, + 0x22200002, 0x22200200, 0x22200202, 0x22210101, 0x22220000, 0x22220002, 0x22220200, 0x22220202, + 0x20200111, 0x20200211, 0x20210011, 0x20210110, 0x20210112, 0x20210211, 0x20210212, 0x21200112, + 0x21200211, 0x21210011, 0x21210111, 0x21210210, 0x21210212, 0x21220011, 0x21220110, 0x22200111, + 0x22210010, 0x22210012, 0x22210112, 0x22210211, 0x20200022, 0x20200220, 0x20200222, 0x20210020, + 0x20210221, 0x20220022, 0x20220220, 0x20220222, 0x21200121, 0x21210021, 0x21210122, 0x21210221, + 0x21220121, 0x22200020, 0x22200022, 0x22200220, 0x22200222, 0x22210121, 0x22220020, 0x22220022, + 0x22220220, 0x22220222, 0x20211201, 0x20221101, 0x21201001, 0x21201100, 0x21211000, 0x21211100, + 0x21211101, 0x21211200, 0x21211202, 0x21221001, 0x21221101, 0x21221102, 0x21221200, 0x21221201, + 0x22201101, 0x20201112, 0x20201211, 0x20211010, 0x20211012, 0x20211111, 0x20211210, 0x20221112, + 0x20221211, 0x21201012, 0x21201111, 0x21211011, 0x21211110, 0x21211111, 0x21211112, 0x21211211, + 0x21221111, 0x21221212, 0x22201011, 0x22201110, 0x22201111, 0x22201112, 0x22201211, 0x22211012, + 0x22211111, 0x22211210, 0x20201121, 0x20211021, 0x20211122, 0x20211222, 0x20221021, 0x20221121, + 0x21201120, 0x21201122, 0x21201222, 0x21211022, 0x21211121, 0x21211122, 0x21211220, 0x21221020, + 0x21221022, 0x22201122, 0x22211020, 0x22211121, 0x22211122, 0x22211221, 0x22221021, 0x22221120, + 0x22221122, 0x20202000, 0x20202002, 0x20202200, 0x20202202, 0x20222000, 0x20222002, 0x20222200, + 0x20222202, 0x21212001, 0x21212100, 0x21212102, 0x21212201, 0x22202000, 0x22202002, 0x22202200, + 0x22202202, 0x22212101, 0x22222000, 0x22222002, 0x22222200, 0x22222202, 0x20202111, 0x20212110, + 0x20212211, 0x20222011, 0x20222111, 0x21202011, 0x21212010, 0x21212111, 0x21212212, 0x21222011, + 0x21222112, 0x21222211, 0x22212010, 0x22212112, 0x20202020, 0x20202022, 0x20202220, 0x20202222, + 0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020, + 0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222, GGML_TABLE_END() +#endif #endif // GGML_COMMON_IMPL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c207ff87a281a..d2945d3c2048d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -565,8 +565,8 @@ static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N #define QI1_S (QK_K / (4*QR1_S)) typedef struct { half d; - uint8_t qs[QK_K/8]; - uint8_t scales[QK_K/16]; + uint8_t qs[QK_K/8]; + uint16_t qh[QK_K/32]; } block_iq1_s; static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); @@ -1722,11 +1722,22 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const int i8 = 4*ib+il; - uint8_t h = x[i].scales[i8/2] >> 4*(i8%2); - const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5))); - const float d = (float)x[i].d * (2*(h & 7) + 1); - for (int j = 0; j < 8; ++j) y[j] = d * grid[j]; + const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 0xf) + 1); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = *((const int *)(iq1s_grid_gpu + (x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)))); + grid32[1] = __vsub4((grid32[0] >> 4) & 0x0f0f0f0f, 0x01010101); + grid32[0] = __vsub4(grid32[0] & 0x0f0f0f0f, 0x01010101); + for (int j = 0; j < 8; ++j) { + y[j] = d * q[j]; + } +#else + const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8))); + for (int j = 0; j < 4; ++j) { + y[j+0] = d * ((grid[j] & 0xf) - 1); + y[j+4] = d * ((grid[j] >> 4) - 1); + } +#endif #else assert(false); #endif @@ -4538,44 +4549,33 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( #endif } - static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; - int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; - const uint8_t h1 = bq1->scales[2*ib32+0]; - const uint8_t h2 = bq1->scales[2*ib32+1]; + int sumi = 0; #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics const int * q8 = (const int *)bq8_1[ib32].qs; - const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); - const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); - const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); - const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); - for (int j = 0; j < 2; ++j) { - sumi1 = __dp4a(q8[j+0], grid1[j], sumi1); - sumi2 = __dp4a(q8[j+2], grid2[j], sumi2); - sumi3 = __dp4a(q8[j+4], grid3[j], sumi3); - sumi4 = __dp4a(q8[j+6], grid4[j], sumi4); + for (int l = 0; l < 4; ++l) { + const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); + int grid0 = __vsub4(grid[0] & 0x0f0f0f0f, 0x01010101); + int grid1 = __vsub4((grid[0] >> 4) & 0x0f0f0f0f, 0x01010101); + sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi)); } #else const int8_t * q8 = bq8_1[ib32].qs; - const int8_t * grid1 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); - const int8_t * grid2 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); - const int8_t * grid3 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); - const int8_t * grid4 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j+ 0] * grid1[j]; - sumi2 += q8[j+ 8] * grid2[j]; - sumi3 += q8[j+16] * grid3[j]; - sumi4 += q8[j+24] * grid4[j]; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); + for (int j = 0; j < 4; ++j) { + sumi += q8[j] * ((grid[j] & 0xf) - 1) + q8[j+4] * ((grid[j] >> 4) - 1); + } + q8 += 8; } #endif const float d = (float)bq1->d * __low2float(bq8_1[ib32].ds); - return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) + - sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1)); + return d * sumi * (2*(bq1->qh[ib32] >> 12) + 1); #else assert(false); return 0.f; diff --git a/ggml-metal.metal b/ggml-metal.metal index 50185ae4dea09..912822ee64bc3 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2595,8 +2595,8 @@ typedef struct { typedef struct { half d; - uint8_t qs[QK_K/8]; - uint8_t scales[QK_K/16]; + uint8_t qs[QK_K/8]; + uint16_t qh[QK_K/32]; } block_iq1_s; // Non-linear quants @@ -4338,48 +4338,53 @@ void kernel_mul_mv_iq1_s_f32_impl( device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0; device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - float yl[16]; + float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - const int ix = tiisg/2; - const int il = tiisg%2; + const int ix = tiisg; - device const float * y4 = y + 32 * ix + 16 * il; + device const float * y4 = y + 32 * ix; - for (int ib32 = ix; ib32 < nb32; ib32 += 16) { + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - for (int i = 0; i < 16; ++i) { + float sumy = 0; + for (int i = 0; i < 32; ++i) { yl[i] = y4[i]; + sumy += yl[i]; } const int ibl = ib32 / (QK_K / 32); const int ib = ib32 % (QK_K / 32); device const block_iq1_s * xr = x + ibl; - device const uint8_t * qs = xr->qs + 4 * ib + 2 * il; - device const uint8_t * sc = xr->scales + 2 * ib + il; - device const half * dh = &xr->d; + device const uint8_t * qs = xr->qs + 4 * ib; + device const uint16_t * qh = xr->qh + ib; + device const half * dh = &xr->d; for (int row = 0; row < N_DST; row++) { - constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); - constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 5) & 0x700))); + constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[0] << 2) & 0x700))); + constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[0] >> 1) & 0x700))); - float2 sum = {0}; - for (int j = 0; j < 8; ++j) { - sum[0] += yl[j+ 0] * grid1[j]; - sum[1] += yl[j+ 8] * grid2[j]; + float sum = 0; + for (int j = 0; j < 4; ++j) { + sum += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4) + + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4) + + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4) + + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4); } - sumf[row] += (float)dh[0] * (sum[0] * (2*(sc[0] & 7) + 1) + sum[1] * (2*((sc[0] >> 4) & 7) + 1)); + sumf[row] += (float)dh[0] * (sum - sumy) * (2*(qh[0] >> 12) + 1); dh += nb*sizeof(block_iq1_s)/2; qs += nb*sizeof(block_iq1_s); - sc += nb*sizeof(block_iq1_s); + qh += nb*sizeof(block_iq1_s)/2; } - y4 += 16 * 32; + y4 += 32 * 32; } for (int row = 0; row < N_DST; ++row) { @@ -5066,16 +5071,19 @@ void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & template void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; const float d = xb->d; - device const uint8_t * qs = xb->qs + 2*il; - device const uint8_t * sc = xb->scales + il; - const float dl1 = d * (2*(sc[0] & 7) + 1); - const float dl2 = d * (2*((sc[0] >> 4) & 7) + 1); - constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); - constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); - for (int i = 0; i < 8; ++i) { - reg[i/4+0][i%4] = dl1 * grid1[i]; - reg[i/4+2][i%4] = dl2 * grid2[i]; + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint16_t * qh = xb->qh; + const float dl = d * (2*(qh[ib32] >> 12) + 1); + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | (((qh[ib32] >> (6*il+0)) & 7) << 8))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | (((qh[ib32] >> (6*il+3)) & 7) << 8))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * (grid1[i] & 0xf) - dl; + reg[1][i] = dl * (grid1[i] >> 4) - dl; + reg[2][i] = dl * (grid2[i] & 0xf) - dl; + reg[3][i] = dl * (grid2[i] >> 4) - dl; } } diff --git a/ggml-quants.c b/ggml-quants.c index 42d8a5d805144..f9a3d9fd229e1 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3449,39 +3449,22 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in assert(k % QK_K == 0); const int nb = k / QK_K; - float db[4]; - uint16_t idx[4]; - //const int8_t * grid[4]; - for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); - const uint8_t * sc = x[i].scales; - const uint8_t * qs = x[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; - for (int i8 = 0; i8 < QK_K/8; i8 += 4) { - idx[0] = qs[0] | ((sc[0] & 0x08) << 5); - idx[1] = qs[1] | ((sc[0] & 0x80) << 1); - idx[2] = qs[2] | ((sc[1] & 0x08) << 5); - idx[3] = qs[3] | ((sc[1] & 0x80) << 1); - //grid[0] = (const int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5))); - //grid[1] = (const int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1))); - //grid[2] = (const int8_t *)(iq1s_grid + (qs[2] | ((sc[1] & 0x08) << 5))); - //grid[3] = (const int8_t *)(iq1s_grid + (qs[3] | ((sc[1] & 0x80) << 1))); - db[0] = d * (2*(sc[0] & 7) + 1); - db[1] = d * (2*((sc[0] >> 4) & 7) + 1); - db[2] = d * (2*(sc[1] & 7) + 1); - db[3] = d * (2*((sc[1] >> 4) & 7) + 1); + for (int ib = 0; ib < QK_K/32; ++ib) { + const float dl = d * (2*(qh[ib] >> 12) + 1); for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]); + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); for (int j = 0; j < 8; ++j) { - //y[j] = db[l] * grid[l][j]; - y[j] = db[l] * grid[j]; + y[j] = dl * grid[j]; } y += 8; } qs += 4; - sc += 2; } } } @@ -9587,113 +9570,72 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const int nb = n / QK_K; - // TODO: implement for QK_K = 64 -#if defined __ARM_NEON && QK_K == 256 - - const uint8x16_t m8 = vdupq_n_u8(0x08); - const uint8x16_t m7 = vdupq_n_u8(0x07); - const uint8x16_t m1 = vdupq_n_u8(0x01); - const int32x4_t vzero = vdupq_n_s32(0); +#if defined __ARM_NEON - uint16_t gindex[8]; - uint16x8x2_t vindex; - int8x16x4_t q1b; + ggml_int8x16x4_t q1b; ggml_int8x16x4_t q8b; - uint16x8x4_t scales; - int32x4x2_t sumi; - int32x4x2_t dotq; float sumf = 0; for (int i = 0; i < nb; ++i) { - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * sc = x[i].scales; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; - sumi.val[0] = sumi.val[1] = vzero; + int sumi1 = 0, sumi2 = 0; - for (int i128 = 0; i128 < QK_K/128; ++i128) { - const uint8x16_t ql = vld1q_u8(qs); qs += 16; - const uint8x8_t tm1 = vld1_u8 (sc); sc += 8; - const uint8x8_t tm2 = vshr_n_u8(tm1, 4); - const uint8x16_t qh = vcombine_u8(vzip1_u8(tm1, tm2), vzip2_u8(tm1, tm2)); - const uint8x16_t hbit = vandq_u8(qh, m8); - vindex.val[0] = vorrq_u16(vmovl_u8(vget_low_u8 (ql)), vshlq_n_u16(vmovl_u8(vget_low_u8 (hbit)), 5)); - vindex.val[1] = vorrq_u16(vmovl_u8(vget_high_u8(ql)), vshlq_n_u16(vmovl_u8(vget_high_u8(hbit)), 5)); - const uint8x16_t scales8 = vorrq_u8(vshlq_n_u8(vandq_u8(qh, m7), 1), m1); - scales.val[0] = vmovl_u8(vget_low_u8 (scales8)); - scales.val[1] = vmovl_u8(vget_high_u8 (scales8)); + for (int ib = 0; ib < QK_K/32; ib += 2) { - for (int l = 0; l < 2; ++l) { - vst1q_u16(gindex+0, vindex.val[l]); - q1b.val[0] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[0])), vld1_s8((const void *)(iq1s_grid+gindex[1]))); - q1b.val[1] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[2])), vld1_s8((const void *)(iq1s_grid+gindex[3]))); - q1b.val[2] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[4])), vld1_s8((const void *)(iq1s_grid+gindex[5]))); - q1b.val[3] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[6])), vld1_s8((const void *)(iq1s_grid+gindex[7]))); - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700))))); + q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700))))); + q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700))))); + q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700))))); + qs += 8; + + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - dotq.val[0] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(vzero, q1b.val[1], q8b.val[1])); - dotq.val[1] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(vzero, q1b.val[3], q8b.val[3])); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * (2*(qh[ib+0] >> 12) + 1); + sumi2 += vaddvq_s32(p2) * (2*(qh[ib+1] >> 12) + 1); - sumi.val[0] = vmlaq_s32(sumi.val[0], dotq.val[0], vreinterpretq_s32_u32(vmovl_u16(vget_low_u16 (scales.val[l])))); - sumi.val[1] = vmlaq_s32(sumi.val[1], dotq.val[1], vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales.val[l])))); - } } - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * vaddvq_s32(vaddq_s32(sumi.val[0], sumi.val[1])); + sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2); } *s = sumf; - // TODO: implement for QK_K = 64 -#elif defined __AVX2__ && QK_K == 256 - - const __m128i m8 = _mm_set1_epi8(0x08); - const __m128i m7 = _mm_set1_epi8(0x07); - const __m128i m1 = _mm_set1_epi8(0x01); - const __m128i shuffle_h = _mm_set_epi8(15, 7, 14, 6, 13, 5, 12, 4, 11, 3, 10, 2, 9, 1, 8, 0); - const __m128i shuffle_s[4] = { - _mm_set_epi32(0x03030303, 0x02020202, 0x01010101, 0x00000000), - _mm_set_epi32(0x07070707, 0x06060606, 0x05050505, 0x04040404), - _mm_set_epi32(0x0b0b0b0b, 0x0a0a0a0a, 0x09090909, 0x08080808), - _mm_set_epi32(0x0f0f0f0f, 0x0e0e0e0e, 0x0d0d0d0d, 0x0c0c0c0c) - }; - - uint64_t aux64; - - typedef union m256i_uint16 { - __m256i reg; - uint16_t s[16]; - } m256i_uint16_t; - - m256i_uint16_t v_gindex; +#elif defined __AVX2__ __m256 accum = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * sc = x[i].scales; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; __m256i sumi = _mm256_setzero_si256(); - for (int i128 = 0; i128 < QK_K/128; ++i128) { - const __m128i ql = _mm_loadu_si128((const __m128i*)qs); qs += 16; - memcpy(&aux64, sc, 8); sc += 8; - const __m128i qh = _mm_shuffle_epi8(_mm_set_epi64x(aux64 >> 4, aux64), shuffle_h); - const __m256i hbit = _mm256_cvtepu8_epi16(_mm_and_si128(qh, m8)); - v_gindex.reg = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); - const __m128i scales = _mm_or_si128(_mm_slli_epi16(_mm_and_si128(qh, m7), 1), m1); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], + iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); + const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], + iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); + qs += 8; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - for (int i32 = 0; i32 < 4; ++i32) { - const __m256i q8b = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q1b = _mm256_set_epi64x(iq1s_grid[v_gindex.s[4*i32+3]], iq1s_grid[v_gindex.s[4*i32+2]], - iq1s_grid[v_gindex.s[4*i32+1]], iq1s_grid[v_gindex.s[4*i32+0]]); - const __m256i dot = mul_add_epi8(q1b, q8b); - const __m256i s16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, shuffle_s[i32])); - const __m256i p = _mm256_madd_epi16(s16, dot); - sumi = _mm256_add_epi32(sumi, p); - } + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*(qh[ib+0] >> 12) + 1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*(qh[ib+1] >> 12) + 1)); + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); } accum = _mm256_fmadd_ps(_mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)), _mm256_cvtepi32_ps(sumi), accum); @@ -9704,35 +9646,26 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void #else - int db[4]; - uint16_t idx[4]; - float sumf = 0; - for (int i = 0; i < nb; ++i) { + for (int i = 0; i < nb; i++) { - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * sc = x[i].scales; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; int sumi = 0; - for (int i32 = 0; i32 < QK_K/32; ++i32) { - idx[0] = qs[0] | ((sc[0] & 0x08) << 5); - idx[1] = qs[1] | ((sc[0] & 0x80) << 1); - idx[2] = qs[2] | ((sc[1] & 0x08) << 5); - idx[3] = qs[3] | ((sc[1] & 0x80) << 1); - db[0] = (2*(sc[0] & 7) + 1); - db[1] = (2*((sc[0] >> 4) & 7) + 1); - db[2] = (2*(sc[1] & 7) + 1); - db[3] = (2*((sc[1] >> 4) & 7) + 1); + for (int ib = 0; ib < QK_K/32; ++ib) { + const int ls = 2*(qh[ib] >> 12) + 1; + int lsum = 0; for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]); - int suml = 0; - for (int j = 0; j < 8; ++j) suml += q8[j] * grid[j]; - sumi += db[l] * suml; + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); + for (int j = 0; j < 8; ++j) { + lsum += q8[j] * grid[j]; + } q8 += 8; } + sumi += ls * lsum; qs += 4; - sc += 2; } sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * sumi; @@ -9996,7 +9929,7 @@ static inline int iq2_grid_size(enum ggml_type type) { GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); return type == GGML_TYPE_IQ2_XXS ? 256 : type == GGML_TYPE_IQ2_XS ? 512 : - type == GGML_TYPE_IQ1_S ? 512 : 1024; + type == GGML_TYPE_IQ1_S ? NGRID_IQ1S : 1024; } static int iq2_compare_func(const void * left, const void * right) { @@ -10063,39 +9996,135 @@ void iq2xs_init_impl(enum ggml_type type) { 40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048, 42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690, }; - static const uint16_t kgrid_1bit_512[512] = { - 10, 33, 41, 85, 132, 134, 160, 162, 277, 337, 340, 345, 357, 405, 516, 545, - 553, 598, 641, 650, 681, 1042, 1044, 1097, 1169, 1176, 1320, 1345, 1365, 1378, 1434, 1444, - 1545, 1617, 1642, 1685, 2053, 2080, 2089, 2133, 2176, 2182, 2208, 2214, 2306, 2384, 2393, 2440, - 2453, 2581, 2664, 2690, 2721, 4117, 4161, 4182, 4184, 4261, 4357, 4369, 4372, 4377, 4390, 4422, - 4432, 4437, 4449, 4457, 4485, 4497, 4505, 4629, 4677, 4696, 4774, 5205, 5217, 5225, 5386, 5397, - 5409, 5445, 5457, 5460, 5461, 5462, 5465, 5472, 5477, 5525, 5545, 5650, 5668, 5717, 5729, 5769, - 5777, 6212, 6234, 6244, 6293, 6424, 6482, 6485, 6502, 6505, 6529, 6538, 6565, 6656, 6682, 6788, - 6806, 6820, 8218, 8224, 8226, 8232, 8277, 8326, 8354, 8469, 8521, 8530, 8549, 8596, 8737, 8794, - 9221, 9253, 9348, 9369, 9380, 9474, 9557, 9633, 9732, 9753, 9793, 9830, 9862, 9880, 10240, 10272, - 10282, 10321, 10406, 10517, 10530, 10566, 10585, 10645, 10896, 16466, 16468, 16473, 16485, 16646, 16660, 16665, - 16725, 16793, 16806, 16914, 16969, 16977, 16996, 17028, 17057, 17408, 17416, 17434, 17493, 17512, 17578, 17685, - 17696, 17733, 17745, 17748, 17749, 17750, 17753, 17765, 17794, 17813, 17946, 17984, 18005, 18072, 18453, 18529, - 18569, 18722, 18756, 18762, 18773, 18794, 18833, 18853, 18945, 19026, 19033, 19077, 20489, 20497, 20500, 20517, - 20565, 20586, 20610, 20633, 20757, 20769, 20776, 20805, 20817, 20820, 20821, 20822, 20825, 20837, 20864, 20872, - 20885, 20896, 21002, 21029, 21077, 21146, 21510, 21525, 21573, 21585, 21588, 21589, 21590, 21593, 21605, 21653, - 21665, 21765, 21777, 21780, 21781, 21782, 21785, 21797, 21825, 21828, 21829, 21830, 21833, 21840, 21841, 21842, - 21844, 21846, 21848, 21849, 21850, 21857, 21860, 21861, 21862, 21865, 21893, 21905, 21908, 21909, 21910, 21913, - 21925, 22024, 22037, 22085, 22097, 22100, 22101, 22102, 22105, 22117, 22165, 22545, 22566, 22568, 22594, 22608, - 22613, 22676, 22697, 22793, 22805, 22853, 22865, 22868, 22869, 22870, 22873, 22885, 22933, 22946, 23046, 23072, - 23125, 23209, 24597, 24640, 24665, 24673, 24725, 24833, 24840, 24869, 24917, 24934, 24965, 25001, 25108, 25110, - 25152, 25184, 25192, 25234, 25616, 25618, 25625, 25685, 25704, 25738, 25744, 25770, 25877, 25897, 25925, 25937, - 25940, 25941, 25942, 25945, 25957, 25986, 26005, 26186, 26197, 26276, 26632, 26634, 26725, 26757, 26770, 26885, - 26965, 26976, 26986, 27032, 27153, 27174, 27200, 27208, 27240, 27269, 27282, 27290, 32778, 32800, 32802, 32808, - 32810, 32853, 32904, 32922, 32930, 32932, 33105, 33110, 33112, 33125, 33157, 33280, 33288, 33301, 33312, 33320, - 33424, 33797, 33829, 33858, 34068, 34133, 34146, 34176, 34217, 34306, 34342, 34441, 34454, 34468, 34832, 34918, - 34965, 34984, 35094, 35137, 35161, 35208, 35232, 35332, 35338, 35368, 35429, 36932, 36934, 36953, 37009, 37125, - 37136, 37138, 37145, 37157, 37205, 37220, 37258, 37290, 37444, 37446, 37465, 37478, 37525, 37905, 37968, 37973, - 38040, 38054, 38145, 38154, 38165, 38180, 38186, 38213, 38225, 38228, 38229, 38230, 38233, 38245, 38293, 38485, - 38504, 38530, 38938, 38985, 38993, 39012, 39040, 39173, 39192, 39253, 39265, 39301, 39316, 39322, 39442, 39497, - 39504, 39590, 40970, 40984, 40992, 41002, 41045, 41120, 41128, 41237, 41289, 41297, 41317, 41364, 41366, 41514, - 41557, 41633, 41989, 42021, 42056, 42068, 42074, 42113, 42242, 42265, 42274, 42325, 42340, 42402, 42501, 42512, - 42533, 42624, 42632, 42666, 43040, 43093, 43106, 43168, 43176, 43264, 43286, 43345, 43429, 43590, 43618, 43680, + static const uint16_t kgrid_1bit_2048[NGRID_IQ1S] = { + 0, 2, 5, 8, 10, 17, 21, 32, 34, 40, 42, 69, 81, 84, 86, 101, + 128, 130, 136, 138, 149, 160, 162, 168, 170, 260, 261, 273, 276, 278, 281, 282, + 293, 321, 326, 329, 338, 341, 346, 353, 356, 358, 360, 389, 401, 404, 406, 421, + 512, 514, 520, 522, 533, 544, 546, 552, 554, 581, 593, 601, 612, 617, 640, 642, + 648, 650, 657, 661, 665, 672, 674, 680, 682, 1041, 1044, 1046, 1061, 1089, 1097, 1109, + 1114, 1124, 1125, 1169, 1177, 1189, 1281, 1284, 1285, 1286, 1301, 1304, 1306, 1321, 1344, 1349, + 1354, 1360, 1361, 1364, 1365, 1366, 1369, 1376, 1378, 1381, 1384, 1386, 1409, 1425, 1429, 1432, + 1434, 1441, 1444, 1445, 1446, 1449, 1556, 1561, 1601, 1604, 1616, 1618, 1621, 1624, 1632, 1633, + 1638, 1641, 1669, 1681, 1684, 1689, 2048, 2050, 2056, 2058, 2069, 2080, 2082, 2088, 2090, 2117, + 2129, 2134, 2149, 2176, 2178, 2184, 2186, 2197, 2208, 2210, 2216, 2218, 2309, 2321, 2324, 2329, + 2340, 2341, 2369, 2384, 2385, 2389, 2401, 2404, 2409, 2449, 2452, 2454, 2457, 2469, 2560, 2562, + 2568, 2570, 2581, 2592, 2594, 2600, 2602, 2629, 2641, 2649, 2657, 2661, 2688, 2690, 2693, 2696, + 2698, 2709, 2720, 2722, 2728, 2730, 4112, 4113, 4116, 4121, 4132, 4133, 4161, 4164, 4176, 4181, + 4184, 4193, 4196, 4197, 4201, 4241, 4244, 4246, 4257, 4261, 4353, 4356, 4358, 4361, 4368, 4370, + 4373, 4376, 4385, 4388, 4393, 4421, 4426, 4432, 4433, 4434, 4436, 4437, 4438, 4441, 4448, 4453, + 4484, 4498, 4501, 4513, 4516, 4625, 4628, 4630, 4645, 4672, 4678, 4681, 4690, 4693, 4696, 4698, + 4708, 4710, 4741, 4753, 4756, 4758, 4773, 5121, 5126, 5129, 5140, 5141, 5144, 5145, 5153, 5158, + 5185, 5189, 5190, 5192, 5194, 5201, 5204, 5205, 5206, 5209, 5218, 5221, 5224, 5252, 5257, 5264, + 5268, 5269, 5272, 5273, 5274, 5281, 5284, 5285, 5289, 5378, 5381, 5386, 5393, 5396, 5397, 5398, + 5401, 5408, 5410, 5413, 5416, 5418, 5441, 5444, 5445, 5446, 5457, 5458, 5460, 5461, 5462, 5465, + 5466, 5473, 5476, 5477, 5478, 5481, 5504, 5506, 5508, 5509, 5512, 5514, 5520, 5521, 5524, 5525, + 5526, 5529, 5530, 5536, 5538, 5541, 5633, 5636, 5637, 5638, 5653, 5654, 5656, 5658, 5665, 5670, + 5696, 5698, 5700, 5701, 5704, 5706, 5713, 5717, 5718, 5720, 5721, 5729, 5732, 5733, 5736, 5737, + 5738, 5766, 5770, 5778, 5781, 5796, 5801, 6161, 6166, 6181, 6209, 6212, 6214, 6217, 6224, 6229, + 6232, 6234, 6240, 6241, 6244, 6246, 6249, 6277, 6289, 6292, 6309, 6416, 6418, 6421, 6426, 6433, + 6437, 6466, 6468, 6469, 6472, 6481, 6484, 6485, 6486, 6489, 6490, 6496, 6501, 6506, 6537, 6545, + 6546, 6549, 6552, 6561, 6566, 6569, 6665, 6678, 6692, 6694, 6724, 6726, 6729, 6736, 6738, 6741, + 6744, 6753, 6758, 6761, 6789, 6801, 6806, 6810, 8192, 8194, 8200, 8202, 8213, 8224, 8226, 8229, + 8232, 8234, 8261, 8273, 8281, 8289, 8293, 8320, 8322, 8328, 8330, 8341, 8352, 8354, 8357, 8360, + 8362, 8453, 8465, 8468, 8473, 8485, 8514, 8516, 8521, 8533, 8536, 8538, 8545, 8548, 8549, 8550, + 8581, 8592, 8598, 8601, 8613, 8705, 8712, 8714, 8721, 8725, 8736, 8738, 8744, 8746, 8773, 8785, + 8790, 8793, 8805, 8833, 8840, 8842, 8849, 8853, 8864, 8866, 8872, 8874, 9221, 9236, 9238, 9241, + 9253, 9284, 9285, 9286, 9289, 9298, 9301, 9304, 9306, 9318, 9349, 9361, 9364, 9369, 9377, 9381, + 9481, 9493, 9505, 9513, 9536, 9541, 9544, 9553, 9556, 9557, 9561, 9570, 9573, 9576, 9609, 9616, + 9620, 9621, 9624, 9626, 9633, 9636, 9638, 9641, 9733, 9744, 9746, 9753, 9765, 9793, 9801, 9813, + 9824, 9825, 9833, 9860, 9862, 9872, 9882, 10240, 10242, 10248, 10250, 10261, 10272, 10274, 10280, 10282, + 10309, 10321, 10324, 10341, 10368, 10370, 10376, 10378, 10400, 10402, 10408, 10410, 10505, 10513, 10516, 10521, + 10533, 10566, 10569, 10578, 10581, 10593, 10596, 10598, 10601, 10629, 10640, 10646, 10649, 10660, 10661, 10752, + 10754, 10760, 10762, 10784, 10786, 10792, 10794, 10821, 10833, 10838, 10841, 10853, 10880, 10882, 10888, 10890, + 10901, 10912, 10914, 10920, 10922, 16389, 16401, 16406, 16421, 16457, 16466, 16469, 16472, 16474, 16481, 16484, + 16486, 16532, 16537, 16545, 16550, 16640, 16641, 16644, 16646, 16649, 16658, 16661, 16662, 16664, 16666, 16673, + 16678, 16681, 16709, 16712, 16714, 16721, 16724, 16725, 16726, 16729, 16730, 16741, 16744, 16746, 16769, 16772, + 16774, 16784, 16786, 16789, 16800, 16801, 16802, 16901, 16913, 16916, 16918, 16933, 16961, 16978, 16981, 16986, + 16996, 17001, 17033, 17044, 17061, 17409, 17429, 17433, 17449, 17477, 17480, 17482, 17489, 17492, 17493, 17494, + 17505, 17506, 17509, 17512, 17514, 17537, 17542, 17545, 17552, 17554, 17557, 17568, 17569, 17577, 17665, 17666, + 17669, 17674, 17681, 17684, 17685, 17686, 17689, 17696, 17701, 17706, 17729, 17732, 17733, 17734, 17737, 17744, + 17745, 17748, 17749, 17750, 17752, 17753, 17761, 17764, 17765, 17766, 17769, 17794, 17796, 17797, 17800, 17809, + 17812, 17813, 17814, 17817, 17818, 17829, 17832, 17834, 17921, 17925, 17929, 17940, 17941, 17944, 17946, 17953, + 17956, 17961, 17984, 17986, 17989, 17992, 18000, 18001, 18002, 18005, 18006, 18009, 18018, 18021, 18024, 18049, + 18053, 18058, 18068, 18069, 18081, 18084, 18086, 18437, 18449, 18453, 18458, 18469, 18498, 18505, 18512, 18517, + 18520, 18529, 18532, 18534, 18537, 18565, 18577, 18580, 18582, 18585, 18597, 18689, 18693, 18694, 18698, 18704, + 18708, 18709, 18712, 18721, 18724, 18726, 18752, 18757, 18762, 18769, 18770, 18772, 18773, 18774, 18777, 18784, + 18786, 18789, 18790, 18794, 18822, 18825, 18834, 18837, 18838, 18840, 18849, 18852, 18854, 18857, 18966, 19012, + 19014, 19017, 19029, 19032, 19034, 19044, 19049, 19092, 19109, 20481, 20484, 20485, 20486, 20489, 20498, 20501, + 20506, 20513, 20516, 20521, 20544, 20549, 20552, 20561, 20564, 20565, 20566, 20569, 20581, 20584, 20614, 20617, + 20629, 20632, 20640, 20641, 20646, 20649, 20741, 20744, 20745, 20746, 20753, 20756, 20757, 20758, 20760, 20761, + 20768, 20773, 20774, 20776, 20778, 20801, 20804, 20805, 20806, 20809, 20816, 20817, 20818, 20820, 20821, 20822, + 20824, 20825, 20826, 20833, 20836, 20837, 20838, 20841, 20866, 20869, 20881, 20884, 20885, 20886, 20889, 20896, + 20901, 20906, 20993, 20998, 21010, 21013, 21018, 21025, 21028, 21058, 21061, 21066, 21073, 21076, 21077, 21078, + 21081, 21090, 21093, 21125, 21136, 21138, 21141, 21145, 21146, 21156, 21508, 21509, 21521, 21524, 21525, 21526, + 21528, 21529, 21537, 21541, 21544, 21546, 21569, 21572, 21573, 21574, 21577, 21578, 21584, 21585, 21588, 21589, + 21590, 21592, 21593, 21594, 21601, 21602, 21604, 21605, 21606, 21609, 21632, 21640, 21642, 21649, 21652, 21653, + 21654, 21657, 21665, 21668, 21669, 21674, 21761, 21762, 21764, 21765, 21766, 21769, 21776, 21777, 21778, 21780, + 21781, 21782, 21785, 21786, 21793, 21796, 21797, 21798, 21801, 21824, 21825, 21826, 21828, 21829, 21830, 21832, + 21833, 21840, 21841, 21842, 21844, 21845, 21846, 21848, 21849, 21850, 21856, 21857, 21860, 21861, 21862, 21864, + 21865, 21866, 21889, 21892, 21893, 21897, 21898, 21904, 21905, 21908, 21909, 21910, 21912, 21913, 21921, 21924, + 21925, 21926, 21929, 22016, 22017, 22018, 22020, 22022, 22024, 22025, 22033, 22036, 22037, 22040, 22041, 22048, + 22049, 22050, 22052, 22053, 22054, 22056, 22057, 22081, 22085, 22086, 22088, 22089, 22090, 22096, 22097, 22098, + 22100, 22101, 22102, 22104, 22105, 22106, 22113, 22116, 22117, 22121, 22146, 22149, 22150, 22152, 22153, 22154, + 22161, 22165, 22170, 22178, 22181, 22182, 22184, 22185, 22532, 22533, 22534, 22537, 22544, 22549, 22552, 22561, + 22570, 22597, 22600, 22602, 22609, 22612, 22613, 22614, 22616, 22617, 22624, 22626, 22628, 22629, 22658, 22665, + 22672, 22674, 22677, 22680, 22689, 22697, 22785, 22786, 22789, 22794, 22801, 22804, 22805, 22806, 22809, 22821, + 22849, 22852, 22853, 22854, 22857, 22864, 22865, 22866, 22868, 22869, 22870, 22872, 22873, 22874, 22881, 22884, + 22885, 22886, 22889, 22913, 22917, 22921, 22929, 22932, 22933, 22934, 22936, 22937, 22949, 23044, 23048, 23061, + 23066, 23072, 23077, 23078, 23081, 23109, 23112, 23113, 23121, 23125, 23126, 23128, 23129, 23138, 23141, 23144, + 23146, 23169, 23178, 23186, 23189, 23190, 23192, 23194, 23201, 24581, 24596, 24598, 24601, 24613, 24644, 24656, + 24661, 24662, 24664, 24666, 24673, 24676, 24678, 24681, 24705, 24726, 24741, 24833, 24836, 24838, 24841, 24850, + 24853, 24865, 24866, 24870, 24873, 24901, 24905, 24913, 24917, 24918, 24921, 24933, 24934, 24938, 24964, 24970, + 24978, 24981, 24993, 24998, 25001, 25105, 25110, 25113, 25152, 25153, 25158, 25173, 25174, 25176, 25184, 25221, + 25233, 25238, 25253, 25617, 25618, 25621, 25622, 25626, 25633, 25638, 25641, 25664, 25666, 25669, 25672, 25674, + 25681, 25684, 25685, 25686, 25689, 25690, 25696, 25698, 25701, 25732, 25733, 25737, 25744, 25746, 25748, 25749, + 25750, 25752, 25754, 25761, 25764, 25769, 25861, 25864, 25866, 25873, 25877, 25878, 25881, 25924, 25925, 25926, + 25929, 25936, 25937, 25940, 25941, 25942, 25945, 25953, 25956, 25957, 25958, 25961, 25990, 25993, 25994, 26001, + 26005, 26006, 26009, 26010, 26018, 26021, 26022, 26024, 26114, 26121, 26133, 26144, 26150, 26152, 26153, 26176, + 26181, 26184, 26186, 26193, 26196, 26197, 26198, 26200, 26202, 26208, 26213, 26216, 26240, 26242, 26245, 26250, + 26260, 26262, 26264, 26265, 26272, 26276, 26278, 26282, 26646, 26649, 26661, 26689, 26706, 26709, 26714, 26721, + 26729, 26757, 26769, 26776, 26790, 26881, 26884, 26896, 26901, 26913, 26916, 26918, 26921, 26944, 26945, 26949, + 26950, 26952, 26961, 26964, 26965, 26966, 26969, 26976, 26981, 26986, 27010, 27012, 27018, 27029, 27041, 27044, + 27045, 27049, 27153, 27158, 27160, 27201, 27204, 27209, 27216, 27221, 27224, 27226, 27236, 27237, 27241, 27270, + 27284, 27288, 27290, 27302, 32768, 32770, 32776, 32778, 32800, 32802, 32808, 32810, 32837, 32848, 32849, 32852, + 32854, 32857, 32869, 32896, 32898, 32904, 32906, 32917, 32928, 32930, 32936, 32938, 33029, 33041, 33044, 33046, + 33049, 33061, 33089, 33092, 33097, 33104, 33106, 33109, 33110, 33112, 33113, 33124, 33126, 33129, 33157, 33161, + 33172, 33174, 33177, 33189, 33280, 33282, 33288, 33290, 33301, 33312, 33314, 33320, 33322, 33361, 33364, 33369, + 33381, 33408, 33410, 33416, 33418, 33429, 33440, 33442, 33448, 33450, 33812, 33817, 33857, 33860, 33873, 33877, + 33882, 33889, 33892, 33897, 33940, 33945, 34049, 34057, 34066, 34069, 34074, 34086, 34089, 34112, 34113, 34117, + 34120, 34129, 34132, 34133, 34134, 34137, 34138, 34149, 34150, 34152, 34154, 34177, 34180, 34182, 34185, 34192, + 34194, 34197, 34200, 34214, 34321, 34326, 34329, 34341, 34369, 34372, 34377, 34378, 34384, 34389, 34393, 34394, + 34401, 34406, 34410, 34437, 34449, 34458, 34468, 34816, 34818, 34824, 34826, 34837, 34848, 34850, 34856, 34858, + 34881, 34885, 34897, 34900, 34905, 34917, 34921, 34944, 34946, 34952, 34954, 34965, 34976, 34978, 34984, 34986, + 35077, 35078, 35089, 35092, 35094, 35109, 35137, 35140, 35142, 35145, 35152, 35154, 35157, 35162, 35169, 35172, + 35205, 35222, 35225, 35237, 35328, 35330, 35336, 35338, 35349, 35360, 35362, 35368, 35370, 35397, 35409, 35412, + 35414, 35456, 35458, 35464, 35466, 35477, 35488, 35490, 35496, 35498, 36869, 36881, 36886, 36888, 36889, 36901, + 36929, 36934, 36937, 36949, 36952, 36954, 36969, 36970, 36997, 37009, 37012, 37014, 37017, 37029, 37121, 37124, + 37126, 37129, 37136, 37141, 37144, 37146, 37153, 37156, 37158, 37161, 37184, 37189, 37200, 37201, 37204, 37205, + 37206, 37209, 37218, 37221, 37252, 37254, 37266, 37269, 37272, 37281, 37284, 37286, 37289, 37381, 37393, 37396, + 37401, 37413, 37444, 37446, 37449, 37456, 37458, 37461, 37464, 37478, 37481, 37509, 37524, 37526, 37545, 37889, + 37892, 37894, 37904, 37909, 37912, 37926, 37952, 37962, 37969, 37972, 37973, 37974, 37976, 37977, 37984, 37985, + 37986, 37989, 38020, 38022, 38034, 38036, 38037, 38040, 38049, 38057, 38144, 38149, 38152, 38154, 38160, 38161, + 38164, 38165, 38166, 38169, 38177, 38181, 38185, 38186, 38209, 38212, 38213, 38214, 38217, 38224, 38225, 38226, + 38228, 38229, 38230, 38232, 38233, 38234, 38241, 38244, 38245, 38246, 38249, 38273, 38277, 38280, 38289, 38290, + 38292, 38293, 38294, 38297, 38298, 38304, 38306, 38309, 38312, 38314, 38401, 38404, 38416, 38421, 38425, 38432, + 38438, 38441, 38469, 38472, 38473, 38481, 38482, 38485, 38486, 38489, 38501, 38504, 38530, 38532, 38537, 38538, + 38546, 38548, 38549, 38564, 38566, 38569, 38917, 38934, 38937, 38949, 38977, 38982, 38992, 38994, 38997, 38998, + 39002, 39012, 39013, 39045, 39057, 39062, 39065, 39077, 39172, 39174, 39177, 39184, 39186, 39189, 39192, 39194, + 39200, 39201, 39204, 39206, 39232, 39234, 39237, 39240, 39242, 39249, 39252, 39253, 39254, 39257, 39266, 39269, + 39270, 39274, 39297, 39300, 39312, 39314, 39317, 39322, 39329, 39334, 39429, 39445, 39461, 39492, 39494, 39497, + 39504, 39509, 39512, 39521, 39557, 39569, 39572, 39573, 39574, 40960, 40962, 40968, 40970, 40981, 40992, 40994, + 41000, 41002, 41029, 41041, 41044, 41046, 41049, 41088, 41090, 41096, 41098, 41109, 41120, 41122, 41128, 41130, + 41221, 41225, 41233, 41236, 41238, 41241, 41242, 41286, 41289, 41297, 41301, 41304, 41306, 41313, 41316, 41349, + 41360, 41362, 41366, 41369, 41474, 41480, 41482, 41488, 41497, 41506, 41512, 41514, 41541, 41553, 41558, 41561, + 41573, 41600, 41602, 41608, 41610, 41621, 41632, 41634, 41640, 41642, 42009, 42021, 42049, 42052, 42064, 42068, + 42069, 42072, 42074, 42081, 42085, 42086, 42088, 42089, 42117, 42246, 42249, 42256, 42258, 42261, 42264, 42278, + 42281, 42306, 42309, 42321, 42324, 42325, 42326, 42329, 42341, 42346, 42369, 42372, 42373, 42374, 42377, 42386, + 42389, 42392, 42501, 42513, 42518, 42522, 42529, 42533, 42564, 42566, 42570, 42578, 42581, 42582, 42584, 42592, + 42594, 42630, 42640, 42645, 42646, 42649, 42657, 42660, 42662, 43008, 43010, 43016, 43018, 43040, 43042, 43048, + 43050, 43089, 43092, 43094, 43097, 43136, 43138, 43144, 43146, 43157, 43168, 43170, 43176, 43178, 43269, 43284, + 43289, 43297, 43301, 43329, 43344, 43349, 43354, 43361, 43366, 43369, 43408, 43414, 43520, 43522, 43528, 43530, + 43552, 43554, 43560, 43562, 43601, 43604, 43606, 43648, 43650, 43656, 43658, 43669, 43680, 43682, 43688, 43690, }; static const uint16_t kgrid_2bit_1024[1024] = { 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, @@ -10169,7 +10198,7 @@ void iq2xs_init_impl(enum ggml_type type) { const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2; const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 : type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : - type == GGML_TYPE_IQ1_S ? kgrid_1bit_512 : kgrid_2bit_1024; + type == GGML_TYPE_IQ1_S ? kgrid_1bit_2048 : kgrid_2bit_1024; uint64_t * kgrid_q2xs; int * kmap_q2xs; uint16_t * kneighbors_q2xs; @@ -11408,12 +11437,70 @@ static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const u return grid_index; } +static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const uint64_t * restrict grid, + const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L, int ngrid) { + int num_neighbors = neighbours[0]; + GGML_ASSERT(num_neighbors > 0); + float best_score = FLT_MAX; + int grid_index = -1; + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float d2 = 0; + for (int i = 0; i < 8; ++i) { + float q = (pg[i] - 3)/2; + float w = weight[i]; + float diff = scale*q - xval[i]; + d2 += w*diff*diff; + } + if (d2 < best_score) { + best_score = d2; + grid_index = neighbours[j]; + } + } + if (grid_index < 0) { + for (int i = 0; i < ngrid; ++i) { + const int8_t * grid_i = (const int8_t *)(grid + i); + float d2 = 0; + for (int j = 0; j < 8; ++j) { + float w = weight[j]; + float q = (grid_i[j] - 3)/2; + float diff = scale*q - xval[i]; + d2 += w*diff*diff; + } + if (d2 < best_score) { + best_score = d2; + grid_index = i; + } + } + } + if (grid_index < 0) { + printf("Oops, did not find grid point\n"); + printf("Have %d neighbours\n", num_neighbors); + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 8; ++i) { + float q = (pg[i] - 3)/2; + float w = weight[i]; + sumqx += w*q*xval[i]; + sumq2 += w*q*q; + } + printf(" neighbour %d: sumqx = %g sumq2 = %g\n", j, (double)sumqx, (double)sumq2); + } + } + GGML_ASSERT(grid_index >= 0); + const int8_t * pg = (const int8_t *)(grid + grid_index); + for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2; + return grid_index; +} + static int iq1_sort_helper(const void * left, const void * right) { const float * l = left; const float * r = right; return *l < *r ? -1 : *l > *r ? 1 : 0; } +#define IQ1S_BLOCK_SIZE 32 static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { const int gindex = iq2_data_index(GGML_TYPE_IQ1_S); @@ -11432,37 +11519,37 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy block_iq1_s * y = vy; - float scales[QK_K/8]; - float weight[8]; - int8_t L[8]; - float sumx[9]; - float sumw[9]; - float pairs[16]; + float scales[QK_K/IQ1S_BLOCK_SIZE]; + float weight[IQ1S_BLOCK_SIZE]; + int8_t L[IQ1S_BLOCK_SIZE]; + float sumx[IQ1S_BLOCK_SIZE+1]; + float sumw[IQ1S_BLOCK_SIZE+1]; + float pairs[2*IQ1S_BLOCK_SIZE]; int * idx = (int *)(pairs + 1); - uint8_t hbit[QK_K/8]; + uint16_t index[IQ1S_BLOCK_SIZE/8]; for (int ibl = 0; ibl < nbl; ++ibl) { y[ibl].d = GGML_FP32_TO_FP16(0.f); memset(y[ibl].qs, 0, QK_K/8); - memset(y[ibl].scales, 0, QK_K/16); + memset(y[ibl].qh, 0, QK_K/16); float max_scale = 0; const float * xbl = x + QK_K*ibl; float sumx2 = 0; for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; - float sigma2 = sumx2/QK_K; + float sigma2 = 2*sumx2/QK_K; - for (int ib = 0; ib < QK_K/8; ++ib) { - const float * xb = xbl + 8*ib; - const float * qw = quant_weights + QK_K*ibl + 8*ib; - for (int i = 0; i < 8; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) { + const float * xb = xbl + IQ1S_BLOCK_SIZE*ib; + const float * qw = quant_weights + QK_K*ibl + IQ1S_BLOCK_SIZE*ib; + for (int i = 0; i < IQ1S_BLOCK_SIZE; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); float max = fabsf(xb[0]); - for (int i = 1; i < 8; ++i) max = MAX(max, fabsf(xb[i])); + for (int i = 1; i < IQ1S_BLOCK_SIZE; ++i) max = MAX(max, fabsf(xb[i])); if (!max) { scales[ib] = 0; - memset(L, 1, 8); + memset(L, 1, IQ1S_BLOCK_SIZE); continue; } // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem. @@ -11471,14 +11558,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale // for each possible and score for each split. - for (int j = 0; j < 8; ++j) { + for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) { pairs[2*j] = xb[j]; idx[2*j] = j; } - qsort(pairs, 8, 2*sizeof(float), iq1_sort_helper); + qsort(pairs, IQ1S_BLOCK_SIZE, 2*sizeof(float), iq1_sort_helper); { sumx[0] = sumw[0] = 0; - for (int j = 0; j < 8; ++j) { + for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) { int i = idx[2*j]; sumx[j+1] = sumx[j] + weight[i]*xb[i]; sumw[j+1] = sumw[j] + weight[i]; @@ -11486,10 +11573,10 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } float best_score = 0, scale = max; int besti1 = 0, besti2 = 0; - for (int i1 = 0; i1 <= 8; ++i1) { - for (int i2 = i1; i2 <= 8; ++i2) { - float sumqx = -(sumx[i1] - sumx[0]) + (sumx[8] - sumx[i2]); - float sumq2 = (sumw[i1] - sumw[0]) + (sumw[8] - sumw[i2]); + for (int i1 = 0; i1 <= IQ1S_BLOCK_SIZE; ++i1) { + for (int i2 = i1; i2 <= IQ1S_BLOCK_SIZE; ++i2) { + float sumqx = -(sumx[i1] - sumx[0]) + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2]); + float sumq2 = (sumw[i1] - sumw[0]) + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2]); if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { scale = sumqx/sumq2; best_score = scale*sumqx; besti1 = i1; besti2 = i2; @@ -11498,23 +11585,43 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; - for (int j = besti2; j < 8; ++j) L[idx[2*j]] = 2; + for (int j = besti2; j < IQ1S_BLOCK_SIZE; ++j) L[idx[2*j]] = 2; if (scale < 0) { - for (int j = 0; j < 8; ++j) L[j] = 2 - L[j]; + for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) L[j] = 2 - L[j]; scale = -scale; } - // Now we check if the solution found above corresponds to a grid point and, if not, use a neighbouring - // grid point that minimizes SSD. - uint16_t u = 0; - for (int j = 0; j < 8; ++j) u |= (L[j] << 2*j); - int grid_index = kmap_q2xs[u]; - if (grid_index < 0) { - const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; - grid_index = iq1_find_best_neighbour(neighbours, kgrid_q2xs, xb, weight, &scale, L, NGRID_IQ2XXS); - GGML_ASSERT(grid_index >= 0); - } - y[ibl].qs[ib] = grid_index & 255; - hbit[ib] = grid_index >> 8; + bool all_on_grid = true; + for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { + uint16_t u = 0; + for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + all_on_grid = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, L + 8*k, NGRID_IQ1S); + GGML_ASSERT(grid_index >= 0); + } + index[k] = grid_index; + } + if (!all_on_grid) { + float sumqx = 0, sumq2 = 0; + for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { + const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]); + for (int j = 0; j < 8; ++j) { + float w = weight[8*k + j]; + float q = (pg[j] - 3)/2; + sumqx += w*q*xb[8*k+j]; + sumq2 += w*q*q; + } + } + if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2; + } + uint16_t h = 0; + for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { + y[ibl].qs[(IQ1S_BLOCK_SIZE/8)*ib + k] = index[k] & 255; + h |= (index[k] >> 8) << 3*k; + } + y[ibl].qh[ib] = h; GGML_ASSERT(scale >= 0); scales[ib] = scale; max_scale = MAX(max_scale, scale); @@ -11525,14 +11632,13 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy continue; } - float d = max_scale/15; - y[ibl].d = GGML_FP32_TO_FP16(d*1.085f); // 1.085f is another fudge factor. Don't ask me why it is needed. + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.085f is another fudge factor. Don't ask me why it is needed. float id = 1/d; - for (int ib = 0; ib < QK_K/8; ++ib) { + for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); - l = MAX(0, MIN(7, l)); - if (hbit[ib]) l |= 8; - y[ibl].scales[ib/2] |= (l << 4*(ib%2)); + l = MAX(0, MIN(15, l)); + y[ibl].qh[ib] |= (l << 12); } } } diff --git a/ggml-quants.h b/ggml-quants.h index 47dd52856422a..74aabf4156385 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -217,8 +217,8 @@ static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N typedef struct { ggml_fp16_t d; - uint8_t qs[QK_K/8]; - uint8_t scales[QK_K/16]; + uint8_t qs[QK_K/8]; + uint16_t qh[QK_K/32]; } block_iq1_s; static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); From ee35600b9061b1ea0c4ea87fce6844297632b2a8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 11 Mar 2024 09:56:47 +0200 Subject: [PATCH 230/401] llama : fix F16/F32 downcast + improve names (#5980) --- llama.cpp | 67 +++++++++++++++++++++++++++++-------------------------- llama.h | 2 +- 2 files changed, 36 insertions(+), 33 deletions(-) diff --git a/llama.cpp b/llama.cpp index 249442166d24e..110e509ccd5b6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11636,7 +11636,7 @@ static void llama_tensor_dequantize_internal( workers.clear(); } -static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) { +static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) { const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants @@ -11951,40 +11951,40 @@ static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const flo } static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) { - ggml_type quantized_type; + ggml_type default_type; llama_ftype ftype = params->ftype; switch (params->ftype) { - case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break; - case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break; - case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break; - case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break; - case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break; - case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break; - case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break; + case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break; + case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break; + case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break; + case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break; + case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break; + case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break; + case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break; // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K_S: - case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; - case LLAMA_FTYPE_MOSTLY_IQ3_XS: quantized_type = GGML_TYPE_IQ3_S; break; + case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break; case LLAMA_FTYPE_MOSTLY_Q3_K_S: case LLAMA_FTYPE_MOSTLY_Q3_K_M: - case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; + case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break; case LLAMA_FTYPE_MOSTLY_Q4_K_S: - case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break; + case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break; case LLAMA_FTYPE_MOSTLY_Q5_K_S: - case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; - case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XS: quantized_type = GGML_TYPE_IQ2_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_S: quantized_type = GGML_TYPE_IQ2_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_M: quantized_type = GGML_TYPE_IQ2_S; break; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break; - case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break; - case LLAMA_FTYPE_MOSTLY_IQ4_XS: quantized_type = GGML_TYPE_IQ4_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ3_S: quantized_type = GGML_TYPE_IQ3_S; break; - case LLAMA_FTYPE_MOSTLY_IQ3_M: quantized_type = GGML_TYPE_IQ3_S; break; + case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break; + case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; + case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break; + case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break; + case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } @@ -12125,23 +12125,26 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // do not quantize Mamba's small yet 2D weights // NOTE: can't use LLM_TN here because the layer number is not known quantize &= name.find("ssm_conv1d.weight") == std::string::npos; - quantize &= name.find("ssm_x.weight") == std::string::npos; - quantize &= name.find("ssm_dt.weight") == std::string::npos; + quantize &= name.find("ssm_x.weight") == std::string::npos; + quantize &= name.find("ssm_dt.weight") == std::string::npos; enum ggml_type new_type; void * new_data; size_t new_size; if (quantize) { - new_type = quantized_type; - if (!params->pure) { - new_type = get_k_quant_type(qs, new_type, tensor, ftype); + new_type = default_type; + + // get more optimal quantization type based on the tensor shape, layer, etc. + if (!params->pure && ggml_is_quantized(default_type)) { + new_type = llama_tensor_get_type(qs, new_type, tensor, ftype); } // If we've decided to quantize to the same type the tensor is already // in then there's nothing to do. quantize = tensor->type != new_type; } + if (!quantize) { new_type = tensor->type; new_data = tensor->data; @@ -12187,7 +12190,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s f32_data = (float *) f32_conv_buf.data(); } - LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type)); + LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type)); fflush(stdout); if (work.size() < nelements * 4) { @@ -12235,7 +12238,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); if (qs.n_fallback > 0) { - LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n", + LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n", __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); } } diff --git a/llama.h b/llama.h index c8e05aadd9913..ccf65ca4e87a5 100644 --- a/llama.h +++ b/llama.h @@ -278,7 +278,7 @@ extern "C" { bool allow_requantize; // allow quantizing non-f32/f16 tensors bool quantize_output_tensor; // quantize output.weight bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored - bool pure; // disable k-quant mixtures and quantize all tensors to the same type + bool pure; // quantize all tensors to the default type void * imatrix; // pointer to importance matrix data } llama_model_quantize_params; From ecab1c75de68de7c41c254e2ae170d3b07bee6d4 Mon Sep 17 00:00:00 2001 From: Gilad S Date: Mon, 11 Mar 2024 10:00:08 +0200 Subject: [PATCH 231/401] cmake : fix subdir for `LLAMA_METAL_EMBED_LIBRARY` (#5985) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 00a26391a6626..60b592bf53de7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -208,7 +208,7 @@ if (LLAMA_METAL) enable_language(ASM) add_compile_definitions(GGML_METAL_EMBED_LIBRARY) - set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal") + set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s") From 332bdfd7980718abf664bfa5460f2288a3314984 Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Mon, 11 Mar 2024 17:09:32 +0900 Subject: [PATCH 232/401] server : maintain chat completion id for streaming responses (#5988) * server: maintain chat completion id for streaming responses * Update examples/server/utils.hpp * Update examples/server/utils.hpp --------- Co-authored-by: Georgi Gerganov --- examples/server/server.cpp | 7 ++++--- examples/server/utils.hpp | 12 ++++++------ 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c7d3ed01b6347..3951507aa5fbe 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3195,11 +3195,12 @@ int main(int argc, char ** argv) { ctx_server.queue_results.add_waiting_task_id(id_task); ctx_server.request_completion(id_task, -1, data, false, false); + const auto completion_id = gen_chatcmplid(); if (!json_value(data, "stream", false)) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { - json result_oai = format_final_response_oaicompat(data, result.data); + json result_oai = format_final_response_oaicompat(data, result.data, completion_id); res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { @@ -3208,11 +3209,11 @@ int main(int argc, char ** argv) { } ctx_server.queue_results.remove_waiting_task_id(id_task); } else { - const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { + const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) { while (true) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error) { - std::vector result_array = format_partial_response_oaicompat(result.data); + std::vector result_array = format_partial_response_oaicompat(result.data, completion_id); for (auto it = result_array.begin(); it != result_array.end(); ++it) { if (!it->empty()) { diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index df0a27782e646..f27af81e99ef5 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -378,7 +378,7 @@ static json oaicompat_completion_params_parse( return llama_params; } -static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) { +static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) { bool stopped_word = result.count("stopped_word") != 0; bool stopped_eos = json_value(result, "stopped_eos", false); int num_tokens_predicted = json_value(result, "tokens_predicted", 0); @@ -412,7 +412,7 @@ static json format_final_response_oaicompat(const json & request, json result, b {"prompt_tokens", num_prompt_tokens}, {"total_tokens", num_tokens_predicted + num_prompt_tokens} }}, - {"id", gen_chatcmplid()} + {"id", completion_id} }; if (server_verbose) { @@ -427,7 +427,7 @@ static json format_final_response_oaicompat(const json & request, json result, b } // return value is vector as there is one case where we might need to generate two responses -static std::vector format_partial_response_oaicompat(json result) { +static std::vector format_partial_response_oaicompat(json result, const std::string & completion_id) { if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { return std::vector({result}); } @@ -471,7 +471,7 @@ static std::vector format_partial_response_oaicompat(json result) { {"role", "assistant"} }}}})}, {"created", t}, - {"id", gen_chatcmplid()}, + {"id", completion_id}, {"model", modelname}, {"object", "chat.completion.chunk"}}; @@ -482,7 +482,7 @@ static std::vector format_partial_response_oaicompat(json result) { {"content", content}}} }})}, {"created", t}, - {"id", gen_chatcmplid()}, + {"id", completion_id}, {"model", modelname}, {"object", "chat.completion.chunk"}}; @@ -509,7 +509,7 @@ static std::vector format_partial_response_oaicompat(json result) { json ret = json { {"choices", choices}, {"created", t}, - {"id", gen_chatcmplid()}, + {"id", completion_id}, {"model", modelname}, {"object", "chat.completion.chunk"} }; From 3202361c5b1ba15e695b31209567ef42c22c5c32 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Mon, 11 Mar 2024 10:28:51 +0100 Subject: [PATCH 233/401] ggml, ci : Windows ARM runner and build fixes (#5979) * windows arm ci * fix `error C2078: too many initializers` with ggml_vld1q_u32 macro for MSVC ARM64 * fix `warning C4146: unary minus operator applied to unsigned type, result still unsigned` * fix `error C2065: '__fp16': undeclared identifier` --- .github/workflows/build.yml | 4 +++- ggml-impl.h | 8 ++++++-- ggml-quants.c | 16 ++++++++-------- ggml.c | 4 ++-- llama.cpp | 4 ++-- 5 files changed, 21 insertions(+), 15 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9144f926628a5..d39cd6bc338e0 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -425,6 +425,8 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' - build: 'vulkan' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' + - build: 'arm64' + defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -520,7 +522,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 diff --git a/ggml-impl.h b/ggml-impl.h index c5637e4d45d8c..e68b728775c41 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -53,26 +53,30 @@ extern "C" { // #include +typedef __fp16 ggml_fp16_internal_t; + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - __fp16 tmp; + ggml_fp16_internal_t tmp; memcpy(&tmp, &h, sizeof(ggml_fp16_t)); return (float)tmp; } static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { ggml_fp16_t res; - __fp16 tmp = f; + ggml_fp16_internal_t tmp = f; memcpy(&res, &tmp, sizeof(ggml_fp16_t)); return res; } #else +typedef uint16_t ggml_fp16_internal_t; + #ifdef __wasm_simd128__ #include #else diff --git a/ggml-quants.c b/ggml-quants.c index f9a3d9fd229e1..86b0764cbae18 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9374,15 +9374,15 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); - const uint32x4_t aux32x4_0 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; - const uint32x4_t aux32x4_1 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); - const uint32x4_t aux32x4_2 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; - const uint32x4_t aux32x4_3 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); diff --git a/ggml.c b/ggml.c index 80efa6f2ac900..9a7bd1d8c527b 100644 --- a/ggml.c +++ b/ggml.c @@ -857,7 +857,7 @@ inline static float vaddvq_f32(float32x4_t v) { #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const __fp16 *)(x)) + #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 @@ -900,7 +900,7 @@ inline static float vaddvq_f32(float32x4_t v) { #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const __fp16 *)(x))) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 diff --git a/llama.cpp b/llama.cpp index 110e509ccd5b6..f61ea791b5be2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13980,7 +13980,7 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } else if (llama_is_user_defined_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; if (length < (int) result.length()) { - return -result.length(); + return -(int) result.length(); } memcpy(buf, result.c_str(), result.length()); return result.length(); @@ -14015,7 +14015,7 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } else if (llama_is_user_defined_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; if (length < (int) result.length()) { - return -result.length(); + return -(int) result.length(); } memcpy(buf, result.c_str(), result.length()); return result.length(); From caa106d4e05a0ab94225c220b81f9e2cd522339b Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 11 Mar 2024 10:56:41 +0100 Subject: [PATCH 234/401] Server: format error to json (#5961) * server: format error to json * server: do not crash on grammar error * fix api key test case * revert limit max n_predict * small fix * correct coding style * update completion.js * launch_slot_with_task * update docs * update_slots * update webui * update readme --- examples/server/README.md | 46 +- examples/server/completion.js.hpp | 469 +++++++++--------- examples/server/public/completion.js | 20 +- examples/server/server.cpp | 159 +++--- examples/server/tests/features/steps/steps.py | 2 +- examples/server/utils.hpp | 51 ++ 6 files changed, 430 insertions(+), 317 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 23606b32a2c81..3767390511e5f 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -556,9 +556,51 @@ Run with bash: bash chat.sh ``` -### API like OAI +### OAI-like API -The HTTP server supports OAI-like API +The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi + +### API errors + +Server returns error in the same format as OAI: https://github.com/openai/openai-openapi + +Example of an error: + +```json +{ + "error": { + "code": 401, + "message": "Invalid API Key", + "type": "authentication_error" + } +} +``` + +Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp: + +**When /metrics or /slots endpoint is disabled** + +```json +{ + "error": { + "code": 501, + "message": "This server does not support metrics endpoint.", + "type": "not_supported_error" + } +} +``` + +**When the server receives invalid grammar via */completions endpoint** + +```json +{ + "error": { + "code": 400, + "message": "Failed to parse grammar", + "type": "invalid_request_error" + } +} +``` ### Extending or building alternative Web Front End diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index f5e696e17edfe..10734091eeaf6 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -231,255 +231,256 @@ unsigned char completion_js[] = { 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x29, 0x20, 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b/examples/server/public/completion.js index ab38a7b409df1..835ce6e68422a 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -96,18 +96,18 @@ export async function* llama(prompt, params = {}, config = {}) { } } if (result.error) { - result.error = JSON.parse(result.error); - if (result.error.content.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error: ${result.error.content}`); + try { + result.error = JSON.parse(result.error); + if (result.error.message.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); + } + } catch(e) { + console.error(`llama.cpp error ${result.error}`) } } - if (result.error) { - result.error = JSON.parse(result.error); - console.error(`llama.cpp error: ${result.error.content}`); - } } } } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3951507aa5fbe..b63a6f243b696 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -396,7 +396,7 @@ struct server_queue { // callback functions std::function callback_new_task; std::function callback_finish_multitask; - std::function callback_run_slots; + std::function callback_update_slots; // Add a new task to the end of the queue int post(server_task task) { @@ -435,8 +435,8 @@ struct server_queue { } // Register the function to be called when all slots data is ready to be processed - void on_run_slots(std::function callback) { - callback_run_slots = std::move(callback); + void on_update_slots(std::function callback) { + callback_update_slots = std::move(callback); } // Call when the state of one slot is changed @@ -461,7 +461,7 @@ struct server_queue { * - Wait until a new task arrives * - Process the task (i.e. maybe copy data into slot) * - Check if multitask is finished - * - Run all slots + * - Update all slots */ void start_loop() { running = true; @@ -499,9 +499,9 @@ struct server_queue { } // all tasks in the current loop is processed, slots data is now ready - LOG_VERBOSE("callback_run_slots", {}); + LOG_VERBOSE("callback_update_slots", {}); - callback_run_slots(); + callback_update_slots(); LOG_VERBOSE("wait for new task", {}); { @@ -805,9 +805,10 @@ struct server_context { return last_used; } - bool launch_slot_with_data(server_slot & slot, json data) const { + bool launch_slot_with_task(server_slot & slot, const server_task & task) { slot_params default_params; llama_sampling_params default_sparams; + auto & data = task.data; if (data.count("__oaicompat") != 0) { slot.oaicompat = true; @@ -864,10 +865,15 @@ struct server_context { { const auto & prompt = data.find("prompt"); if (prompt == data.end()) { - slot.prompt = ""; + send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST); + return false; } else { slot.prompt = *prompt; } + if (slot.prompt.is_array() && slot.prompt.size() == 0) { + send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST); + return false; + } } // penalize user-provided tokens @@ -926,6 +932,7 @@ struct server_context { if (logit_bias != data.end() && logit_bias->is_array()) { const int n_vocab = llama_n_vocab(model); for (const auto & el : *logit_bias) { + // TODO: we may want to throw errors here, in case "el" is incorrect if (el.is_array() && el.size() == 2) { float bias; if (el[1].is_number()) { @@ -985,6 +992,11 @@ struct server_context { llama_sampling_free(slot.ctx_sampling); } slot.ctx_sampling = llama_sampling_init(slot.sparams); + if (slot.ctx_sampling == nullptr) { + // for now, the only error that may happen here is invalid grammar + send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); + return false; + } llama_set_rng_seed(ctx, slot.params.seed); } @@ -1226,15 +1238,23 @@ struct server_context { }; } - void send_error(const server_task & task, const std::string & error) { - LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); + void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + send_error(task.id, task.id_multi, error, type); + } + + void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + send_error(slot.id_task, slot.id_multi, error, type); + } + + void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + LOG_TEE("task %i - error: %s\n", id_task, error.c_str()); server_task_result res; - res.id = task.id; - res.id_multi = task.id_multi; + res.id = id_task; + res.id_multi = id_multi; res.stop = false; res.error = true; - res.data = { { "content", error } }; + res.data = format_error_response(error, type); queue_results.send(res); } @@ -1468,9 +1488,8 @@ struct server_context { slot->infill = task.infill; slot->embedding = task.embedding; - if (!launch_slot_with_data(*slot, task.data)) { - // send error result - send_error(task, "internal_error"); + if (!launch_slot_with_task(*slot, task)) { + LOG_ERROR("error while launching slot", task.data); break; } } break; @@ -1587,7 +1606,7 @@ struct server_context { queue_results.send(result); } - bool update_slots() { + void update_slots() { if (system_need_update) { system_prompt_update(); } @@ -1630,7 +1649,7 @@ struct server_context { kv_cache_clear(); } - return true; + return; } } @@ -1975,8 +1994,7 @@ struct server_context { if (batch.n_tokens == 0) { LOG_VERBOSE("no tokens to decode", {}); - - return true; + return; } LOG_VERBOSE("decoding batch", { @@ -2033,7 +2051,13 @@ struct server_context { if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); - return false; + for (auto & slot : slots) { + slot.state = SLOT_STATE_PROCESSING; + slot.command = SLOT_COMMAND_NONE; + slot.release(); + send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size."); + } + break; // break loop of n_batch } LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); @@ -2042,12 +2066,12 @@ struct server_context { n_batch /= 2; i -= n_batch; - continue; + continue; // continue loop of n_batch } for (auto & slot : slots) { if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { - continue; + continue; // continue loop of slots } // prompt evaluated for embedding @@ -2055,7 +2079,7 @@ struct server_context { send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; - continue; + continue; // continue loop of slots } completion_token_output result; @@ -2097,9 +2121,7 @@ struct server_context { } } - LOG_VERBOSE("slots updated", {}); - - return true; + LOG_VERBOSE("run slots completed", {}); } json model_meta() const { @@ -2745,32 +2767,32 @@ int main(int argc, char ** argv) { svr->set_logger(log_server_request); - svr->set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { - const char fmt[] = "500 Internal Server Error\n%s"; + auto res_error = [](httplib::Response & res, json error_data) { + json final_response {{"error", error_data}}; + res.set_content(final_response.dump(), "application/json; charset=utf-8"); + res.status = json_value(error_data, "code", 500); + }; - char buf[BUFSIZ]; + svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { + std::string message; try { std::rethrow_exception(std::move(ep)); - } catch (std::exception &e) { - snprintf(buf, sizeof(buf), fmt, e.what()); + } catch (std::exception & e) { + message = e.what(); } catch (...) { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); + message = "Unknown Exception"; } - res.set_content(buf, "text/plain; charset=utf-8"); - res.status = 500; + json formatted_error = format_error_response(message, ERROR_TYPE_SERVER); + LOG_VERBOSE("Got exception", formatted_error); + res_error(res, formatted_error); }); - svr->set_error_handler([](const httplib::Request &, httplib::Response & res) { - if (res.status == 401) { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } + svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) { if (res.status == 404) { - res.set_content("File Not Found", "text/plain; charset=utf-8"); + res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND)); } + // for other error codes, we skip processing here because it's already done by res_error() }); // set timeouts and change hostname and port @@ -2835,7 +2857,7 @@ int main(int argc, char ** argv) { // Middlewares // - auto middleware_validate_api_key = [&sparams](const httplib::Request & req, httplib::Response & res) { + auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) { // TODO: should we apply API key to all endpoints, including "/health" and "/models"? static const std::set protected_endpoints = { "/props", @@ -2876,8 +2898,7 @@ int main(int argc, char ** argv) { // API key is invalid or not provided // TODO: make another middleware for CORS related logic res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); - res.status = 401; // Unauthorized + res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION)); LOG_WARNING("Unauthorized: Invalid API Key", {}); @@ -2940,21 +2961,18 @@ int main(int argc, char ** argv) { } case SERVER_STATE_LOADING_MODEL: { - res.set_content(R"({"status": "loading model"})", "application/json"); - res.status = 503; // HTTP Service Unavailable + res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE)); } break; case SERVER_STATE_ERROR: { - res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); - res.status = 500; // HTTP Internal Server Error + res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER)); } break; } }; const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { if (!sparams.slots_endpoint) { - res.status = 501; - res.set_content("This server does not support slots endpoint.", "text/plain; charset=utf-8"); + res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -2978,8 +2996,7 @@ int main(int argc, char ** argv) { const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { if (!sparams.metrics_endpoint) { - res.status = 501; - res.set_content("This server does not support metrics endpoint.", "text/plain; charset=utf-8"); + res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -3090,7 +3107,7 @@ int main(int argc, char ** argv) { res.set_content(data.dump(), "application/json; charset=utf-8"); }; - const auto handle_completions = [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = json::parse(req.body); @@ -3105,8 +3122,7 @@ int main(int argc, char ** argv) { if (!result.error && result.stop) { res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { - res.status = 500; - res.set_content(result.data["content"], "text/plain; charset=utf-8"); + res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); @@ -3186,7 +3202,7 @@ int main(int argc, char ** argv) { res.set_content(models.dump(), "application/json; charset=utf-8"); }; - const auto handle_chat_completions = [&ctx_server, &sparams](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); @@ -3204,8 +3220,7 @@ int main(int argc, char ** argv) { res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { - res.status = 500; - res.set_content(result.data["content"], "text/plain; charset=utf-8"); + res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); } else { @@ -3259,7 +3274,7 @@ int main(int argc, char ** argv) { } }; - const auto handle_infill = [&ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = json::parse(req.body); @@ -3274,8 +3289,7 @@ int main(int argc, char ** argv) { if (!result.error && result.stop) { res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { - res.status = 404; - res.set_content(result.data["content"], "text/plain; charset=utf-8"); + res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); @@ -3346,7 +3360,7 @@ int main(int argc, char ** argv) { return res.set_content(data.dump(), "application/json; charset=utf-8"); }; - const auto handle_embeddings = [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) { + const auto handle_embeddings = [¶ms, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; @@ -3375,8 +3389,8 @@ int main(int argc, char ** argv) { std::string content = body["content"]; prompts.push_back(content); } else { - // TODO @ngxson : should return an error here - prompts.push_back(""); + res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); + return; } // process all prompts @@ -3392,9 +3406,14 @@ int main(int argc, char ** argv) { // get the result server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); - - // append to the responses - responses.push_back(result.data); + if (!result.error) { + // append to the responses + responses.push_back(result.data); + } else { + // error received, ignore everything else + res_error(res, result.data); + return; + } } // write JSON response @@ -3488,7 +3507,7 @@ int main(int argc, char ** argv) { &server_context::process_single_task, &ctx_server, std::placeholders::_1)); ctx_server.queue_tasks.on_finish_multitask(std::bind( &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1)); - ctx_server.queue_tasks.on_run_slots(std::bind( + ctx_server.queue_tasks.on_update_slots(std::bind( &server_context::update_slots, &ctx_server)); ctx_server.queue_results.on_multitask_update(std::bind( &server_queue::update_multitask, diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 4e81a255a08d9..98c2b61743cbf 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -801,7 +801,7 @@ async def oai_chat_completions(user_prompt, stream=enable_streaming, seed=seed ) - except openai.error.APIError as e: + except openai.error.AuthenticationError as e: if expect_api_error is not None and expect_api_error: return 401 else: diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index f27af81e99ef5..48aeef4ebea67 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -14,6 +14,17 @@ using json = nlohmann::json; +// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 +enum error_type { + ERROR_TYPE_INVALID_REQUEST, + ERROR_TYPE_AUTHENTICATION, + ERROR_TYPE_SERVER, + ERROR_TYPE_NOT_FOUND, + ERROR_TYPE_PERMISSION, + ERROR_TYPE_UNAVAILABLE, // custom error + ERROR_TYPE_NOT_SUPPORTED, // custom error +}; + extern bool server_verbose; extern bool server_log_json; @@ -542,3 +553,43 @@ static json format_detokenized_response(const std::string & content) { {"content", content} }; } + +static json format_error_response(const std::string & message, const enum error_type type) { + std::string type_str; + int code = 500; + switch (type) { + case ERROR_TYPE_INVALID_REQUEST: + type_str = "invalid_request_error"; + code = 400; + break; + case ERROR_TYPE_AUTHENTICATION: + type_str = "authentication_error"; + code = 401; + break; + case ERROR_TYPE_NOT_FOUND: + type_str = "not_found_error"; + code = 404; + break; + case ERROR_TYPE_SERVER: + type_str = "server_error"; + code = 500; + break; + case ERROR_TYPE_PERMISSION: + type_str = "permission_error"; + code = 403; + break; + case ERROR_TYPE_NOT_SUPPORTED: + type_str = "not_supported_error"; + code = 501; + break; + case ERROR_TYPE_UNAVAILABLE: + type_str = "unavailable_error"; + code = 503; + break; + } + return json { + {"code", code}, + {"message", message}, + {"type", type_str}, + }; +} From 828defefb66fc8a25404f5de845897145bf34061 Mon Sep 17 00:00:00 2001 From: Jakub N Date: Mon, 11 Mar 2024 14:40:42 +0100 Subject: [PATCH 235/401] Update server docker image URLs (#5997) --- examples/server/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 3767390511e5f..8f8454affaecd 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -123,10 +123,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can ### Docker ```bash -docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 +docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 # or, with CUDA: -docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 +docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 ``` ## Testing with CURL From 83796e62bc9f6caae6228168e359890f51e60fee Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 11 Mar 2024 17:47:47 +0200 Subject: [PATCH 236/401] llama : refactor unicode stuff (#5992) * llama : refactor unicode stuff ggml-ci * unicode : names * make : fix c++ compiler * unicode : names * unicode : straighten tables * zig : fix build * unicode : put nfd normalization behind API ggml-ci * swift : fix build * unicode : add BOM * unicode : add ggml-ci * unicode : pass as cpts as const ref --- CMakeLists.txt | 2 + Makefile | 7 +- Package.swift | 1 + build.zig | 15 +- llama.cpp | 87 +- tests/test-tokenizer-1-bpe.cpp | 4 +- tests/test-tokenizer-1-llama.cpp | 2 +- unicode.cpp | 1672 ++++++++++++++++++++++++++++++ unicode.h | 790 +------------- 9 files changed, 1744 insertions(+), 836 deletions(-) create mode 100644 unicode.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 60b592bf53de7..7ab13cbd56e8a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1141,6 +1141,8 @@ endif() add_library(llama llama.cpp llama.h + unicode.h + unicode.cpp ) target_include_directories(llama PUBLIC .) diff --git a/Makefile b/Makefile index 6492da8a02fb7..c8fd3f5c5026a 100644 --- a/Makefile +++ b/Makefile @@ -633,9 +633,12 @@ ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h $(CC) $(CFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o +unicode.o: unicode.cpp unicode.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o -llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h +llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ COMMON_H_DEPS = common/common.h common/sampling.h common/log.h diff --git a/Package.swift b/Package.swift index b24c9204a7d83..4696ae5e4b44c 100644 --- a/Package.swift +++ b/Package.swift @@ -31,6 +31,7 @@ let package = Package( sources: [ "ggml.c", "llama.cpp", + "unicode.cpp", "ggml-alloc.c", "ggml-backend.c", "ggml-quants.c", diff --git a/build.zig b/build.zig index c0af454dc9e92..90609359bfe0b 100644 --- a/build.zig +++ b/build.zig @@ -115,6 +115,7 @@ pub fn build(b: *std.build.Builder) !void { const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); + const unicode = make.obj("unicode", "unicode.cpp"); const llama = make.obj("llama", "llama.cpp"); const buildinfo = make.obj("common", "common/build-info.cpp"); const common = make.obj("common", "common/common.cpp"); @@ -125,14 +126,14 @@ pub fn build(b: *std.build.Builder) !void { const clip = make.obj("clip", "examples/llava/clip.cpp"); const llava = make.obj("llava", "examples/llava/llava.cpp"); - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train }); + _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/llama.cpp b/llama.cpp index f61ea791b5be2..6aa88ce54baef 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3703,7 +3703,7 @@ static void llm_load_vocab( for (int i = 0; i < n_merges; i++) { const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i); - GGML_ASSERT(codepoints_from_utf8(word).size() > 0); + GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); std::string first; std::string second; @@ -3748,7 +3748,7 @@ static void llm_load_vocab( for (uint32_t i = 0; i < n_vocab; i++) { std::string word = gguf_get_arr_str(ctx, token_idx, i); - GGML_ASSERT(codepoints_from_utf8(word).size() > 0); + GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); vocab.token_to_id[word] = i; @@ -9340,7 +9340,7 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { } case LLAMA_VOCAB_TYPE_BPE: { GGML_ASSERT(false); - return unicode_to_bytes_bpe(token_data.text); + return unicode_utf8_to_byte(token_data.text); } case LLAMA_VOCAB_TYPE_WPM: { GGML_ASSERT(false); @@ -9365,7 +9365,7 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { } case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_BPE: { - return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); + return vocab.token_to_id.at(unicode_byte_to_utf8(ch)); } default: GGML_ASSERT(false); @@ -9705,9 +9705,9 @@ struct llm_tokenizer_bpe { bpe_words.reserve(text.size()); bpe_encoded_words.reserve(text.size()); - auto cps = codepoints_from_utf8(text); - for (size_t i = 0; i < cps.size(); ++i) - text_utf.emplace_back(codepoint_to_utf8(cps[i])); + const auto cpts = unicode_cpts_from_utf8(text); + for (size_t i = 0; i < cpts.size(); ++i) + text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i])); for (int i = 0; i < (int)text_utf.size(); i++) { const std::string & utf_char = text_utf[i]; @@ -9757,40 +9757,40 @@ struct llm_tokenizer_bpe { } if (!split_condition && !collecting) { - if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) { + if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) { collecting_letter = true; collecting = true; } - else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { collecting_numeric = true; collecting = true; } else if ( - ((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || - (!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) + ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || + (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) ) { collecting_special = true; collecting = true; } - else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) { + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) { collecting_whitespace_lookahead = true; collecting = true; } - else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) { + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) { split_condition = true; } } else if (!split_condition && collecting) { - if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) { + if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) { split_condition = true; } - else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) { + else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) { split_condition = true; } - else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { + else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { split_condition = true; } - else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { split_condition = true; } } @@ -9819,7 +9819,7 @@ struct llm_tokenizer_bpe { for (std::string & word : bpe_words) { std::string encoded_token = ""; for (char & c : word) { - encoded_token += bytes_to_unicode_bpe(c); + encoded_token += unicode_byte_to_utf8(c); } bpe_encoded_words.emplace_back(encoded_token); } @@ -9893,25 +9893,13 @@ struct llm_tokenizer_wpm { } std::vector preprocess(const std::string & text) { - // normalalization form D - std::vector codepoints = codepoints_from_utf8(text); - std::vector nfd_codepoints; - for (uint32_t code : codepoints) { - auto it = nfd_map.equal_range(code); - if (it.first != it.second) { - for (auto jt = it.first; jt != it.second; jt++) { - nfd_codepoints.push_back(jt->second); - } - } else { - nfd_codepoints.push_back(code); - } - } + std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); // strip accents, strip control, uniformize whitespace, // to lowercase, pad chinese characters, pad punctuation std::string new_str = ""; - for (uint32_t code : nfd_codepoints) { - int type = codepoint_type(code); + for (uint32_t code : cpts_nfd) { + int type = unicode_cpt_type(code); if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) { continue; } @@ -9919,7 +9907,7 @@ struct llm_tokenizer_wpm { if (type == CODEPOINT_TYPE_WHITESPACE) { code = ' '; } - std::string s = codepoint_to_utf8(code); + std::string s = unicode_cpt_to_utf8(code); if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) { new_str += " "; new_str += s; @@ -9939,8 +9927,7 @@ struct llm_tokenizer_wpm { if (r > l) words.push_back(new_str.substr(l, (r - l))); l = r + 1; r = l; - } - else { + } else { r += 1; } } @@ -9964,17 +9951,17 @@ struct llm_tokenizer_wpm { return code < 256 && ispunct(code); } - bool is_chinese_char(uint32_t codepoint) { - if ((codepoint >= 0x4E00 && codepoint <= 0x9FFF) || - (codepoint >= 0x3400 && codepoint <= 0x4DBF) || - (codepoint >= 0x20000 && codepoint <= 0x2A6DF) || - (codepoint >= 0x2A700 && codepoint <= 0x2B73F) || - (codepoint >= 0x2B740 && codepoint <= 0x2B81F) || - (codepoint >= 0x2B920 && codepoint <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920 - (codepoint >= 0xF900 && codepoint <= 0xFAFF) || - (codepoint >= 0x2F800 && codepoint <= 0x2FA1F) || - (codepoint >= 0x3000 && codepoint <= 0x303F) || - (codepoint >= 0xFF00 && codepoint <= 0xFFEF)) { + bool is_chinese_char(uint32_t cpt) { + if ((cpt >= 0x4E00 && cpt <= 0x9FFF) || + (cpt >= 0x3400 && cpt <= 0x4DBF) || + (cpt >= 0x20000 && cpt <= 0x2A6DF) || + (cpt >= 0x2A700 && cpt <= 0x2B73F) || + (cpt >= 0x2B740 && cpt <= 0x2B81F) || + (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920 + (cpt >= 0xF900 && cpt <= 0xFAFF) || + (cpt >= 0x2F800 && cpt <= 0x2FA1F) || + (cpt >= 0x3000 && cpt <= 0x303F) || + (cpt >= 0xFF00 && cpt <= 0xFFEF)) { return true; // NOLINT } return false; @@ -13953,9 +13940,9 @@ int32_t llama_tokenize( static std::string llama_decode_text(const std::string & text) { std::string decoded_text; - auto unicode_sequences = codepoints_from_utf8(text); - for (auto& unicode_sequence : unicode_sequences) { - decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence)); + auto unicode_sequences = unicode_cpts_from_utf8(text); + for (auto & unicode_sequence : unicode_sequences) { + decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence)); } return decoded_text; diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index 3596ce55af2ce..a0e2caf9427eb 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -64,7 +64,7 @@ int main(int argc, char **argv) { for (int i = 0; i < n_vocab; ++i) { std::string str = llama_detokenize_bpe(ctx, std::vector(1, i)); try { - auto cps = codepoints_from_utf8(str); + auto cps = unicode_cpts_from_utf8(str); std::vector tokens = llama_tokenize(ctx, str, false); std::string check = llama_detokenize_bpe(ctx, tokens); if (check != str) { @@ -97,7 +97,7 @@ int main(int argc, char **argv) { continue; } - std::string str = codepoint_to_utf8(cp); + std::string str = unicode_cpt_to_utf8(cp); std::vector tokens = llama_tokenize(ctx, str, false); std::string check = llama_detokenize_bpe(ctx, tokens); if (cp != 9601 && str != check) { diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-llama.cpp index 9333f8686fa1c..8caf0b24eab2a 100644 --- a/tests/test-tokenizer-1-llama.cpp +++ b/tests/test-tokenizer-1-llama.cpp @@ -85,7 +85,7 @@ int main(int argc, char **argv) { continue; } - std::string str = codepoint_to_utf8(cp); + std::string str = unicode_cpt_to_utf8(cp); std::vector tokens = llama_tokenize(ctx, str, false); std::string check = llama_detokenize_spm(ctx, tokens); if (cp != 9601 && str != check) { diff --git a/unicode.cpp b/unicode.cpp new file mode 100644 index 0000000000000..7fce6fb34aaf4 --- /dev/null +++ b/unicode.cpp @@ -0,0 +1,1672 @@ +#include "unicode.h" + +#include +#include +#include +#include +#include +#include + +static const std::vector> unicode_ranges_digit = { +{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x00000660, 0x00000669}, +{0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, {0x000009E6, 0x000009EF}, +{0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, {0x00000B66, 0x00000B6F}, {0x00000BE6, 0x00000BEF}, +{0x00000C66, 0x00000C6F}, {0x00000CE6, 0x00000CEF}, {0x00000D66, 0x00000D6F}, {0x00000DE6, 0x00000DEF}, +{0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F29}, {0x00001040, 0x00001049}, +{0x00001090, 0x00001099}, {0x00001369, 0x00001371}, {0x000017E0, 0x000017E9}, {0x00001810, 0x00001819}, +{0x00001946, 0x0000194F}, {0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, +{0x00001B50, 0x00001B59}, {0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, +{0x00002070, 0x00002070}, {0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002460, 0x00002468}, +{0x00002474, 0x0000247C}, {0x00002488, 0x00002490}, {0x000024EA, 0x000024EA}, {0x000024F5, 0x000024FD}, +{0x000024FF, 0x000024FF}, {0x00002776, 0x0000277E}, {0x00002780, 0x00002788}, {0x0000278A, 0x00002792}, +{0x0000A620, 0x0000A629}, {0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, +{0x0000A9F0, 0x0000A9F9}, {0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, +{0x000104A0, 0x000104A9}, {0x00010A40, 0x00010A43}, {0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E68}, +{0x00011052, 0x0001105A}, {0x00011066, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, +{0x000111D0, 0x000111D9}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, {0x000114D0, 0x000114D9}, +{0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x00011739}, {0x000118E0, 0x000118E9}, +{0x00011950, 0x00011959}, {0x00011C50, 0x00011C59}, {0x00011D50, 0x00011D59}, {0x00011DA0, 0x00011DA9}, +{0x00016A60, 0x00016A69}, {0x00016B50, 0x00016B59}, {0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, +{0x0001E2F0, 0x0001E2F9}, {0x0001E950, 0x0001E959}, {0x0001F100, 0x0001F10A}, {0x0001FBF0, 0x0001FBF9}, +}; + +static const std::vector> unicode_ranges_letter = { +{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5}, +{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1}, +{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE}, +{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F}, +{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1}, +{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556}, +{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2}, +{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5}, +{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF}, +{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1}, +{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815}, +{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858}, +{0x00000860, 0x0000086A}, {0x000008A0, 0x000008B4}, {0x000008B6, 0x000008C7}, {0x00000904, 0x00000939}, +{0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, {0x00000971, 0x00000980}, +{0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, {0x000009AA, 0x000009B0}, +{0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, {0x000009CE, 0x000009CE}, +{0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, {0x000009FC, 0x000009FC}, +{0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, {0x00000A2A, 0x00000A30}, +{0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, {0x00000A59, 0x00000A5C}, +{0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, {0x00000A8F, 0x00000A91}, +{0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, {0x00000AB5, 0x00000AB9}, +{0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, {0x00000AF9, 0x00000AF9}, +{0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, {0x00000B2A, 0x00000B30}, +{0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, {0x00000B5C, 0x00000B5D}, +{0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, {0x00000B85, 0x00000B8A}, +{0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, {0x00000B9C, 0x00000B9C}, +{0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, {0x00000BAE, 0x00000BB9}, +{0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, {0x00000C12, 0x00000C28}, +{0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, {0x00000C60, 0x00000C61}, +{0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, {0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, +{0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, {0x00000CBD, 0x00000CBD}, {0x00000CDE, 0x00000CDE}, +{0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, {0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, +{0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, {0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, +{0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, {0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, +{0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, {0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, +{0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, {0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, +{0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, {0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, +{0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, {0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, +{0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, {0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, +{0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, {0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, +{0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, {0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, +{0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, {0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, +{0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, {0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, +{0x00001250, 0x00001256}, {0x00001258, 0x00001258}, {0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, +{0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, {0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, +{0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, {0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, +{0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, {0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, +{0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, {0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, +{0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, {0x00001700, 0x0000170C}, {0x0000170E, 0x00001711}, +{0x00001720, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, {0x0000176E, 0x00001770}, +{0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, {0x00001820, 0x00001878}, +{0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, {0x000018B0, 0x000018F5}, +{0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, {0x00001980, 0x000019AB}, +{0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, {0x00001AA7, 0x00001AA7}, +{0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4B}, {0x00001B83, 0x00001BA0}, {0x00001BAE, 0x00001BAF}, +{0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, {0x00001C5A, 0x00001C7D}, +{0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, {0x00001CE9, 0x00001CEC}, +{0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, {0x00001D00, 0x00001DBF}, +{0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, {0x00001F48, 0x00001F4D}, +{0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, {0x00001F5D, 0x00001F5D}, +{0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, {0x00001FBE, 0x00001FBE}, +{0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, {0x00001FD6, 0x00001FDB}, +{0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, {0x00002071, 0x00002071}, +{0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, {0x00002107, 0x00002107}, +{0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, {0x00002124, 0x00002124}, +{0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, {0x0000212F, 0x00002139}, +{0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, {0x00002183, 0x00002184}, +{0x00002C00, 0x00002C2E}, {0x00002C30, 0x00002C5E}, {0x00002C60, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, +{0x00002CF2, 0x00002CF3}, {0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, +{0x00002D30, 0x00002D67}, {0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, +{0x00002DA8, 0x00002DAE}, {0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, +{0x00002DC8, 0x00002DCE}, {0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, +{0x00003005, 0x00003006}, {0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, +{0x0000309D, 0x0000309F}, {0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, +{0x00003131, 0x0000318E}, {0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, +{0x00004E00, 0x00009FFC}, {0x0000A000, 0x0000A48C}, {0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, +{0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, {0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, +{0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, {0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7BF}, +{0x0000A7C2, 0x0000A7CA}, {0x0000A7F5, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, +{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7}, +{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946}, +{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4}, +{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42}, +{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF}, +{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0}, +{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4}, +{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26}, +{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2}, +{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D}, +{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D}, +{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E}, +{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D}, +{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74}, +{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE}, +{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC}, +{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D}, +{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C}, +{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349}, +{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF}, +{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527}, +{0x00010530, 0x00010563}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, +{0x00010800, 0x00010805}, {0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, +{0x0001083C, 0x0001083C}, {0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, +{0x000108E0, 0x000108F2}, {0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, +{0x00010980, 0x000109B7}, {0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, +{0x00010A15, 0x00010A17}, {0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, +{0x00010AC0, 0x00010AC7}, {0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, +{0x00010B60, 0x00010B72}, {0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, +{0x00010CC0, 0x00010CF2}, {0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, +{0x00010F00, 0x00010F1C}, {0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010FB0, 0x00010FC4}, +{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, +{0x00011103, 0x00011126}, {0x00011144, 0x00011144}, {0x00011147, 0x00011147}, {0x00011150, 0x00011172}, +{0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, {0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, +{0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, {0x00011213, 0x0001122B}, {0x00011280, 0x00011286}, +{0x00011288, 0x00011288}, {0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, +{0x000112B0, 0x000112DE}, {0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, +{0x0001132A, 0x00011330}, {0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, +{0x00011350, 0x00011350}, {0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, +{0x0001145F, 0x00011461}, {0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, +{0x00011580, 0x000115AE}, {0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, +{0x00011680, 0x000116AA}, {0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011800, 0x0001182B}, +{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913}, +{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941}, +{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3}, +{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50}, +{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AC0, 0x00011AF8}, {0x00011C00, 0x00011C08}, +{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06}, +{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65}, +{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2}, +{0x00011FB0, 0x00011FB0}, {0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00013000, 0x0001342E}, +{0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, {0x00016AD0, 0x00016AED}, +{0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, {0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, +{0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, {0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, +{0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, {0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, +{0x00018D00, 0x00018D08}, {0x0001B000, 0x0001B11E}, {0x0001B150, 0x0001B152}, {0x0001B164, 0x0001B167}, +{0x0001B170, 0x0001B2FB}, {0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, +{0x0001BC90, 0x0001BC99}, {0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, +{0x0001D4A2, 0x0001D4A2}, {0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, +{0x0001D4BB, 0x0001D4BB}, {0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, +{0x0001D50D, 0x0001D514}, {0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, +{0x0001D540, 0x0001D544}, {0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, +{0x0001D6A8, 0x0001D6C0}, {0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, +{0x0001D716, 0x0001D734}, {0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, +{0x0001D78A, 0x0001D7A8}, {0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001E100, 0x0001E12C}, +{0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, {0x0001E2C0, 0x0001E2EB}, {0x0001E800, 0x0001E8C4}, +{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F}, +{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32}, +{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42}, +{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F}, +{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59}, +{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62}, +{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77}, +{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, +{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DD}, +{0x0002A700, 0x0002B734}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, +{0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, +}; + +static const std::vector> unicode_ranges_whitespace = { +{0x00000009, 0x0000000D}, {0x0000001C, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, +{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, +{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +}; + +static const std::vector> unicode_ranges_accent_mark = { +{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF}, +{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A}, +{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4}, +{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A}, +{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819}, +{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B}, +{0x000008D3, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, {0x0000093E, 0x0000094F}, +{0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, {0x000009BC, 0x000009BC}, +{0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, {0x000009D7, 0x000009D7}, +{0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, {0x00000A3C, 0x00000A3C}, +{0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, {0x00000A51, 0x00000A51}, +{0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, {0x00000ABC, 0x00000ABC}, +{0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, {0x00000AE2, 0x00000AE3}, +{0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, {0x00000B3E, 0x00000B44}, +{0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, {0x00000B62, 0x00000B63}, +{0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, {0x00000BCA, 0x00000BCD}, +{0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, +{0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, {0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, +{0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, {0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, +{0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, +{0x00000D3E, 0x00000D44}, {0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, +{0x00000D62, 0x00000D63}, {0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, +{0x00000DD6, 0x00000DD6}, {0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, +{0x00000E34, 0x00000E3A}, {0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, +{0x00000EC8, 0x00000ECD}, {0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, +{0x00000F39, 0x00000F39}, {0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, +{0x00000F8D, 0x00000F97}, {0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, +{0x00001056, 0x00001059}, {0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, +{0x00001071, 0x00001074}, {0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, +{0x0000135D, 0x0000135F}, {0x00001712, 0x00001714}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, +{0x00001772, 0x00001773}, {0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, +{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B}, +{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F}, +{0x00001AB0, 0x00001AC0}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, +{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37}, +{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4}, +{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DF9}, {0x00001DFB, 0x00001DFF}, {0x000020D0, 0x000020F0}, +{0x00002CEF, 0x00002CF1}, {0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, +{0x00003099, 0x0000309A}, {0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, +{0x0000A6F0, 0x0000A6F1}, {0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, +{0x0000A823, 0x0000A827}, {0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, +{0x0000A8E0, 0x0000A8F1}, {0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, +{0x0000A980, 0x0000A983}, {0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, +{0x0000AA43, 0x0000AA43}, {0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, +{0x0000AAB2, 0x0000AAB4}, {0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, +{0x0000AAEB, 0x0000AAEF}, {0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, +{0x0000FB1E, 0x0000FB1E}, {0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, +{0x000102E0, 0x000102E0}, {0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, +{0x00010A0C, 0x00010A0F}, {0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, +{0x00010D24, 0x00010D27}, {0x00010EAB, 0x00010EAC}, {0x00010F46, 0x00010F50}, {0x00011000, 0x00011002}, +{0x00011038, 0x00011046}, {0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x00011100, 0x00011102}, +{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182}, +{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237}, +{0x0001123E, 0x0001123E}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, {0x0001133B, 0x0001133C}, +{0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, {0x00011357, 0x00011357}, +{0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, {0x00011435, 0x00011446}, +{0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, {0x000115B8, 0x000115C0}, +{0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, {0x0001171D, 0x0001172B}, +{0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, {0x0001193B, 0x0001193E}, +{0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, {0x000119DA, 0x000119E0}, +{0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, {0x00011A3B, 0x00011A3E}, +{0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, {0x00011C2F, 0x00011C36}, +{0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, {0x00011D31, 0x00011D36}, +{0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, {0x00011D47, 0x00011D47}, +{0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, {0x00011EF3, 0x00011EF6}, +{0x00016AF0, 0x00016AF4}, {0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, +{0x00016F8F, 0x00016F92}, {0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, +{0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, {0x0001D185, 0x0001D18B}, +{0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, {0x0001DA3B, 0x0001DA6C}, +{0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, {0x0001DAA1, 0x0001DAAF}, +{0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, {0x0001E023, 0x0001E024}, +{0x0001E026, 0x0001E02A}, {0x0001E130, 0x0001E136}, {0x0001E2EC, 0x0001E2EF}, {0x0001E8D0, 0x0001E8D6}, +{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF}, +}; + +static const std::vector> unicode_ranges_punctuation = { +{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B}, +{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B}, +{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB}, +{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E}, +{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE}, +{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4}, +{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061E, 0x0000061F}, +{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9}, +{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970}, +{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77}, +{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B}, +{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85}, +{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB}, +{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C}, +{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA}, +{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6}, +{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001BFC, 0x00001BFF}, {0x00001C3B, 0x00001C3F}, +{0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, {0x00002010, 0x00002027}, +{0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, {0x0000207D, 0x0000207E}, +{0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, {0x00002768, 0x00002775}, +{0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, {0x000029D8, 0x000029DB}, +{0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, {0x00002D70, 0x00002D70}, +{0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E52}, {0x00003001, 0x00003003}, +{0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, {0x0000303D, 0x0000303D}, +{0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, {0x0000A60D, 0x0000A60F}, +{0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, {0x0000A874, 0x0000A877}, +{0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, {0x0000A92E, 0x0000A92F}, +{0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, {0x0000AA5C, 0x0000AA5F}, +{0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, {0x0000FD3E, 0x0000FD3F}, +{0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, {0x0000FE63, 0x0000FE63}, +{0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, {0x0000FF05, 0x0000FF0A}, +{0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, {0x0000FF3B, 0x0000FF3D}, +{0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, {0x0000FF5F, 0x0000FF65}, +{0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, {0x0001056F, 0x0001056F}, +{0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, {0x00010A50, 0x00010A58}, +{0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, {0x00010B99, 0x00010B9C}, +{0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, +{0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, {0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, +{0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, {0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, +{0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, {0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, +{0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, {0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, +{0x0001173C, 0x0001173E}, {0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, +{0x00011A3F, 0x00011A46}, {0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011C41, 0x00011C45}, +{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011FFF, 0x00011FFF}, {0x00012470, 0x00012474}, +{0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, {0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, +{0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, {0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, +{0x0001E95E, 0x0001E95F}, +}; + +static const std::vector> unicode_ranges_symbol = { +{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E}, +{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6}, +{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4}, +{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5}, +{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF}, +{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482}, +{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F}, +{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6}, +{0x000007FE, 0x000007FF}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, {0x00000AF1, 0x00000AF1}, +{0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, {0x00000D4F, 0x00000D4F}, +{0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, {0x00000F13, 0x00000F13}, +{0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, {0x00000F36, 0x00000F36}, +{0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, {0x00000FCE, 0x00000FCF}, +{0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, {0x0000166D, 0x0000166D}, +{0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, {0x00001B61, 0x00001B6A}, +{0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, {0x00001FCD, 0x00001FCF}, +{0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, {0x00002044, 0x00002044}, +{0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, {0x000020A0, 0x000020BF}, +{0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, {0x00002114, 0x00002114}, +{0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, {0x00002127, 0x00002127}, +{0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, {0x00002140, 0x00002144}, +{0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, {0x00002190, 0x00002307}, +{0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, {0x0000249C, 0x000024E9}, +{0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, {0x000027F0, 0x00002982}, +{0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, {0x00002B76, 0x00002B95}, +{0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, {0x00002E80, 0x00002E99}, +{0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFB}, {0x00003004, 0x00003004}, +{0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, {0x0000303E, 0x0000303F}, +{0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, {0x000031C0, 0x000031E3}, +{0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, {0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, +{0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, {0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, +{0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, {0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, +{0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, {0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, +{0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC1}, {0x0000FDFC, 0x0000FDFD}, {0x0000FE62, 0x0000FE62}, +{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, +{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, +{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, +{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, +{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, +{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, +{0x0001BC9C, 0x0001BC9C}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, {0x0001D129, 0x0001D164}, +{0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, {0x0001D1AE, 0x0001D1E8}, +{0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, {0x0001D6C1, 0x0001D6C1}, +{0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, {0x0001D735, 0x0001D735}, +{0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, {0x0001D7A9, 0x0001D7A9}, +{0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, {0x0001DA6D, 0x0001DA74}, +{0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, {0x0001E2FF, 0x0001E2FF}, +{0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, {0x0001EEF0, 0x0001EEF1}, +{0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, {0x0001F0B1, 0x0001F0BF}, +{0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, {0x0001F1E6, 0x0001F202}, +{0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, {0x0001F260, 0x0001F265}, +{0x0001F300, 0x0001F6D7}, {0x0001F6E0, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, {0x0001F700, 0x0001F773}, +{0x0001F780, 0x0001F7D8}, {0x0001F7E0, 0x0001F7EB}, {0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, +{0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, {0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, +{0x0001F900, 0x0001F978}, {0x0001F97A, 0x0001F9CB}, {0x0001F9CD, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, +{0x0001FA70, 0x0001FA74}, {0x0001FA78, 0x0001FA7A}, {0x0001FA80, 0x0001FA86}, {0x0001FA90, 0x0001FAA8}, +{0x0001FAB0, 0x0001FAB6}, {0x0001FAC0, 0x0001FAC2}, {0x0001FAD0, 0x0001FAD6}, {0x0001FB00, 0x0001FB92}, +{0x0001FB94, 0x0001FBCA}, +}; + +static const std::vector> unicode_ranges_control = { +{0x00000000, 0x00000008}, {0x0000000E, 0x0000001B}, {0x0000007F, 0x00000084}, {0x00000086, 0x0000009F}, +{0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, {0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, +{0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, {0x00000530, 0x00000530}, {0x00000557, 0x00000558}, +{0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, {0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, +{0x000005F5, 0x00000605}, {0x0000061C, 0x0000061D}, {0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, +{0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, {0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, +{0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, {0x0000085F, 0x0000085F}, {0x0000086B, 0x0000089F}, +{0x000008B5, 0x000008B5}, {0x000008C8, 0x000008D2}, {0x000008E2, 0x000008E2}, {0x00000984, 0x00000984}, +{0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, {0x000009B1, 0x000009B1}, +{0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, {0x000009C9, 0x000009CA}, +{0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, {0x000009E4, 0x000009E5}, +{0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, {0x00000A11, 0x00000A12}, +{0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, {0x00000A37, 0x00000A37}, +{0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, {0x00000A49, 0x00000A4A}, +{0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, {0x00000A5F, 0x00000A65}, +{0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, {0x00000A92, 0x00000A92}, +{0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, {0x00000ABA, 0x00000ABB}, +{0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, {0x00000AD1, 0x00000ADF}, +{0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, {0x00000B04, 0x00000B04}, +{0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, {0x00000B31, 0x00000B31}, +{0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, {0x00000B49, 0x00000B4A}, +{0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E}, {0x00000B64, 0x00000B65}, +{0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D}, {0x00000B91, 0x00000B91}, +{0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D}, {0x00000BA0, 0x00000BA2}, +{0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD}, {0x00000BC3, 0x00000BC5}, +{0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6}, {0x00000BD8, 0x00000BE5}, +{0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11}, {0x00000C29, 0x00000C29}, +{0x00000C3A, 0x00000C3C}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49}, {0x00000C4E, 0x00000C54}, +{0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5F}, {0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, +{0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91}, {0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, +{0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5}, {0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, +{0x00000CD7, 0x00000CDD}, {0x00000CDF, 0x00000CDF}, {0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, +{0x00000CF3, 0x00000CFF}, {0x00000D0D, 0x00000D0D}, {0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, +{0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53}, {0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, +{0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99}, {0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, +{0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9}, {0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, +{0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5}, {0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, +{0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80}, {0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, +{0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4}, {0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, +{0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7}, {0x00000ECE, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, +{0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48}, {0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, +{0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD}, {0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, +{0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF}, {0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, +{0x00001257, 0x00001257}, {0x00001259, 0x00001259}, {0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, +{0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1}, {0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, +{0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7}, {0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, +{0x00001316, 0x00001317}, {0x0000135B, 0x0000135C}, {0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, +{0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF}, {0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, +{0x0000170D, 0x0000170D}, {0x00001715, 0x0000171F}, {0x00001737, 0x0000173F}, {0x00001754, 0x0000175F}, +{0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F}, {0x000017DE, 0x000017DF}, +{0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180F}, {0x0000181A, 0x0000181F}, +{0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF}, {0x0000191F, 0x0000191F}, +{0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943}, {0x0000196E, 0x0000196F}, +{0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF}, {0x000019DB, 0x000019DD}, +{0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E}, {0x00001A8A, 0x00001A8F}, +{0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001AC1, 0x00001AFF}, {0x00001B4C, 0x00001B4F}, +{0x00001B7D, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A}, {0x00001C4A, 0x00001C4C}, +{0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF}, {0x00001CFB, 0x00001CFF}, +{0x00001DFA, 0x00001DFA}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47}, +{0x00001F4E, 0x00001F4F}, {0x00001F58, 0x00001F58}, {0x00001F5A, 0x00001F5A}, {0x00001F5C, 0x00001F5C}, +{0x00001F5E, 0x00001F5E}, {0x00001F7E, 0x00001F7F}, {0x00001FB5, 0x00001FB5}, {0x00001FC5, 0x00001FC5}, +{0x00001FD4, 0x00001FD5}, {0x00001FDC, 0x00001FDC}, {0x00001FF0, 0x00001FF1}, {0x00001FF5, 0x00001FF5}, +{0x00001FFF, 0x00001FFF}, {0x0000200B, 0x0000200F}, {0x0000202A, 0x0000202E}, {0x00002060, 0x0000206F}, +{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C0, 0x000020CF}, +{0x000020F1, 0x000020FF}, {0x0000218C, 0x0000218F}, {0x00002427, 0x0000243F}, {0x0000244B, 0x0000245F}, +{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002C2F, 0x00002C2F}, {0x00002C5F, 0x00002C5F}, +{0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26}, {0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, +{0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E}, {0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, +{0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, {0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, +{0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, {0x00002DDF, 0x00002DDF}, {0x00002E53, 0x00002E7F}, +{0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, {0x00002FD6, 0x00002FEF}, {0x00002FFC, 0x00002FFF}, +{0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104}, {0x00003130, 0x00003130}, +{0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EF}, {0x0000321F, 0x0000321F}, {0x00009FFD, 0x00009FFF}, +{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF}, +{0x0000A7C0, 0x0000A7C1}, {0x0000A7CB, 0x0000A7F4}, {0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, +{0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD}, {0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, +{0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE}, {0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, +{0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F}, {0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, +{0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08}, {0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, +{0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F}, {0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, +{0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF}, {0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, +{0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, {0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, +{0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, {0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, +{0x0000FB45, 0x0000FB45}, {0x0000FBC2, 0x0000FBD2}, {0x0000FD40, 0x0000FD4F}, {0x0000FD90, 0x0000FD91}, +{0x0000FDC8, 0x0000FDEF}, {0x0000FDFE, 0x0000FDFF}, {0x0000FE1A, 0x0000FE1F}, {0x0000FE53, 0x0000FE53}, +{0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, {0x0000FEFD, 0x0000FF00}, +{0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, {0x0000FFD8, 0x0000FFD9}, +{0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, {0x0000FFFE, 0x0000FFFF}, +{0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, {0x0001003E, 0x0001003E}, +{0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF}, {0x00010103, 0x00010106}, +{0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F}, {0x000101A1, 0x000101CF}, +{0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF}, {0x000102FC, 0x000102FF}, +{0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, {0x0001039E, 0x0001039E}, +{0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, {0x000104AA, 0x000104AF}, +{0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, {0x00010564, 0x0001056E}, +{0x00010570, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x000107FF}, +{0x00010806, 0x00010807}, {0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, +{0x0001083D, 0x0001083E}, {0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, +{0x000108F3, 0x000108F3}, {0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, +{0x00010940, 0x0001097F}, {0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, +{0x00010A07, 0x00010A0B}, {0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, +{0x00010A3B, 0x00010A3E}, {0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, +{0x00010AE7, 0x00010AEA}, {0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, +{0x00010B73, 0x00010B77}, {0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, +{0x00010C49, 0x00010C7F}, {0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, +{0x00010D3A, 0x00010E5F}, {0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, +{0x00010EB2, 0x00010EFF}, {0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, +{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011070, 0x0001107E}, {0x000110BD, 0x000110BD}, +{0x000110C2, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, +{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, +{0x00011212, 0x00011212}, {0x0001123F, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, +{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, +{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, +{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, +{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, +{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, +{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, +{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, +{0x0001166D, 0x0001167F}, {0x000116B9, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, +{0x0001172C, 0x0001172F}, {0x00011740, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, +{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, +{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, +{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, +{0x00011AA3, 0x00011ABF}, {0x00011AF9, 0x00011BFF}, {0x00011C09, 0x00011C09}, {0x00011C37, 0x00011C37}, +{0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, {0x00011CA8, 0x00011CA8}, +{0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, {0x00011D37, 0x00011D39}, +{0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, {0x00011D5A, 0x00011D5F}, +{0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, {0x00011D92, 0x00011D92}, +{0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, +{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, +{0x00012544, 0x00012FFF}, {0x0001342F, 0x000143FF}, {0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, +{0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, {0x00016A70, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, +{0x00016AF6, 0x00016AFF}, {0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, +{0x00016B78, 0x00016B7C}, {0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, +{0x00016F88, 0x00016F8E}, {0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, +{0x000187F8, 0x000187FF}, {0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFFF}, {0x0001B11F, 0x0001B14F}, +{0x0001B153, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, +{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CFFF}, +{0x0001D0F6, 0x0001D0FF}, {0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1E9, 0x0001D1FF}, +{0x0001D246, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, +{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, +{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, +{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, +{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, +{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, +{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DFFF}, {0x0001E007, 0x0001E007}, +{0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, {0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E0FF}, +{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E2BF}, +{0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E7FF}, {0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, +{0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, {0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, +{0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, {0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, +{0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, {0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, +{0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, {0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, +{0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, {0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, +{0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, {0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, +{0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, {0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, +{0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, {0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, +{0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, {0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, +{0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, {0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, +{0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, {0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, +{0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, {0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, +{0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, {0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DF}, +{0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, {0x0001F774, 0x0001F77F}, {0x0001F7D9, 0x0001F7DF}, +{0x0001F7EC, 0x0001F7FF}, {0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, +{0x0001F888, 0x0001F88F}, {0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001F979, 0x0001F979}, +{0x0001F9CC, 0x0001F9CC}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, {0x0001FA75, 0x0001FA77}, +{0x0001FA7B, 0x0001FA7F}, {0x0001FA87, 0x0001FA8F}, {0x0001FAA9, 0x0001FAAF}, {0x0001FAB7, 0x0001FABF}, +{0x0001FAC3, 0x0001FACF}, {0x0001FAD7, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, {0x0001FBCB, 0x0001FBEF}, +{0x0001FBFA, 0x0001FFFF}, {0x0002A6DE, 0x0002A6FF}, {0x0002B735, 0x0002B73F}, {0x0002B81E, 0x0002B81F}, +{0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002F7FF}, {0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x000E00FF}, +{0x000E01F0, 0x0010FFFF}, +}; + +static const std::multimap unicode_map_nfd = { +{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301}, +{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303}, +{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A}, +{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300}, +{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302}, +{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300}, +{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302}, +{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303}, +{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301}, +{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303}, +{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300}, +{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302}, +{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301}, +{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301}, +{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303}, +{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A}, +{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300}, +{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302}, +{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300}, +{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302}, +{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303}, +{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301}, +{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303}, +{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300}, +{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302}, +{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301}, +{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304}, +{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306}, +{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328}, +{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301}, +{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302}, +{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307}, +{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C}, +{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C}, +{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304}, +{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306}, +{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307}, +{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328}, +{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C}, +{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302}, +{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306}, +{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307}, +{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327}, +{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302}, +{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303}, +{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304}, +{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306}, +{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 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0x00008728}, {0x0002F9BD, 0x0000876B}, +{0x0002F9BE, 0x00008786}, {0x0002F9BF, 0x000045D7}, {0x0002F9C0, 0x000087E1}, {0x0002F9C1, 0x00008801}, +{0x0002F9C2, 0x000045F9}, {0x0002F9C3, 0x00008860}, {0x0002F9C4, 0x00008863}, {0x0002F9C5, 0x00027667}, +{0x0002F9C6, 0x000088D7}, {0x0002F9C7, 0x000088DE}, {0x0002F9C8, 0x00004635}, {0x0002F9C9, 0x000088FA}, +{0x0002F9CA, 0x000034BB}, {0x0002F9CB, 0x000278AE}, {0x0002F9CC, 0x00027966}, {0x0002F9CD, 0x000046BE}, +{0x0002F9CE, 0x000046C7}, {0x0002F9CF, 0x00008AA0}, {0x0002F9D0, 0x00008AED}, {0x0002F9D1, 0x00008B8A}, +{0x0002F9D2, 0x00008C55}, {0x0002F9D3, 0x00027CA8}, {0x0002F9D4, 0x00008CAB}, {0x0002F9D5, 0x00008CC1}, +{0x0002F9D6, 0x00008D1B}, {0x0002F9D7, 0x00008D77}, {0x0002F9D8, 0x00027F2F}, {0x0002F9D9, 0x00020804}, +{0x0002F9DA, 0x00008DCB}, {0x0002F9DB, 0x00008DBC}, {0x0002F9DC, 0x00008DF0}, {0x0002F9DD, 0x000208DE}, +{0x0002F9DE, 0x00008ED4}, {0x0002F9DF, 0x00008F38}, {0x0002F9E0, 0x000285D2}, {0x0002F9E1, 0x000285ED}, +{0x0002F9E2, 0x00009094}, {0x0002F9E3, 0x000090F1}, {0x0002F9E4, 0x00009111}, {0x0002F9E5, 0x0002872E}, +{0x0002F9E6, 0x0000911B}, {0x0002F9E7, 0x00009238}, {0x0002F9E8, 0x000092D7}, {0x0002F9E9, 0x000092D8}, +{0x0002F9EA, 0x0000927C}, {0x0002F9EB, 0x000093F9}, {0x0002F9EC, 0x00009415}, {0x0002F9ED, 0x00028BFA}, +{0x0002F9EE, 0x0000958B}, {0x0002F9EF, 0x00004995}, {0x0002F9F0, 0x000095B7}, {0x0002F9F1, 0x00028D77}, +{0x0002F9F2, 0x000049E6}, {0x0002F9F3, 0x000096C3}, {0x0002F9F4, 0x00005DB2}, {0x0002F9F5, 0x00009723}, +{0x0002F9F6, 0x00029145}, {0x0002F9F7, 0x0002921A}, {0x0002F9F8, 0x00004A6E}, {0x0002F9F9, 0x00004A76}, +{0x0002F9FA, 0x000097E0}, {0x0002F9FB, 0x0002940A}, {0x0002F9FC, 0x00004AB2}, {0x0002F9FD, 0x00029496}, +{0x0002F9FE, 0x0000980B}, {0x0002F9FF, 0x0000980B}, {0x0002FA00, 0x00009829}, {0x0002FA01, 0x000295B6}, +{0x0002FA02, 0x000098E2}, {0x0002FA03, 0x00004B33}, {0x0002FA04, 0x00009929}, {0x0002FA05, 0x000099A7}, +{0x0002FA06, 0x000099C2}, {0x0002FA07, 0x000099FE}, {0x0002FA08, 0x00004BCE}, {0x0002FA09, 0x00029B30}, +{0x0002FA0A, 0x00009B12}, {0x0002FA0B, 0x00009C40}, {0x0002FA0C, 0x00009CFD}, {0x0002FA0D, 0x00004CCE}, +{0x0002FA0E, 0x00004CED}, {0x0002FA0F, 0x00009D67}, {0x0002FA10, 0x0002A0CE}, {0x0002FA11, 0x00004CF8}, +{0x0002FA12, 0x0002A105}, {0x0002FA13, 0x0002A20E}, {0x0002FA14, 0x0002A291}, {0x0002FA15, 0x00009EBB}, +{0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05}, +{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600}, +}; + +static std::string unicode_cpts_to_utf8(const std::vector & cps) { + std::string result; + for (size_t i = 0; i < cps.size(); ++i) { + result.append(unicode_cpt_to_utf8(cps[i])); + } + return result; +} + +static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) { + assert(offset < utf8.size()); + if (!(utf8[offset + 0] & 0x80)) { + auto result = utf8[offset + 0]; + offset += 1; + return result; + } + if (!(utf8[offset + 0] & 0x40)) { + throw std::invalid_argument("invalid character"); + } + if (!(utf8[offset + 0] & 0x20)) { + if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) { + throw std::invalid_argument("invalid character"); + } + auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f); + offset += 2; + return result; + } + if (!(utf8[offset + 0] & 0x10)) { + if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) { + throw std::invalid_argument("invalid character"); + } + auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f); + offset += 3; + return result; + } + if (!(utf8[offset + 0] & 0x08)) { + if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) { + throw std::invalid_argument("invalid character"); + } + auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f); + offset += 4; + return result; + } + throw std::invalid_argument("invalid string"); +} + +static std::vector unicode_cpt_to_utf16(uint32_t cp) { + std::vector result; + if (/* 0x0000 <= cp && */ cp <= 0xffff) { + result.emplace_back(cp); + } + else if (0x10000 <= cp && cp <= 0x10ffff) { + result.emplace_back(0xd800 | ((cp - 0x10000) >> 10)); + result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff)); + } + else { + throw std::invalid_argument("invalid cpt"); + } + return result; +} + +//static std::vector unicode_cpts_to_utf16(const std::vector & cps) { +// std::vector result; +// for (size_t i = 0; i < cps.size(); ++i) { +// auto temp = unicode_cpt_to_utf16(cps[i]); +// result.insert(result.end(), temp.begin(), temp.end()); +// } +// return result; +//} + +static uint32_t cpt_from_utf16(const std::vector & utf16, size_t & offset) { + assert(offset < utf16.size()); + if (((utf16[0] >> 10) << 10) != 0xd800) { + auto result = utf16[offset + 0]; + offset += 1; + return result; + } + + if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) { + throw std::invalid_argument("invalid character"); + } + + auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); + offset += 2; + return result; +} + +//static std::vector unicode_cpts_from_utf16(const std::vector & utf16) { +// std::vector result; +// size_t offset = 0; +// while (offset < utf16.size()) { +// result.push_back(cpt_from_utf16(utf16, offset)); +// } +// return result; +//} + +static std::unordered_map unicode_cpt_type_map() { + std::unordered_map cpt_types; + for (auto p : unicode_ranges_digit) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_DIGIT; + } + } + for (auto p : unicode_ranges_letter) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_LETTER; + } + } + for (auto p : unicode_ranges_whitespace) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_WHITESPACE; + } + } + for (auto p : unicode_ranges_accent_mark) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK; + } + } + for (auto p : unicode_ranges_punctuation) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION; + } + } + for (auto p : unicode_ranges_symbol) { + for (auto i = p.first; i <= p.second; ++i) { + cpt_types[i] = CODEPOINT_TYPE_SYMBOL; + } + } + for (auto p : unicode_ranges_control) { + for (auto i = p.first; i <= p.second; ++ i) { + cpt_types[i] = CODEPOINT_TYPE_CONTROL; + } + } + return cpt_types; +} + +static std::unordered_map unicode_byte_to_utf8_map() { + std::unordered_map map; + for (int ch = u'!'; ch <= u'~'; ++ch) { + assert(0 <= ch && ch < 256); + map[ch] = unicode_cpt_to_utf8(ch); + } + for (int ch = u'¡'; ch <= u'¬'; ++ch) { + assert(0 <= ch && ch < 256); + map[ch] = unicode_cpt_to_utf8(ch); + } + for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + assert(0 <= ch && ch < 256); + map[ch] = unicode_cpt_to_utf8(ch); + } + auto n = 0; + for (int ch = 0; ch < 256; ++ch) { + if (map.find(ch) == map.end()) { + map[ch] = unicode_cpt_to_utf8(256 + n); + ++n; + } + } + return map; +} + +static std::unordered_map unicode_utf8_to_byte_map() { + std::unordered_map map; + for (int ch = u'!'; ch <= u'~'; ++ch) { + assert(0 <= ch && ch < 256); + map[unicode_cpt_to_utf8(ch)] = ch; + } + for (int ch = u'¡'; ch <= u'¬'; ++ch) { + assert(0 <= ch && ch < 256); + map[unicode_cpt_to_utf8(ch)] = ch; + } + for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + assert(0 <= ch && ch < 256); + map[unicode_cpt_to_utf8(ch)] = ch; + } + auto n = 0; + for (int ch = 0; ch < 256; ++ch) { + if (map.find(unicode_cpt_to_utf8(ch)) == map.end()) { + map[unicode_cpt_to_utf8(256 + n)] = ch; + ++n; + } + } + return map; +} + +// +// interface +// + +std::string unicode_cpt_to_utf8(uint32_t cp) { + std::string result; + if (/* 0x00 <= cp && */ cp <= 0x7f) { + result.push_back(cp); + } + else if (0x80 <= cp && cp <= 0x7ff) { + result.push_back(0xc0 | ((cp >> 6) & 0x1f)); + result.push_back(0x80 | (cp & 0x3f)); + } + else if (0x800 <= cp && cp <= 0xffff) { + result.push_back(0xe0 | ((cp >> 12) & 0x0f)); + result.push_back(0x80 | ((cp >> 6) & 0x3f)); + result.push_back(0x80 | (cp & 0x3f)); + } + else if (0x10000 <= cp && cp <= 0x10ffff) { + result.push_back(0xf0 | ((cp >> 18) & 0x07)); + result.push_back(0x80 | ((cp >> 12) & 0x3f)); + result.push_back(0x80 | ((cp >> 6) & 0x3f)); + result.push_back(0x80 | (cp & 0x3f)); + } + else { + throw std::invalid_argument("invalid codepoint"); + } + return result; +} + +std::vector unicode_cpts_normalize_nfd(const std::vector & cpts) { + std::vector result; + result.reserve(cpts.size()); + for (size_t i = 0; i < cpts.size(); ++i) { + auto it = unicode_map_nfd.find(cpts[i]); + if (it == unicode_map_nfd.end()) { + result.push_back(cpts[i]); + } else { + result.push_back(it->second); + } + } + return result; +} + +std::vector unicode_cpts_from_utf8(const std::string & utf8) { + std::vector result; + size_t offset = 0; + while (offset < utf8.size()) { + result.push_back(unicode_cpt_from_utf8(utf8, offset)); + } + return result; +} + +int unicode_cpt_type(uint32_t cp) { + static std::unordered_map cpt_types = unicode_cpt_type_map(); + const auto it = cpt_types.find(cp); + return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; +} + +int unicode_cpt_type(const std::string & utf8) { + if (utf8.length() == 0) { + return CODEPOINT_TYPE_UNIDENTIFIED; + } + size_t offset = 0; + return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset)); +} + +std::string unicode_byte_to_utf8(uint8_t byte) { + static std::unordered_map map = unicode_byte_to_utf8_map(); + return map.at(byte); +} + +uint8_t unicode_utf8_to_byte(const std::string & utf8) { + static std::unordered_map map = unicode_utf8_to_byte_map(); + return map.at(utf8); +} + diff --git a/unicode.h b/unicode.h index f6be4549bff10..6d14a5a333f57 100644 --- a/unicode.h +++ b/unicode.h @@ -1,784 +1,26 @@ -#pragma once +#pragma once -#include -#include -#include +#include #include -#include #include -static const std::vector> digit_ranges = { -{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F}, -{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F}, -{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468}, -{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909}, -{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A}, -{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739}, -{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9}, -{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9}, -}; - -static const std::vector> letter_ranges = { -{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374}, -{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559}, -{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710}, -{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A}, -{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2}, -{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33}, -{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD}, -{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61}, -{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0}, -{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3}, -{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61}, -{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A}, -{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C}, -{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7}, -{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5}, -{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C}, -{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3}, -{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB}, -{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F}, -{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D}, -{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4}, -{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107}, -{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E}, -{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F}, -{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006}, -{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF}, -{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788}, -{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE}, -{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B}, -{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4}, -{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB}, -{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44}, -{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7}, -{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA}, -{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D}, -{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835}, -{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7}, -{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35}, -{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C}, -{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147}, -{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288}, -{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339}, -{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE}, -{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909}, -{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00}, -{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F}, -{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0}, -{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77}, -{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08}, -{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C}, -{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514}, -{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA}, -{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D}, -{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27}, -{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52}, -{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72}, -{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734}, -{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A}, -}; - -static const std::vector> whitespace_ranges = { -{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000}, -}; - -static const std::vector> accent_mark_ranges = { -{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4}, -{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B}, -{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7}, -{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC}, -{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63}, -{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83}, -{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57}, -{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC}, -{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E}, -{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734}, -{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E}, -{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2}, -{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F}, -{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881}, -{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D}, -{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E}, -{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F}, -{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134}, -{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303}, -{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E}, -{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938}, -{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47}, -{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45}, -{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92}, -{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36}, -{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A}, -{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF}, -}; - -static const std::vector> punctuation_ranges = { -{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB}, -{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D}, -{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76}, -{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA}, -{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A}, -{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027}, -{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998}, -{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F}, -{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF}, -{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F}, -{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20}, -{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857}, -{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D}, -{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9}, -{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946}, -{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F}, -{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F}, -}; - -static const std::vector> symbol_ranges = { -{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7}, -{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608}, -{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA}, -{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5}, -{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C}, -{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF}, -{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B}, -{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4}, -{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3}, -{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3}, -{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A}, -{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69}, -{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F}, -{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F}, -{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241}, -{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789}, -{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC}, -{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD}, -{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8}, -{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53}, -{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA}, -}; - -static const std::vector> control_ranges = { -{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C}, -{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F}, -{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5}, -{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29}, -{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80}, -{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5}, -{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54}, -{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7}, -{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C}, -{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB}, -{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49}, -{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7}, -{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5}, -{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC}, -{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF}, -{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F}, -{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF}, -{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F}, -{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F}, -{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F}, -{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC}, -{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF}, -{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F}, -{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF}, -{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF}, -{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F}, -{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA}, -{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA}, -{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2}, -{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1}, -{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B}, -{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F}, -{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F}, -{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807}, -{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E}, -{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E}, -{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8}, -{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF}, -{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF}, -{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E}, -{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334}, -{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C}, -{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF}, -{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936}, -{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09}, -{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B}, -{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF}, -{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F}, -{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF}, -{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163}, -{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A}, -{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8}, -{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F}, -{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A}, -{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6}, -{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26}, -{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50}, -{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B}, -{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF}, -{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F}, -{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F}, -{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F}, -{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F}, -{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, -}; - -static const std::multimap nfd_map = { -{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43}, -{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301}, -{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F}, -{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301}, -{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63}, -{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301}, -{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F}, -{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301}, -{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61}, -{0x105, 0x328}, {0x106, 0x43}, {0x106, 0x301}, {0x107, 0x63}, {0x107, 0x301}, {0x108, 0x43}, {0x108, 0x302}, {0x109, 0x63}, {0x109, 0x302}, {0x10A, 0x43}, {0x10A, 0x307}, {0x10B, 0x63}, -{0x10B, 0x307}, {0x10C, 0x43}, {0x10C, 0x30C}, {0x10D, 0x63}, {0x10D, 0x30C}, {0x10E, 0x44}, {0x10E, 0x30C}, {0x10F, 0x64}, {0x10F, 0x30C}, {0x112, 0x45}, {0x112, 0x304}, {0x113, 0x65}, -{0x113, 0x304}, {0x114, 0x45}, {0x114, 0x306}, {0x115, 0x65}, {0x115, 0x306}, {0x116, 0x45}, {0x116, 0x307}, {0x117, 0x65}, {0x117, 0x307}, {0x118, 0x45}, {0x118, 0x328}, {0x119, 0x65}, -{0x119, 0x328}, {0x11A, 0x45}, {0x11A, 0x30C}, {0x11B, 0x65}, {0x11B, 0x30C}, {0x11C, 0x47}, {0x11C, 0x302}, {0x11D, 0x67}, {0x11D, 0x302}, {0x11E, 0x47}, {0x11E, 0x306}, {0x11F, 0x67}, -{0x11F, 0x306}, {0x120, 0x47}, {0x120, 0x307}, {0x121, 0x67}, {0x121, 0x307}, {0x122, 0x47}, {0x122, 0x327}, {0x123, 0x67}, {0x123, 0x327}, {0x124, 0x48}, {0x124, 0x302}, {0x125, 0x68}, -{0x125, 0x302}, {0x128, 0x49}, {0x128, 0x303}, {0x129, 0x69}, {0x129, 0x303}, {0x12A, 0x49}, {0x12A, 0x304}, {0x12B, 0x69}, {0x12B, 0x304}, {0x12C, 0x49}, {0x12C, 0x306}, {0x12D, 0x69}, -{0x12D, 0x306}, {0x12E, 0x49}, {0x12E, 0x328}, {0x12F, 0x69}, {0x12F, 0x328}, {0x130, 0x49}, {0x130, 0x307}, {0x134, 0x4A}, {0x134, 0x302}, {0x135, 0x6A}, {0x135, 0x302}, {0x136, 0x4B}, -{0x136, 0x327}, {0x137, 0x6B}, {0x137, 0x327}, {0x139, 0x4C}, {0x139, 0x301}, {0x13A, 0x6C}, {0x13A, 0x301}, {0x13B, 0x4C}, {0x13B, 0x327}, {0x13C, 0x6C}, {0x13C, 0x327}, {0x13D, 0x4C}, -{0x13D, 0x30C}, {0x13E, 0x6C}, {0x13E, 0x30C}, {0x143, 0x4E}, {0x143, 0x301}, {0x144, 0x6E}, {0x144, 0x301}, {0x145, 0x4E}, {0x145, 0x327}, {0x146, 0x6E}, {0x146, 0x327}, {0x147, 0x4E}, -{0x147, 0x30C}, {0x148, 0x6E}, {0x148, 0x30C}, {0x14C, 0x4F}, {0x14C, 0x304}, {0x14D, 0x6F}, {0x14D, 0x304}, {0x14E, 0x4F}, {0x14E, 0x306}, {0x14F, 0x6F}, {0x14F, 0x306}, {0x150, 0x4F}, -{0x150, 0x30B}, {0x151, 0x6F}, {0x151, 0x30B}, {0x154, 0x52}, {0x154, 0x301}, {0x155, 0x72}, {0x155, 0x301}, {0x156, 0x52}, {0x156, 0x327}, {0x157, 0x72}, {0x157, 0x327}, {0x158, 0x52}, -{0x158, 0x30C}, {0x159, 0x72}, {0x159, 0x30C}, {0x15A, 0x53}, {0x15A, 0x301}, {0x15B, 0x73}, {0x15B, 0x301}, {0x15C, 0x53}, {0x15C, 0x302}, {0x15D, 0x73}, {0x15D, 0x302}, {0x15E, 0x53}, -{0x15E, 0x327}, {0x15F, 0x73}, {0x15F, 0x327}, {0x160, 0x53}, {0x160, 0x30C}, {0x161, 0x73}, {0x161, 0x30C}, {0x162, 0x54}, {0x162, 0x327}, {0x163, 0x74}, {0x163, 0x327}, {0x164, 0x54}, -{0x164, 0x30C}, {0x165, 0x74}, {0x165, 0x30C}, {0x168, 0x55}, {0x168, 0x303}, {0x169, 0x75}, {0x169, 0x303}, {0x16A, 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0x3A6C}, {0x2F8C8, 0x654F}, {0x2F8C9, 0x656C}, {0x2F8CA, 0x2300A}, {0x2F8CB, 0x65E3}, {0x2F8CC, 0x66F8}, {0x2F8CD, 0x6649}, -{0x2F8CE, 0x3B19}, {0x2F8CF, 0x6691}, {0x2F8D0, 0x3B08}, {0x2F8D1, 0x3AE4}, {0x2F8D2, 0x5192}, {0x2F8D3, 0x5195}, {0x2F8D4, 0x6700}, {0x2F8D5, 0x669C}, {0x2F8D6, 0x80AD}, {0x2F8D7, 0x43D9}, -{0x2F8D8, 0x6717}, {0x2F8D9, 0x671B}, {0x2F8DA, 0x6721}, {0x2F8DB, 0x675E}, {0x2F8DC, 0x6753}, {0x2F8DD, 0x233C3}, {0x2F8DE, 0x3B49}, {0x2F8DF, 0x67FA}, {0x2F8E0, 0x6785}, {0x2F8E1, 0x6852}, -{0x2F8E2, 0x6885}, {0x2F8E3, 0x2346D}, {0x2F8E4, 0x688E}, {0x2F8E5, 0x681F}, {0x2F8E6, 0x6914}, {0x2F8E7, 0x3B9D}, {0x2F8E8, 0x6942}, {0x2F8E9, 0x69A3}, {0x2F8EA, 0x69EA}, {0x2F8EB, 0x6AA8}, -{0x2F8EC, 0x236A3}, {0x2F8ED, 0x6ADB}, {0x2F8EE, 0x3C18}, {0x2F8EF, 0x6B21}, {0x2F8F0, 0x238A7}, {0x2F8F1, 0x6B54}, {0x2F8F2, 0x3C4E}, {0x2F8F3, 0x6B72}, {0x2F8F4, 0x6B9F}, {0x2F8F5, 0x6BBA}, -{0x2F8F6, 0x6BBB}, {0x2F8F7, 0x23A8D}, {0x2F8F8, 0x21D0B}, {0x2F8F9, 0x23AFA}, {0x2F8FA, 0x6C4E}, {0x2F8FB, 0x23CBC}, {0x2F8FC, 0x6CBF}, {0x2F8FD, 0x6CCD}, {0x2F8FE, 0x6C67}, {0x2F8FF, 0x6D16}, -{0x2F900, 0x6D3E}, {0x2F901, 0x6D77}, {0x2F902, 0x6D41}, {0x2F903, 0x6D69}, {0x2F904, 0x6D78}, {0x2F905, 0x6D85}, {0x2F906, 0x23D1E}, {0x2F907, 0x6D34}, {0x2F908, 0x6E2F}, {0x2F909, 0x6E6E}, -{0x2F90A, 0x3D33}, {0x2F90B, 0x6ECB}, {0x2F90C, 0x6EC7}, {0x2F90D, 0x23ED1}, {0x2F90E, 0x6DF9}, {0x2F90F, 0x6F6E}, {0x2F910, 0x23F5E}, {0x2F911, 0x23F8E}, {0x2F912, 0x6FC6}, {0x2F913, 0x7039}, -{0x2F914, 0x701E}, {0x2F915, 0x701B}, {0x2F916, 0x3D96}, {0x2F917, 0x704A}, {0x2F918, 0x707D}, {0x2F919, 0x7077}, {0x2F91A, 0x70AD}, {0x2F91B, 0x20525}, {0x2F91C, 0x7145}, {0x2F91D, 0x24263}, -{0x2F91E, 0x719C}, {0x2F91F, 0x243AB}, {0x2F920, 0x7228}, {0x2F921, 0x7235}, {0x2F922, 0x7250}, {0x2F923, 0x24608}, {0x2F924, 0x7280}, {0x2F925, 0x7295}, {0x2F926, 0x24735}, {0x2F927, 0x24814}, -{0x2F928, 0x737A}, {0x2F929, 0x738B}, {0x2F92A, 0x3EAC}, {0x2F92B, 0x73A5}, {0x2F92C, 0x3EB8}, {0x2F92D, 0x3EB8}, {0x2F92E, 0x7447}, {0x2F92F, 0x745C}, {0x2F930, 0x7471}, {0x2F931, 0x7485}, -{0x2F932, 0x74CA}, {0x2F933, 0x3F1B}, {0x2F934, 0x7524}, {0x2F935, 0x24C36}, {0x2F936, 0x753E}, {0x2F937, 0x24C92}, {0x2F938, 0x7570}, {0x2F939, 0x2219F}, {0x2F93A, 0x7610}, {0x2F93B, 0x24FA1}, -{0x2F93C, 0x24FB8}, {0x2F93D, 0x25044}, {0x2F93E, 0x3FFC}, {0x2F93F, 0x4008}, {0x2F940, 0x76F4}, {0x2F941, 0x250F3}, {0x2F942, 0x250F2}, {0x2F943, 0x25119}, {0x2F944, 0x25133}, {0x2F945, 0x771E}, -{0x2F946, 0x771F}, {0x2F947, 0x771F}, {0x2F948, 0x774A}, {0x2F949, 0x4039}, {0x2F94A, 0x778B}, {0x2F94B, 0x4046}, {0x2F94C, 0x4096}, {0x2F94D, 0x2541D}, {0x2F94E, 0x784E}, {0x2F94F, 0x788C}, -{0x2F950, 0x78CC}, {0x2F951, 0x40E3}, {0x2F952, 0x25626}, {0x2F953, 0x7956}, {0x2F954, 0x2569A}, {0x2F955, 0x256C5}, {0x2F956, 0x798F}, {0x2F957, 0x79EB}, {0x2F958, 0x412F}, {0x2F959, 0x7A40}, -{0x2F95A, 0x7A4A}, {0x2F95B, 0x7A4F}, {0x2F95C, 0x2597C}, {0x2F95D, 0x25AA7}, {0x2F95E, 0x25AA7}, {0x2F95F, 0x7AEE}, {0x2F960, 0x4202}, {0x2F961, 0x25BAB}, {0x2F962, 0x7BC6}, {0x2F963, 0x7BC9}, -{0x2F964, 0x4227}, {0x2F965, 0x25C80}, {0x2F966, 0x7CD2}, {0x2F967, 0x42A0}, {0x2F968, 0x7CE8}, {0x2F969, 0x7CE3}, {0x2F96A, 0x7D00}, {0x2F96B, 0x25F86}, {0x2F96C, 0x7D63}, {0x2F96D, 0x4301}, -{0x2F96E, 0x7DC7}, {0x2F96F, 0x7E02}, {0x2F970, 0x7E45}, {0x2F971, 0x4334}, {0x2F972, 0x26228}, {0x2F973, 0x26247}, {0x2F974, 0x4359}, {0x2F975, 0x262D9}, {0x2F976, 0x7F7A}, {0x2F977, 0x2633E}, -{0x2F978, 0x7F95}, {0x2F979, 0x7FFA}, {0x2F97A, 0x8005}, {0x2F97B, 0x264DA}, {0x2F97C, 0x26523}, {0x2F97D, 0x8060}, {0x2F97E, 0x265A8}, {0x2F97F, 0x8070}, {0x2F980, 0x2335F}, {0x2F981, 0x43D5}, -{0x2F982, 0x80B2}, {0x2F983, 0x8103}, {0x2F984, 0x440B}, {0x2F985, 0x813E}, {0x2F986, 0x5AB5}, {0x2F987, 0x267A7}, {0x2F988, 0x267B5}, {0x2F989, 0x23393}, {0x2F98A, 0x2339C}, {0x2F98B, 0x8201}, -{0x2F98C, 0x8204}, {0x2F98D, 0x8F9E}, {0x2F98E, 0x446B}, {0x2F98F, 0x8291}, {0x2F990, 0x828B}, {0x2F991, 0x829D}, {0x2F992, 0x52B3}, {0x2F993, 0x82B1}, {0x2F994, 0x82B3}, {0x2F995, 0x82BD}, -{0x2F996, 0x82E6}, {0x2F997, 0x26B3C}, {0x2F998, 0x82E5}, {0x2F999, 0x831D}, {0x2F99A, 0x8363}, {0x2F99B, 0x83AD}, {0x2F99C, 0x8323}, {0x2F99D, 0x83BD}, {0x2F99E, 0x83E7}, {0x2F99F, 0x8457}, -{0x2F9A0, 0x8353}, {0x2F9A1, 0x83CA}, {0x2F9A2, 0x83CC}, {0x2F9A3, 0x83DC}, {0x2F9A4, 0x26C36}, {0x2F9A5, 0x26D6B}, {0x2F9A6, 0x26CD5}, {0x2F9A7, 0x452B}, {0x2F9A8, 0x84F1}, {0x2F9A9, 0x84F3}, -{0x2F9AA, 0x8516}, {0x2F9AB, 0x273CA}, {0x2F9AC, 0x8564}, {0x2F9AD, 0x26F2C}, {0x2F9AE, 0x455D}, {0x2F9AF, 0x4561}, {0x2F9B0, 0x26FB1}, {0x2F9B1, 0x270D2}, {0x2F9B2, 0x456B}, {0x2F9B3, 0x8650}, -{0x2F9B4, 0x865C}, {0x2F9B5, 0x8667}, {0x2F9B6, 0x8669}, {0x2F9B7, 0x86A9}, {0x2F9B8, 0x8688}, {0x2F9B9, 0x870E}, {0x2F9BA, 0x86E2}, {0x2F9BB, 0x8779}, {0x2F9BC, 0x8728}, {0x2F9BD, 0x876B}, -{0x2F9BE, 0x8786}, {0x2F9BF, 0x45D7}, {0x2F9C0, 0x87E1}, {0x2F9C1, 0x8801}, {0x2F9C2, 0x45F9}, {0x2F9C3, 0x8860}, {0x2F9C4, 0x8863}, {0x2F9C5, 0x27667}, {0x2F9C6, 0x88D7}, {0x2F9C7, 0x88DE}, -{0x2F9C8, 0x4635}, {0x2F9C9, 0x88FA}, {0x2F9CA, 0x34BB}, {0x2F9CB, 0x278AE}, {0x2F9CC, 0x27966}, {0x2F9CD, 0x46BE}, {0x2F9CE, 0x46C7}, {0x2F9CF, 0x8AA0}, {0x2F9D0, 0x8AED}, {0x2F9D1, 0x8B8A}, -{0x2F9D2, 0x8C55}, {0x2F9D3, 0x27CA8}, {0x2F9D4, 0x8CAB}, {0x2F9D5, 0x8CC1}, {0x2F9D6, 0x8D1B}, {0x2F9D7, 0x8D77}, {0x2F9D8, 0x27F2F}, {0x2F9D9, 0x20804}, {0x2F9DA, 0x8DCB}, {0x2F9DB, 0x8DBC}, -{0x2F9DC, 0x8DF0}, {0x2F9DD, 0x208DE}, {0x2F9DE, 0x8ED4}, {0x2F9DF, 0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E}, -{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995}, -{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76}, -{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33}, -{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE}, -{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9}, -{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600}, -}; - -static std::string codepoint_to_utf8(uint32_t cp) { - std::string result; - if (/* 0x00 <= cp && */ cp <= 0x7f) { - result.push_back(cp); - } - else if (0x80 <= cp && cp <= 0x7ff) { - result.push_back(0xc0 | ((cp >> 6) & 0x1f)); - result.push_back(0x80 | (cp & 0x3f)); - } - else if (0x800 <= cp && cp <= 0xffff) { - result.push_back(0xe0 | ((cp >> 12) & 0x0f)); - result.push_back(0x80 | ((cp >> 6) & 0x3f)); - result.push_back(0x80 | (cp & 0x3f)); - } - else if (0x10000 <= cp && cp <= 0x10ffff) { - result.push_back(0xf0 | ((cp >> 18) & 0x07)); - result.push_back(0x80 | ((cp >> 12) & 0x3f)); - result.push_back(0x80 | ((cp >> 6) & 0x3f)); - result.push_back(0x80 | (cp & 0x3f)); - } - else { - throw std::invalid_argument("invalid codepoint"); - } - return result; -} - -static std::string codepoints_to_utf8(const std::vector & cps) { - std::string result; - for (size_t i = 0; i < cps.size(); ++i) { - result.append(codepoint_to_utf8(cps[i])); - } - return result; -} - -static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) { - assert(offset < utf8.size()); - if (!(utf8[offset + 0] & 0x80)) { - auto result = utf8[offset + 0]; - offset += 1; - return result; - } - if (!(utf8[offset + 0] & 0x40)) { - throw std::invalid_argument("invalid character"); - } - if (!(utf8[offset + 0] & 0x20)) { - if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) { - throw std::invalid_argument("invalid character"); - } - auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f); - offset += 2; - return result; - } - if (!(utf8[offset + 0] & 0x10)) { - if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) { - throw std::invalid_argument("invalid character"); - } - auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f); - offset += 3; - return result; - } - if (!(utf8[offset + 0] & 0x08)) { - if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) { - throw std::invalid_argument("invalid character"); - } - auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f); - offset += 4; - return result; - } - throw std::invalid_argument("invalid string"); -} - -static std::vector codepoints_from_utf8(const std::string & utf8) { - std::vector result; - size_t offset = 0; - while (offset < utf8.size()) { - result.push_back(codepoint_from_utf8(utf8, offset)); - } - return result; -} - -static std::vector codepoint_to_utf16(uint32_t cp) { - std::vector result; - if (/* 0x0000 <= cp && */ cp <= 0xffff) { - result.emplace_back(cp); - } - else if (0x10000 <= cp && cp <= 0x10ffff) { - result.emplace_back(0xd800 | ((cp - 0x10000) >> 10)); - result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff)); - } - else { - throw std::invalid_argument("invalid codepoint"); - } - return result; -} - -static std::vector codepoints_to_utf16(const std::vector & cps) { - std::vector result; - for (size_t i = 0; i < cps.size(); ++i) { - auto temp = codepoint_to_utf16(cps[i]); - result.insert(result.end(), temp.begin(), temp.end()); - } - return result; -} - -static uint32_t codepoint_from_utf16(const std::vector & utf16, size_t & offset) { - assert(offset < utf16.size()); - if (((utf16[0] >> 10) << 10) != 0xd800) { - auto result = utf16[offset + 0]; - offset += 1; - return result; - } - - if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) { - throw std::invalid_argument("invalid character"); - } - - auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); - offset += 2; - return result; -} - -static std::vector codepoints_from_utf16(const std::vector & utf16) { - std::vector result; - size_t offset = 0; - while (offset < utf16.size()) { - result.push_back(codepoint_from_utf16(utf16, offset)); - } - return result; -} - #define CODEPOINT_TYPE_UNIDENTIFIED 0 -#define CODEPOINT_TYPE_DIGIT 1 -#define CODEPOINT_TYPE_LETTER 2 -#define CODEPOINT_TYPE_WHITESPACE 3 -#define CODEPOINT_TYPE_ACCENT_MARK 4 -#define CODEPOINT_TYPE_PUNCTUATION 5 -#define CODEPOINT_TYPE_SYMBOL 6 -#define CODEPOINT_TYPE_CONTROL 7 - -static std::unordered_map codepoint_type_map() { - std::unordered_map codepoint_types; - for (auto p : digit_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_DIGIT; - } - } - for (auto p : letter_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_LETTER; - } - } - for (auto p : whitespace_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE; - } - } - for (auto p : accent_mark_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK; - } - } - for (auto p : punctuation_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION; - } - } - for (auto p : symbol_ranges) { - for (auto i = p.first; i <= p.second; ++i) { - codepoint_types[i] = CODEPOINT_TYPE_SYMBOL; - } - } - for (auto p : control_ranges) { - for (auto i = p.first; i <= p.second; ++ i) { - codepoint_types[i] = CODEPOINT_TYPE_CONTROL; - } - } - return codepoint_types; -} - -static int codepoint_type(uint32_t cp) { - static std::unordered_map codepoint_types = codepoint_type_map(); - const auto it = codepoint_types.find(cp); - return it == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; -} - -static int codepoint_type(const std::string & utf8) { - if (utf8.length() == 0) { - return CODEPOINT_TYPE_UNIDENTIFIED; - } - size_t offset = 0; - return codepoint_type(codepoint_from_utf8(utf8, offset)); -} +#define CODEPOINT_TYPE_DIGIT 1 +#define CODEPOINT_TYPE_LETTER 2 +#define CODEPOINT_TYPE_WHITESPACE 3 +#define CODEPOINT_TYPE_ACCENT_MARK 4 +#define CODEPOINT_TYPE_PUNCTUATION 5 +#define CODEPOINT_TYPE_SYMBOL 6 +#define CODEPOINT_TYPE_CONTROL 7 -static std::unordered_map bytes_to_unicode_map_bpe() { - std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { - assert(0 <= ch && ch < 256); - map[ch] = codepoint_to_utf8(ch); - } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { - assert(0 <= ch && ch < 256); - map[ch] = codepoint_to_utf8(ch); - } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { - assert(0 <= ch && ch < 256); - map[ch] = codepoint_to_utf8(ch); - } - auto n = 0; - for (int ch = 0; ch < 256; ++ch) { - if (map.find(ch) == map.end()) { - map[ch] = codepoint_to_utf8(256 + n); - ++n; - } - } - return map; -} +std::string unicode_cpt_to_utf8(uint32_t cp); +std::vector unicode_cpts_from_utf8(const std::string & utf8); -static std::string bytes_to_unicode_bpe(uint8_t byte) { - static std::unordered_map map = bytes_to_unicode_map_bpe(); - return map.at(byte); -} +std::vector unicode_cpts_normalize_nfd(const std::vector & cpts); -static std::unordered_map unicode_to_bytes_map_bpe() { - std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { - assert(0 <= ch && ch < 256); - map[codepoint_to_utf8(ch)] = ch; - } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { - assert(0 <= ch && ch < 256); - map[codepoint_to_utf8(ch)] = ch; - } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { - assert(0 <= ch && ch < 256); - map[codepoint_to_utf8(ch)] = ch; - } - auto n = 0; - for (int ch = 0; ch < 256; ++ch) { - if (map.find(codepoint_to_utf8(ch)) == map.end()) { - map[codepoint_to_utf8(256 + n)] = ch; - ++n; - } - } - return map; -} +int unicode_cpt_type(uint32_t cp); +int unicode_cpt_type(const std::string & utf8); -static uint8_t unicode_to_bytes_bpe(const std::string & utf8) { - static std::unordered_map map = unicode_to_bytes_map_bpe(); - return map.at(utf8); -} +std::string unicode_byte_to_utf8(uint8_t byte); +uint8_t unicode_utf8_to_byte(const std::string & utf8); From 05b06210c954491cf0f12034b0a62bd4d69ce78b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 11 Mar 2024 17:49:47 +0200 Subject: [PATCH 237/401] llama : more consistent names of count variables (#5994) * llama : more consistent names of count variables ggml-ci * llama : n_parallel -> n_seq_max * common : fix param name * examples : fix param name --- README.md | 2 +- common/common.cpp | 18 +++++++++--------- examples/batched-bench/batched-bench.cpp | 2 +- examples/batched/batched.cpp | 2 +- examples/main/main.cpp | 1 + examples/perplexity/perplexity.cpp | 6 +++--- llama.cpp | 24 ++++++++++++------------ llama.h | 14 +++++++------- 8 files changed, 35 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 98fdc6808ffe3..54bf84bec67bb 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes -- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 +- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 diff --git a/common/common.cpp b/common/common.cpp index 16ef4d7f74dd9..2f38ac632b45a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1288,7 +1288,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_ctx = params.n_ctx; cparams.n_batch = params.n_batch; - cparams.n_parallel = params.n_parallel; + cparams.n_seq_max = params.n_parallel; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; @@ -1786,17 +1786,17 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", - view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); llama_kv_cache_view_cell * c_curr = view.cells; llama_seq_id * cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { if (i % row_size == 0) { printf("\n%5d: ", i); } int seq_count = 0; - for (int j = 0; j < view.n_max_seq; j++) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] >= 0) { seq_count++; } } putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]); @@ -1809,14 +1809,14 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", - view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); std::unordered_map seqs; llama_kv_cache_view_cell * c_curr = view.cells; llama_seq_id * cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { - for (int j = 0; j < view.n_max_seq; j++) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] < 0) { continue; } if (seqs.find(cs_curr[j]) == seqs.end()) { if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } @@ -1835,11 +1835,11 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { c_curr = view.cells; cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { if (i % row_size == 0) { printf("\n%5d: ", i); } - for (int j = 0; j < view.n_max_seq; j++) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] >= 0) { const auto & it = seqs.find(cs_curr[j]); putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+'); diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index dff6c68ec2e69..22bc93bca817c 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -106,7 +106,7 @@ int main(int argc, char ** argv) { ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; // ensure enough sequences are available - ctx_params.n_parallel = *std::max_element(n_pl.begin(), n_pl.end()); + ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end()); llama_context * ctx = llama_new_context_with_model(model, ctx_params); diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index dde4d5a068e24..ee1f8f1bf5dd2 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -80,7 +80,7 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_len, n_parallel); - ctx_params.n_parallel = n_parallel; + ctx_params.n_seq_max = n_parallel; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 47059e582a0d4..e2d07a6319d50 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -878,6 +878,7 @@ int main(int argc, char ** argv) { const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); + LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 293eb52c33653..fdfc8f5dcd3e5 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -841,7 +841,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1118,7 +1118,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int max_tasks_per_batch = 128; - const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); + const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); @@ -1470,7 +1470,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params const int n_batch = params.n_batch; const int max_tasks_per_batch = 32; - const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx)); + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); diff --git a/llama.cpp b/llama.cpp index 6aa88ce54baef..98ec147aee5d5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12538,7 +12538,7 @@ struct llama_context_params llama_context_default_params() { /*.seed =*/ LLAMA_DEFAULT_SEED, /*.n_ctx =*/ 512, /*.n_batch =*/ 512, - /*.n_parallel =*/ 1, + /*.n_seq_max =*/ 1, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, @@ -12700,7 +12700,7 @@ struct llama_context * llama_new_context_with_model( auto & cparams = ctx->cparams; cparams.n_batch = params.n_batch; - // TODO: maybe add n_parallel here too + // TODO: maybe add n_seq_max here too cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch; cparams.yarn_ext_factor = params.yarn_ext_factor; @@ -12767,7 +12767,7 @@ struct llama_context * llama_new_context_with_model( // Mamba only needs a constant number of KV cache cells per sequence if (model->arch == LLM_ARCH_MAMBA) { // Mamba needs at least as many KV cells as there are sequences kept at any time - kv_size = std::max((uint32_t) 1, params.n_parallel); + kv_size = std::max((uint32_t) 1, params.n_seq_max); // it's probably best to keep as much precision as possible for the states type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states @@ -13024,7 +13024,7 @@ uint32_t llama_n_batch(const struct llama_context * ctx) { return ctx->cparams.n_batch; } -uint32_t llama_n_max_seq(const struct llama_context * ctx) { +uint32_t llama_n_seq_max(const struct llama_context * ctx) { return ctx->kv_self.size; } @@ -13188,10 +13188,10 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const } } -struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) { +struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) { struct llama_kv_cache_view result = { /*.n_cells = */ 0, - /*.n_max_seq = */ n_max_seq, + /*.n_seq_max = */ n_seq_max, /*.token_count = */ 0, /*.used_cells = */ llama_get_kv_cache_used_cells(ctx), /*.max_contiguous = */ 0, @@ -13219,7 +13219,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells); GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells"); view->cells = (struct llama_kv_cache_view_cell *)p; - p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells); + p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells); GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences"); view->cells_sequences = (llama_seq_id *)p; } @@ -13233,7 +13233,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k uint32_t max_contig = 0; int32_t max_contig_idx = -1; - for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) { + for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) { const size_t curr_size = kv_cells[i].seq_id.size(); token_count += curr_size; c_curr->pos = kv_cells[i].pos + kv_cells[i].delta; @@ -13250,7 +13250,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k int seq_idx = 0; for (const llama_seq_id it : kv_cells[i].seq_id) { - if (seq_idx >= view->n_max_seq) { + if (seq_idx >= view->n_seq_max) { break; } cs_curr[seq_idx] = it; @@ -13259,7 +13259,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k if (seq_idx != 0) { used_cells++; } - for (; seq_idx < view->n_max_seq; seq_idx++) { + for (; seq_idx < view->n_seq_max; seq_idx++) { cs_curr[seq_idx] = -1; } } @@ -13921,12 +13921,12 @@ int32_t llama_tokenize( const char * text, int32_t text_len, llama_token * tokens, - int32_t n_max_tokens, + int32_t n_tokens_max, bool add_bos, bool special) { auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special); - if (n_max_tokens < (int) res.size()) { + if (n_tokens_max < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); return -((int) res.size()); } diff --git a/llama.h b/llama.h index ccf65ca4e87a5..446899da6e38e 100644 --- a/llama.h +++ b/llama.h @@ -235,7 +235,7 @@ extern "C" { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model uint32_t n_batch; // prompt processing maximum batch size - uint32_t n_parallel; // number of parallel sequences (i.e. distinct states for recurrent models) + uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -377,7 +377,7 @@ extern "C" { LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); - LLAMA_API uint32_t llama_n_max_seq (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); @@ -456,7 +456,7 @@ extern "C" { // Maximum number of sequences that can exist in a cell. It's not an error // if there are more sequences in a cell than this value, however they will // not be visible in the view cells_sequences. - int32_t n_max_seq; + int32_t n_seq_max; // Number of tokens in the cache. For example, if there are two populated // cells, the first with 1 sequence id in it and the second with 2 sequence @@ -476,12 +476,12 @@ extern "C" { // Information for an individual cell. struct llama_kv_cache_view_cell * cells; - // The sequences for each cell. There will be n_max_seq items per cell. + // The sequences for each cell. There will be n_seq_max items per cell. llama_seq_id * cells_sequences; }; // Create an empty KV cache view. (use only for debugging purposes) - LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq); + LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max); // Free a KV cache view. (use only for debugging purposes) LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view); @@ -708,7 +708,7 @@ extern "C" { /// @details Convert the provided text into tokens. /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. - /// @return Returns the number of tokens on success, no more than n_max_tokens + /// @return Returns the number of tokens on success, no more than n_tokens_max /// @return Returns a negative number on failure - the number of tokens that would have been returned /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext. /// Does not insert a leading space. @@ -717,7 +717,7 @@ extern "C" { const char * text, int32_t text_len, llama_token * tokens, - int32_t n_max_tokens, + int32_t n_tokens_max, bool add_bos, bool special); From 44ca159faf4fbe1a7ace13a962845ba7cdfd95ec Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:53:15 +0100 Subject: [PATCH 238/401] 1.5 bit: we can do even better (#5999) * iq1_s: we can do even better Spent one of the 4 scale bits on a signs of a 0.125 shift. I.e., quants are now -1 + delta, delta, 1 + delta, where delta is +/- 0.125. CUDA works, same performance as before. PPL(LLaMA-v2-7B) is now 11.85! * iq1_s: make scalar and AVX2 work with the new version * iq1_s: make Neon work with new version. ~10% drop in performance, so will need some more work. * iq1_s: make Metal work with new version * iq1_s: very slightly faster dequantize on Metal * iq1_s: fix dequantize on the CPU --------- Co-authored-by: Iwan Kawrakow --- ggml-common.h | 1 + ggml-cuda.cu | 36 ++++++++++----------- ggml-metal.metal | 18 ++++++----- ggml-quants.c | 83 ++++++++++++++++++++++++++++++++---------------- 4 files changed, 83 insertions(+), 55 deletions(-) diff --git a/ggml-common.h b/ggml-common.h index 5dd918081c8dd..2402b773c073c 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -645,6 +645,7 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512) GGML_TABLE_END() #define NGRID_IQ1S 2048 +#define IQ1S_DELTA 0.125f #if defined(GGML_COMMON_IMPL_C) GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S) 0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff, diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d2945d3c2048d..01b1f15ee8778 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1722,22 +1722,15 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 0xf) + 1); -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = *((const int *)(iq1s_grid_gpu + (x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)))); - grid32[1] = __vsub4((grid32[0] >> 4) & 0x0f0f0f0f, 0x01010101); - grid32[0] = __vsub4(grid32[0] & 0x0f0f0f0f, 0x01010101); + const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; + const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; for (int j = 0; j < 8; ++j) { - y[j] = d * q[j]; - } -#else - const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8))); - for (int j = 0; j < 4; ++j) { - y[j+0] = d * ((grid[j] & 0xf) - 1); - y[j+4] = d * ((grid[j] >> 4) - 1); + y[j] = d * (q[j] + delta); } -#endif #else assert(false); #endif @@ -4560,22 +4553,25 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const int * q8 = (const int *)bq8_1[ib32].qs; for (int l = 0; l < 4; ++l) { const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); - int grid0 = __vsub4(grid[0] & 0x0f0f0f0f, 0x01010101); - int grid1 = __vsub4((grid[0] >> 4) & 0x0f0f0f0f, 0x01010101); + int grid0 = grid[0] & 0x0f0f0f0f; + int grid1 = (grid[0] >> 4) & 0x0f0f0f0f; sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi)); } #else - const int8_t * q8 = bq8_1[ib32].qs; + const int8_t * q8 = bq8_1[ib32].qs; for (int l = 0; l < 4; ++l) { const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); for (int j = 0; j < 4; ++j) { - sumi += q8[j] * ((grid[j] & 0xf) - 1) + q8[j+4] * ((grid[j] >> 4) - 1); + sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4); } q8 += 8; } #endif - const float d = (float)bq1->d * __low2float(bq8_1[ib32].ds); - return d * sumi * (2*(bq1->qh[ib32] >> 12) + 1); + const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA; + const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1); + const float d = d1q * __low2float (bq8_1[ib32].ds); + const float m = d1q * __high2float(bq8_1[ib32].ds); + return d * sumi + m * delta; #else assert(false); return 0.f; diff --git a/ggml-metal.metal b/ggml-metal.metal index 912822ee64bc3..21298a8c24cc7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -4377,7 +4377,7 @@ void kernel_mul_mv_iq1_s_f32_impl( + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4) + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4); } - sumf[row] += (float)dh[0] * (sum - sumy) * (2*(qh[0] >> 12) + 1); + sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1); dh += nb*sizeof(block_iq1_s)/2; qs += nb*sizeof(block_iq1_s); @@ -5076,14 +5076,16 @@ void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & const float d = xb->d; device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; device const uint16_t * qh = xb->qh; - const float dl = d * (2*(qh[ib32] >> 12) + 1); - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | (((qh[ib32] >> (6*il+0)) & 7) << 8))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | (((qh[ib32] >> (6*il+3)) & 7) << 8))); + const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); + const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); + const uint16_t h = qh[ib32] >> 6*il; + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) - dl; - reg[1][i] = dl * (grid1[i] >> 4) - dl; - reg[2][i] = dl * (grid2[i] & 0xf) - dl; - reg[3][i] = dl * (grid2[i] >> 4) - dl; + reg[0][i] = dl * (grid1[i] & 0xf) + ml; + reg[1][i] = dl * (grid1[i] >> 4) + ml; + reg[2][i] = dl * (grid2[i] & 0xf) + ml; + reg[3][i] = dl * (grid2[i] >> 4) + ml; } } diff --git a/ggml-quants.c b/ggml-quants.c index 86b0764cbae18..06665eb2d9725 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3456,11 +3456,12 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in const uint16_t * qh = x[i].qh; for (int ib = 0; ib < QK_K/32; ++ib) { - const float dl = d * (2*(qh[ib] >> 12) + 1); + const float dl = d * (2*((qh[ib] >> 12) & 7) + 1); + const float delta = qh[ib] & 0x8000 ? -IQ1S_DELTA : IQ1S_DELTA; for (int l = 0; l < 4; ++l) { const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); for (int j = 0; j < 8; ++j) { - y[j] = dl * grid[j]; + y[j] = dl * (grid[j] + delta); } y += 8; } @@ -9582,7 +9583,7 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qs = x[i].qs; const uint16_t * qh = x[i].qh; - int sumi1 = 0, sumi2 = 0; + int sumi1 = 0, sumi2 = 0, sumi3 = 0; for (int ib = 0; ib < QK_K/32; ib += 2) { @@ -9601,12 +9602,16 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); - sumi1 += vaddvq_s32(p1) * (2*(qh[ib+0] >> 12) + 1); - sumi2 += vaddvq_s32(p2) * (2*(qh[ib+1] >> 12) + 1); + const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + sumi1 += vaddvq_s32(p1) * ls1; + sumi2 += vaddvq_s32(p2) * ls2; + sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1) + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1); } - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2); + sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3); } *s = sumf; @@ -9614,6 +9619,7 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void #elif defined __AVX2__ __m256 accum = _mm256_setzero_ps(); + float accum1 = 0; for (int i = 0; i < nb; ++i) { const int8_t * q8 = y[i].qs; @@ -9621,6 +9627,7 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const uint16_t * qh = x[i].qh; __m256i sumi = _mm256_setzero_si256(); + int sumi1 = 0; for (int ib = 0; ib < QK_K/32; ib += 2) { const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); @@ -9632,17 +9639,23 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*(qh[ib+0] >> 12) + 1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*(qh[ib+1] >> 12) + 1)); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2)); sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; } - accum = _mm256_fmadd_ps(_mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)), _mm256_cvtepi32_ps(sumi), accum); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum); + accum1 += d * sumi1; } - *s = hsum_float_8(accum); + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; #else @@ -9653,9 +9666,10 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qs = x[i].qs; const uint16_t * qh = x[i].qh; - int sumi = 0; + int sumi = 0, sumi1 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { - const int ls = 2*(qh[ib] >> 12) + 1; + const int ls = 2*((qh[ib] >> 12) & 7) + 1; + const int delta = qh[ib] & 0x8000 ? -1 : 1; int lsum = 0; for (int l = 0; l < 4; ++l) { const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); @@ -9664,11 +9678,12 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void } q8 += 8; } - sumi += ls * lsum; + sumi += ls * lsum; + sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]); qs += 4; } - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * sumi; + sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1); } *s = sumf; @@ -11438,7 +11453,7 @@ static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const u } static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const uint64_t * restrict grid, - const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L, int ngrid) { + const float * restrict xval, const float * restrict weight, float scale, const float * restrict xg, int8_t * restrict L, int ngrid) { int num_neighbors = neighbours[0]; GGML_ASSERT(num_neighbors > 0); float best_score = FLT_MAX; @@ -11447,7 +11462,7 @@ static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const const int8_t * pg = (const int8_t *)(grid + neighbours[j]); float d2 = 0; for (int i = 0; i < 8; ++i) { - float q = (pg[i] - 3)/2; + float q = xg[(pg[i] - 1)/2]; float w = weight[i]; float diff = scale*q - xval[i]; d2 += w*diff*diff; @@ -11463,7 +11478,7 @@ static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const float d2 = 0; for (int j = 0; j < 8; ++j) { float w = weight[j]; - float q = (grid_i[j] - 3)/2; + float q = xg[(grid_i[j] - 1)/2]; float diff = scale*q - xval[i]; d2 += w*diff*diff; } @@ -11480,7 +11495,7 @@ static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const const int8_t * pg = (const int8_t *)(grid + neighbours[j]); float sumqx = 0, sumq2 = 0; for (int i = 0; i < 8; ++i) { - float q = (pg[i] - 3)/2; + float q = xg[(pg[i] - 1)/2]; float w = weight[i]; sumqx += w*q*xval[i]; sumq2 += w*q*q; @@ -11519,6 +11534,9 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy block_iq1_s * y = vy; + const float x_p[3] = {-1 + IQ1S_DELTA, IQ1S_DELTA, 1 + IQ1S_DELTA}; + const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA}; + float scales[QK_K/IQ1S_BLOCK_SIZE]; float weight[IQ1S_BLOCK_SIZE]; int8_t L[IQ1S_BLOCK_SIZE]; @@ -11527,6 +11545,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy float pairs[2*IQ1S_BLOCK_SIZE]; int * idx = (int *)(pairs + 1); uint16_t index[IQ1S_BLOCK_SIZE/8]; + int8_t shifts[QK_K/IQ1S_BLOCK_SIZE]; for (int ibl = 0; ibl < nbl; ++ibl) { @@ -11572,25 +11591,33 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } } float best_score = 0, scale = max; - int besti1 = 0, besti2 = 0; + int besti1 = -1, besti2 = -1, best_shift = 0; for (int i1 = 0; i1 <= IQ1S_BLOCK_SIZE; ++i1) { for (int i2 = i1; i2 <= IQ1S_BLOCK_SIZE; ++i2) { - float sumqx = -(sumx[i1] - sumx[0]) + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2]); - float sumq2 = (sumw[i1] - sumw[0]) + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2]); + float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_p[2]; + float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_p[2]*x_p[2]; + if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { + scale = sumqx/sumq2; best_score = scale*sumqx; + besti1 = i1; besti2 = i2; best_shift = 1; + } + sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_m[2]; + sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_m[2]*x_m[2]; if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { scale = sumqx/sumq2; best_score = scale*sumqx; - besti1 = i1; besti2 = i2; + besti1 = i1; besti2 = i2; best_shift = -1; } } } + GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0); for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; for (int j = besti2; j < IQ1S_BLOCK_SIZE; ++j) L[idx[2*j]] = 2; if (scale < 0) { for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) L[j] = 2 - L[j]; - scale = -scale; + scale = -scale; best_shift = -best_shift; } bool all_on_grid = true; + const float * xx = best_shift == 1 ? x_p : x_m; for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { uint16_t u = 0; for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j); @@ -11598,7 +11625,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy if (grid_index < 0) { all_on_grid = false; const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; - grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, L + 8*k, NGRID_IQ1S); + grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S); GGML_ASSERT(grid_index >= 0); } index[k] = grid_index; @@ -11609,7 +11636,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]); for (int j = 0; j < 8; ++j) { float w = weight[8*k + j]; - float q = (pg[j] - 3)/2; + float q = xx[(pg[j] - 1)/2]; sumqx += w*q*xb[8*k+j]; sumq2 += w*q*q; } @@ -11624,6 +11651,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy y[ibl].qh[ib] = h; GGML_ASSERT(scale >= 0); scales[ib] = scale; + shifts[ib] = best_shift; max_scale = MAX(max_scale, scale); } @@ -11632,12 +11660,13 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy continue; } - float d = max_scale/31; + float d = max_scale/15; y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.085f is another fudge factor. Don't ask me why it is needed. float id = 1/d; for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); - l = MAX(0, MIN(15, l)); + l = MAX(0, MIN(7, l)); + if (shifts[ib] == -1) l |= 8; y[ibl].qh[ib] |= (l << 12); } } From 5cdb371731caa2c41fcca42d4d2d43f94f6883b4 Mon Sep 17 00:00:00 2001 From: gliptic Date: Mon, 11 Mar 2024 20:59:03 +0100 Subject: [PATCH 239/401] grammar : fix unnecessarily retained pointer to rules (#6003) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 98ec147aee5d5..ad7b7b7d4bcf2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10538,7 +10538,7 @@ struct llama_grammar * llama_grammar_init( // loop over alternates of start rule to build initial stacks std::vector> stacks; - pos = rules[start_rule_index]; + pos = vec_rules[start_rule_index].data(); do { std::vector stack; if (!llama_grammar_is_end_of_sequence(pos)) { From 48358b2e5b3983c41ba7e61a493e84d3901dc7b9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 12 Mar 2024 11:15:05 +0200 Subject: [PATCH 240/401] sycl : update IQ1_S kernels (WIP - not working!) (#5995) * sycl : try to fix after IQ1_S changes * sycl : iq1s_grid -> iq1s_grid_gpu * sycl : fix grid type --- ggml-sycl.cpp | 66 ++++++++++++++++++++++++++------------------------- 1 file changed, 34 insertions(+), 32 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 6d56845821f69..24187e7d5bc2d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3514,8 +3514,8 @@ static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N #define QI1_S (QK_K / (4*QR1_S)) typedef struct { sycl::half d; - uint8_t qs[QK_K/8]; - uint8_t scales[QK_K/16]; + uint8_t qs[QK_K/8]; + uint16_t qh[QK_K/32]; } block_iq1_s; static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); @@ -4891,10 +4891,9 @@ static void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restr template static void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy, const sycl::nd_item<3> &item_ct1, - const uint64_t *iq1s_grid, + const uint32_t *iq1s_grid, const uint8_t *ksigns_iq2xs, const uint8_t *kmask_iq2xs) { - const int i = item_ct1.get_group(2); const block_iq1_s * x = (const block_iq1_s *) vx; @@ -4903,11 +4902,15 @@ static void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restr const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const int i8 = 4*ib+il; - uint8_t h = x[i].scales[i8/2] >> 4*(i8%2); - const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5))); - const float d = (float)x[i].d * (2*(h & 7) + 1); - for (int j = 0; j < 8; ++j) y[j] = d * grid[j]; + const uint8_t * qs = x[i].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq1s_grid + qs[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq1s_grid + qs[2*il+1]); + const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 0xf) + 1); + const uint8_t signs = ksigns_iq2xs[(x[i].qh[ib] >> 3*il) & 7]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } #else assert(false); #endif @@ -7803,28 +7806,27 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, static __dpct_inline__ float vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, - const uint64_t *iq1s_grid, const uint64_t *ksigns64) { + const uint32_t *iq1s_grid, const uint64_t *ksigns64) { #if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; - int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; - const uint8_t h1 = bq1->scales[2*ib32+0]; - const uint8_t h2 = bq1->scales[2*ib32+1]; - const int * q8 = (const int *)bq8_1[ib32].qs; - const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5))); - const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1))); - const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5))); - const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1))); - for (int j = 0; j < 2; ++j) { - sumi1 = dpct::dp4a(q8[j+0], grid1[j], sumi1); - sumi2 = dpct::dp4a(q8[j+2], grid2[j], sumi2); - sumi3 = dpct::dp4a(q8[j+4], grid3[j], sumi3); - sumi4 = dpct::dp4a(q8[j+6], grid4[j], sumi4); - } - const float d = (float)bq1->d * bq8_1[ib32].ds[0]; - return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) + - sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1)); + const uint8_t * qs = bq1->qs + 4*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq1s_grid + qs[l]); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (qs[l] >> 8)); + const int grid_l = dpct::vectorized_binary( + grid[0] ^ signs[0], signs[0], std::minus<>()); + const int grid_h = dpct::vectorized_binary( + grid[1] ^ signs[1], signs[1], std::minus<>()); + sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + q8 += 8; + } + const float d = (float)bq1->d * bq8_1[ib32].ds[0] * 0.25f; + return d * sumi; #else assert(false); return 0.f; @@ -8644,7 +8646,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void * __restrict__ vx, const void * template static void mul_mat_vec_q_iq1_s_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, const sycl::nd_item<3> &item_ct1, - const uint64_t *iq1s_grid_ptr, const uint64_t *ksigns64_ptr ) { + const uint32_t *iq1s_grid_ptr, const uint64_t *ksigns64_ptr ) { const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); @@ -10406,7 +10408,7 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; { - iq1s_grid.init(*stream); + iq1s_grid_gpu.init(*stream); ksigns_iq2xs.init(*stream); kmask_iq2xs.init(*stream); @@ -10414,7 +10416,7 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k, {sycl::aspect::fp16}); stream->submit([&](sycl::handler &cgh) { - auto iq1s_grid_ptr_ct1 = iq1s_grid.get_ptr(); + auto iq1s_grid_ptr_ct1 = iq1s_grid_gpu.get_ptr(); auto ksigns_iq2xs_ptr_ct1 = ksigns_iq2xs.get_ptr(); auto kmask_iq2xs_ptr_ct1 = kmask_iq2xs.get_ptr(); @@ -11154,11 +11156,11 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - iq1s_grid.init(*stream); + iq1s_grid_gpu.init(*stream); ksigns64.init(*stream); stream->submit([&](sycl::handler &cgh) { - auto iq1s_grid_ptr_ct1 = iq1s_grid.get_ptr(); + auto iq1s_grid_ptr_ct1 = iq1s_grid_gpu.get_ptr(); auto ksigns64_ptr_ct1 = ksigns64.get_ptr(); cgh.parallel_for( From 184215e783dfad76aded2c68244c327a5c507df5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 12 Mar 2024 13:49:55 +0200 Subject: [PATCH 241/401] ggml : fix UB in IQ2_S and IQ3_S (#6012) --- ggml-quants.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 06665eb2d9725..3ad558a1b140e 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9025,7 +9025,7 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); qs += 8; - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); @@ -9034,7 +9034,7 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vceqq_u8(vs.val[0], mask2); @@ -9105,12 +9105,12 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); qs += 8; - __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); - aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); @@ -9386,7 +9386,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); @@ -9395,7 +9395,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); From 8030da7afea2d89f997aeadbd14183d399a017b9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 12 Mar 2024 14:27:20 +0200 Subject: [PATCH 242/401] ggml : reuse quantum structs across backends (#5943) * ggml : reuse quant blocks across backends ggml-ci * ggml : define helper constants only for CUDA and SYCL ggml-ci * ggml : define helper quantum constants for SYCL ggml-ci --- ggml-common.h | 409 ++++++++++++++++++++++++++++++++++++++++++++++- ggml-cuda.cu | 244 ++-------------------------- ggml-metal.m | 2 +- ggml-metal.metal | 180 +-------------------- ggml-quants.c | 78 ++++----- ggml-quants.h | 244 +--------------------------- ggml-sycl.cpp | 192 +--------------------- 7 files changed, 469 insertions(+), 880 deletions(-) diff --git a/ggml-common.h b/ggml-common.h index 2402b773c073c..0257c928cea52 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -1,4 +1,408 @@ -#pragma once +#ifndef GGML_COMMON_DECL + +#if defined(GGML_COMMON_DECL_C) +#include + +typedef uint16_t ggml_half; +typedef uint32_t ggml_half2; + +#define GGML_COMMON_AGGR + +#define GGML_COMMON_DECL +#elif defined(GGML_COMMON_DECL_METAL) +#include + +typedef half ggml_half; +typedef half2 ggml_half2; + +#define GGML_COMMON_AGGR + +#define GGML_COMMON_DECL +#elif defined(GGML_COMMON_DECL_CUDA) +#include +#include + +typedef half ggml_half; +typedef half2 ggml_half2; + +#define GGML_COMMON_AGGR data + +#define GGML_COMMON_DECL +#elif defined(GGML_COMMON_DECL_HIP) +#include +#include + +typedef half ggml_half; +typedef half2 ggml_half2; + +#define GGML_COMMON_AGGR data + +#define GGML_COMMON_DECL +#elif defined(GGML_COMMON_DECL_SYCL) +#include +#include + +typedef sycl::half ggml_half; +typedef sycl::half2 ggml_half2; + +#define GGML_COMMON_AGGR data + +#define GGML_COMMON_DECL +#endif + +#if defined(GGML_COMMON_DECL) + +#ifndef __cplusplus +#ifndef static_assert +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) +#define static_assert(cond, msg) _Static_assert(cond, msg) +#else +#define static_assert(cond, msg) struct global_scope_noop_trick +#endif +#endif +#endif // __cplusplus + +// QK = number of values after dequantization +// QK_K = super-block size + +#ifdef GGML_QKK_64 +#define QK_K 64 +#define K_SCALE_SIZE 4 +#else +#define QK_K 256 +#define K_SCALE_SIZE 12 +#endif // GGML_QKK_64 + +#if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL) +// QR = QK / number of values before dequantization +// QI = number of 32 bit integers before dequantization + +#define QI4_0 (QK4_0 / (4 * QR4_0)) +#define QR4_0 2 + +#define QI4_1 (QK4_1 / (4 * QR4_1)) +#define QR4_1 2 + +#define QI5_0 (QK5_0 / (4 * QR5_0)) +#define QR5_0 2 + +#define QI5_1 (QK5_1 / (4 * QR5_1)) +#define QR5_1 2 + +#define QI8_0 (QK8_0 / (4 * QR8_0)) +#define QR8_0 1 + +#define QI8_1 (QK8_1 / (4 * QR8_1)) +#define QR8_1 1 + +#define QI2_K (QK_K / (4*QR2_K)) +#define QR2_K 4 + +#define QI3_K (QK_K / (4*QR3_K)) +#define QR3_K 4 + +#define QI4_K (QK_K / (4*QR4_K)) +#define QR4_K 2 + +#define QI5_K (QK_K / (4*QR5_K)) +#define QR5_K 2 + +#define QI6_K (QK_K / (4*QR6_K)) +#define QR6_K 2 + +#define QI2_XXS (QK_K / (4*QR2_XXS)) +#define QR2_XXS 8 + +#define QI2_XS (QK_K / (4*QR2_XS)) +#define QR2_XS 8 + +#define QI2_S (QK_K / (4*QR2_S)) +#define QR2_S 8 + +#define QI3_XXS (QK_K / (4*QR3_XXS)) +#define QR3_XXS 8 + +#define QI3_XS (QK_K / (4*QR3_XS)) +#define QR3_XS 8 + +#define QI1_S (QK_K / (4*QR1_S)) +#define QR1_S 8 + +#define QI4_NL (QK4_NL / (4*QR4_NL)) +#define QR4_NL 2 + +#if QK_K == 64 +#define QI4_XS QI4_NL +#define QR4_XS QR4_NL +#else +#define QI4_XS (QK_K / (4*QR4_XS)) +#define QR4_XS 8 +#endif + +#endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP + +#define QK4_0 32 +typedef struct { + ggml_half d; // delta + uint8_t qs[QK4_0 / 2]; // nibbles / quants +} block_q4_0; +static_assert(sizeof(block_q4_0) == sizeof(ggml_half) + QK4_0 / 2, "wrong q4_0 block size/padding"); + +#define QK4_1 32 +typedef struct { + union { + struct { + ggml_half d; // delta + ggml_half m; // min + } GGML_COMMON_AGGR; + ggml_half2 dm; + }; + uint8_t qs[QK4_1 / 2]; // nibbles / quants +} block_q4_1; +static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding"); + +#define QK5_0 32 +typedef struct { + ggml_half d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; +static_assert(sizeof(block_q5_0) == sizeof(ggml_half) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); + +#define QK5_1 32 +typedef struct { + union { + struct { + ggml_half d; // delta + ggml_half m; // min + } GGML_COMMON_AGGR; + ggml_half2 dm; + }; + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; +static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_half) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); + +#define QK8_0 32 +typedef struct { + ggml_half d; // delta + int8_t qs[QK8_0]; // quants +} block_q8_0; +static_assert(sizeof(block_q8_0) == sizeof(ggml_half) + QK8_0, "wrong q8_0 block size/padding"); + +#define QK8_1 32 +typedef struct { + union { + struct { + ggml_half d; // delta + ggml_half s; // d * sum(qs[i]) + } GGML_COMMON_AGGR; + ggml_half2 ds; + }; + int8_t qs[QK8_1]; // quants +} block_q8_1; +static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding"); + +// +// Super-block quantization structures +// + +// 2-bit quantization +// weight is represented as x = a * q + b +// 16 blocks of 16 elements each +// Effectively 2.625 bits per weight +typedef struct { + uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits + uint8_t qs[QK_K/4]; // quants + union { + struct { + ggml_half d; // super-block scale for quantized scales + ggml_half dmin; // super-block scale for quantized mins + } GGML_COMMON_AGGR; + ggml_half2 dm; + }; +} block_q2_K; +static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); + +// 3-bit quantization +// weight is represented as x = a * q +// 16 blocks of 16 elements each +// Effectively 3.4375 bits per weight +#ifdef GGML_QKK_64 +typedef struct { + uint8_t hmask[QK_K/8]; // quants - high bit + uint8_t qs[QK_K/4]; // quants - low 2 bits + uint8_t scales[2]; + ggml_half d; // super-block scale +} block_q3_K; +static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); +#else +typedef struct { + uint8_t hmask[QK_K/8]; // quants - high bit + uint8_t qs[QK_K/4]; // quants - low 2 bits + uint8_t scales[12]; // scales, quantized with 6 bits + ggml_half d; // super-block scale +} block_q3_K; +static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); +#endif + +// 4-bit quantization +// 8 blocks of 32 elements each +// weight is represented as x = a * q + b +// Effectively 4.5 bits per weight +#ifdef GGML_QKK_64 +typedef struct { + ggml_half d[2]; // super-block scales/mins + uint8_t scales[2]; // 4-bit block scales/mins + uint8_t qs[QK_K/2]; // 4--bit quants +} block_q4_K; +static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding"); +#else +typedef struct { + union { + struct { + ggml_half d; // super-block scale for quantized scales + ggml_half dmin; // super-block scale for quantized mins + } GGML_COMMON_AGGR; + ggml_half2 dm; + }; + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits + uint8_t qs[QK_K/2]; // 4--bit quants +} block_q4_K; +static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); +#endif + +// 5-bit quantization +// 8 blocks of 32 elements each +// weight is represented as x = a * q + b +// Effectively 5.5 bits per weight +#ifdef GGML_QKK_64 +typedef struct { + ggml_half d; // super-block scale + int8_t scales[QK_K/16]; // 8-bit block scales + uint8_t qh[QK_K/8]; // quants, high bit + uint8_t qs[QK_K/2]; // quants, low 4 bits +} block_q5_K; +static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); +#else +typedef struct { + union { + struct { + ggml_half d; // super-block scale for quantized scales + ggml_half dmin; // super-block scale for quantized mins + } GGML_COMMON_AGGR; + ggml_half2 dm; + }; + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits + uint8_t qh[QK_K/8]; // quants, high bit + uint8_t qs[QK_K/2]; // quants, low 4 bits +} block_q5_K; +static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); +#endif + +// 6-bit quantization +// weight is represented as x = a * q +// 16 blocks of 16 elements each +// Effectively 6.5625 bits per weight +typedef struct { + uint8_t ql[QK_K/2]; // quants, lower 4 bits + uint8_t qh[QK_K/4]; // quants, upper 2 bits + int8_t scales[QK_K/16]; // scales, quantized with 8 bits + ggml_half d; // super-block scale +} block_q6_K; +static_assert(sizeof(block_q6_K) == sizeof(ggml_half) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding"); + +// This is only used for intermediate quantization and dot products +typedef struct { + float d; // delta + int8_t qs[QK_K]; // quants + int16_t bsums[QK_K/16]; // sum of quants in groups of 16 +} block_q8_K; +static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding"); + +// (Almost) "true" 2-bit quantization. +// Due to the need to use blocks as per ggml design, it ends up using +// 2.0625 bpw because of the 16-bit scale for each block of 256. +typedef struct { + ggml_half d; + uint16_t qs[QK_K/8]; +} block_iq2_xxs; +static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); + +// 2.3125 bpw quants +typedef struct { + ggml_half d; + uint16_t qs[QK_K/8]; + uint8_t scales[QK_K/32]; +} block_iq2_xs; +static_assert(sizeof(block_iq2_xs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); + +// 2.5625 bpw quants +typedef struct { + ggml_half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; +static_assert(sizeof(block_iq2_s) == sizeof(ggml_half) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); + +// (Almost) "true" 3-bit quantization. +// Due to the need to use blocks as per ggml design, it ends up using +// 3.0625 bpw because of the 16-bit scale for each block of 256. +typedef struct { + ggml_half d; + uint8_t qs[3*QK_K/8]; +} block_iq3_xxs; +static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); + +// 3.4375 bpw +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif +typedef struct { + ggml_half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t signs[QK_K/8]; + uint8_t scales[IQ3S_N_SCALE]; +} block_iq3_s; +static_assert(sizeof(block_iq3_s) == sizeof(ggml_half) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); + +typedef struct { + ggml_half d; + uint8_t qs[QK_K/8]; + uint16_t qh[QK_K/32]; +} block_iq1_s; +static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); + +// Non-linear quants +#define QK4_NL 32 +typedef struct { + ggml_half d; + uint8_t qs[QK4_NL/2]; +} block_iq4_nl; +static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding"); + +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +#else +typedef struct { + ggml_half d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +#endif + +#endif // GGML_COMMON_DECL +#endif // GGML_COMMON_DECL + +//////////////////////////////////////////////////////////////////////////////// + +#ifndef GGML_COMMON_IMPL #if defined(GGML_COMMON_IMPL_C) #include @@ -14,7 +418,7 @@ #define GGML_TABLE_END() }; #define GGML_COMMON_IMPL -#elif defined(GGML_COMMON_IMPL_CUDA) +#elif defined(GGML_COMMON_IMPL_CUDA) || defined(GGML_COMMON_IMPL_HIP) #include #define GGML_TABLE_BEGIN(type, name, size) static const __device__ type name[size] = { @@ -1423,3 +1827,4 @@ GGML_TABLE_END() #endif #endif // GGML_COMMON_IMPL +#endif // GGML_COMMON_IMPL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 01b1f15ee8778..b8834ed05eed0 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2,7 +2,13 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#if defined(GGML_USE_HIPBLAS) +#define GGML_COMMON_DECL_HIP +#define GGML_COMMON_IMPL_HIP +#else +#define GGML_COMMON_DECL_CUDA #define GGML_COMMON_IMPL_CUDA +#endif #include "ggml-common.h" #include @@ -359,66 +365,6 @@ typedef void (*ggml_cuda_op_flatten_t)( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream); -// QK = number of values after dequantization -// QR = QK / number of values before dequantization -// QI = number of 32 bit integers before dequantization - -#define QK4_0 32 -#define QR4_0 2 -#define QI4_0 (QK4_0 / (4 * QR4_0)) -typedef struct { - half d; // delta - uint8_t qs[QK4_0 / 2]; // nibbles / quants -} block_q4_0; -static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); - -#define QK4_1 32 -#define QR4_1 2 -#define QI4_1 (QK4_1 / (4 * QR4_1)) -typedef struct { - half2 dm; // dm.x = delta, dm.y = min - uint8_t qs[QK4_1 / 2]; // nibbles / quants -} block_q4_1; -static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding"); - -#define QK5_0 32 -#define QR5_0 2 -#define QI5_0 (QK5_0 / (4 * QR5_0)) -typedef struct { - half d; // delta - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_0 / 2]; // nibbles / quants -} block_q5_0; -static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); - -#define QK5_1 32 -#define QR5_1 2 -#define QI5_1 (QK5_1 / (4 * QR5_1)) -typedef struct { - half2 dm; // dm.x = delta, dm.y = min - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_1 / 2]; // nibbles / quants -} block_q5_1; -static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); - -#define QK8_0 32 -#define QR8_0 1 -#define QI8_0 (QK8_0 / (4 * QR8_0)) -typedef struct { - half d; // delta - int8_t qs[QK8_0]; // quants -} block_q8_0; -static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); - -#define QK8_1 32 -#define QR8_1 1 -#define QI8_1 (QK8_1 / (4 * QR8_1)) -typedef struct { - half2 ds; // ds.x = delta, ds.y = sum - int8_t qs[QK8_0]; // quants -} block_q8_1; -static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding"); - typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc); typedef void (*load_tiles_cuda_t)( @@ -428,174 +374,6 @@ typedef float (*vec_dot_q_mul_mat_cuda_t)( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); -//================================= k-quants - -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else -#define QK_K 256 -#define K_SCALE_SIZE 12 -#endif - -#define QR2_K 4 -#define QI2_K (QK_K / (4*QR2_K)) -typedef struct { - uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits - uint8_t qs[QK_K/4]; // quants - half2 dm; // super-block scale for quantized scales/mins -} block_q2_K; -static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); - -#define QR3_K 4 -#define QI3_K (QK_K / (4*QR3_K)) -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits -#ifdef GGML_QKK_64 - uint8_t scales[2]; // scales, quantized with 8 bits -#else - uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits -#endif - half d; // super-block scale -} block_q3_K; -//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding"); - -#define QR4_K 2 -#define QI4_K (QK_K / (4*QR4_K)) -#ifdef GGML_QKK_64 -typedef struct { - half dm[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else -typedef struct { - half2 dm; // super-block scale for quantized scales/mins - uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding"); -#endif - -#define QR5_K 2 -#define QI5_K (QK_K / (4*QR5_K)) -#ifdef GGML_QKK_64 -typedef struct { - half d; // super-block scale - int8_t scales[QK_K/16]; // block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else -typedef struct { - half2 dm; // super-block scale for quantized scales/mins - uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif - -#define QR6_K 2 -#define QI6_K (QK_K / (4*QR6_K)) -typedef struct { - uint8_t ql[QK_K/2]; // quants, lower 4 bits - uint8_t qh[QK_K/4]; // quants, upper 2 bits - int8_t scales[QK_K/16]; // scales - half d; // delta -} block_q6_K; -static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); - -#define QR2_XXS 8 -#define QI2_XXS (QK_K / (4*QR2_XXS)) -typedef struct { - half d; - uint16_t qs[QK_K/8]; -} block_iq2_xxs; -static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); - -#define QR2_XS 8 -#define QI2_XS (QK_K / (4*QR2_XS)) -typedef struct { - half d; - uint16_t qs[QK_K/8]; - uint8_t scales[QK_K/32]; -} block_iq2_xs; -static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); - -// 2.5625 bpw quants -#define QR2_S 8 -#define QI2_S (QK_K / (4*QR2_S)) -typedef struct { - half d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t scales[QK_K/32]; -} block_iq2_s; -static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); - -#define QR3_XXS 8 -#define QI3_XXS (QK_K / (4*QR3_XXS)) -typedef struct { - half d; - uint8_t qs[3*(QK_K/8)]; -} block_iq3_xxs; -static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); - -#define QR3_XS 8 -#define QI3_XS (QK_K / (4*QR3_XS)) -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else -#define IQ3S_N_SCALE QK_K/64 -#endif -typedef struct { - half d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t signs[QK_K/8]; - uint8_t scales[IQ3S_N_SCALE]; -} block_iq3_s; -static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); - -#define QR1_S 8 -#define QI1_S (QK_K / (4*QR1_S)) -typedef struct { - half d; - uint8_t qs[QK_K/8]; - uint16_t qh[QK_K/32]; -} block_iq1_s; -static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); - -#define QK4_NL 32 -#define QR4_NL 2 -#define QI4_NL (QK4_NL / (4*QR4_NL)) -typedef struct { - half d; - uint8_t qs[QK4_NL/2]; -} block_iq4_nl; -static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); - -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -#define QR4_XS QR4_NL -#define QI4_XS QI4_NL -#else -// QR4_XS = 8 is very slightly faster than QR4_XS = 4 -#define QR4_XS 8 -#define QI4_XS (QK_K / (4*QR4_XS)) -typedef struct { - half d; - uint16_t scales_h; - uint8_t scales_l[QK_K/64]; - uint8_t qs[QK_K/2]; -} block_iq4_xs; -static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); -#endif - #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -3570,7 +3348,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1( #pragma unroll for (int i = 0; i < QR2_K; ++ i) { u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); - d8[i] = __low2half(bq8_1[bq8_offset + i].ds); + d8[i] = __low2float(bq8_1[bq8_offset + i].ds); } return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8); @@ -3692,7 +3470,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( #pragma unroll for (int i = 0; i < QR3_K; ++i) { u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); - d8[i] = __low2half(bq8_1[bq8_offset + i].ds); + d8[i] = __low2float(bq8_1[bq8_offset + i].ds); } return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8); @@ -3861,7 +3639,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( for (int i = 0; i < QR4_K; ++i) { const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; - d8[i] = __low2half(bq8i->ds); + d8[i] = __low2float(bq8i->ds); const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); u[2*i+0] = q8[0]; @@ -4226,7 +4004,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( #pragma unroll for (int i = 0; i < QR6_K; ++i) { u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); - d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds); + d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds); } return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8); @@ -4763,7 +4541,7 @@ static __device__ __forceinline__ void mul_mat_q( *dsi_dst = *dsi_src; } else { float * dfi_dst = (float *) dsi_dst; - *dfi_dst = __low2half(*dsi_src); + *dfi_dst = __low2float(*dsi_src); } } diff --git a/ggml-metal.m b/ggml-metal.m index 3cf80de7bf2e0..1825d3320ee7a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -336,7 +336,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ NSMutableDictionary * prep = [NSMutableDictionary dictionary]; #ifdef GGML_QKK_64 - prep[@"QK_K"] = @(64); + prep[@"GGML_QKK_64"] = @(1); #endif MTLCompileOptions* options = [MTLCompileOptions new]; diff --git a/ggml-metal.metal b/ggml-metal.metal index 21298a8c24cc7..ebf2f5b478e46 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1,3 +1,7 @@ +#define GGML_COMMON_DECL_METAL +#define GGML_COMMON_IMPL_METAL +#include "ggml-common.h" + #include #define GGML_COMMON_IMPL_METAL @@ -9,41 +13,6 @@ using namespace metal; #define MIN(x, y) ((x) < (y) ? (x) : (y)) #define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; } -#define QK4_0 32 -#define QR4_0 2 -typedef struct { - half d; // delta - uint8_t qs[QK4_0 / 2]; // nibbles / quants -} block_q4_0; - -#define QK4_1 32 -typedef struct { - half d; // delta - half m; // min - uint8_t qs[QK4_1 / 2]; // nibbles / quants -} block_q4_1; - -#define QK5_0 32 -typedef struct { - half d; // delta - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_0 / 2]; // nibbles / quants -} block_q5_0; - -#define QK5_1 32 -typedef struct { - half d; // delta - half m; // min - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_1 / 2]; // nibbles / quants -} block_q5_1; - -#define QK8_0 32 -typedef struct { - half d; // delta - int8_t qs[QK8_0]; // quants -} block_q8_0; - #define N_SIMDWIDTH 32 // assuming SIMD group size is 32 enum ggml_sort_order { @@ -2478,147 +2447,6 @@ kernel void kernel_concat( } } -//============================================ k-quants ====================================================== - -#ifndef QK_K -#define QK_K 256 -#else -static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64"); -#endif - -#if QK_K == 256 -#define K_SCALE_SIZE 12 -#else -#define K_SCALE_SIZE 4 -#endif - -typedef struct { - uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits - uint8_t qs[QK_K/4]; // quants - half d; // super-block scale for quantized scales - half dmin; // super-block scale for quantized mins -} block_q2_K; -// 84 bytes / block - -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits -#if QK_K == 64 - uint8_t scales[2]; -#else - uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits -#endif - half d; // super-block scale -} block_q3_K; - -#if QK_K == 64 -typedef struct { - half d[2]; // super-block scales/mins - uint8_t scales[2]; - uint8_t qs[QK_K/2]; // 4-bit quants -} block_q4_K; -#else -typedef struct { - half d; // super-block scale for quantized scales - half dmin; // super-block scale for quantized mins - uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -#endif - -#if QK_K == 64 -typedef struct { - half d; // super-block scales/mins - int8_t scales[QK_K/16]; // 8-bit block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -#else -typedef struct { - half d; // super-block scale for quantized scales - half dmin; // super-block scale for quantized mins - uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -// 176 bytes / block -#endif - -typedef struct { - uint8_t ql[QK_K/2]; // quants, lower 4 bits - uint8_t qh[QK_K/4]; // quants, upper 2 bits - int8_t scales[QK_K/16]; // scales, quantized with 8 bits - half d; // super-block scale -} block_q6_K; -// 210 bytes / block - -typedef struct { - half d; - uint16_t qs[QK_K/8]; -} block_iq2_xxs; -// 66 bytes / block for QK_K = 256, so 2.0625 bpw - -typedef struct { - half d; - uint16_t qs[QK_K/8]; - uint8_t scales[QK_K/32]; -} block_iq2_xs; -// 74 bytes / block for QK_K = 256, so 2.3125 bpw - -// 2.5625 bpw quants -typedef struct { - half d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t scales[QK_K/32]; -} block_iq2_s; - -typedef struct { - half d; - uint8_t qs[3*QK_K/8]; -} block_iq3_xxs; -// 98 bytes / block for QK_K = 256, so 3.0625 bpw - -// 3.4375 bpw -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else -#define IQ3S_N_SCALE QK_K/64 -#endif -typedef struct { - half d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t signs[QK_K/8]; - uint8_t scales[IQ3S_N_SCALE]; -} block_iq3_s; - -typedef struct { - half d; - uint8_t qs[QK_K/8]; - uint16_t qh[QK_K/32]; -} block_iq1_s; - -// Non-linear quants -#define QK4_NL 32 -typedef struct { - half d; - uint8_t qs[QK4_NL/2]; -} block_iq4_nl; - -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -#else -typedef struct { - half d; - uint16_t scales_h; - uint8_t scales_l[QK_K/64]; - uint8_t qs[QK_K/2]; -} block_iq4_xs; -#endif - -//====================================== dot products ========================= - void kernel_mul_mv_q2_K_f32_impl( device const void * src0, device const float * src1, diff --git a/ggml-quants.c b/ggml-quants.c index 3ad558a1b140e..109dd6660d856 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1,3 +1,6 @@ +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + #include "ggml-quants.h" #include "ggml-impl.h" @@ -951,7 +954,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = d; + y[i].d = GGML_FP32_TO_FP16(d); int sum = 0; @@ -966,7 +969,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict sum += y[i].qs[QK8_1/2 + j]; } - y[i].s = sum*d; + y[i].s = GGML_FP32_TO_FP16(sum*d); } } @@ -994,7 +997,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = d; + y[i].d = GGML_FP32_TO_FP16(d); int32x4_t accv = vdupq_n_s32(0); @@ -1010,7 +1013,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { accv = vaddq_s32(accv, vi); } - y[i].s = d * vaddvq_s32(accv); + y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv)); } #elif defined(__wasm_simd128__) for (int i = 0; i < nb; i++) { @@ -1033,7 +1036,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = d; + y[i].d = GGML_FP32_TO_FP16(d); v128_t accv = wasm_i32x4_splat(0); @@ -1049,10 +1052,11 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { accv = wasm_i32x4_add(accv, vi); } - y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) + - wasm_i32x4_extract_lane(accv, 1) + - wasm_i32x4_extract_lane(accv, 2) + - wasm_i32x4_extract_lane(accv, 3)); + y[i].s = GGML_FP32_TO_FP16( + d * (wasm_i32x4_extract_lane(accv, 0) + + wasm_i32x4_extract_lane(accv, 1) + + wasm_i32x4_extract_lane(accv, 2) + + wasm_i32x4_extract_lane(accv, 3))); } #elif defined(__AVX2__) || defined(__AVX__) for (int i = 0; i < nb; i++) { @@ -1077,7 +1081,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { // Quantize these floats const float d = maxScalar / 127.f; - y[i].d = d; + y[i].d = GGML_FP32_TO_FP16(d); const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); @@ -1101,7 +1105,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { #if defined(__AVX2__) // Compute the sum of the quants and set y[i].s - y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)))); // Convert int32 to int16 i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 @@ -1131,7 +1135,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { // Compute the sum of the quants and set y[i].s const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); - y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1))); // Convert int32 to int16 ni0 = _mm_packs_epi32( ni0, ni1 ); @@ -1162,7 +1166,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; - y[i].d = d; + y[i].d = GGML_FP32_TO_FP16(d); vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); @@ -1179,7 +1183,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { // set y[i].s int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); - y[i].s = sum*d; + y[i].s = GGML_FP32_TO_FP16(sum*d); } #else GGML_UNUSED(nb); @@ -4019,10 +4023,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict b_y0 = &vy0[i]; const block_q8_1 * restrict b_y1 = &vy1[i]; - float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * b_y0->s, - GGML_FP16_TO_FP32(b_x1->m) * b_y0->s, - GGML_FP16_TO_FP32(b_x0->m) * b_y1->s, - GGML_FP16_TO_FP32(b_x1->m) * b_y1->s}; + float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; summs0 += summs_t; const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -4087,7 +4091,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict y0 = &y[i + 0]; const block_q8_1 * restrict y1 = &y[i + 1]; - summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s; + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -4110,8 +4114,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d); + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; @@ -4124,9 +4128,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r // Main loop for (int i = 0; i < nb; ++i) { const float d0 = GGML_FP16_TO_FP32(x[i].d); - const float d1 = y[i].d; + const float d1 = GGML_FP16_TO_FP32(y[i].d); - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); const __m256 d0v = _mm256_set1_ps( d0 ); const __m256 d1v = _mm256_set1_ps( d1 ); @@ -4178,7 +4182,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s); } *s = sumf; @@ -4196,7 +4200,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); } - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s); } *s = sumf; @@ -4532,8 +4536,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r const uint8x16_t m4b = vdupq_n_u8(0x0F); - summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s; - summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s; + summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); + summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); // extract the 5th bit via lookup table ((b) << 4) memcpy(&qh0, x0->qh, sizeof(qh0)); @@ -4577,10 +4581,10 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; @@ -4597,7 +4601,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q5_1 * restrict x0 = &x[i]; const block_q8_1 * restrict y0 = &y[i]; - summs += GGML_FP16_TO_FP32(x0->m) * y0->s; + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); const v128_t m4b = wasm_i8x16_splat(0x0F); @@ -4644,7 +4648,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r wasm_i32x4_dot_i16x8(v0lfh, v1lh)), wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d))); + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); } *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + @@ -4659,14 +4663,14 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; i++) { const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); __m256i qx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); qx = _mm256_or_si256(qx, bxhi); - const __m256 dy = _mm256_set1_ps(y[i].d); + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d)); const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_us8_pairs_float(qx, qy); @@ -4686,7 +4690,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; i++) { const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); - summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); __m256i bx_0 = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); @@ -4700,7 +4704,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r bxh = _mm_or_si128(bxh, bxhih); bx_0 = MM256_SET_M128I(bxh, bxl); - const __m256 dy = _mm256_set1_ps(y[i].d); + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d)); const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); @@ -4767,7 +4771,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s); } *s = sumf; @@ -4791,7 +4795,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); } - sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s); } *s = sumf; diff --git a/ggml-quants.h b/ggml-quants.h index 74aabf4156385..aa7e54a16e867 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -1,247 +1,11 @@ #pragma once -// GGML internal header - -#include "ggml-impl.h" - -#include -#include - -#define QK4_0 32 -typedef struct { - ggml_fp16_t d; // delta - uint8_t qs[QK4_0 / 2]; // nibbles / quants -} block_q4_0; -static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); - -#define QK4_1 32 -typedef struct { - ggml_fp16_t d; // delta - ggml_fp16_t m; // min - uint8_t qs[QK4_1 / 2]; // nibbles / quants -} block_q4_1; -static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding"); - -#define QK5_0 32 -typedef struct { - ggml_fp16_t d; // delta - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_0 / 2]; // nibbles / quants -} block_q5_0; -static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); - -#define QK5_1 32 -typedef struct { - ggml_fp16_t d; // delta - ggml_fp16_t m; // min - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_1 / 2]; // nibbles / quants -} block_q5_1; -static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); - -#define QK8_0 32 -typedef struct { - ggml_fp16_t d; // delta - int8_t qs[QK8_0]; // quants -} block_q8_0; -static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); - -#define QK8_1 32 -typedef struct { - float d; // delta - float s; // d * sum(qs[i]) - int8_t qs[QK8_1]; // quants -} block_q8_1; -static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding"); - -// -// Super-block quantization structures -// - -// Super-block size -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else -#define QK_K 256 -#define K_SCALE_SIZE 12 -#endif - -// 2-bit quantization -// weight is represented as x = a * q + b -// 16 blocks of 16 elements each -// Effectively 2.625 bits per weight -typedef struct { - uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits - uint8_t qs[QK_K/4]; // quants - ggml_fp16_t d; // super-block scale for quantized scales - ggml_fp16_t dmin; // super-block scale for quantized mins -} block_q2_K; -static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); - -// 3-bit quantization -// weight is represented as x = a * q -// 16 blocks of 16 elements each -// Effectively 3.4375 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[2]; - ggml_fp16_t d; // super-block scale -} block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); -#else -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[12]; // scales, quantized with 6 bits - ggml_fp16_t d; // super-block scale -} block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); -#endif +#define GGML_COMMON_DECL_C +#include "ggml-common.h" -// 4-bit quantization -// 8 blocks of 32 elements each -// weight is represented as x = a * q + b -// Effectively 4.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_fp16_t d[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else -typedef struct { - ggml_fp16_t d; // super-block scale for quantized scales - ggml_fp16_t dmin; // super-block scale for quantized mins - uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); -#endif - -// 5-bit quantization -// 8 blocks of 32 elements each -// weight is represented as x = a * q + b -// Effectively 5.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_fp16_t d; // super-block scale - int8_t scales[QK_K/16]; // 8-bit block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else -typedef struct { - ggml_fp16_t d; // super-block scale for quantized scales - ggml_fp16_t dmin; // super-block scale for quantized mins - uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif - -// 6-bit quantization -// weight is represented as x = a * q -// 16 blocks of 16 elements each -// Effectively 6.5625 bits per weight -typedef struct { - uint8_t ql[QK_K/2]; // quants, lower 4 bits - uint8_t qh[QK_K/4]; // quants, upper 2 bits - int8_t scales[QK_K/16]; // scales, quantized with 8 bits - ggml_fp16_t d; // super-block scale -} block_q6_K; -static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding"); - -// This is only used for intermediate quantization and dot products -typedef struct { - float d; // delta - int8_t qs[QK_K]; // quants - int16_t bsums[QK_K/16]; // sum of quants in groups of 16 -} block_q8_K; -static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding"); - -// (Almost) "true" 2-bit quantization. -// Due to the need to use blocks as per ggml design, it ends up using -// 2.0625 bpw because of the 16-bit scale for each block of 256. -typedef struct { - ggml_fp16_t d; - uint16_t qs[QK_K/8]; -} block_iq2_xxs; -static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); - -// 2.3125 bpw quants -typedef struct { - ggml_fp16_t d; - uint16_t qs[QK_K/8]; - uint8_t scales[QK_K/32]; -} block_iq2_xs; -static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); - -// 2.5625 bpw quants -typedef struct { - ggml_fp16_t d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t scales[QK_K/32]; -} block_iq2_s; -static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); +#include "ggml.h" -// (Almost) "true" 3-bit quantization. -// Due to the need to use blocks as per ggml design, it ends up using -// 3.0625 bpw because of the 16-bit scale for each block of 256. -typedef struct { - ggml_fp16_t d; - uint8_t qs[3*QK_K/8]; -} block_iq3_xxs; -static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); - -// 3.4375 bpw -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else -#define IQ3S_N_SCALE QK_K/64 -#endif -typedef struct { - ggml_fp16_t d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t signs[QK_K/8]; - uint8_t scales[IQ3S_N_SCALE]; -} block_iq3_s; -static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); - -typedef struct { - ggml_fp16_t d; - uint8_t qs[QK_K/8]; - uint16_t qh[QK_K/32]; -} block_iq1_s; -static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); - -// Non-linear quants -#define QK4_NL 32 -typedef struct { - ggml_fp16_t d; - uint8_t qs[QK4_NL/2]; -} block_iq4_nl; -static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); - -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -//typedef struct block_iq4_nl block_iq4_xs; -#else -typedef struct { - ggml_fp16_t d; - uint16_t scales_h; - uint8_t scales_l[QK_K/64]; - uint8_t qs[QK_K/2]; -} block_iq4_xs; -static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); -#endif +// GGML internal header #ifdef __cplusplus extern "C" { diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 24187e7d5bc2d..cfb09934d40c3 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3144,6 +3144,7 @@ namespace dpct } // COPY from DPCT head files +#define GGML_COMMON_DECL_SYCL #define GGML_COMMON_IMPL_SYCL #include "ggml-common.h" @@ -3312,66 +3313,6 @@ typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0, const float *src1_dd, float *dst_dd, const dpct::queue_ptr &main_stream); -// QK = number of values after dequantization -// QR = QK / number of values before dequantization -// QI = number of 32 bit integers before dequantization - -#define QK4_0 32 -#define QR4_0 2 -#define QI4_0 (QK4_0 / (4 * QR4_0)) -typedef struct dpct_type_block_q4_0 { - sycl::half d; // delta - uint8_t qs[QK4_0 / 2]; // nibbles / quants -} block_q4_0; -static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); - -#define QK4_1 32 -#define QR4_1 2 -#define QI4_1 (QK4_1 / (4 * QR4_1)) -typedef struct dpct_type_block_q4_1 { - sycl::half2 dm; // dm.x = delta, dm.y = min - uint8_t qs[QK4_1 / 2]; // nibbles / quants -} block_q4_1; -static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding"); - -#define QK5_0 32 -#define QR5_0 2 -#define QI5_0 (QK5_0 / (4 * QR5_0)) -typedef struct dpct_type_block_q5_0 { - sycl::half d; // delta - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_0 / 2]; // nibbles / quants -} block_q5_0; -static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); - -#define QK5_1 32 -#define QR5_1 2 -#define QI5_1 (QK5_1 / (4 * QR5_1)) -typedef struct dpct_type_block_q5_1 { - sycl::half2 dm; // dm.x = delta, dm.y = min - uint8_t qh[4]; // 5-th bit of quants - uint8_t qs[QK5_1 / 2]; // nibbles / quants -} block_q5_1; -static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); - -#define QK8_0 32 -#define QR8_0 1 -#define QI8_0 (QK8_0 / (4 * QR8_0)) -typedef struct dpct_type_block_q8_0 { - sycl::half d; // delta - int8_t qs[QK8_0]; // quants -} block_q8_0; -static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); - -#define QK8_1 32 -#define QR8_1 1 -#define QI8_1 (QK8_1 / (4 * QR8_1)) -typedef struct dpct_type_block_q8_1 { - sycl::half2 ds; // ds.x = delta, ds.y = sum - int8_t qs[QK8_0]; // quants -} block_q8_1; -static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding"); - typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc); @@ -3388,137 +3329,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms, const int &i, const int &j, const int &k); -//================================= k-quants - -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else -#define QK_K 256 -#define K_SCALE_SIZE 12 -#endif - -#define QR2_K 4 -#define QI2_K (QK_K / (4*QR2_K)) -typedef struct dpct_type_block_q2_K { - uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits - uint8_t qs[QK_K/4]; // quants - sycl::half2 dm; // super-block scale for quantized scales/mins -} block_q2_K; -static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); - -#define QR3_K 4 -#define QI3_K (QK_K / (4*QR3_K)) -typedef struct dpct_type_block_q3_K { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits -#ifdef GGML_QKK_64 - uint8_t scales[2]; // scales, quantized with 8 bits -#else - uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits -#endif - sycl::half d; // super-block scale -} block_q3_K; -//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding"); - -#define QR4_K 2 -#define QI4_K (QK_K / (4*QR4_K)) -#ifdef GGML_QKK_64 -typedef struct { - sycl::half dm[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == sizeof(sycl::half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else -typedef struct dpct_type_block_q4_K { - sycl::half2 dm; // super-block scale for quantized scales/mins - uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding"); -#endif - -#define QR5_K 2 -#define QI5_K (QK_K / (4*QR5_K)) -#ifdef GGML_QKK_64 -typedef struct { - sycl::half d; // super-block scale - int8_t scales[QK_K/16]; // block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else -typedef struct dpct_type_block_q5_K { - sycl::half2 dm; // super-block scale for quantized scales/mins - uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif - -#define QR6_K 2 -#define QI6_K (QK_K / (4*QR6_K)) -typedef struct dpct_type_block_q6_K { - uint8_t ql[QK_K/2]; // quants, lower 4 bits - uint8_t qh[QK_K/4]; // quants, upper 2 bits - int8_t scales[QK_K/16]; // scales - sycl::half d; // delta -} block_q6_K; -static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); - -#define QR2_XXS 8 -#define QI2_XXS (QK_K / (4*QR2_XXS)) -typedef struct dpct_type_block_iq2_xxs { - sycl::half d; - uint16_t qs[QK_K/8]; -} block_iq2_xxs; -static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding"); - -#define QR2_XS 8 -#define QI2_XS (QK_K / (4*QR2_XS)) -typedef struct dpct_type_block_iq2_xs { - sycl::half d; - uint16_t qs[QK_K/8]; - uint8_t scales[QK_K/32]; -} block_iq2_xs; -static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); - -#define QR3_XXS 8 -#define QI3_XXS (QK_K / (4*QR3_XXS)) -typedef struct dpct_type_block_iq3_xxs { - sycl::half d; - uint8_t qs[3*(QK_K/8)]; -} block_iq3_xxs; -static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); - -#define QR3_XS 8 -#define QI3_XS (QK_K / (4*QR3_XS)) -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else -#define IQ3S_N_SCALE QK_K/64 -#endif -typedef struct { - sycl::half d; - uint8_t qs[QK_K/4]; - uint8_t qh[QK_K/32]; - uint8_t signs[QK_K/8]; - uint8_t scales[IQ3S_N_SCALE]; -} block_iq3_s; -static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); - -#define QR1_S 8 -#define QI1_S (QK_K / (4*QR1_S)) -typedef struct { - sycl::half d; - uint8_t qs[QK_K/8]; - uint16_t qh[QK_K/32]; -} block_iq1_s; -static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); - #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses From 306d34be7ad19e768975409fc80791a274ea0230 Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 12 Mar 2024 16:55:19 +0100 Subject: [PATCH 243/401] ci : remove tidy-review (#6021) --- .github/workflows/tidy-post.yml | 20 -------------------- .github/workflows/tidy-review.yml | 23 ----------------------- 2 files changed, 43 deletions(-) delete mode 100644 .github/workflows/tidy-post.yml delete mode 100644 .github/workflows/tidy-review.yml diff --git a/.github/workflows/tidy-post.yml b/.github/workflows/tidy-post.yml deleted file mode 100644 index 03652760c80dc..0000000000000 --- a/.github/workflows/tidy-post.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: clang-tidy review post comments - -on: - workflow_dispatch: - workflows: ["clang-tidy-review"] - types: - - completed - -jobs: - build: - runs-on: ubuntu-latest - - steps: - - uses: ZedThree/clang-tidy-review/post@v0.13.0 - # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup - with: - # adjust options as necessary - lgtm_comment_body: '' - annotations: false - max_comments: 25 diff --git a/.github/workflows/tidy-review.yml b/.github/workflows/tidy-review.yml deleted file mode 100644 index a4bc8d976560e..0000000000000 --- a/.github/workflows/tidy-review.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: clang-tidy-review - -on: - pull_request: - branches: - - master - -jobs: - clang-tidy-review: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - - uses: ZedThree/clang-tidy-review@v0.13.0 - id: review - with: - lgtm_comment_body: '' - build_dir: build - cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on - split_workflow: true - - - uses: ZedThree/clang-tidy-review/upload@v0.13.0 From 99b71c068f624521ad977e08e41589e2971fa1c7 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Wed, 13 Mar 2024 11:39:11 +0100 Subject: [PATCH 244/401] Server: Use multi-task for embeddings endpoint (#6001) * use multitask for embd endpoint * specify types * remove redundant {"n_predict", 0} --- examples/server/server.cpp | 76 ++++++++++++++------------------------ examples/server/utils.hpp | 12 +++++- 2 files changed, 38 insertions(+), 50 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b63a6f243b696..3172d96dd0353 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2763,6 +2763,7 @@ int main(int argc, char ** argv) { res.set_header("Access-Control-Allow-Credentials", "true"); res.set_header("Access-Control-Allow-Methods", "POST"); res.set_header("Access-Control-Allow-Headers", "*"); + return res.set_content("", "application/json; charset=utf-8"); }); svr->set_logger(log_server_request); @@ -3371,44 +3372,37 @@ int main(int argc, char ** argv) { const json body = json::parse(req.body); bool is_openai = false; - // an input prompt can string or a list of tokens (integer) - std::vector prompts; + // an input prompt can be a string or a list of tokens (integer) + json prompt; if (body.count("input") != 0) { is_openai = true; - if (body["input"].is_array()) { - // support multiple prompts - for (const json & elem : body["input"]) { - prompts.push_back(elem); - } - } else { - // single input prompt - prompts.push_back(body["input"]); - } + prompt = body["input"]; } else if (body.count("content") != 0) { - // only support single prompt here - std::string content = body["content"]; - prompts.push_back(content); + // with "content", we only support single prompt + prompt = std::vector{body["content"]}; } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); return; } - // process all prompts - json responses = json::array(); - for (auto & prompt : prompts) { - // TODO @ngxson : maybe support multitask for this endpoint? - // create and queue the task + // create and queue the task + json responses; + { const int id_task = ctx_server.queue_tasks.get_new_id(); - ctx_server.queue_results.add_waiting_task_id(id_task); - ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true); + ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true); // get the result server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); if (!result.error) { - // append to the responses - responses.push_back(result.data); + if (result.data.count("results")) { + // result for multi-task + responses = result.data["results"]; + } else { + // result for single task + responses = std::vector{result.data}; + } } else { // error received, ignore everything else res_error(res, result.data); @@ -3417,24 +3411,19 @@ int main(int argc, char ** argv) { } // write JSON response - json root; - if (is_openai) { - json res_oai = json::array(); - int i = 0; - for (auto & elem : responses) { - res_oai.push_back(json{ - {"embedding", json_value(elem, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }); - } - root = format_embeddings_response_oaicompat(body, res_oai); - } else { - root = responses[0]; - } + json root = is_openai + ? format_embeddings_response_oaicompat(body, responses) + : responses[0]; return res.set_content(root.dump(), "application/json; charset=utf-8"); }; + auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { + return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(content), len, mime_type); + return false; + }; + }; + // // Router // @@ -3446,17 +3435,6 @@ int main(int argc, char ** argv) { } // using embedded static files - auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { - return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(content), len, mime_type); - return false; - }; - }; - - svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) { - // TODO @ngxson : I have no idea what it is... maybe this is redundant? - return res.set_content("", "application/json; charset=utf-8"); - }); svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 48aeef4ebea67..2ddb2cd21f8d6 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -529,6 +529,16 @@ static std::vector format_partial_response_oaicompat(json result, const st } static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { + json data = json::array(); + int i = 0; + for (auto & elem : embeddings) { + data.push_back(json{ + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }); + } + json res = json { {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, {"object", "list"}, @@ -536,7 +546,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso {"prompt_tokens", 0}, {"total_tokens", 0} }}, - {"data", embeddings} + {"data", data} }; return res; From b3d978600f07f22e94f2e797f18a8b5f6df23c89 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Wed, 13 Mar 2024 13:17:54 +0000 Subject: [PATCH 245/401] Update get version (#6025) --- ggml-sycl.cpp | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index cfb09934d40c3..c2ab13034ba85 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -202,24 +202,29 @@ namespace dpct // Version string has the following format: // a. OpenCL // b. + // c. e.g gfx1030 std::string ver; ver = dev.get_info(); std::string::size_type i = 0; - while (i < ver.size()) - { - if (isdigit(ver[i])) - break; - i++; + while (i < ver.size()) { + if (isdigit(ver[i])) + break; + i++; } major = std::stoi(&(ver[i])); - while (i < ver.size()) - { - if (ver[i] == '.') - break; - i++; + while (i < ver.size()) { + if (ver[i] == '.') + break; + i++; + } + if (i < ver.size()) { + // a. and b. + i++; + minor = std::stoi(&(ver[i])); + } else { + // c. + minor = 0; } - i++; - minor = std::stoi(&(ver[i])); } template From d8fd0ccf6ac8b07791ffd1575eed436930854ae3 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 13 Mar 2024 14:58:30 +0100 Subject: [PATCH 246/401] test-backend-ops : skip CPU backend by default (#6028) --- tests/test-backend-ops.cpp | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index fc5edcc4bf6fc..c2916c3e480e0 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2222,8 +2222,8 @@ static void usage(char ** argv) { int main(int argc, char ** argv) { test_mode mode = MODE_TEST; - const char * op_name = NULL; - const char * backend = NULL; + const char * op_name_filter = NULL; + const char * backend_filter = NULL; for (int i = 1; i < argc; i++) { if (strcmp(argv[i], "test") == 0) { @@ -2232,14 +2232,14 @@ int main(int argc, char ** argv) { mode = MODE_PERF; } else if (strcmp(argv[i], "-o") == 0) { if (i + 1 < argc) { - op_name = argv[++i]; + op_name_filter = argv[++i]; } else { usage(argv); return 1; } } else if (strcmp(argv[i], "-b") == 0) { if (i + 1 < argc) { - backend = argv[++i]; + backend_filter = argv[++i]; } else { usage(argv); return 1; @@ -2258,7 +2258,7 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) { printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i)); - if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) { + if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) { printf(" Skipping\n"); n_ok++; continue; @@ -2266,9 +2266,17 @@ int main(int argc, char ** argv) { ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL); GGML_ASSERT(backend != NULL); + + if (backend_filter == NULL && ggml_backend_is_cpu(backend)) { + printf(" Skipping CPU backend\n"); + ggml_backend_free(backend); + n_ok++; + continue; + } + printf(" Backend name: %s\n", ggml_backend_name(backend)); - bool ok = test_backend(backend, mode, op_name); + bool ok = test_backend(backend, mode, op_name_filter); printf(" Backend %s: ", ggml_backend_name(backend)); if (ok) { From f30ea47a87ed4446ad55adb265755dc9102956a2 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 13 Mar 2024 18:54:21 +0100 Subject: [PATCH 247/401] llama : add pipeline parallelism support (#6017) * llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs ggml-ci * server : add -ub, --ubatch-size parameter * fix server embedding test * llama : fix Mamba inference for pipeline parallelism Tested to work correctly with both `main` and `parallel` examples. * llama : limit max batch size to n_batch * add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism default increase to 4 (from 2) changing this value may improve performance for some systems, but increases memory usage * fix hip build * fix sycl build (disable cpy_tensor_async) * fix hip build * llama : limit n_batch and n_ubatch to n_ctx during context creation * llama : fix norm backend * batched-bench : sync after decode * swiftui : sync after decode * ggml : allow ggml_get_rows to use multiple threads if they are available * check n_ubatch >= n_tokens with non-casual attention * llama : do not limit n_batch to n_ctx with non-casual attn * server : construct batch with size of llama_n_batch * ggml_backend_cpu_graph_compute : fix return value when alloc fails * llama : better n_batch and n_ubatch comment * fix merge * small fix * reduce default n_batch to 2048 --------- Co-authored-by: Francis Couture-Harpin Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 3 + Makefile | 4 + common/common.cpp | 14 +- common/common.h | 3 +- examples/batched-bench/batched-bench.cpp | 2 + examples/embedding/embedding.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 53 +- .../llama.cpp.swift/LibLlama.swift | 2 + examples/perplexity/perplexity.cpp | 3 +- examples/server/server.cpp | 32 +- .../server/tests/features/embeddings.feature | 1 + examples/server/tests/features/steps/steps.py | 8 + ggml-alloc.c | 109 +- ggml-alloc.h | 18 +- ggml-backend-impl.h | 17 +- ggml-backend.c | 493 +++++-- ggml-backend.h | 58 +- ggml-cuda.cu | 175 ++- ggml-kompute.cpp | 5 + ggml-metal.m | 5 + ggml-sycl.cpp | 7 +- ggml-vulkan.cpp | 5 + ggml.c | 113 +- llama.cpp | 1131 +++++++++-------- llama.h | 9 +- 25 files changed, 1426 insertions(+), 846 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7ab13cbd56e8a..a8abf4088a01c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -118,6 +118,7 @@ option(LLAMA_SYCL "llama: use SYCL" option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device") option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF) +set(LLAMA_SCHED_MAX_COPIES "4" CACHE STRING "llama: max input copies for pipeline parallelism") option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) @@ -147,6 +148,8 @@ set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads REQUIRED) include(CheckCXXCompilerFlag) +add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES}) + # enable libstdc++ assertions for debug builds if (CMAKE_SYSTEM_NAME MATCHES "Linux") add_compile_definitions($<$:_GLIBCXX_ASSERTIONS>) diff --git a/Makefile b/Makefile index c8fd3f5c5026a..db9968efbddd8 100644 --- a/Makefile +++ b/Makefile @@ -167,6 +167,10 @@ ifeq ($(UNAME_S),OpenBSD) MK_CPPFLAGS += -D_BSD_SOURCE endif +ifdef LLAMA_SCHED_MAX_COPIES + MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES) +endif + ifdef LLAMA_DEBUG MK_CFLAGS += -O0 -g MK_CXXFLAGS += -O0 -g diff --git a/common/common.cpp b/common/common.cpp index 2f38ac632b45a..73b1b61ba1b74 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -483,6 +483,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_batch = std::stoi(argv[i]); + } else if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_ubatch = std::stoi(argv[i]); } else if (arg == "--keep") { if (++i >= argc) { invalid_param = true; @@ -977,7 +983,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" binary file containing multiple choice tasks.\n"); printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); + printf(" -ub N, --ubatch-size N\n"); + printf(" physical maximum batch size (default: %d)\n", params.n_ubatch); printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n"); printf(" (default: %s)\n", sampler_type_names.c_str()); printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); @@ -1287,8 +1295,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param auto cparams = llama_context_default_params(); cparams.n_ctx = params.n_ctx; - cparams.n_batch = params.n_batch; cparams.n_seq_max = params.n_parallel; + cparams.n_batch = params.n_batch; + cparams.n_ubatch = params.n_ubatch; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; @@ -1379,6 +1388,7 @@ std::tuple llama_init_from_gpt_par std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); llama_kv_cache_clear(lctx); + llama_synchronize(lctx); llama_reset_timings(lctx); } diff --git a/common/common.h b/common/common.h index f8d82b8713c87..0f178b9eb1de3 100644 --- a/common/common.h +++ b/common/common.h @@ -51,7 +51,8 @@ struct gpt_params { int32_t n_threads_batch_draft = -1; int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_draft = 5; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 22bc93bca817c..19674dfd36708 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -138,6 +138,8 @@ int main(int argc, char ** argv) { LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); return false; } + + llama_synchronize(ctx); } return true; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index a553ae1c3f35d..49302a199977e 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -107,7 +107,7 @@ int main(int argc, char ** argv) { // max batch size const uint64_t n_batch = params.n_batch; - GGML_ASSERT(params.n_batch == params.n_ctx); + GGML_ASSERT(params.n_batch >= params.n_ctx); // tokenize the prompts and trim std::vector> inputs; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 2ff86ef6f1146..bf94e7e7a60a4 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -164,6 +164,7 @@ struct cmd_params { std::vector n_prompt; std::vector n_gen; std::vector n_batch; + std::vector n_ubatch; std::vector type_k; std::vector type_v; std::vector n_threads; @@ -183,7 +184,8 @@ static const cmd_params cmd_params_defaults = { /* model */ {"models/7B/ggml-model-q4_0.gguf"}, /* n_prompt */ {512}, /* n_gen */ {128}, - /* n_batch */ {512}, + /* n_batch */ {2048}, + /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, /* type_v */ {GGML_TYPE_F16}, /* n_threads */ {get_num_physical_cores()}, @@ -208,6 +210,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub N, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); @@ -217,7 +220,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); - printf(" -ts, --tensor_split (default: 0)\n"); + printf(" -ts, --tensor-split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); @@ -297,6 +300,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); + } else if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); } else if (arg == "-ctk" || arg == "--cache-type-k") { if (++i >= argc) { invalid_param = true; @@ -455,6 +465,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; } if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; } if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } + if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; } if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } @@ -474,6 +485,7 @@ struct cmd_params_instance { int n_prompt; int n_gen; int n_batch; + int n_ubatch; ggml_type type_k; ggml_type type_v; int n_threads; @@ -511,6 +523,7 @@ struct cmd_params_instance { cparams.n_ctx = n_prompt + n_gen; cparams.n_batch = n_batch; + cparams.n_ubatch = n_ubatch; cparams.type_k = type_k; cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; @@ -532,6 +545,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & mmp : params.use_mmap) for (const auto & embd : params.embeddings) for (const auto & nb : params.n_batch) + for (const auto & nub : params.n_ubatch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) for (const auto & nkvo : params.no_kv_offload) @@ -545,6 +559,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_prompt = */ n_prompt, /* .n_gen = */ 0, /* .n_batch = */ nb, + /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, /* .n_threads = */ nt, @@ -568,6 +583,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_prompt = */ 0, /* .n_gen = */ n_gen, /* .n_batch = */ nb, + /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, /* .n_threads = */ nt, @@ -604,6 +620,7 @@ struct test { uint64_t model_size; uint64_t model_n_params; int n_batch; + int n_ubatch; int n_threads; ggml_type type_k; ggml_type type_v; @@ -627,6 +644,7 @@ struct test { model_size = llama_model_size(lmodel); model_n_params = llama_model_n_params(lmodel); n_batch = inst.n_batch; + n_ubatch = inst.n_ubatch; n_threads = inst.n_threads; type_k = inst.type_k; type_v = inst.type_v; @@ -705,7 +723,8 @@ struct test { "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", - "n_batch", "n_threads", "type_k", "type_v", + "n_batch", "n_ubatch", + "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", "tensor_split", "use_mmap", "embeddings", @@ -719,7 +738,8 @@ struct test { enum field_type {STRING, BOOL, INT, FLOAT}; static field_type get_field_type(const std::string & field) { - if (field == "build_number" || field == "n_batch" || field == "n_threads" || + if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || + field == "n_threads" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || field == "main_gpu" || field == "n_prompt" || field == "n_gen" || @@ -759,7 +779,8 @@ struct test { std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), - std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), + std::to_string(n_batch), std::to_string(n_ubatch), + std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), @@ -957,6 +978,9 @@ struct markdown_printer : public printer { if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) { fields.emplace_back("n_batch"); } + if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) { + fields.emplace_back("n_ubatch"); + } if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) { fields.emplace_back("type_k"); } @@ -1096,25 +1120,32 @@ struct sql_printer : public printer { }; static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { + llama_set_n_threads(ctx, n_threads, n_threads); + + //std::vector tokens(n_prompt, llama_token_bos(llama_get_model(ctx))); + //llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt, n_past, 0)); + //GGML_UNUSED(n_batch); + std::vector tokens(n_batch, llama_token_bos(llama_get_model(ctx))); int n_processed = 0; - llama_set_n_threads(ctx, n_threads, n_threads); - while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); n_processed += n_tokens; } + + llama_synchronize(ctx); } static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { - llama_token token = llama_token_bos(llama_get_model(ctx)); - llama_set_n_threads(ctx, n_threads, n_threads); + llama_token token = llama_token_bos(llama_get_model(ctx)); + for (int i = 0; i < n_gen; i++) { llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); + llama_synchronize(ctx); } } @@ -1203,7 +1234,8 @@ int main(int argc, char ** argv) { // warmup run if (t.n_prompt > 0) { - test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads); + //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); } if (t.n_gen > 0) { test_gen(ctx, 1, 0, t.n_threads); @@ -1219,6 +1251,7 @@ int main(int argc, char ** argv) { if (t.n_gen > 0) { test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads); } + uint64_t t_ns = get_time_ns() - t_start; t.samples_ns.push_back(t_ns); } diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 58fcf40c6fb69..c249291aea110 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -221,6 +221,7 @@ actor LlamaContext { if llama_decode(context, batch) != 0 { print("llama_decode() failed during prompt") } + llama_synchronize(context) let t_pp_end = ggml_time_us() @@ -240,6 +241,7 @@ actor LlamaContext { if llama_decode(context, batch) != 0 { print("llama_decode() failed during text generation") } + llama_synchronize(context) } let t_tg_end = ggml_time_us() diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index fdfc8f5dcd3e5..d766aef6ac1b1 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -589,9 +589,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par } } - const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { + llama_synchronize(ctx); + const auto t_end = std::chrono::high_resolution_clock::now(); const float t_total = std::chrono::duration(t_end - t_start).count(); fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); int total_seconds = (int)(t_total*n_chunk/n_seq); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3172d96dd0353..895d608fdcc06 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -147,7 +147,7 @@ struct server_slot { int32_t n_decoded = 0; int32_t n_remaining = -1; int32_t i_batch = -1; - int32_t n_predict = -1; + int32_t n_predict = -1; // TODO: disambiguate from params.n_predict int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; @@ -739,7 +739,13 @@ struct server_context { default_generation_settings_for_props = get_formated_generation(slots.front()); default_generation_settings_for_props["seed"] = -1; - batch = llama_batch_init(n_ctx, 0, params.n_parallel); + // the update_slots() logic will always submit a maximum of n_batch tokens + // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) + { + const int32_t n_batch = llama_n_batch(ctx); + + batch = llama_batch_init(n_batch, 0, params.n_parallel); + } metrics.init(); } @@ -1036,8 +1042,10 @@ struct server_context { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) { - const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i)); + const int32_t n_batch = llama_n_batch(ctx); + + for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i); llama_batch batch_view = { n_tokens, batch.token + i, @@ -1226,7 +1234,7 @@ struct server_context { {"mirostat_eta", slot.sparams.mirostat_eta}, {"penalize_nl", slot.sparams.penalize_nl}, {"stop", slot.params.antiprompt}, - {"n_predict", slot.params.n_predict}, + {"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict {"n_keep", params.n_keep}, {"ignore_eos", ignore_eos}, {"stream", slot.params.stream}, @@ -1738,7 +1746,8 @@ struct server_context { } // process in chunks of params.n_batch - int32_t n_batch = params.n_batch; + int32_t n_batch = llama_n_batch(ctx); + int32_t n_ubatch = llama_n_ubatch(ctx); // next, batch any pending prompts without exceeding n_batch if (params.cont_batching || batch.n_tokens == 0) { @@ -1811,7 +1820,7 @@ struct server_context { if (slot.embedding) { // this prompt is too large to process - discard it - if (slot.n_prompt_tokens > n_batch) { + if (slot.n_prompt_tokens > n_ubatch) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); @@ -2157,7 +2166,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); printf(" -dt N, --defrag-thold N\n"); printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); + printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_supports_mlock()) { @@ -2424,6 +2434,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } params.n_batch = std::stoi(argv[i]); + } else if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_ubatch = std::stoi(argv[i]); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature index b47661e943ca6..57359b267a668 100644 --- a/examples/server/tests/features/embeddings.feature +++ b/examples/server/tests/features/embeddings.feature @@ -9,6 +9,7 @@ Feature: llama.cpp server And 42 as server seed And 2 slots And 1024 as batch size + And 1024 as ubatch size And 2048 KV cache size And embeddings extraction Then the server is starting diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 98c2b61743cbf..cfa9f96ec5306 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -33,6 +33,7 @@ def step_server_config(context, server_fqdn, server_port): context.model_alias = None context.n_batch = None + context.n_ubatch = None context.n_ctx = None context.n_ga = None context.n_ga_w = None @@ -278,6 +279,11 @@ def step_n_batch(context, n_batch): context.n_batch = n_batch +@step('{n_ubatch:d} as ubatch size') +def step_n_ubatch(context, n_ubatch): + context.n_ubatch = n_ubatch + + @step('{seed:d} as seed') def step_seed(context, seed): context.seed = seed @@ -1029,6 +1035,8 @@ def start_server_background(context): ] if context.n_batch: server_args.extend(['--batch-size', context.n_batch]) + if context.n_ubatch: + server_args.extend(['--ubatch-size', context.n_ubatch]) if context.n_gpu_layer: server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.server_continuous_batching: diff --git a/ggml-alloc.c b/ggml-alloc.c index e675306c8c3f1..8ac1d3e51470c 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -61,7 +61,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -// TODO: GGML_PAD ? static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { assert(alignment && !(alignment & (alignment - 1))); // power of 2 size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment; @@ -69,25 +68,14 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen } // tallocr -struct ggml_tallocr { - ggml_backend_buffer_t buffer; - void * base; - size_t alignment; - size_t offset; -}; - -ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) { - ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr)); - if (talloc == NULL) { - return NULL; - } +struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) { void * base = ggml_backend_buffer_get_base(buffer); size_t align = ggml_backend_buffer_get_alignment(buffer); assert(align && !(align & (align - 1))); // power of 2 - *talloc = (struct ggml_tallocr) { + struct ggml_tallocr talloc = (struct ggml_tallocr) { /*.buffer = */ buffer, /*.base = */ base, /*.alignment = */ align, @@ -96,11 +84,7 @@ ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) { return talloc; } -void ggml_tallocr_free(ggml_tallocr_t talloc) { - free(talloc); -} - -void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) { +void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) { size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor); size = GGML_PAD(size, talloc->alignment); @@ -354,12 +338,16 @@ struct hash_node { bool allocated; }; -// struct tensor_alloc { size_t offset; size_t size_max; // 0 = pre-allocated, unused, or view }; +struct leaf_alloc { + int buffer_id; + struct tensor_alloc leaf; +}; + struct node_alloc { int buffer_id; struct tensor_alloc dst; @@ -378,7 +366,7 @@ struct ggml_gallocr { struct node_alloc * node_allocs; // [n_nodes] int n_nodes; - struct tensor_alloc * leaf_allocs; // [n_leafs] + struct leaf_alloc * leaf_allocs; // [n_leafs] int n_leafs; }; @@ -543,13 +531,20 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) { return node_buffer_ids ? node_buffer_ids[i] : 0; } -static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) { +static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { // clear hash tables memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *)); memset(galloc->hash_values, 0, galloc->hash_set.size * sizeof(struct hash_node)); + // allocate leafs + // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i)); + } + // count number of children and views - // allocate all graph inputs and leafs first to avoid overwriting them + // allocate other graph inputs and leafs first to avoid overwriting them for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -577,19 +572,6 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr } } - // allocate the remaining leafs that are unused on the graph - // these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes - for (int i = 0; i < graph->n_leafs; i++) { - struct ggml_tensor * leaf = graph->leafs[i]; - struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); - - if (hn->n_children == 0) { - assert(!hn->allocated); - // since buffer ids are only given for nodes, these leafs are always allocated in the first buffer - ggml_gallocr_allocate_node(galloc, leaf, 0); - } - } - // allocate tensors for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -652,7 +634,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr } } -bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) { +bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { size_t hash_size = graph->visited_hash_table.size; // initialize hash table @@ -676,7 +658,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c } // allocate in hash table - ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids); + ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids); // set the node_allocs from the hash table if (galloc->n_nodes < graph->n_nodes) { @@ -711,15 +693,16 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c } if (galloc->n_leafs < graph->n_leafs) { free(galloc->leaf_allocs); - galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs); + galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs); GGML_ASSERT(galloc->leaf_allocs != NULL); } galloc->n_leafs = graph->n_leafs; for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); - galloc->leaf_allocs[i].offset = hn->offset; - galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf); + galloc->leaf_allocs[i].buffer_id = hn->buffer_id; + galloc->leaf_allocs[i].leaf.offset = hn->offset; + galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf); } // reallocate buffers if needed @@ -727,7 +710,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0; size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]); - if (new_size > cur_size) { + // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views + if (new_size > cur_size || galloc->buffers[i] == NULL) { #ifndef NDEBUG fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif @@ -744,30 +728,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c } bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { - return ggml_gallocr_reserve_n(galloc, graph, NULL); + return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL); } -static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) { - assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max); +static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) { + assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max); - if (node->view_src != NULL) { - if (node->buffer == NULL) { + if (tensor->view_src != NULL) { + if (tensor->buffer == NULL) { assert(tensor_alloc->offset == SIZE_MAX); - if (node->view_src->buffer == NULL) { + if (tensor->view_src->buffer == NULL) { // this tensor was allocated without ggml-backend return; } - ggml_backend_view_init(galloc->buffers[buffer_id], node); + ggml_backend_view_init(galloc->buffers[buffer_id], tensor); } } else { - if (node->data == NULL) { + if (tensor->data == NULL) { assert(tensor_alloc->offset != SIZE_MAX); - assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max); + assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max); void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]); void * addr = (char *)base + tensor_alloc->offset; - ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr); + ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr); } else { - if (node->buffer == NULL) { + if (tensor->buffer == NULL) { // this tensor was allocated without ggml-backend return; } @@ -843,13 +827,18 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) // reset buffers for (int i = 0; i < galloc->n_buffers; i++) { - // zero size buffers are not allocated if (galloc->buffers[i] != NULL) { ggml_backend_buffer_reset(galloc->buffers[i]); } } // allocate the graph tensors from the previous assignments + // leafs + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i]; + ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf); + } // nodes for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -863,12 +852,6 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) } ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst); } - // leafs - for (int i = 0; i < graph->n_leafs; i++) { - struct ggml_tensor * leaf = graph->leafs[i]; - struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i]; - ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc); - } return true; } @@ -900,12 +883,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx, return false; } - struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer); + struct ggml_tallocr tallocr = ggml_tallocr_new(buffer); for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) { if (t->data == NULL) { if (t->view_src == NULL) { - ggml_tallocr_alloc(tallocr, t); + ggml_tallocr_alloc(&tallocr, t); } else if (t->buffer == NULL) { ggml_backend_view_init(buffer, t); } @@ -917,8 +900,6 @@ static bool alloc_tensor_range(struct ggml_context * ctx, } } - ggml_tallocr_free(tallocr); - *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1)); (*buffers)[(*n_buffers)++] = buffer; diff --git a/ggml-alloc.h b/ggml-alloc.h index 1d9085d15f793..434c13b34a929 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -11,11 +11,15 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t; typedef struct ggml_backend * ggml_backend_t; // Tensor allocator -typedef struct ggml_tallocr * ggml_tallocr_t; +struct ggml_tallocr { + ggml_backend_buffer_t buffer; + void * base; + size_t alignment; + size_t offset; +}; -GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer); -GGML_API void ggml_tallocr_free(ggml_tallocr_t talloc); -GGML_API void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor); +GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer); +GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor); // Graph allocator /* @@ -50,7 +54,11 @@ GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed // returns false if the buffer allocation failed GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph); -GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids); +GGML_API bool ggml_gallocr_reserve_n( + ggml_gallocr_t galloc, + struct ggml_cgraph * graph, + const int * node_buffer_ids, + const int * leaf_buffer_ids); // automatic reallocation if the topology changes when using a single buffer // returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 2e9ba58a931cc..e475e20e5f46a 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -86,12 +86,12 @@ extern "C" { // (optional) asynchronous tensor data access void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); + bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst); // (optional) complete all pending operations void (*GGML_CALL synchronize)(ggml_backend_t backend); - // create a plan for ggml_cgraph and free it + // compute graph with a plan (not used currently) ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); @@ -102,16 +102,27 @@ extern "C" { // check if the backend supports an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + + // (optional) event synchronization + ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend); + void (*GGML_CALL event_free) (ggml_backend_event_t event); + void (*GGML_CALL event_record) (ggml_backend_event_t event); + void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event); + void (*GGML_CALL event_synchronize) (ggml_backend_event_t event); }; struct ggml_backend { ggml_guid_t guid; struct ggml_backend_i iface; - ggml_backend_context_t context; }; + struct ggml_backend_event { + ggml_backend_t backend; + void * context; + }; + // // Backend registry // diff --git a/ggml-backend.c b/ggml-backend.c index d60d984143249..31f8d5a6dd30b 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -221,29 +221,29 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(buf != NULL && "tensor buffer not set"); + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); if (!size) { return; } - tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); + buf->iface.set_tensor(buf, tensor, data, offset, size); } GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); if (!size) { return; } - tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size); + buf->iface.get_tensor(buf, tensor, data, offset, size); } void ggml_backend_synchronize(ggml_backend_t backend) { @@ -255,18 +255,30 @@ void ggml_backend_synchronize(ggml_backend_t backend) { } ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + GGML_ASSERT(backend->iface.graph_plan_create != NULL); + return backend->iface.graph_plan_create(backend, cgraph); } void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(backend->iface.graph_plan_free != NULL); + backend->iface.graph_plan_free(backend, plan); } enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(backend->iface.graph_plan_compute != NULL); + return backend->iface.graph_plan_compute(backend, plan); } enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph); + ggml_backend_synchronize(backend); + return err; +} + +bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -314,34 +326,68 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst } } -void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { +void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); if (src == dst) { return; } - if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) { - if (backend->iface.cpy_tensor_async != NULL) { - if (backend->iface.cpy_tensor_async(backend, src, dst)) { - return; - } + if (backend_dst->iface.cpy_tensor_async != NULL) { + if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) { + return; } } - size_t nbytes = ggml_nbytes(src); + // an async copy would normally happen after all the queued operations on both backends are completed + // sync src, set_async dst if (ggml_backend_buffer_is_host(src->buffer)) { - ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes); - } - else { + ggml_backend_synchronize(backend_src); + ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src)); + } else { + ggml_backend_synchronize(backend_src); ggml_backend_tensor_copy(src, dst); + ggml_backend_synchronize(backend_dst); + } +} + +// events + +ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) { + if (backend->iface.event_new == NULL) { + return NULL; + } + return backend->iface.event_new(backend); +} + +void ggml_backend_event_free(ggml_backend_event_t event) { + if (event == NULL) { + return; } + event->backend->iface.event_free(event); +} + +void ggml_backend_event_record(ggml_backend_event_t event) { + GGML_ASSERT(event->backend->iface.event_record != NULL); + + event->backend->iface.event_record(event); +} + +void ggml_backend_event_synchronize(ggml_backend_event_t event) { + GGML_ASSERT(event->backend->iface.event_synchronize != NULL); + + event->backend->iface.event_synchronize(event); } +void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + GGML_ASSERT(backend->iface.event_wait != NULL); + + backend->iface.event_wait(backend, event); +} // backend registry -#define GGML_MAX_BACKENDS_REG 16 +#define GGML_REG_MAX_BACKENDS 16 struct ggml_backend_reg { char name[128]; @@ -350,7 +396,7 @@ struct ggml_backend_reg { void * user_data; }; -static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG]; +static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS]; static size_t ggml_backend_registry_count = 0; GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); @@ -395,7 +441,7 @@ GGML_CALL static void ggml_backend_registry_init(void) { } GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { - GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG); + GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS); size_t id = ggml_backend_registry_count; @@ -746,8 +792,12 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); if (cpu_ctx->work_size < cplan.work_size) { - // TODO: may be faster to free and use malloc to avoid the copy - cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size); + free(cpu_ctx->work_data); + cpu_ctx->work_data = malloc(cplan.work_size); + if (cpu_ctx->work_data == NULL) { + cpu_ctx->work_size = 0; + return GGML_STATUS_ALLOC_FAILED; + } cpu_ctx->work_size = cplan.work_size; } cplan.work_data = cpu_ctx->work_data; @@ -784,6 +834,11 @@ static struct ggml_backend_i cpu_backend_i = { /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, /* .graph_compute = */ ggml_backend_cpu_graph_compute, /* .supports_op = */ ggml_backend_cpu_supports_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, }; static ggml_guid_t ggml_backend_cpu_guid(void) { @@ -939,15 +994,27 @@ static bool ggml_is_view_op(enum ggml_op op) { // scheduler -#define GGML_MAX_BACKENDS 16 -#define GGML_MAX_SPLITS 256 -#define GGML_MAX_SPLIT_INPUTS 16 +#ifndef GGML_SCHED_MAX_BACKENDS +#define GGML_SCHED_MAX_BACKENDS 16 +#endif + +#ifndef GGML_SCHED_MAX_SPLITS +#define GGML_SCHED_MAX_SPLITS 256 +#endif + +#ifndef GGML_SCHED_MAX_SPLIT_INPUTS +#define GGML_SCHED_MAX_SPLIT_INPUTS 16 +#endif + +#ifndef GGML_SCHED_MAX_COPIES +#define GGML_SCHED_MAX_COPIES 4 +#endif struct ggml_backend_sched_split { int backend_id; int i_start; int i_end; - struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; + struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; int n_inputs; // graph view of this split struct ggml_cgraph graph; @@ -955,45 +1022,53 @@ struct ggml_backend_sched_split { struct ggml_backend_sched { bool is_reset; // true if the scheduler has been reset since the last graph split + bool is_alloc; int n_backends; - ggml_backend_t backends[GGML_MAX_BACKENDS]; - ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS]; + ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS]; + ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS]; ggml_gallocr_t galloc; // hash keys of the nodes in the graph struct ggml_hash_set hash_set; // hash values int * tensor_backend_id; - struct ggml_tensor * (* tensor_copies)[GGML_MAX_BACKENDS]; + struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES]; - int * node_backend_ids; // [n_nodes] - int n_nodes; + int * node_backend_ids; // [graph_size] + int * leaf_backend_ids; // [graph_size] // copy of the graph with modified inputs struct ggml_cgraph * graph; - struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; + // graph splits + struct ggml_backend_sched_split splits[GGML_SCHED_MAX_SPLITS]; int n_splits; + // pipeline parallelism support + int n_copies; + int cur_copy; + ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES]; + struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; + int n_graph_inputs; + struct ggml_context * ctx; ggml_backend_sched_eval_callback callback_eval; void * callback_eval_user_data; // align context_buffer to GGML_MEM_ALIGN - #ifdef _MSC_VER +#ifdef _MSC_VER __declspec(align(GGML_MEM_ALIGN)) - #else +#else __attribute__((aligned(GGML_MEM_ALIGN))) - #endif - char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; +#endif + char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; }; -#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node) -#define tensor_backend_id(node) sched->tensor_backend_id[hash_id(node)] -#define tensor_backend(node) (tensor_backend_id(node) == -1 ? NULL : sched->backends[tensor_backend_id(node)]) +#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor) +#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)] // returns the priority of the backend, lower id is higher priority static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) { @@ -1005,7 +1080,8 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen return -1; } -static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { +static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) { + ggml_backend_buffer_t buffer = tensor->buffer; if (buffer == NULL) { return -1; } @@ -1016,12 +1092,16 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, gg return i; } } - GGML_ASSERT(false && "tensor buffer type not supported by any backend"); - return -1; // silence warning + + fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n", + __func__, ggml_backend_buffer_name(buffer), tensor->name); + GGML_ASSERT(false); + + return -1; } #if 0 -static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only +static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) #define GET_CAUSE(node) causes[hash_id(node)] #else @@ -1035,19 +1115,28 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st // assign pre-allocated nodes to their backend // dst - int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->buffer); + int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor); if (cur_backend != -1) { - SET_CAUSE(node, "1.dst"); + SET_CAUSE(tensor, "1.dst"); return cur_backend; } + // view_src if (tensor->view_src != NULL) { - cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src->buffer); + cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src); if (cur_backend != -1) { - SET_CAUSE(node, "1.vsrc"); + SET_CAUSE(tensor, "1.vsrc"); return cur_backend; } } + + // input + if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { + cur_backend = sched->n_backends - 1; // last backend (assumed CPU) + SET_CAUSE(tensor, "1.inp"); + return cur_backend; + } + // assign nodes that use weights to the backend of the weights for (int i = 0; i < GGML_MAX_SRC; i++) { const struct ggml_tensor * src = tensor->src[i]; @@ -1055,9 +1144,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st continue; } if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer); + int src_backend = ggml_backend_sched_backend_from_buffer(sched, src); // operations with weights are always run on the same backend as the weights - SET_CAUSE(node, "1.wgt%d", i); + SET_CAUSE(tensor, "1.wgt%d", i); return src_backend; } } @@ -1093,7 +1182,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str if (ggml_is_view_op(node->op)) { continue; } - ggml_backend_t tensor_backend = tensor_backend(node); + ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); for (int j = 0; j < GGML_MAX_SRC; j++) { @@ -1101,7 +1190,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str if (src == NULL) { continue; } - ggml_backend_t src_backend = tensor_backend(src); + ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } @@ -1118,6 +1207,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { // reset splits sched->n_splits = 0; + sched->n_graph_inputs = 0; sched->is_reset = false; struct ggml_init_params params = { @@ -1163,7 +1253,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } #ifdef DEBUG_PASS1 - fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif // pass 2: expand current backend assignments @@ -1171,10 +1261,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend) // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops - // pass 2.1 expand gpu up + + // pass 2.2 expand gpu down { int cur_backend_id = -1; - for (int i = graph->n_nodes - 1; i >= 0; i--) { + for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; @@ -1189,15 +1280,15 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } else { tensor_backend_id(node) = cur_backend_id; - SET_CAUSE(node, "2.1"); + SET_CAUSE(node, "2.2"); } } } - // pass 2.2 expand gpu down + // pass 2.1 expand gpu up { int cur_backend_id = -1; - for (int i = 0; i < graph->n_nodes; i++) { + for (int i = graph->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; @@ -1212,15 +1303,16 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } else { tensor_backend_id(node) = cur_backend_id; - SET_CAUSE(node, "2.2"); + SET_CAUSE(node, "2.1"); } } } - // pass 2.3 expand rest up + + // pass 2.4 expand rest down { int cur_backend_id = -1; - for (int i = graph->n_nodes - 1; i >= 0; i--) { + for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; @@ -1230,15 +1322,14 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg cur_backend_id = tensor_backend_id; } else { tensor_backend_id(node) = cur_backend_id; - SET_CAUSE(node, "2.3"); + SET_CAUSE(node, "2.4"); } } } - - // pass 2.4 expand rest down + // pass 2.3 expand rest up { int cur_backend_id = -1; - for (int i = 0; i < graph->n_nodes; i++) { + for (int i = graph->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; @@ -1248,12 +1339,13 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg cur_backend_id = tensor_backend_id; } else { tensor_backend_id(node) = cur_backend_id; - SET_CAUSE(node, "2.4"); + SET_CAUSE(node, "2.3"); } } } + #ifdef DEBUG_PASS2 - fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif // pass 3: assign backends to remaining src from dst and view_src @@ -1283,7 +1375,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } #ifdef DEBUG_PASS3 - fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif // pass 4: split graph, find tensors that need to be copied @@ -1315,7 +1407,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (tensor_backend_id != cur_backend_id) { sched->splits[cur_split].i_end = i; cur_split++; - GGML_ASSERT(cur_split < GGML_MAX_SPLITS); + GGML_ASSERT(cur_split < GGML_SCHED_MAX_SPLITS); sched->splits[cur_split].backend_id = tensor_backend_id; sched->splits[cur_split].i_start = i; sched->splits[cur_split].n_inputs = 0; @@ -1328,25 +1420,57 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (src == NULL) { continue; } + int src_backend_id = tensor_backend_id(src); assert(src_backend_id != -1); // all inputs should be assigned by now + + if (src->flags & GGML_TENSOR_FLAG_INPUT) { + size_t id = hash_id(src); + if (sched->tensor_copies[id][src_backend_id][0] == NULL) { + ggml_backend_t backend = sched->backends[src_backend_id]; + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * tensor_copy; + if (c == sched->cur_copy) { + tensor_copy = src; // use the original tensor as the current copy + } else { + tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c); + } + if (sched->n_copies > 1) { + ggml_set_input(tensor_copy); + ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor + } + sched->tensor_copies[id][src_backend_id][c] = tensor_copy; + tensor_backend_id(tensor_copy) = src_backend_id; + SET_CAUSE(tensor_copy, "4.cpy"); + } + int n_graph_inputs = sched->n_graph_inputs++; + GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); + sched->graph_inputs[n_graph_inputs] = src; + } + } + if (src_backend_id != tensor_backend_id) { // create a copy of the input in the split's backend size_t id = hash_id(src); - if (sched->tensor_copies[id][cur_backend_id] == NULL) { + if (sched->tensor_copies[id][cur_backend_id][0] == NULL) { ggml_backend_t backend = sched->backends[cur_backend_id]; - struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); - ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); - - sched->tensor_copies[id][cur_backend_id] = tensor_copy; - tensor_backend_id(tensor_copy) = cur_backend_id; - SET_CAUSE(tensor_copy, "4.cpy"); - + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c); + if (sched->n_copies > 1) { + ggml_set_input(tensor_copy); + ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor + } + sched->tensor_copies[id][cur_backend_id][c] = tensor_copy; + tensor_backend_id(tensor_copy) = cur_backend_id; + SET_CAUSE(tensor_copy, "4.cpy"); + } int n_inputs = sched->splits[cur_split].n_inputs++; - GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); + GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); sched->splits[cur_split].inputs[n_inputs] = src; } - node->src[j] = sched->tensor_copies[id][cur_backend_id]; + node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy]; } } } @@ -1354,37 +1478,39 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg sched->n_splits = cur_split + 1; } #ifdef DEBUG_PASS4 - fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif #ifndef NDEBUG // sanity check: all sources should have the same backend as the node for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_backend_t tensor_backend = tensor_backend(node); + ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); if (tensor_backend == NULL) { fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); } - if (node->view_src != NULL && tensor_backend != tensor_backend(node->view_src)) { + if (node->view_src != NULL && tensor_backend != ggml_backend_sched_get_tensor_backend(sched, node->view_src)) { fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n", node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", - node->view_src->name, tensor_backend(node->view_src) ? ggml_backend_name(tensor_backend(node->view_src)) : "NULL"); + node->view_src->name, ggml_backend_sched_get_tensor_backend(sched, node->view_src) ? + ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, node->view_src)) : "NULL"); } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { continue; } - ggml_backend_t src_backend = tensor_backend(src); + ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); if (src_backend != tensor_backend /* && src_backend != NULL */) { fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL"); } - if (src->view_src != NULL && src_backend != tensor_backend(src->view_src)) { + if (src->view_src != NULL && src_backend != ggml_backend_sched_get_tensor_backend(sched, src->view_src)) { fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n", src->name, src_backend ? ggml_backend_name(src_backend) : "NULL", - src->view_src->name, tensor_backend(src->view_src) ? ggml_backend_name(tensor_backend(src->view_src)) : "NULL"); + src->view_src->name, ggml_backend_sched_get_tensor_backend(sched, src->view_src) ? + ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, src->view_src)) : "NULL"); } } } @@ -1392,18 +1518,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg #endif // create copies of the graph for each split - // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way - struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false); + // TODO: avoid this copy + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS, false); for (int i = 0; i < sched->n_splits; i++) { struct ggml_backend_sched_split * split = &sched->splits[i]; split->graph = ggml_graph_view(graph, split->i_start, split->i_end); + // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id]; + struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id][sched->cur_copy]; // add a dependency to the input source so that it is not freed before the copy is done struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input); + input_dep->src[0] = input; sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input); graph_copy->nodes[graph_copy->n_nodes++] = input_dep; @@ -1417,18 +1545,56 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; } } + + if (sched->n_copies > 1) { + // add input copies as leafs so that they are allocated first + for (int i = 0; i < sched->n_graph_inputs; i++) { + struct ggml_tensor * input = sched->graph_inputs[i]; + size_t id = hash_id(input); + int backend_id = tensor_backend_id(input); + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c]; + sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id; + graph_copy->leafs[graph_copy->n_leafs++] = input_cpy; + } + } + + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &sched->splits[i]; + int backend_id = split->backend_id; + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input = split->inputs[j]; + size_t id = hash_id(input); + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c]; + sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id; + graph_copy->leafs[graph_copy->n_leafs++] = input_cpy; + } + } + } + } + + // add leafs from the original graph + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf); + graph_copy->leafs[graph_copy->n_leafs++] = leaf; + } + sched->graph = graph_copy; } static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { - // ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids); + // allocate graph if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { + // the re-allocation may cause the split inputs to be moved to a different address + ggml_backend_sched_synchronize(sched); #ifndef NDEBUG - fprintf(stderr, "ggml_backend_sched: failed to allocate graph, reserving\n"); + fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__); #endif - ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids); + ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids); if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { - fprintf(stderr, "ggml_backend_sched: failed to allocate graph\n"); + fprintf(stderr, "%s: failed to allocate graph\n", __func__); return false; } } @@ -1437,9 +1603,6 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { } static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { - uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; - uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; - struct ggml_backend_sched_split * splits = sched->splits; for (int i = 0; i < sched->n_splits; i++) { @@ -1448,34 +1611,36 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s ggml_backend_t split_backend = sched->backends[split_backend_id]; // copy the input tensors to the split backend - uint64_t copy_start_us = ggml_time_us(); for (int j = 0; j < split->n_inputs; j++) { + ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]); struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id]; + struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy]; - GGML_ASSERT(input->buffer != NULL); - GGML_ASSERT(input_cpy->buffer != NULL); + if (input->flags & GGML_TENSOR_FLAG_INPUT) { + // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]); + } else { + ggml_backend_synchronize(split_backend); + } + ggml_backend_tensor_copy(input, input_cpy); + } else { + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]); + } else { + ggml_backend_synchronize(split_backend); + ggml_backend_synchronize(input_backend); + } - ggml_backend_tensor_copy_async(split_backend, input, input_cpy); + ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy); + } } - //ggml_backend_synchronize(split_backend); // necessary to measure copy time - int64_t copy_end_us = ggml_time_us(); - copy_us[split_backend_id] += copy_end_us - copy_start_us; -#if 0 - char split_filename[GGML_MAX_NAME]; - snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend)); - ggml_graph_dump_dot(split->graph, NULL, split_filename); -#endif - - - uint64_t compute_start_us = ggml_time_us(); if (!sched->callback_eval) { - enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph); + enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph); if (ec != GGML_STATUS_SUCCESS) { return ec; } - //ggml_backend_synchronize(split_backend); // necessary to measure compute time } else { // similar to ggml_backend_compare_graph_backend for (int j0 = 0; j0 < split->graph.n_nodes; j0++) { @@ -1494,11 +1659,14 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); - enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv); + enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv); if (ec != GGML_STATUS_SUCCESS) { return ec; } + // TODO: pass backend to the callback, then the user can decide if they want to synchronize + ggml_backend_synchronize(split_backend); + if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { break; } @@ -1506,39 +1674,54 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s j0 = j1; } } - uint64_t compute_end_us = ggml_time_us(); - compute_us[split_backend_id] += compute_end_us - compute_start_us; - } -#if 0 - // per-backend timings - fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits); - for (int i = 0; i < sched->n_backends; i++) { - if (copy_us[i] > 0 || compute_us[i] > 0) { - fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]); + // record the event of this copy + if (split->n_inputs > 0) { + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]); + } } } -#endif + + sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies; return GGML_STATUS_SUCCESS; } -ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { +ggml_backend_sched_t ggml_backend_sched_new( + ggml_backend_t * backends, + ggml_backend_buffer_type_t * bufts, + int n_backends, + size_t graph_size, + bool parallel) { GGML_ASSERT(n_backends > 0); - GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); + GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); + GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); // initialize hash table - sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + sched->hash_set = ggml_hash_set_new(graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS); sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size); sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size); sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), graph_size); + sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), graph_size); sched->n_backends = n_backends; - for (int i = 0; i < n_backends; i++) { - sched->backends[i] = backends[i]; - sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]); + + sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; + + GGML_ASSERT(sched->n_copies <= GGML_SCHED_MAX_COPIES); + + for (int b = 0; b < n_backends; b++) { + sched->backends[b] = backends[b]; + sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); + GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b])); + if (sched->n_copies > 1) { + for (int c = 0; c < sched->n_copies; c++) { + sched->events[b][c] = ggml_backend_event_new(backends[b]); + } + } } sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends); @@ -1552,12 +1735,18 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { if (sched == NULL) { return; } + for (int b = 0; b < sched->n_backends; b++) { + for (int c = 0; c < sched->n_copies; c++) { + ggml_backend_event_free(sched->events[b][c]); + } + } ggml_gallocr_free(sched->galloc); ggml_free(sched->ctx); free(sched->hash_set.keys); free(sched->tensor_backend_id); free(sched->tensor_copies); free(sched->node_backend_ids); + free(sched->leaf_backend_ids); free(sched); } @@ -1569,34 +1758,63 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) { memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size); sched->is_reset = true; + sched->is_alloc = false; } bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { ggml_backend_sched_split_graph(sched, measure_graph); - if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids)) { + // TODO: extract this to a separate function + if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) { return false; } ggml_backend_sched_reset(sched); + ggml_backend_sched_synchronize(sched); + + return true; +} + +bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS); + + ggml_backend_sched_split_graph(sched, graph); + + if (!ggml_backend_sched_alloc_splits(sched)) { + return false; + } + + sched->is_alloc = true; + return true; } enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph); + ggml_backend_sched_synchronize(sched); + return err; +} - if (!sched->is_reset) { +enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + if (!sched->is_reset && !sched->is_alloc) { ggml_backend_sched_reset(sched); } - ggml_backend_sched_split_graph(sched, graph); - if (!ggml_backend_sched_alloc_splits(sched)) { - return GGML_STATUS_ALLOC_FAILED; + if (!sched->is_alloc) { + if (!ggml_backend_sched_alloc_graph(sched, graph)) { + return GGML_STATUS_ALLOC_FAILED; + } } return ggml_backend_sched_compute_splits(sched); } +void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) { + for (int i = 0; i < sched->n_backends; i++) { + ggml_backend_synchronize(sched->backends[i]); + } +} + void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { sched->callback_eval = callback; sched->callback_eval_user_data = user_data; @@ -1606,19 +1824,24 @@ int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) { return sched->n_splits; } +int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) { + return sched->n_copies; +} + size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); + return ggml_gallocr_get_buffer_size(sched->galloc, backend_index); } -void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { +void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); tensor_backend_id(node) = backend_index; } -ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { +ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { int backend_index = tensor_backend_id(node); if (backend_index == -1) { return NULL; diff --git a/ggml-backend.h b/ggml-backend.h index 8bed22578a907..099d9c258794e 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -9,6 +9,7 @@ extern "C" { typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t; typedef struct ggml_backend_buffer * ggml_backend_buffer_t; + typedef struct ggml_backend_event * ggml_backend_event_t; typedef struct ggml_backend * ggml_backend_t; typedef void * ggml_backend_graph_plan_t; @@ -72,11 +73,24 @@ extern "C" { GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); - GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy + + // asynchronous copy + // the copy is performed after all the currently queued operations in backend_src + // backend_dst will wait for the copy to complete before performing other operations + // automatic fallback to sync copy if async is not supported + GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst); + + // events + GGML_API ggml_backend_event_t ggml_backend_event_new (ggml_backend_t backend); + GGML_API void ggml_backend_event_free (ggml_backend_event_t event); + GGML_API void ggml_backend_event_record (ggml_backend_event_t event); + GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event); + GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event // // CPU backend @@ -123,27 +137,31 @@ extern "C" { /* Example usage: - sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends); - // sched is initialized with measure allocators and cannot be used until allocated with a measure graph + // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned + // preferrably to run on the same backend as the buffer + ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); - // initialize buffers from a measure graph - measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed + sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false); - // in build_graph: - build_graph(...) { - // manually assign nodes to a backend (optional, should not be needed in most cases) - struct ggml_tensor * node = ggml_mul_mat(ctx, ...); - ggml_backend_sched_set_node_backend(sched, node, backend_gpu); - } + // initialize buffers from a max size graph (optional) + reserve_graph = build_graph(sched, max_batch_size); - // allocate backend buffers from measure graph - ggml_backend_sched_init_measure(sched, measure_graph); + // manually assign nodes to a backend (optional, should not be needed in most cases) + struct ggml_tensor * node = ggml_mul_mat(ctx, ...); + ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu); - // the scheduler is now ready to compute graphs + ggml_backend_sched_reserve(sched, reserve_graph); // compute graph = build_graph(sched); ggml_backend_sched_graph_compute(sched, graph); + + // if there are graph inputs: + ggml_backend_sched_reset(sched); + ggml_backend_sched_alloc_graph(sched, graph); + ggml_backend_tensor_set(input_tensor, ...); + ggml_backend_sched_graph_compute(sched, graph); + } */ struct ggml_backend_sched; @@ -158,20 +176,26 @@ extern "C" { typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel); GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + // Initialize backend buffers from a measure graph GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + // Get the number of splits of the last graph GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); + GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched); GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); - GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); + GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); // Allocate and compute graph on the backend scheduler + GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched); // Reset all assignments and allocators - must be called before changing the node backends GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b8834ed05eed0..d1b5e52ba9011 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -72,6 +72,7 @@ #define cudaEventCreateWithFlags hipEventCreateWithFlags #define cudaEventDisableTiming hipEventDisableTiming #define cudaEventRecord hipEventRecord +#define cudaEventSynchronize hipEventSynchronize #define cudaEvent_t hipEvent_t #define cudaEventDestroy hipEventDestroy #define cudaFree hipFree @@ -81,6 +82,7 @@ #define cudaGetDeviceProperties hipGetDeviceProperties #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError +#define cudaLaunchHostFunc hipLaunchHostFunc #ifdef GGML_HIP_UMA #define cudaMalloc hipMallocManaged #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size) @@ -104,6 +106,7 @@ #define cudaStreamCreateWithFlags hipStreamCreateWithFlags #define cudaStreamFireAndForget hipStreamFireAndForget #define cudaStreamNonBlocking hipStreamNonBlocking +#define cudaStreamPerThread hipStreamPerThread #define cudaStreamSynchronize hipStreamSynchronize #define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags) #define cudaStream_t hipStream_t @@ -10641,8 +10644,20 @@ GGML_CALL void ggml_cuda_get_device_description(int device, char * description, #define UNUSED GGML_UNUSED struct ggml_backend_cuda_context { + explicit ggml_backend_cuda_context(int device) : + device(device), + name(GGML_CUDA_NAME + std::to_string(device)) { + } + + ~ggml_backend_cuda_context() { + if (copy_event != nullptr) { + CUDA_CHECK(cudaEventDestroy(copy_event)); + } + } + int device; std::string name; + cudaEvent_t copy_event = nullptr; }; // cuda buffer @@ -10732,9 +10747,8 @@ GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice)); - CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread)); + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -10743,26 +10757,25 @@ GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost)); - CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_cuda(src->buffer)) { ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; - ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - ggml_cuda_set_device(src_ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - ggml_cuda_set_device(dst_ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy((char *)dst->data, (const char *)src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice)); - CUDA_CHECK(cudaDeviceSynchronize()); - + ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; + if (src_ctx->device == dst_ctx->device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); + } else { + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); + } + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); return true; } return false; + + UNUSED(buffer); } GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -11007,7 +11020,11 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buf } const char * buf_host = (const char *)data + offset_split; - CUDA_CHECK(cudaMemcpy(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice)); + CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread)); + } + + for (int id = 0; id < g_device_count; ++id) { + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } } @@ -11041,7 +11058,11 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buf } char * buf_host = (char *)data + offset_split; - CUDA_CHECK(cudaMemcpy(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost)); + CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); + } + + for (int id = 0; id < g_device_count; ++id) { + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } } @@ -11220,6 +11241,10 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { return &ggml_backend_cuda_buffer_type_host; } +//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) { +// return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name; +//} + // backend GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) { @@ -11243,8 +11268,9 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); + GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); @@ -11252,22 +11278,61 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); + GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } -GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; +GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { + GGML_ASSERT(ggml_backend_is_cuda(backend_src) || ggml_backend_is_cuda(backend_dst)); - if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx->device][0])); - return true; + ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer; + ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer; + + if (!ggml_backend_buffer_is_cuda(src->buffer)) { + return false; } - return false; + if (!ggml_backend_buffer_is_cuda(dst->buffer)) { + return false; + } + + // device -> device + ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context; + ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context; + + if (backend_src != backend_dst) { + ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context; + ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context; + + GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device); + GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device); + + if (!cuda_ctx_src->copy_event) { + ggml_cuda_set_device(cuda_ctx_src->device); + CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming)); + } + + // copy on src stream + if (cuda_ctx_src->device == cuda_ctx_dst->device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0])); + } else { + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), g_cudaStreams[cuda_ctx_src->device][0])); + } + + // record event on src stream + CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, g_cudaStreams[cuda_ctx_src->device][0])); + + // wait on dst stream for the copy to complete + CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx_dst->device][0], cuda_ctx_src->copy_event, 0)); + } else { + // src and dst are on the same backend + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0])); + } + return true; } GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { @@ -11444,6 +11509,52 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons UNUSED(backend); } +static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + ggml_cuda_set_device(cuda_ctx->device); + + cudaEvent_t event; + CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming)); + + return new ggml_backend_event { + /* .backend = */ backend, + /* .context = */ event, + }; +} + +static void ggml_backend_cuda_event_free(ggml_backend_event_t event) { + CUDA_CHECK(cudaEventDestroy((cudaEvent_t)event->context)); + + delete event; +} + +static void ggml_backend_cuda_event_record(ggml_backend_event_t event) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)event->backend->context; + + CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, g_cudaStreams[cuda_ctx->device][0])); +} + +static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + if (ggml_backend_is_cuda(event->backend)) { + CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx->device][0], (cudaEvent_t)event->context, 0)); + } else { + // untested + auto wait_fn = [](void * user_data) { + ggml_backend_event_t event = (ggml_backend_event_t)user_data; + ggml_backend_event_synchronize(event); + }; + + CUDA_CHECK(cudaLaunchHostFunc(g_cudaStreams[cuda_ctx->device][0], wait_fn, event)); + } +} + +static void ggml_backend_cuda_event_synchronize(ggml_backend_event_t event) { + CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context)); +} + static ggml_backend_i ggml_backend_cuda_interface = { /* .get_name = */ ggml_backend_cuda_name, /* .free = */ ggml_backend_cuda_free, @@ -11457,6 +11568,11 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, /* .supports_op = */ ggml_backend_cuda_supports_op, + /* .event_new = */ ggml_backend_cuda_event_new, + /* .event_free = */ ggml_backend_cuda_event_free, + /* .event_record = */ ggml_backend_cuda_event_record, + /* .event_wait = */ ggml_backend_cuda_event_wait, + /* .event_synchronize = */ ggml_backend_cuda_event_synchronize, }; static ggml_guid_t ggml_backend_cuda_guid() { @@ -11475,10 +11591,11 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_cuda_set_main_device(device); - ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context { - /* .device = */ device, - /* .name = */ GGML_CUDA_NAME + std::to_string(device), - }; + ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); + if (ctx == nullptr) { + fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + return nullptr; + } ggml_backend_t cuda_backend = new ggml_backend { /* .guid = */ ggml_backend_cuda_guid(), diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 83a7822fdbe9d..4caf2c9e78b02 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1951,6 +1951,11 @@ static struct ggml_backend_i kompute_backend_i = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_kompute_graph_compute, /* .supports_op = */ ggml_backend_kompute_supports_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, }; static ggml_guid_t ggml_backend_kompute_guid() { diff --git a/ggml-metal.m b/ggml-metal.m index 1825d3320ee7a..3a5476c52f1a5 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2820,6 +2820,11 @@ GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, con /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, /* .supports_op = */ ggml_backend_metal_supports_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, }; void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index c2ab13034ba85..9f6506383cc0d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -17249,13 +17249,18 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ ggml_backend_sycl_cpy_tensor_async, + /* .cpy_tensor_async = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface /* .synchronize = */ ggml_backend_sycl_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, }; static ggml_guid_t ggml_backend_sycl_guid() { diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index d41aa7d22f096..7cce616ba714f 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5693,6 +5693,11 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_vk_graph_compute, /* .supports_op = */ ggml_backend_vk_supports_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, }; static ggml_guid_t ggml_backend_vk_guid() { diff --git a/ggml.c b/ggml.c index 9a7bd1d8c527b..fbc66f65b1052 100644 --- a/ggml.c +++ b/ggml.c @@ -11560,8 +11560,6 @@ static void ggml_compute_forward_get_rows_q( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - assert(params->ith == 0); - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11569,7 +11567,7 @@ static void ggml_compute_forward_get_rows_q( GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr); + const int64_t nr = ggml_nelements(src1); const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; @@ -11579,17 +11577,25 @@ static void ggml_compute_forward_get_rows_q( assert(nb00 == ggml_type_size(type)); assert(ggml_nrows(dst) == nr); - // TODO: multi-thread - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + const int ith = params->ith; + const int nth = params->nth; - dequantize_row_q( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } - } + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + dequantize_row_q( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } } @@ -11600,8 +11606,6 @@ static void ggml_compute_forward_get_rows_f16( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - assert(params->ith == 0); - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11609,24 +11613,32 @@ static void ggml_compute_forward_get_rows_f16( GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr); + const int64_t nr = ggml_nelements(src1); assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == sizeof(ggml_fp16_t)); assert(ggml_nrows(dst) == nr); - // TODO: multi-thread - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; - ggml_fp16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } - } + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + ggml_fp16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } } @@ -11637,8 +11649,6 @@ static void ggml_compute_forward_get_rows_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - assert(params->ith == 0); - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } @@ -11646,24 +11656,32 @@ static void ggml_compute_forward_get_rows_f32( GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr); + const int64_t nr = ggml_nelements(src1); assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == sizeof(float)); assert(ggml_nrows(dst) == nr); - // TODO: multi-thread - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + const int ith = params->ith; + const int nth = params->nth; - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), - (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); - } - } + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), + (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); } } @@ -17796,7 +17814,7 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const node->perf_time_us += time_us_cur; } -static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) { int n_tasks = 0; switch (node->op) { @@ -17877,6 +17895,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_GET_ROWS: + { + // FIXME: the cost of launching additional threads decreases performance with GPU offloading + //n_tasks = MIN(n_threads, ggml_nelements(node->src[1])); + n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1])); + } break; case GGML_OP_SCALE: case GGML_OP_SET: case GGML_OP_CONT: @@ -17884,7 +17908,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS: case GGML_OP_GET_ROWS_BACK: case GGML_OP_DIAG: { @@ -18102,7 +18125,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* FINALIZE */ struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { - params.nth = ggml_get_n_tasks(node, n_threads); + params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); ggml_compute_forward(¶ms, node); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -18112,7 +18135,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { while (++node_n < cgraph->n_nodes) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = ggml_get_n_tasks(node, n_threads); + const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); @@ -18160,7 +18183,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* INIT & COMPUTE */ struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = ggml_get_n_tasks(node, n_threads); + const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); struct ggml_compute_params params = { /*.type =*/ GGML_TASK_TYPE_INIT, @@ -18225,7 +18248,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; - const int n_tasks = ggml_get_n_tasks(node, n_threads); + const int n_tasks = ggml_get_n_tasks(node, n_threads, 1); max_tasks = MAX(max_tasks, n_tasks); diff --git a/llama.cpp b/llama.cpp index ad7b7b7d4bcf2..38e7036a72720 100644 --- a/llama.cpp +++ b/llama.cpp @@ -978,21 +978,6 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { } } -// -// ggml helpers -// - -static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { - struct ggml_cplan plan = ggml_graph_plan(graph, n_threads); - - if (plan.work_size > 0) { - buf.resize(plan.work_size); - plan.work_data = buf.data(); - } - - ggml_graph_compute(graph, &plan); -} - // // llama helpers // @@ -1728,6 +1713,7 @@ struct llama_hparams { struct llama_cparams { uint32_t n_ctx; // context size used during inference uint32_t n_batch; + uint32_t n_ubatch; uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -2024,8 +2010,7 @@ struct llama_context { ggml_vk_free_cpu_assist(); #endif - ggml_backend_buffer_free(buf_input); - ggml_free(ctx_input); + ggml_backend_buffer_free(buf_output); } llama_cparams cparams; @@ -2051,12 +2036,20 @@ struct llama_context { int64_t t_p_eval_us = 0; int64_t t_eval_us = 0; + int64_t t_compute_start_us = 0; + int64_t n_queued_tokens = 0; + int32_t n_sample = 0; // number of tokens sampled int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1) int32_t n_eval = 0; // number of eval calls - // logits output (2-dimensional array: [n_tokens][n_vocab]) - std::vector logits; + // host buffer for the model output (logits and embeddings) + ggml_backend_buffer_t buf_output = nullptr; + + // decode output (2-dimensional array: [n_tokens][n_vocab]) + size_t logits_size = 0; + float * logits = nullptr; + #ifndef NDEBUG // guard against access to unset logits std::vector logits_valid; @@ -2065,7 +2058,8 @@ struct llama_context { // embeddings output (2-dimensional array: [n_tokens][n_embd]) // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE - std::vector embd; + size_t embd_size = 0; + float * embd = nullptr; // sequence embeddings output (map of [n_embd] vectors) // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE @@ -2079,8 +2073,6 @@ struct llama_context { void * abort_callback_data = nullptr; // input tensors - ggml_backend_buffer_t buf_input = nullptr; - ggml_context * ctx_input = nullptr; struct ggml_tensor * inp_tokens; // I32 [n_batch] struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] struct ggml_tensor * inp_pos; // I32 [n_batch] @@ -2090,7 +2082,7 @@ struct llama_context { struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] struct ggml_tensor * inp_s_copy; // I32 [kv_size] - struct ggml_tensor * inp_s_mask; // F32 [kv_size] + struct ggml_tensor * inp_s_mask; // F32 [1, kv_size] struct ggml_tensor * inp_s_seq; // I32 [kv_size, n_batch] #ifdef GGML_USE_MPI @@ -4005,6 +3997,7 @@ static bool llm_load_tensors( // there is very little benefit to offloading the input layer, so always keep it on the CPU model.buft_input = llama_default_buffer_type_cpu(true); + //model.buft_input = llama_default_buffer_type_offload(main_gpu); model.buft_layer.resize(n_layer); @@ -5094,29 +5087,32 @@ enum llm_norm_type { static struct ggml_tensor * llm_build_inp_embd( struct ggml_context * ctx, + struct llama_context & lctx, const llama_hparams & hparams, const llama_batch & batch, struct ggml_tensor * tok_embd, - struct ggml_tensor * inp_tokens, - struct ggml_tensor * inp_embd, const llm_build_cb & cb) { const int64_t n_embd = hparams.n_embd; struct ggml_tensor * inpL; if (batch.token) { - struct ggml_tensor * inp_tokens_v = ggml_view_1d(ctx, inp_tokens, batch.n_tokens, 0); - cb(inp_tokens, "inp_tokens", -1); + lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens); + cb(lctx.inp_tokens, "inp_tokens", -1); + ggml_set_input(lctx.inp_tokens); - inpL = ggml_get_rows(ctx, tok_embd, inp_tokens_v); + inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { #ifdef GGML_USE_MPI GGML_ASSERT(false && "not implemented"); #endif - - inpL = ggml_view_2d(ctx, inp_embd, n_embd, batch.n_tokens, inp_embd->nb[1], 0); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + inpL = lctx.inp_embd; + ggml_set_input(lctx.inp_embd); } + cb(inpL, "inp_embd", -1); + return inpL; } @@ -5420,7 +5416,7 @@ static struct ggml_tensor * llm_build_kv( struct llm_build_context { const llama_model & model; - const llama_context & lctx; + llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; const llama_batch & batch; @@ -5513,6 +5509,18 @@ struct llm_build_context { }; ctx0 = ggml_init(params); + + lctx.inp_tokens = nullptr; + lctx.inp_embd = nullptr; + lctx.inp_pos = nullptr; + lctx.inp_KQ_mask = nullptr; + lctx.inp_KQ_pos = nullptr; + lctx.inp_K_shift = nullptr; + lctx.inp_mean = nullptr; + lctx.inp_cls = nullptr; + lctx.inp_s_copy = nullptr; + lctx.inp_s_mask = nullptr; + lctx.inp_s_seq = nullptr; } void free() { @@ -5527,6 +5535,10 @@ struct llm_build_context { GGML_ASSERT(kv_self.size == n_ctx); + lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); + cb(lctx.inp_K_shift, "K_shift", -1); + ggml_set_input(lctx.inp_K_shift); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions @@ -5550,12 +5562,14 @@ struct llm_build_context { GGML_ASSERT(kv_self.recurrent); + struct ggml_tensor * state_copy = build_inp_s_copy(); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); - conv_states = ggml_get_rows(ctx0, conv_states, lctx.inp_s_copy); - ssm_states = ggml_get_rows(ctx0, ssm_states, lctx.inp_s_copy); + conv_states = ggml_get_rows(ctx0, conv_states, state_copy); + ssm_states = ggml_get_rows(ctx0, ssm_states, state_copy); // TODO: name the intermediate tensors with cb() @@ -5615,6 +5629,66 @@ struct llm_build_context { return gf; } + struct ggml_tensor * build_inp_pos() { + lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(lctx.inp_pos, "inp_pos", -1); + ggml_set_input(lctx.inp_pos); + return lctx.inp_pos; + } + + struct ggml_tensor * build_inp_KQ_mask(bool causal = true) { + if (causal) { + lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens); + } else { + lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); + } + cb(lctx.inp_KQ_mask, "KQ_mask", -1); + ggml_set_input(lctx.inp_KQ_mask); + return lctx.inp_KQ_mask; + } + + struct ggml_tensor * build_inp_KQ_pos() { + lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); + cb(lctx.inp_KQ_pos, "KQ_pos", -1); + ggml_set_input(lctx.inp_KQ_pos); + return lctx.inp_KQ_pos; + } + + struct ggml_tensor * build_inp_mean() { + lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); + cb(lctx.inp_mean, "inp_mean", -1); + ggml_set_input(lctx.inp_mean); + return lctx.inp_mean; + } + + struct ggml_tensor * build_inp_cls() { + lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(lctx.inp_cls, "inp_cls", -1); + ggml_set_input(lctx.inp_cls); + return lctx.inp_cls; + } + + struct ggml_tensor * build_inp_s_copy() { + lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size); + cb(lctx.inp_s_copy, "inp_s_copy", -1); + ggml_set_input(lctx.inp_s_copy); + return lctx.inp_s_copy; + } + + struct ggml_tensor * build_inp_s_mask() { + lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv); + cb(lctx.inp_s_mask, "inp_s_mask", -1); + ggml_set_input(lctx.inp_s_mask); + return lctx.inp_s_mask; + } + + struct ggml_tensor * build_inp_s_seq() { + lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens); + cb(lctx.inp_s_seq, "inp_s_seq", -1); + ggml_set_input(lctx.inp_s_seq); + return lctx.inp_s_seq; + } + struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -5625,16 +5699,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -5686,7 +5757,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -5804,20 +5874,16 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); - cb(KQ_pos, "KQ_pos", -1); + struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -5865,7 +5931,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -5921,16 +5986,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -5984,7 +6046,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = cur; @@ -6035,21 +6096,17 @@ struct llm_build_context { GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; - struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); cb(pos, "pos_embd", -1); inpL = ggml_add(ctx0, inpL, pos); @@ -6083,7 +6140,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // add the input @@ -6135,16 +6191,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * residual = inpL; @@ -6284,7 +6337,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); @@ -6338,16 +6390,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); - cb(KQ_pos, "KQ_pos", -1); + struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6377,7 +6426,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -6433,15 +6481,12 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - // get input vectors with right size - const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type); - - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0); - struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); + struct ggml_tensor * inp_pos = build_inp_pos(); + struct ggml_tensor * inp_mean = build_inp_mean(); + struct ggml_tensor * inp_cls = build_inp_cls(); // construct input embeddings (token, type, position) - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // token types are hardcoded to zero ("Sentence A") struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); @@ -6456,8 +6501,7 @@ struct llm_build_context { cb(inpL, "inp_norm", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_cont(ctx0, ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_tokens, n_tokens, n_tokens*ggml_type_size(lctx.inp_KQ_mask->type), 0)); - cb(KQ_mask, "KQ_mask", -1); // [n_tokens, n_tokens] + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false); // iterate layers for (int il = 0; il < n_layer; ++il) { @@ -6619,16 +6663,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); - cb(KQ_pos, "KQ_pos", -1); + struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, @@ -6664,7 +6705,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // Add the input @@ -6716,16 +6756,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0); - cb(KQ_pos, "KQ_pos", -1); + struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -6766,7 +6803,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // Add the input @@ -6821,16 +6857,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6883,7 +6916,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -6939,16 +6971,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -6993,7 +7022,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -7048,16 +7076,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7109,7 +7134,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -7164,16 +7188,13 @@ struct llm_build_context { struct ggml_tensor * ffn_output; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { attn_norm_output = llm_build_norm(ctx0, inpL, hparams, @@ -7231,7 +7252,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); - cb(cur, "kqv_out", il); } // FF @@ -7281,16 +7301,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { @@ -7329,7 +7346,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * sa_out = cur; @@ -7383,16 +7399,13 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); cb(pos, "pos_embd", -1); @@ -7428,7 +7441,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // add the input @@ -7481,16 +7493,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { cur = llm_build_norm(ctx0, inpL, hparams, @@ -7532,7 +7541,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // add the input @@ -7584,16 +7592,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7645,7 +7650,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -7698,16 +7702,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7759,7 +7760,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -7821,20 +7821,17 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); cb(inpL, "inp_scaled", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7886,7 +7883,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } // scale_res - scale the hidden states for residual connection @@ -7953,22 +7949,18 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { - // norm cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, @@ -8005,7 +7997,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL); @@ -8060,16 +8051,13 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); + struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -8178,11 +8166,10 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - struct ggml_tensor * state_mask = ggml_view_2d(ctx0, lctx.inp_s_mask, 1, n_kv, lctx.inp_s_mask->nb[0], 0); - struct ggml_tensor * state_seq = ggml_view_2d(ctx0, lctx.inp_s_seq, n_kv, n_tokens, n_kv*ggml_element_size(lctx.inp_s_seq), 0); + struct ggml_tensor * state_mask = build_inp_s_mask(); + struct ggml_tensor * state_seq = build_inp_s_seq(); for (int il = 0; il < n_layer; ++il) { // (ab)using the KV cache to store the states @@ -8234,7 +8221,7 @@ struct llm_build_context { ggml_build_forward_expand(gf, ggml_cpy(ctx0, ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)), - ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_self.head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv)))); + ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv)))); // extract x from x_conv x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0); @@ -8268,7 +8255,7 @@ struct llm_build_context { ggml_build_forward_expand(gf, ggml_cpy(ctx0, ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)), - ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_self.head*d_state*d_inner*ggml_element_size(ssm_states)))); + ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states)))); struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0); @@ -8372,7 +8359,18 @@ static struct ggml_cgraph * llama_build_graph( if (!lctx.cparams.offload_kqv) { if (strcmp(name, "kqv_merged_cont") == 0) { // all nodes between the KV store and the attention output are run on the CPU - ggml_backend_sched_set_node_backend(lctx.sched, cur, lctx.backend_cpu); + ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu); + } + } + + // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends + // to fix this, we assign the norm layer manually to the backend of its layer + if (il != -1 && strcmp(name, "norm") == 0) { + for (auto * backend : lctx.backends) { + if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) { + ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); + break; + } } } }; @@ -8528,7 +8526,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); } - if (batch.pos) { + if (batch.pos && lctx.inp_pos) { const int64_t n_tokens = batch.n_tokens; ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); @@ -8539,61 +8537,63 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { "non-causal attention with generative models is not supported" ); - // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. - if (cparams.causal_attn) { - const int64_t n_kv = kv_self.n; - const int64_t n_tokens = batch.n_tokens; + if (lctx.inp_KQ_mask) { + // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. + if (cparams.causal_attn) { + const int64_t n_kv = kv_self.n; + const int64_t n_tokens = batch.n_tokens; - assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); - float * data = (float *) lctx.inp_KQ_mask->data; + float * data = (float *) lctx.inp_KQ_mask->data; - // For causal attention, use only the previous KV cells - // of the correct sequence for each token of the batch. - // It's assumed that if a token in the batch has multiple sequences, they are equivalent. - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; + // For causal attention, use only the previous KV cells + // of the correct sequence for each token of the batch. + // It's assumed that if a token in the batch has multiple sequences, they are equivalent. + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_pos pos = batch.pos[j]; + const llama_seq_id seq_id = batch.seq_id[j][0]; - for (int i = 0; i < n_kv; ++i) { - float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { - f = -INFINITY; - } else { - f = 0.0f; + for (int i = 0; i < n_kv; ++i) { + float f; + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + f = -INFINITY; + } else { + f = 0.0f; + } + data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } - data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } - } - } else { - // when using kv cache, the mask needs to match the kv cache size - const int64_t n_tokens = batch.n_tokens; - const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens; + } else { + // when using kv cache, the mask needs to match the kv cache size + const int64_t n_tokens = batch.n_tokens; + const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens; - assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); - float * data = (float *) lctx.inp_KQ_mask->data; + float * data = (float *) lctx.inp_KQ_mask->data; - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_seq_id seq_id = batch.seq_id[j][0]; + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_seq_id seq_id = batch.seq_id[j][0]; - for (int i = 0; i < n_tokens; ++i) { - float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[i]; ++s) { - if (batch.seq_id[i][s] == seq_id) { - f = 0.0f; - break; + for (int i = 0; i < n_tokens; ++i) { + float f = -INFINITY; + for (int s = 0; s < batch.n_seq_id[i]; ++s) { + if (batch.seq_id[i][s] == seq_id) { + f = 0.0f; + break; + } } - } - data[h*(n_tokens*n_tokens) + j*n_stride + i] = f; - } + data[h*(n_tokens*n_tokens) + j*n_stride + i] = f; + } - for (int i = n_tokens; i < n_stride; ++i) { - data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY; + for (int i = n_tokens; i < n_stride; ++i) { + data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY; + } } } } @@ -8602,7 +8602,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (hparams.need_kq_pos) { const int64_t n_kv = kv_self.n; - assert(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer)); + GGML_ASSERT(lctx.inp_KQ_pos); + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer)); float * data = (float *) lctx.inp_KQ_pos->data; @@ -8614,6 +8615,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; + GGML_ASSERT(lctx.inp_mean); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); float * data = (float *) lctx.inp_mean->data; @@ -8645,6 +8647,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { const int64_t n_tokens = batch.n_tokens; + GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); uint32_t * data = (uint32_t *) lctx.inp_cls->data; @@ -8665,7 +8668,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (kv_self.recurrent) { const int64_t n_kv = kv_self.n; - { + if (lctx.inp_s_mask) { GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer)); float * data = (float *) lctx.inp_s_mask->data; @@ -8687,7 +8690,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // update the correct state(s)/sequence(s) for each token of the batch. // Like with the KQ_mask, if a token in the batch has multiple sequences, // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv). - { + if (lctx.inp_s_seq) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer)); @@ -8730,7 +8733,7 @@ static void llama_graph_compute( ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data); } - ggml_backend_sched_graph_compute(lctx.sched, gf); + ggml_backend_sched_graph_compute_async(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); @@ -8750,10 +8753,11 @@ static void llama_graph_compute( // static int llama_decode_internal( llama_context & lctx, - llama_batch batch) { - const uint32_t n_tokens = batch.n_tokens; + llama_batch batch_all) { // TODO: rename back to batch + + const uint32_t n_tokens_all = batch_all.n_tokens; - if (n_tokens == 0) { + if (n_tokens_all == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__); return -1; } @@ -8762,14 +8766,16 @@ static int llama_decode_internal( const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; - const auto n_batch = cparams.n_batch; + GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT - GGML_ASSERT(n_tokens <= n_batch); - GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT + GGML_ASSERT(n_tokens_all <= cparams.n_batch); - int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; + GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens"); - const int64_t t_start_us = ggml_time_us(); + if (lctx.t_compute_start_us == 0) { + lctx.t_compute_start_us = ggml_time_us(); + } + lctx.n_queued_tokens += n_tokens_all; #ifdef GGML_USE_MPI // TODO: needs fix after #3228 @@ -8777,272 +8783,274 @@ static int llama_decode_internal( //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); #endif - GGML_ASSERT(n_threads > 0); - auto & kv_self = lctx.kv_self; const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; - // helpers for smoother batch API transition - // after deprecating the llama_eval calls, these will be removed - std::vector pos; - std::vector n_seq_id; - std::vector seq_id_arr; - std::vector> seq_id; + auto * logits_out = lctx.logits; - if (batch.pos == nullptr) { - pos.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - pos[i] = batch.all_pos_0 + i*batch.all_pos_1; - } +#ifndef NDEBUG + auto & logits_valid = lctx.logits_valid; + logits_valid.clear(); + logits_valid.resize(n_tokens_all); - batch.pos = pos.data(); - } + memset(logits_out, 0, lctx.logits_size*sizeof(float)); +#endif - if (batch.seq_id == nullptr) { - n_seq_id.resize(n_tokens); - seq_id.resize(n_tokens); - seq_id_arr.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - n_seq_id[i] = 1; - seq_id[i].resize(1); - seq_id[i][0] = batch.all_seq_id; - seq_id_arr[i] = seq_id[i].data(); - } + const auto n_ubatch = cparams.n_ubatch; - batch.n_seq_id = n_seq_id.data(); - batch.seq_id = seq_id_arr.data(); - } + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_arr; + std::vector> seq_id; - // non-causal masks do not use the KV cache - if (hparams.causal_attn) { - llama_kv_cache_update(&lctx); + for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) { + const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token); + llama_batch u_batch = { + /* .n_tokens = */ (int32_t) n_tokens, + /* .token = */ batch_all.token ? batch_all.token + cur_token : nullptr, + /* .embd = */ batch_all.embd ? batch_all.embd + cur_token*n_embd : nullptr, + /* .pos = */ batch_all.pos ? batch_all.pos + cur_token : nullptr, + /* .n_seq_id = */ batch_all.n_seq_id ? batch_all.n_seq_id + cur_token : nullptr, + /* .seq_id = */ batch_all.seq_id ? batch_all.seq_id + cur_token : nullptr, + /* .logits = */ batch_all.logits ? batch_all.logits + cur_token : nullptr, + /* .all_pos_0 = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1, + /* .all_pos_1 = */ batch_all.all_pos_1, + /* .all_seq_id = */ batch_all.all_seq_id, + }; - // if we have enough unused cells before the current head -> - // better to start searching from the beginning of the cache, hoping to fill it - if (kv_self.head > kv_self.used + 2*n_tokens) { - kv_self.head = 0; - } + int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; + GGML_ASSERT(n_threads > 0); - if (!llama_kv_cache_find_slot(kv_self, batch)) { - return 1; - } + // helpers for smoother batch API transition + // after deprecating the llama_eval calls, these will be removed + if (u_batch.pos == nullptr) { + pos.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1; + } - if (!kv_self.recurrent) { - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); - //kv_self.n = llama_kv_cache_cell_max(kv_self); + u_batch.pos = pos.data(); } - } - - //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - ggml_backend_sched_reset(lctx.sched); - ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + if (u_batch.seq_id == nullptr) { + n_seq_id.resize(n_tokens); + seq_id.resize(n_tokens); + seq_id_arr.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + n_seq_id[i] = 1; + seq_id[i].resize(1); + seq_id[i][0] = u_batch.all_seq_id; + seq_id_arr[i] = seq_id[i].data(); + } - ggml_cgraph * gf = llama_build_graph(lctx, batch, false); + u_batch.n_seq_id = n_seq_id.data(); + u_batch.seq_id = seq_id_arr.data(); + } - // the output is always the last tensor in the graph - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; + // non-causal masks do not use the KV cache + if (hparams.causal_attn) { + llama_kv_cache_update(&lctx); - if (!hparams.causal_attn) { - res = nullptr; // do not extract logits for embedding models such as BERT + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (kv_self.head > kv_self.used + 2*n_tokens) { + kv_self.head = 0; + } - // token or sequence embeddings - embd = gf->nodes[gf->n_nodes - 1]; + if (!llama_kv_cache_find_slot(kv_self, u_batch)) { + return 1; + } - GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0); - } else { - if (strcmp(res->name, "result_output") == 0) { - // the token embeddings could be the second to last tensor, or the third to last tensor - if (strcmp(embd->name, "result_norm") != 0) { - embd = gf->nodes[gf->n_nodes - 3]; - GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + if (!kv_self.recurrent) { + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + //kv_self.n = llama_kv_cache_cell_max(kv_self); } - } else { - GGML_ASSERT(false && "missing result_output tensor"); } - } - // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); + //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - // for big prompts, if BLAS is enabled, it is better to use only one thread - // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance - // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well - // we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering - // with the BLAS calls. need a better solution - // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is - // being processed then Accelerate/BLAS will not be involved, so capping would limit performance. - if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { - n_threads = std::min(4, n_threads); - } + ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); - llama_set_inputs(lctx, batch); + ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false); - llama_graph_compute(lctx, gf, n_threads); + // the output is always the last tensor in the graph + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; - // update the kv ring buffer - { - kv_self.head += n_tokens; - - // Ensure kv cache head points to a valid index. - if (kv_self.head >= kv_self.size) { - kv_self.head = 0; - } - } + if (!hparams.causal_attn) { + res = nullptr; // do not extract logits for embedding models such as BERT - // decide if we need to defrag the kv cache - if (cparams.defrag_thold >= 0.0f) { - const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used + n_tokens)/float(kv_self.n) : 0.0f; + // token or sequence embeddings + embd = gf->nodes[gf->n_nodes - 1]; - // queue defragmentation for next llama_kv_cache_update - if (fragmentation > cparams.defrag_thold) { - //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation); + GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0); + } else { + if (strcmp(res->name, "result_output") == 0) { + // the token embeddings could be the second to last tensor, or the third to last tensor + if (strcmp(embd->name, "result_norm") != 0) { + embd = gf->nodes[gf->n_nodes - 3]; + GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + } + } else { + GGML_ASSERT(false && "missing result_output tensor"); + } + } + // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); - llama_kv_cache_defrag(kv_self); + // for big prompts, if BLAS is enabled, it is better to use only one thread + // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance + // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well + // we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering + // with the BLAS calls. need a better solution + // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is + // being processed then Accelerate/BLAS will not be involved, so capping would limit performance. + if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { + n_threads = std::min(4, n_threads); } - } -#ifdef GGML_PERF - // print timing information per ggml operation (for debugging purposes) - // requires GGML_PERF to be defined - ggml_graph_print(gf); -#endif + ggml_backend_sched_alloc_graph(lctx.sched, gf); - // plot the computation graph in dot format (for debugging purposes) - //if (n_past%100 == 0) { - // ggml_graph_dump_dot(gf, NULL, "llama.dot"); - //} + llama_set_inputs(lctx, u_batch); - // extract logits - // TODO: do not compute and extract logits if only embeddings are needed - // need to update the graphs to skip "result_output" - if (res) { - auto & logits_out = lctx.logits; + llama_graph_compute(lctx, gf, n_threads); -#ifndef NDEBUG - auto & logits_valid = lctx.logits_valid; - logits_valid.clear(); - logits_valid.resize(n_tokens); + // update the kv ring buffer + { + kv_self.head += n_tokens; - logits_out.clear(); -#endif + // Ensure kv cache head points to a valid index. + if (kv_self.head >= kv_self.size) { + kv_self.head = 0; + } + } - ggml_backend_t backend_res = ggml_backend_sched_get_node_backend(lctx.sched, res); - GGML_ASSERT(backend_res != nullptr); +#ifdef GGML_PERF + // print timing information per ggml operation (for debugging purposes) + // requires GGML_PERF to be defined + ggml_graph_print(gf); +#endif - if (batch.logits) { - logits_out.resize(n_vocab * n_tokens); - int32_t i_first = -1; - for (uint32_t i = 0; i < n_tokens; i++) { - if (batch.logits[i] && i_first == -1) { - i_first = (int32_t) i; - } - if (batch.logits[i] == 0 || i == n_tokens - 1) { - if (i_first != -1) { - int i_last = batch.logits[i] == 0 ? i : i + 1; - // extract logits for the range [i_first, i_last) - // group the requests to minimize the number of calls to the backend - ggml_backend_tensor_get_async(backend_res, res, - logits_out.data() + (n_vocab*i_first), - (n_vocab*i_first)*sizeof(float), - (i_last - i_first)*n_vocab*sizeof(float)); - i_first = -1; + // plot the computation graph in dot format (for debugging purposes) + //if (n_past%100 == 0) { + // ggml_graph_dump_dot(gf, NULL, "llama.dot"); + //} + + // extract logits + // TODO: do not compute and extract logits if only embeddings are needed + // update the graphs to skip "result_output" if logits are not needed + if (res) { + ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res); + GGML_ASSERT(backend_res != nullptr); + if (u_batch.logits) { + int32_t i_first = -1; + for (uint32_t i = 0; i < n_tokens; i++) { + if (u_batch.logits[i] && i_first == -1) { + i_first = (int32_t) i; + } + if (u_batch.logits[i] == 0 || i == n_tokens - 1) { + if (i_first != -1) { + int i_last = u_batch.logits[i] == 0 ? i : i + 1; + // extract logits for the range [i_first, i_last) + // group the requests to minimize the number of calls to the backend + ggml_backend_tensor_get_async(backend_res, res, + logits_out + n_vocab*(cur_token + i_first), + i_first*n_vocab*sizeof(float), + (i_last - i_first)*n_vocab*sizeof(float)); + i_first = -1; + } } - } #ifndef NDEBUG - logits_valid[i] = batch.logits[i] != 0; + logits_valid[cur_token + i] = u_batch.logits[i] != 0;; #endif - } - } else if (lctx.logits_all) { - logits_out.resize(n_vocab*n_tokens); - ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); + } + } else if (lctx.logits_all) { + ggml_backend_tensor_get_async(backend_res, res, logits_out + n_vocab*cur_token, 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG - std::fill(logits_valid.begin(), logits_valid.end(), true); + std::fill(logits_valid.begin() + cur_token, logits_valid.begin() + cur_token + n_tokens, true); #endif - } else { - logits_out.resize(n_vocab); - ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); + } else { + if (cur_token + n_tokens >= n_tokens_all) { + ggml_backend_tensor_get_async(backend_res, res, logits_out, n_vocab*(n_tokens - 1)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG - logits_valid[0] = true; + logits_valid[0] = true; #endif + } + } } - ggml_backend_synchronize(backend_res); - } - // extract embeddings - if (cparams.embeddings && embd) { - ggml_backend_t backend_embd = ggml_backend_sched_get_node_backend(lctx.sched, embd); - GGML_ASSERT(backend_embd != nullptr); - - switch (cparams.pooling_type) { - case LLAMA_POOLING_TYPE_NONE: - { - // extract token embeddings - auto & embd_out = lctx.embd; + // extract embeddings + if (cparams.embeddings && embd) { + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + GGML_ASSERT(backend_embd != nullptr); - if (batch.logits) { - embd_out.resize(n_embd * n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - if (batch.logits[i] == 0) { - continue; + switch (cparams.pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // extract token embeddings + auto & embd_out = lctx.embd; + + if (u_batch.logits) { + //embd_out.resize(n_embd * n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + if (u_batch.logits[i] == 0) { + continue; + } + ggml_backend_tensor_get_async(backend_embd, embd, embd_out + n_embd*(i + cur_token), (n_embd*i)*sizeof(float), n_embd*sizeof(float)); } - - ggml_backend_tensor_get_async(backend_embd, embd, embd_out.data() + (n_embd*i), (n_embd*i)*sizeof(float), n_embd*sizeof(float)); } - } - } break; - case LLAMA_POOLING_TYPE_CLS: - case LLAMA_POOLING_TYPE_MEAN: - { - GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0); + } break; + case LLAMA_POOLING_TYPE_CLS: + case LLAMA_POOLING_TYPE_MEAN: + { + GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0); - // extract sequence embeddings - auto & embd_seq_out = lctx.embd_seq; - embd_seq_out.clear(); + // extract sequence embeddings + auto & embd_seq_out = lctx.embd_seq; + embd_seq_out.clear(); - for (uint32_t i = 0; i < n_tokens; i++) { - const llama_seq_id seq_id = batch.seq_id[i][0]; - if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { - continue; + for (uint32_t i = 0; i < n_tokens; i++) { + const llama_seq_id seq_id = u_batch.seq_id[i][0]; + if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { + continue; + } + embd_seq_out[seq_id].resize(n_embd); + ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); } - embd_seq_out[seq_id].resize(n_embd); - ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); - } - } break; - case LLAMA_POOLING_TYPE_UNSPECIFIED: - { - GGML_ASSERT(false && "unknown pooling type"); - } break; + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ASSERT(false && "unknown pooling type"); + } break; + } } - ggml_backend_synchronize(backend_embd); } - // measure the performance only for the single-token evals - if (n_tokens == 1) { - lctx.t_eval_us += ggml_time_us() - t_start_us; - lctx.n_eval++; - } - else if (n_tokens > 1) { - lctx.t_p_eval_us += ggml_time_us() - t_start_us; - lctx.n_p_eval += n_tokens; - } + // wait for the computation to finish (automatically done when obtaining the model output) + //llama_synchronize(&lctx); - // get a more accurate load time, upon first eval - // TODO: fix this - if (!lctx.has_evaluated_once) { - lctx.t_load_us = ggml_time_us() - lctx.t_start_us; - lctx.has_evaluated_once = true; + // decide if we need to defrag the kv cache + if (cparams.defrag_thold >= 0.0f) { + const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used + n_tokens_all)/float(kv_self.n) : 0.0f; + + // queue defragmentation for next llama_kv_cache_update + if (fragmentation > cparams.defrag_thold) { + //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation); + + llama_kv_cache_defrag(kv_self); + } } return 0; } + // find holes from the beginning of the KV cache and fill them by moving data from the end of the cache static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { auto & kv_self = lctx.kv_self; @@ -9242,6 +9250,8 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { #else // ggml_graph defrag + ggml_backend_sched_reset(lctx.sched); + ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids); llama_graph_compute(lctx, gf, lctx.cparams.n_threads); @@ -9253,14 +9263,22 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { } static void llama_kv_cache_update_internal(struct llama_context & lctx) { + bool need_reserve = false; + // apply K-shift if needed if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) { - llama_set_k_shift(lctx); - { + ggml_backend_sched_reset(lctx.sched); + ggml_cgraph * gf = llama_build_graph_k_shift(lctx); + ggml_backend_sched_alloc_graph(lctx.sched, gf); + + llama_set_k_shift(lctx); + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); + + need_reserve = true; } { @@ -9275,12 +9293,18 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { } if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) { - llama_set_s_copy(lctx); - { + ggml_backend_sched_reset(lctx.sched); + ggml_cgraph * gf = llama_build_graph_s_copy(lctx); + ggml_backend_sched_alloc_graph(lctx.sched, gf); + + llama_set_s_copy(lctx); + llama_graph_compute(lctx, gf, lctx.cparams.n_threads); + + need_reserve = true; } { @@ -9298,8 +9322,26 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { if (lctx.kv_self.do_defrag) { llama_kv_cache_defrag_internal(lctx); + need_reserve = true; + lctx.kv_self.do_defrag = false; } + + // reserve a worst case graph again + if (need_reserve) { + // TODO: extract to a function + // build worst-case graph + int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch); + int n_past = lctx.cparams.n_ctx - n_tokens; + llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph + ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true); + + // initialize scheduler with the worst-case graph + ggml_backend_sched_reset(lctx.sched); + if (!ggml_backend_sched_reserve(lctx.sched, gf)) { + LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); + } + } } // @@ -12537,7 +12579,8 @@ struct llama_context_params llama_context_default_params() { struct llama_context_params result = { /*.seed =*/ LLAMA_DEFAULT_SEED, /*.n_ctx =*/ 512, - /*.n_batch =*/ 512, + /*.n_batch =*/ 2048, + /*.n_ubatch =*/ 512, /*.n_seq_max =*/ 1, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, @@ -12691,6 +12734,17 @@ struct llama_context * llama_new_context_with_model( struct llama_context_params params) { if (!model) { + LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__); + return nullptr; + } + + if (params.n_batch == 0 && params.n_ubatch == 0) { + LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__); + return nullptr; + } + + if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) { + LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__); return nullptr; } @@ -12699,7 +12753,6 @@ struct llama_context * llama_new_context_with_model( const auto & hparams = model->hparams; auto & cparams = ctx->cparams; - cparams.n_batch = params.n_batch; // TODO: maybe add n_seq_max here too cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch; @@ -12716,6 +12769,11 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; + // with causal attention, the batch size is limited by the context size + cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; + cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); + + cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx : hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx : hparams.n_ctx_train; @@ -12751,6 +12809,8 @@ struct llama_context * llama_new_context_with_model( } LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); + LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); + LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); @@ -12895,54 +12955,31 @@ struct llama_context * llama_new_context_with_model( ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); } - // resized during inference, reserve maximum - ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); + // graph outputs buffer + { + // resized during inference, reserve maximum + ctx->logits_size = hparams.n_vocab*cparams.n_batch; + ctx->embd_size = params.embeddings ? hparams.n_embd*cparams.n_batch : 0; - if (params.embeddings) { - ctx->embd.reserve(hparams.n_embd*cparams.n_batch); - } + const size_t buf_output_size = (ctx->logits_size + ctx->embd_size)*sizeof(float); - // graph inputs - { - ggml_init_params init_params = { - /* .mem_size */ ggml_tensor_overhead()*(8 + 3*(ctx->kv_self.recurrent)), - /* .mem_buffer */ nullptr, - /* .no_alloc */ true, - }; - ctx->ctx_input = ggml_init(init_params); - - ctx->inp_tokens = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); - ctx->inp_embd = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch); - ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); - ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, kv_size, cparams.n_batch); - ctx->inp_KQ_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); - ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); - ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); - ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); - if (ctx->kv_self.recurrent) { - ctx->inp_s_copy = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, kv_size); - ctx->inp_s_mask = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, kv_size); - ctx->inp_s_seq = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_I32, kv_size, cparams.n_batch); - } - - ggml_set_name(ctx->inp_tokens, "inp_tokens"); - ggml_set_name(ctx->inp_embd, "inp_embd"); - ggml_set_name(ctx->inp_pos, "inp_pos"); - ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask"); - ggml_set_name(ctx->inp_KQ_pos, "inp_KQ_pos"); - ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); - ggml_set_name(ctx->inp_mean, "inp_mean"); - ggml_set_name(ctx->inp_cls, "inp_cls"); - if (ctx->kv_self.recurrent) { - ggml_set_name(ctx->inp_s_copy, "inp_s_copy"); - ggml_set_name(ctx->inp_s_mask, "inp_s_mask"); - ggml_set_name(ctx->inp_s_seq, "inp_s_seq"); - } - - ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); - LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, - ggml_backend_buffer_name(ctx->buf_input), - ggml_backend_buffer_get_size(ctx->buf_input) / 1024.0 / 1024.0); + ctx->buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buf_output_size); + if (ctx->buf_output == nullptr) { + LLAMA_LOG_ERROR("%s: failed to allocate logits buffer\n", __func__); + llama_free(ctx); + return nullptr; + } + ggml_backend_buffer_clear(ctx->buf_output, 0); + + + ctx->logits = (float *) ggml_backend_buffer_get_base(ctx->buf_output); + if (params.embeddings) { + ctx->embd = ctx->logits + ctx->logits_size; + } + + LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__, + ggml_backend_buffer_name(ctx->buf_output), + ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0); } // scheduler and compute buffers @@ -12961,10 +12998,21 @@ struct llama_context * llama_new_context_with_model( // buffer used to store the computation graph and the tensor meta data ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false)); - ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES); + // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary + bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER; +#ifndef GGML_USE_CUBLAS + // pipeline parallelism requires support for async compute and events + // currently this is only implemented in the CUDA backend + pipeline_parallel = false; +#endif + ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel); + + if (pipeline_parallel) { + LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched)); + } // build worst-case graph - int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); + int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch); int n_past = cparams.n_ctx - n_tokens; llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true); @@ -12987,7 +13035,7 @@ struct llama_context * llama_new_context_with_model( // note: the number of splits during measure is higher than during inference due to the kv shift int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); - LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits); + LLAMA_LOG_INFO("%s: graph splits: %d\n", __func__, n_splits); } } @@ -13024,6 +13072,10 @@ uint32_t llama_n_batch(const struct llama_context * ctx) { return ctx->cparams.n_batch; } +uint32_t llama_n_ubatch(const struct llama_context * ctx) { + return ctx->cparams.n_ubatch; +} + uint32_t llama_n_seq_max(const struct llama_context * ctx) { return ctx->kv_self.size; } @@ -13347,9 +13399,9 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_rng = LLAMA_MAX_RNG_STATE; const size_t s_logits_size = sizeof(size_t); // assume worst case for logits although only currently set ones are serialized - const size_t s_logits = ctx->logits.capacity() * sizeof(float); + const size_t s_logits = ctx->logits_size * sizeof(float); const size_t s_embedding_size = sizeof(size_t); - const size_t s_embedding = ctx->embd.capacity() * sizeof(float); + const size_t s_embedding = ctx->embd_size * sizeof(float); const size_t s_kv_buf_size = sizeof(size_t); const size_t s_kv_head = sizeof(uint32_t); const size_t s_kv_size = sizeof(uint32_t); @@ -13447,23 +13499,23 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat // copy logits { - const size_t logits_size = ctx->logits.size(); + const size_t logits_size = ctx->logits_size; data_ctx->write(&logits_size, sizeof(logits_size)); if (logits_size) { - data_ctx->write(ctx->logits.data(), logits_size * sizeof(float)); + data_ctx->write(ctx->logits, logits_size * sizeof(float)); } } // copy embeddings { - const size_t embeddings_size = ctx->embd.size(); + const size_t embeddings_size = ctx->embd_size; data_ctx->write(&embeddings_size, sizeof(embeddings_size)); if (embeddings_size) { - data_ctx->write(ctx->embd.data(), embeddings_size * sizeof(float)); + data_ctx->write(ctx->embd, embeddings_size * sizeof(float)); } } @@ -13566,12 +13618,10 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size); - GGML_ASSERT(ctx->logits.capacity() >= logits_size); + GGML_ASSERT(ctx->logits_size >= logits_size); if (logits_size) { - ctx->logits.resize(logits_size); - - memcpy(ctx->logits.data(), inp, logits_size * sizeof(float)); + memcpy(ctx->logits, inp, logits_size * sizeof(float)); inp += logits_size * sizeof(float); } } @@ -13582,12 +13632,10 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size); - GGML_ASSERT(ctx->embd.capacity() == embeddings_size); + GGML_ASSERT(ctx->embd_size == embeddings_size); if (embeddings_size) { - ctx->embd.resize(embeddings_size); - - memcpy(ctx->embd.data(), inp, embeddings_size * sizeof(float)); + memcpy(ctx->embd, inp, embeddings_size * sizeof(float)); inp += embeddings_size * sizeof(float); } } @@ -13842,24 +13890,61 @@ int32_t llama_decode( return ret; } +void llama_synchronize(struct llama_context * ctx) { + ggml_backend_sched_synchronize(ctx->sched); + + // FIXME: if multiple single tokens are evaluated without a synchronization, + // the stats will be added to the prompt evaluation stats + // this should only happen when using batch size 1 to evaluate a batch + + // add the evaluation to the stats + if (ctx->n_queued_tokens == 1) { + ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us; + ctx->n_eval++; + } else if (ctx->n_queued_tokens > 1) { + ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us; + ctx->n_p_eval += ctx->n_queued_tokens; + } + + // get a more accurate load time, upon first eval + if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) { + ctx->t_load_us = ggml_time_us() - ctx->t_start_us; + ctx->has_evaluated_once = true; + } + + ctx->n_queued_tokens = 0; + ctx->t_compute_start_us = 0; +} + float * llama_get_logits(struct llama_context * ctx) { - return ctx->logits.data(); + llama_synchronize(ctx); + + return ctx->logits; } float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { assert(ctx->logits_valid.at(i)); - return ctx->logits.data() + i*ctx->model.hparams.n_vocab; + + llama_synchronize(ctx); + + return ctx->logits + i*ctx->model.hparams.n_vocab; } float * llama_get_embeddings(struct llama_context * ctx) { - return ctx->embd.data(); + llama_synchronize(ctx); + + return ctx->embd; } float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) { - return ctx->embd.data() + i*ctx->model.hparams.n_embd; + llama_synchronize(ctx); + + return ctx->embd + i*ctx->model.hparams.n_embd; } float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) { + llama_synchronize(ctx); + auto it = ctx->embd_seq.find(seq_id); if (it == ctx->embd_seq.end()) { return nullptr; diff --git a/llama.h b/llama.h index 446899da6e38e..2d16cc9b9fa2c 100644 --- a/llama.h +++ b/llama.h @@ -234,7 +234,8 @@ extern "C" { struct llama_context_params { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model - uint32_t n_batch; // prompt processing maximum batch size + uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode + uint32_t n_ubatch; // physical maximum batch size uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing @@ -377,6 +378,7 @@ extern "C" { LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); @@ -650,6 +652,11 @@ extern "C" { // Set abort callback LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); + // Wait until all computations are finished + // This is automatically done when using one of the functions below to obtain the computation results + // and is not necessary to call it explicitly in most cases + LLAMA_API void llama_synchronize(struct llama_context * ctx); + // Token logits obtained from the last call to llama_decode() // The logits for the last token are stored in the last row // Logits for which llama_batch.logits[i] == 0 are undefined From 463628372d5fe3a0c1e5864aa5fc57deb7387039 Mon Sep 17 00:00:00 2001 From: Clint Herron Date: Wed, 13 Mar 2024 14:10:40 -0400 Subject: [PATCH 248/401] grammar : handle missing "root" node (#6004) --- common/sampling.cpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/common/sampling.cpp b/common/sampling.cpp index 823031febc7e2..5a54509827cbf 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -17,6 +17,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ return nullptr; } + // Ensure that there is a "root" node. + if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) { + fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__); + delete result; + return nullptr; + } + std::vector grammar_rules(result->parsed_grammar.c_rules()); result->grammar = llama_grammar_init( From 76a936c8939c249a7c3e8e66dfefbab13eae194f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 13 Mar 2024 20:33:56 +0200 Subject: [PATCH 249/401] readme : update API changes and hot topics --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 54bf84bec67bb..80037782fe9d6 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,14 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017 - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 ### Hot topics +- Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017 - Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981 - Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962 - Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328 From 19885d205e768579ab090d1e99281cae58c21b54 Mon Sep 17 00:00:00 2001 From: Linwei Wang Date: Thu, 14 Mar 2024 02:34:40 +0800 Subject: [PATCH 250/401] readme : update details about running llama in Termux on Android (#6039) --- README.md | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 80037782fe9d6..61bedc3f86309 100644 --- a/README.md +++ b/README.md @@ -904,6 +904,9 @@ First, install the essential packages for termux: pkg install clang wget git cmake ``` Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: + +You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux. + ``` $ mkdir build-android $ cd build-android @@ -912,7 +915,28 @@ $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROI $ make ``` Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card. -Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone: +Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: + +(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) +``` +$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/ +$cd /data/data/com.termux/files/home/bin +$chmod +x ./* +``` + +Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/` + +``` +$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/ +``` + +Now, you can start chatting: +``` +$cd /data/data/com.termux/files/home/bin +$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml +``` + +Here is a demo of an interactive session running on Pixel 5 phone: https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 From 0fd6c1f015f6cccf3b527f7dbd8386a434728126 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 10:12:29 +0200 Subject: [PATCH 251/401] embedding : print cosine similarity (#899) --- common/common.cpp | 13 +++++++++++++ common/common.h | 1 + examples/embedding/embedding.cpp | 21 ++++++++++++++++----- examples/gritlm/gritlm.cpp | 26 ++++++-------------------- 4 files changed, 36 insertions(+), 25 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 73b1b61ba1b74..58fbd05aa3516 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1877,3 +1877,16 @@ void llama_embd_normalize(const float * inp, float * out, int n) { } } +float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){ + double sum = 0.0; + double sum1 = 0.0; + double sum2 = 0.0; + + for (int i = 0; i < n; i++) { + sum += embd1[i] * embd2[i]; + sum1 += embd1[i] * embd1[i]; + sum2 += embd2[i] * embd2[i]; + } + + return sum / (sqrt(sum1) * sqrt(sum2)); +} diff --git a/common/common.h b/common/common.h index 0f178b9eb1de3..d250eef8b2b6b 100644 --- a/common/common.h +++ b/common/common.h @@ -268,3 +268,4 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40 void llama_embd_normalize(const float * inp, float * out, int n); +float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n); diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 49302a199977e..f390c40610446 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -168,14 +168,25 @@ int main(int argc, char ** argv) { batch_decode(ctx, batch, out, s, n_embd); // print first 3 embeddings + fprintf(stdout, "\n"); for (int j = 0; j < std::min(3, n_prompts); j++) { - fprintf(stderr, "embedding %d: ", j); - for (int i = 0; i < n_embd; i++) { - fprintf(stderr, "%f ", emb[j * n_embd + i]); + fprintf(stdout, "embedding %d: ", j); + for (int i = 0; i < std::min(16, n_embd); i++) { + fprintf(stdout, "%f ", emb[j * n_embd + i]); } - fprintf(stderr, "\n\n"); + fprintf(stdout, "\n"); + } + + // print cosine similarity matrix + fprintf(stdout, "\n"); + printf("cosine similarity matrix:\n\n"); + for (int i = 0; i < n_prompts; i++) { + for (int j = 0; j < n_prompts; j++) { + float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + fprintf(stdout, "%6.2f ", sim); + } + fprintf(stdout, "\n"); } - fprintf(stderr, "\n"); // clean up llama_print_timings(ctx); diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp index 3d4b085d69b6f..52fd719b38ee5 100644 --- a/examples/gritlm/gritlm.cpp +++ b/examples/gritlm/gritlm.cpp @@ -6,22 +6,6 @@ // #define GRIT_DEBUG -static float dot_product(const std::vector & v1, const std::vector & v2) { - float dot = 0.0f; - for (uint64_t i = 0; i < v1.size(); ++i) { - dot += v1[i] * v2[i]; - } - return dot; -} - -static float norm(const std::vector & v) { - return std::sqrt(dot_product(v, v)); -} - -static float cosine_similarity(const std::vector & v1, const std::vector & v2) { - return dot_product(v1, v2) / (norm(v1) * norm(v2)); -} - static std::vector> encode(llama_context * ctx, const std::vector & sentences, const std::string & instruction) { std::vector> result; @@ -203,10 +187,12 @@ int main(int argc, char * argv[]) { const std::vector> d_rep = encode(ctx, documents, gritlm_instruction("")); const std::vector> q_rep = encode(ctx, queries, gritlm_instruction(instruction)); - const float cosine_sim_q0_d0 = cosine_similarity(q_rep[0], d_rep[0]); - const float cosine_sim_q0_d1 = cosine_similarity(q_rep[0], d_rep[1]); - const float cosine_sim_q1_d0 = cosine_similarity(q_rep[1], d_rep[0]); - const float cosine_sim_q1_d1 = cosine_similarity(q_rep[1], d_rep[1]); + const int n_embd = llama_n_embd(mdl); + + const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd); + const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); From 381da2d9f0940d7009e3e918bed36338c8ff2fbb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 11:55:23 +0200 Subject: [PATCH 252/401] metal : build metallib + fix embed path (#6015) * metal : build metallib + fix embed path ggml-ci * metal : fix embed build + update library load logic ggml-ci * metal : fix embeded library build ggml-ci * ci : fix iOS builds to use embedded library --- .github/workflows/build.yml | 2 ++ .gitignore | 2 ++ CMakeLists.txt | 70 ++++++++++++++++++++----------------- Makefile | 15 ++++---- ggml-metal.m | 49 +++++++++++++++++--------- ggml-metal.metal | 3 -- 6 files changed, 83 insertions(+), 58 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index d39cd6bc338e0..0da01d5ba6ead 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -333,6 +333,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DLLAMA_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ @@ -361,6 +362,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DLLAMA_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ diff --git a/.gitignore b/.gitignore index d28f4d1b801ea..1ad8d929b87e8 100644 --- a/.gitignore +++ b/.gitignore @@ -25,6 +25,8 @@ .vscode/ .idea/ +ggml-metal-embed.metal + lcov-report/ gcovr-report/ diff --git a/CMakeLists.txt b/CMakeLists.txt index a8abf4088a01c..3ac2804a6881a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -200,9 +200,6 @@ if (LLAMA_METAL) add_compile_definitions(GGML_METAL_NDEBUG) endif() - # get full path to the file - #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - # copy ggml-common.h and ggml-metal.metal to bin directory configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) @@ -211,53 +208,62 @@ if (LLAMA_METAL) enable_language(ASM) add_compile_definitions(GGML_METAL_EMBED_LIBRARY) + set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h") set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") + file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") - set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s") + + # merge ggml-common.h and ggml-metal.metal into a single file + set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s") + set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal") add_custom_command( - OUTPUT ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY} - DEPENDS ${METALLIB_SOURCE} + OUTPUT ${METALLIB_EMBED_ASM} + COMMAND echo "Embedding Metal library" + COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED} + COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM} + DEPENDS ggml-metal.metal ggml-common.h COMMENT "Generate assembly for embedded Metal library" ) - set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY}) - endif() - - if (LLAMA_METAL_SHADER_DEBUG) - # custom command to do the following: - # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib - # - # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works - # disabling fast math is needed in order to pass tests/test-backend-ops - # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 - # note: unfortunately, we have to call it default.metallib instead of ggml.metallib - # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 - set(XC_FLAGS -fno-fast-math -fno-inline -g) - if (LLAMA_QKK_64) - set(XC_FLAGS ${XC_FLAGS} -DQK_K=64) + set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM}) + else() + if (LLAMA_METAL_SHADER_DEBUG) + # custom command to do the following: + # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air + # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib + # + # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works + # disabling fast math is needed in order to pass tests/test-backend-ops + # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 + # note: unfortunately, we have to call it default.metallib instead of ggml.metallib + # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 + set(XC_FLAGS -fno-fast-math -fno-inline -g) + else() + set(XC_FLAGS -O3) endif() add_custom_command( OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - DEPENDS ggml-metal.metal + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal + DEPENDS ggml-metal.metal ggml-common.h COMMENT "Compiling Metal kernels" - ) + ) add_custom_target( ggml-metal ALL DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - ) - endif() + ) + endif() # LLAMA_METAL_EMBED_LIBRARY set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${FOUNDATION_LIBRARY} diff --git a/Makefile b/Makefile index db9968efbddd8..cb597b209181b 100644 --- a/Makefile +++ b/Makefile @@ -557,15 +557,16 @@ ggml-metal.o: ggml-metal.m ggml-metal.h $(CC) $(CFLAGS) -c $< -o $@ ifdef LLAMA_METAL_EMBED_LIBRARY -ggml-metal-embed.o: ggml-metal.metal +ggml-metal-embed.o: ggml-metal.metal ggml-common.h @echo "Embedding Metal library" + @sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal $(eval TEMP_ASSEMBLY=$(shell mktemp)) - @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY) - @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY) - @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY) - @echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY) - @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY) - @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY) + @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY) + @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY) + @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY) + @echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY) + @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY) + @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY) @$(AS) $(TEMP_ASSEMBLY) -o $@ @rm -f ${TEMP_ASSEMBLY} endif diff --git a/ggml-metal.m b/ggml-metal.m index 3a5476c52f1a5..c3451a79b103f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -280,6 +280,11 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ id metal_library; // load library + // + // - first check if the library is embedded + // - then check if the library is in the bundle + // - if not found, load the source and compile it + // - if that fails, return NULL { NSBundle * bundle = nil; #ifdef SWIFT_PACKAGE @@ -287,12 +292,21 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ #else bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; #endif + NSError * error = nil; - NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"]; - if (libPath != nil) { + +#if GGML_METAL_EMBED_LIBRARY + const bool try_metallib = false; +#else + const bool try_metallib = true; +#endif + + NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"]; + if (try_metallib && path_lib != nil) { // pre-compiled library found - NSURL * libURL = [NSURL fileURLWithPath:libPath]; - GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); + NSURL * libURL = [NSURL fileURLWithPath:path_lib]; + GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]); + metal_library = [ctx->device newLibraryWithURL:libURL error:&error]; if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); @@ -305,31 +319,34 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ extern const char ggml_metallib_start[]; extern const char ggml_metallib_end[]; - NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding]; + NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding]; #else GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); - NSString * sourcePath; - NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; + NSString * path_source; + NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; - GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil"); + GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil"); - if (ggmlMetalPathResources) { - sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"]; + if (path_resource) { + path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"]; } else { - sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; + path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; } - if (sourcePath == nil) { + + if (path_source == nil) { GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); - sourcePath = @"ggml-metal.metal"; + path_source = @"ggml-metal.metal"; } - GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]); - NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error]; + + GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]); + + NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error]; if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } -#endif +#endif // GGML_METAL_EMBED_LIBRARY @autoreleasepool { // dictionary of preprocessor macros diff --git a/ggml-metal.metal b/ggml-metal.metal index ebf2f5b478e46..63de563251d89 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -4,9 +4,6 @@ #include -#define GGML_COMMON_IMPL_METAL -#include "ggml-common.h" - using namespace metal; #define MAX(x, y) ((x) > (y) ? (x) : (y)) From 68265ebfc6a1bed022973ea0c3145be1450b7e70 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 12:37:20 +0200 Subject: [PATCH 253/401] embedding : print all resulting embeddings (#899) --- examples/embedding/embedding.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index f390c40610446..895469a315271 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -167,9 +167,9 @@ int main(int argc, char ** argv) { float * out = emb + p * n_embd; batch_decode(ctx, batch, out, s, n_embd); - // print first 3 embeddings + // print the first part of the embeddings fprintf(stdout, "\n"); - for (int j = 0; j < std::min(3, n_prompts); j++) { + for (int j = 0; j < n_prompts; j++) { fprintf(stdout, "embedding %d: ", j); for (int i = 0; i < std::min(16, n_embd); i++) { fprintf(stdout, "%f ", emb[j * n_embd + i]); From 3fe8d7a17f84bd721cd4d8db35365da44b69f68b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 12:38:37 +0200 Subject: [PATCH 254/401] ggml : designate enum vals for integer types (#6050) --- ggml.h | 64 +++++++++++++++++++++++++++++----------------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/ggml.h b/ggml.h index 1171088a96526..ab26c8f5908c7 100644 --- a/ggml.h +++ b/ggml.h @@ -337,24 +337,24 @@ extern "C" { struct ggml_object; struct ggml_context; + // NOTE: always add types at the end of the enum to keep backward compatibility enum ggml_type { - GGML_TYPE_F32 = 0, - GGML_TYPE_F16 = 1, - GGML_TYPE_Q4_0 = 2, - GGML_TYPE_Q4_1 = 3, + GGML_TYPE_F32 = 0, + GGML_TYPE_F16 = 1, + GGML_TYPE_Q4_0 = 2, + GGML_TYPE_Q4_1 = 3, // GGML_TYPE_Q4_2 = 4, support has been removed - // GGML_TYPE_Q4_3 (5) support has been removed - GGML_TYPE_Q5_0 = 6, - GGML_TYPE_Q5_1 = 7, - GGML_TYPE_Q8_0 = 8, - GGML_TYPE_Q8_1 = 9, - // k-quantizations - GGML_TYPE_Q2_K = 10, - GGML_TYPE_Q3_K = 11, - GGML_TYPE_Q4_K = 12, - GGML_TYPE_Q5_K = 13, - GGML_TYPE_Q6_K = 14, - GGML_TYPE_Q8_K = 15, + // GGML_TYPE_Q4_3 = 5, support has been removed + GGML_TYPE_Q5_0 = 6, + GGML_TYPE_Q5_1 = 7, + GGML_TYPE_Q8_0 = 8, + GGML_TYPE_Q8_1 = 9, + GGML_TYPE_Q2_K = 10, + GGML_TYPE_Q3_K = 11, + GGML_TYPE_Q4_K = 12, + GGML_TYPE_Q5_K = 13, + GGML_TYPE_Q6_K = 14, + GGML_TYPE_Q8_K = 15, GGML_TYPE_IQ2_XXS = 16, GGML_TYPE_IQ2_XS = 17, GGML_TYPE_IQ3_XXS = 18, @@ -363,9 +363,9 @@ extern "C" { GGML_TYPE_IQ3_S = 21, GGML_TYPE_IQ2_S = 22, GGML_TYPE_IQ4_XS = 23, - GGML_TYPE_I8, - GGML_TYPE_I16, - GGML_TYPE_I32, + GGML_TYPE_I8 = 24, + GGML_TYPE_I16 = 25, + GGML_TYPE_I32 = 26, GGML_TYPE_COUNT, }; @@ -383,20 +383,20 @@ extern "C" { // model file types enum ggml_ftype { - GGML_FTYPE_UNKNOWN = -1, - GGML_FTYPE_ALL_F32 = 0, - GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors - GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors - GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors + GGML_FTYPE_UNKNOWN = -1, + GGML_FTYPE_ALL_F32 = 0, + GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors + GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors + GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 - GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors - GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors - GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors - GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors - GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors - GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors - GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors - GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors + GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors + GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors + GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors + GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors + GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors + GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors + GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors + GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors From 3ca23481dd309bd51cc31c73a4cc34f922cc372f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ond=C5=99ej=20=C4=8Cert=C3=ADk?= Date: Thu, 14 Mar 2024 04:40:14 -0600 Subject: [PATCH 255/401] gguf-py : add support for I8, I16 and I32 (#6045) * Refactor dtype handling to be extensible This code is equivalent as before, but now it is prepared to easily add more NumPy dtypes. * Add support for I8, I16 and I32 These types are allowed in the GGUF specification. * Add support for I8, I16 and I32 to gguf_writer * Add support for I8, I16, I32 to gguf_reader --- gguf-py/gguf/constants.py | 6 ++++++ gguf-py/gguf/gguf_reader.py | 9 +++++++++ gguf-py/gguf/gguf_writer.py | 16 ++++++++++++---- 3 files changed, 27 insertions(+), 4 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index b23badb1019c1..99f71f0a1aa29 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -661,6 +661,9 @@ class GGMLQuantizationType(IntEnum): IQ3_S = 21 IQ2_S = 22 IQ4_XS = 23 + I8 = 24 + I16 = 25 + I32 = 26 class GGUFEndian(IntEnum): @@ -727,6 +730,9 @@ def get_type(val: Any) -> GGUFValueType: GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), + GGMLQuantizationType.I8: (1, 1), + GGMLQuantizationType.I16: (1, 2), + GGMLQuantizationType.I32: (1, 4), } diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 5b6d4ba6bcce9..1c10f57538992 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -248,6 +248,15 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: elif ggml_type == GGMLQuantizationType.F16: item_count = n_elems item_type = np.float16 + elif ggml_type == GGMLQuantizationType.I8: + item_count = n_elems + item_type = np.int8 + elif ggml_type == GGMLQuantizationType.I16: + item_count = n_elems + item_type = np.int16 + elif ggml_type == GGMLQuantizationType.I32: + item_count = n_elems + item_type = np.int32 else: item_count = n_bytes item_type = np.uint8 diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e49c5db6866a2..9c1eeac318c7d 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -196,9 +196,6 @@ def add_tensor_info( if self.state is not WriterState.EMPTY: raise ValueError(f'Expected output file to be empty, got {self.state}') - if raw_dtype is None and tensor_dtype not in (np.float32, np.float16): - raise ValueError("Only F32 and F16 tensors are supported for now") - encoded_name = name.encode("utf8") self.ti_data += self._pack("Q", len(encoded_name)) self.ti_data += encoded_name @@ -207,7 +204,18 @@ def add_tensor_info( for i in range(n_dims): self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: - dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 + if tensor_shape == np.float32: + dtype = GGMLQuantizationType.F32 + elif tensor_dtype == np.float16: + dtype = GGMLQuantizationType.F16 + elif tensor_dtype == np.int8: + dtype = GGMLQuantizationType.I8 + elif tensor_dtype == np.int16: + dtype = GGMLQuantizationType.I16 + elif tensor_dtype == np.int32: + dtype = GGMLQuantizationType.I32 + else: + raise ValueError("Only F32, F16, I8, I16, I32 tensors are supported for now") else: dtype = raw_dtype self.ti_data += self._pack("I", dtype) From 2c4fb69246834503db7b78bcbedcef506bbc60c4 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Thu, 14 Mar 2024 11:56:48 +0100 Subject: [PATCH 256/401] llama : optimize defrag moves + fix fragmentation calculation (#6037) * attempt to reduce the impact of a worst-case scenario * fragmentation calculation fix * Update llama.cpp --------- Co-authored-by: Georgi Gerganov --- llama.cpp | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 38e7036a72720..ff467c5756a1a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9036,8 +9036,8 @@ static int llama_decode_internal( //llama_synchronize(&lctx); // decide if we need to defrag the kv cache - if (cparams.defrag_thold >= 0.0f) { - const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used + n_tokens_all)/float(kv_self.n) : 0.0f; + if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) { + const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f; // queue defragmentation for next llama_kv_cache_update if (fragmentation > cparams.defrag_thold) { @@ -9069,6 +9069,11 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // number of cells moved uint32_t n_moves = 0; + // each move requires 6*n_layer tensors (see build_defrag) + // - source view, destination view, copy operation + // - x2 for keys and values + const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer); + // determine which KV cells to move where // // cell i moves to ids[i] @@ -9095,15 +9100,6 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { nh++; } - // each move requires 6*n_layer tensors (see build_defrag) - // - source view, destination view, copy operation - // - x2 for keys and values - // - if (6*(n_moves + nh)*n_layer >= LLAMA_MAX_NODES) { - // the graph is too big, we cannot move more cells - break; - } - uint32_t nf = 0; uint32_t is = n_kv - 1; @@ -9133,11 +9129,19 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // are we moving a continuous block of memory? bool cont = false; + // should we stop searching for the next move? + bool stop = false; + // go back and move the nf cells to the hole for (; i1 < n_kv; ++i1) { auto & cell1 = kv_self.cells[i1]; if (cell1.is_empty() || ids[i1] != n_kv) { + if (n_moves == max_moves) { + stop = true; + break; + } + cont = false; continue; } @@ -9164,6 +9168,10 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { } } + if (stop || n_moves == max_moves) { + break; + } + //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh); i0 += nh - 1; From a44bc969e4cd62ca9f4332e17fe3c51f2093e7c6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 13:13:06 +0200 Subject: [PATCH 257/401] llama : fix typo --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index ff467c5756a1a..eb48b1e90c8d1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3932,7 +3932,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); - LLAMA_LOG_INFO("%s: causal attm = %d\n", __func__, hparams.causal_attn); + LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn); LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); From 43241adf22e8231ffaf3827d2c9310cc0ffd5ac5 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Thu, 14 Mar 2024 12:15:39 +0100 Subject: [PATCH 258/401] server: disable debug release type sanitizer, simplify trigger (#6047) - increase time out for server - do not fail fast --- .github/workflows/server.yml | 17 ++++++++--------- examples/server/tests/features/steps/steps.py | 9 ++++++++- 2 files changed, 16 insertions(+), 10 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index e385e03f3a245..5e38b3547c659 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -25,17 +25,14 @@ jobs: strategy: matrix: sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug, Release] + build_type: [Debug] include: - build_type: Release sanitizer: "" - exclude: - - build_type: Release - sanitizer: ADDRESS - - build_type: Release + - build_type: Debug sanitizer: THREAD - - build_type: Release - sanitizer: UNDEFINED + disabled_on_pr: true + fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken container: image: ubuntu:latest @@ -81,13 +78,14 @@ jobs: - name: Tests id: server_integration_tests + if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} run: | cd examples/server/tests PORT=8888 ./tests.sh - name: Slow tests id: server_integration_tests_slow - if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }} + if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow @@ -124,13 +122,14 @@ jobs: - name: Tests id: server_integration_tests + if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} run: | cd examples/server/tests behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp - name: Slow tests id: server_integration_tests_slow - if: ${{ github.event.schedule != '' || github.event.inputs.slow_tests == 'true' }} + if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests behave.exe --stop --no-skipped --no-capture --tags slow diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index cfa9f96ec5306..a59a52d21748a 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -119,6 +119,10 @@ def step_server_metrics(context): def step_start_server(context): start_server_background(context) attempts = 0 + max_attempts = 20 + if 'GITHUB_ACTIONS' in os.environ: + max_attempts *= 2 + while True: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: result = sock.connect_ex((context.server_fqdn, context.server_port)) @@ -126,7 +130,7 @@ def step_start_server(context): print("\x1b[33;46mserver started!\x1b[0m") return attempts += 1 - if attempts > 20: + if attempts > max_attempts: assert False, "server not started" print(f"waiting for server to start, connect error code = {result}...") time.sleep(0.1) @@ -943,6 +947,9 @@ async def wait_for_health_status(context, print(f"Starting checking for health for expected_health_status={expected_health_status}\n") interval = 0.5 counter = 0 + if 'GITHUB_ACTIONS' in os.environ: + timeout *= 2 + async with aiohttp.ClientSession() as session: while True: async with await session.get(f'{base_url}/health', params=params) as health_response: From 15a333260ab637a040ed0864c206a2ceaf806bb8 Mon Sep 17 00:00:00 2001 From: Jian Liao Date: Thu, 14 Mar 2024 04:18:23 -0700 Subject: [PATCH 259/401] readme : improve readme for Llava-1.6 example (#6044) Co-authored-by: Jian Liao --- examples/llava/README.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 35e6d9e5d00f7..67cb0f22b7d95 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -63,12 +63,20 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director ```console git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b ``` -2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: + +2) Install the required Python packages: + +```sh +pip install -r examples/llava/requirements.txt +``` + +3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory -3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: + +4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: ```console mkdir vit cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin @@ -76,18 +84,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json ``` -4) Create the visual gguf model: +5) Create the visual gguf model: ```console python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP -5) Then convert the model to gguf format: +6) Then convert the model to gguf format: ```console python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` -6) And finally we can run the llava-cli using the 1.6 model version: +7) And finally we can run the llava-cli using the 1.6 model version: ```console ./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 ``` From 77178eedc83d49f31bf757d8e12315d76460be78 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 13:32:14 +0200 Subject: [PATCH 260/401] gguf-py : fix dtype check (#6045) --- gguf-py/gguf/gguf_writer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 9c1eeac318c7d..4d389be951d72 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -204,7 +204,7 @@ def add_tensor_info( for i in range(n_dims): self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: - if tensor_shape == np.float32: + if tensor_dtype == np.float32: dtype = GGMLQuantizationType.F32 elif tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 From 044ec4b2a567f649459ccd20af2f387c784faa51 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 15:14:14 +0200 Subject: [PATCH 261/401] embedding : add EOS token if not present (#899) --- examples/embedding/embedding.cpp | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 895469a315271..cbf9aa2b560dd 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -112,13 +112,20 @@ int main(int argc, char ** argv) { // tokenize the prompts and trim std::vector> inputs; for (const auto & prompt : prompts) { - auto inp = ::llama_tokenize(ctx, prompt, true); + auto inp = ::llama_tokenize(ctx, prompt, true, false); if (inp.size() > n_batch) { inp.resize(n_batch); } inputs.push_back(inp); } + // add eos if not present + for (auto & inp : inputs) { + if (inp.empty() || inp.back() != llama_token_eos(model)) { + inp.push_back(llama_token_eos(model)); + } + } + // tokenization stats if (params.verbose_prompt) { for (int i = 0; i < (int) inputs.size(); i++) { @@ -172,7 +179,7 @@ int main(int argc, char ** argv) { for (int j = 0; j < n_prompts; j++) { fprintf(stdout, "embedding %d: ", j); for (int i = 0; i < std::min(16, n_embd); i++) { - fprintf(stdout, "%f ", emb[j * n_embd + i]); + fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); } fprintf(stdout, "\n"); } From 69ff61397d2b7b550dcdda4a35b35128892408b0 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Thu, 14 Mar 2024 17:21:56 +0100 Subject: [PATCH 262/401] llama : support models without vocabulary (#5798) * additional methods to read model and ctx parameters * vocab size as a part of a model metadata * models without vocabulary, convert.py part * models without vocabulary, llama.cpp part * PR clean up * converter scrypt fixes * llama_vocab_type update (renamed the new key) * pr review fixes * revert function renaming * one more NoVocab assert --- convert.py | 126 +++++++++++++++++++++--------------- gguf-py/gguf/constants.py | 2 + gguf-py/gguf/gguf_writer.py | 3 + llama.cpp | 92 +++++++++++++++++--------- llama.h | 7 +- 5 files changed, 142 insertions(+), 88 deletions(-) diff --git a/convert.py b/convert.py index c15f8c47ea4f7..161430f3e717e 100755 --- a/convert.py +++ b/convert.py @@ -332,6 +332,9 @@ def load(model_plus: ModelPlus) -> Params: # class BpeVocab: + tokenizer_model = "gpt2" + name = "bpe" + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) if isinstance(self.bpe_tokenizer.get('model'), dict): @@ -390,6 +393,9 @@ def __repr__(self) -> str: class SentencePieceVocab: + tokenizer_model = "llama" + name = "spm" + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) added_tokens: dict[str, int] @@ -453,6 +459,9 @@ def __repr__(self) -> str: class HfVocab: + tokenizer_model = "llama" + name = "hfft" + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None: try: from transformers import AutoTokenizer @@ -553,7 +562,15 @@ def __repr__(self) -> str: return f"" -Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab" +class NoVocab: + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab" # @@ -935,8 +952,10 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N # Handle special case where the model's vocab size is not set if params.n_vocab == -1: raise ValueError( - f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?" + f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}" ) + if isinstance(vocab, NoVocab): + return # model has no vocab # Check for a vocab size mismatch if params.n_vocab == vocab.vocab_size: @@ -977,6 +996,7 @@ def add_meta_arch(self, params: Params) -> None: name = str(params.path_model.parent).split('/')[-1] self.gguf.add_name (name) + self.gguf.add_vocab_size (params.n_vocab) self.gguf.add_context_length (params.n_ctx) self.gguf.add_embedding_length (params.n_embd) self.gguf.add_block_count (params.n_layer) @@ -1013,21 +1033,9 @@ def add_meta_arch(self, params: Params) -> None: if params.ftype is not None: self.gguf.add_file_type(params.ftype) - def handle_tokenizer_model(self, vocab: Vocab) -> str: - # Map the vocab types to the supported tokenizer models - tokenizer_model = { - SentencePieceVocab: "llama", - HfVocab: "llama", - BpeVocab: "gpt2", - }.get(type(vocab)) - - # Block if vocab type is not predefined - if tokenizer_model is None: - raise ValueError("Unknown vocab type: Not supported") - - return tokenizer_model - def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]: + assert not isinstance(vocab, NoVocab) + tokens = [] scores = [] toktypes = [] @@ -1043,11 +1051,8 @@ def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list return tokens, scores, toktypes def add_meta_vocab(self, vocab: Vocab) -> None: - # Handle the tokenizer model - tokenizer_model = self.handle_tokenizer_model(vocab) - # Ensure that tokenizer_model is added to the GGUF model - self.gguf.add_tokenizer_model(tokenizer_model) + self.gguf.add_tokenizer_model(vocab.tokenizer_model) # Extract model vocabulary for model conversion tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab) @@ -1074,6 +1079,26 @@ def write_meta(self) -> None: def write_tensor_info(self) -> None: self.gguf.write_ti_data_to_file() + def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None: + ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency) + if ftype == GGMLFileType.MostlyQ8_0: + ndarrays = bounded_parallel_map( + OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency, + use_processpool_executor=True, + ) + else: + ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) + + start = time.time() + for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + elapsed = time.time() - start + size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) + padi = len(str(len(model))) + print( + f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" + ) + self.gguf.write_tensor_data(ndarray) + def close(self) -> None: self.gguf.close() @@ -1082,7 +1107,7 @@ def write_vocab_only( fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, ) -> None: - check_vocab_size(params, vocab, pad_vocab = pad_vocab) + check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) @@ -1120,8 +1145,11 @@ def write_all( # meta data of.add_meta_arch(params) - of.add_meta_vocab(vocab) - of.add_meta_special_vocab(svocab) + if isinstance(vocab, NoVocab): + of.gguf.add_tokenizer_model(vocab.tokenizer_model) + else: + of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) # tensor info for name, lazy_tensor in model.items(): @@ -1131,24 +1159,7 @@ def write_all( of.write_tensor_info() # tensor data - ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) - if ftype == GGMLFileType.MostlyQ8_0: - ndarrays = bounded_parallel_map( - OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency, - use_processpool_executor=True, - ) - else: - ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) - - start = time.time() - for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): - elapsed = time.time() - start - size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) - padi = len(str(len(model))) - print( - f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" - ) - of.gguf.write_tensor_data(ndarray) + of.write_tensor_data(ftype, model, concurrency) of.close() @@ -1309,8 +1320,8 @@ def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]: return vtype, path raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}") - def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab: - load_merges = vocabtype == "bpe" + def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab: + load_merges = vocab.name == "bpe" n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None return gguf.SpecialVocab( model_parent_path, @@ -1319,30 +1330,34 @@ def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: n_vocab=n_vocab, ) - def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: + def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab: vocab_type, path = self._select_file(vocab_types) print(f"Loading vocab file {path!r}, type {vocab_type!r}") added_tokens_path = path.parent / "added_tokens.json" - vocab: Vocab if vocab_type == "bpe": - vocab = BpeVocab( + return BpeVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocab_type == "spm": - vocab = SentencePieceVocab( + if vocab_type == "spm": + return SentencePieceVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocab_type == "hfft": - vocab = HfVocab( + if vocab_type == "hfft": + return HfVocab( path.parent, added_tokens_path if added_tokens_path.exists() else None ) + raise ValueError(vocab_type) + + def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: + vocab: Vocab + if len(vocab_types) == 1 and "no_vocab" in vocab_types: + vocab = NoVocab() else: - raise ValueError(vocab_type) + vocab = self._create_vocab_by_path(vocab_types) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, - vocab_type, model_parent_path, ) return vocab, special_vocab @@ -1380,6 +1395,7 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--no-vocab", action="store_true", help="store model without the vocab") parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") @@ -1392,6 +1408,10 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") args = parser.parse_args(args_in) + if args.no_vocab: + if args.vocab_only: + raise ValueError("no need to specify --vocab-only if using --no-vocab") + args.vocab_type = "no_vocab" if args.dump_single: model_plus = lazy_load_file(args.model) @@ -1442,7 +1462,7 @@ def main(args_in: list[str] | None = None) -> None: print(f"Wrote {outfile}") return - if model_plus.vocab is not None and args.vocab_dir is None: + if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab: vocab = model_plus.vocab print(f"Vocab info: {vocab}") diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 99f71f0a1aa29..2d7cf16c14ed1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -32,6 +32,7 @@ class General: FILE_TYPE = "general.file_type" class LLM: + VOCAB_SIZE = "{arch}.vocab_size" CONTEXT_LENGTH = "{arch}.context_length" EMBEDDING_LENGTH = "{arch}.embedding_length" BLOCK_COUNT = "{arch}.block_count" @@ -752,6 +753,7 @@ def get_type(val: Any) -> GGUFValueType: KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE # LLM +KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 4d389be951d72..81b2eb884d485 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -321,6 +321,9 @@ def add_custom_alignment(self, alignment: int) -> None: self.data_alignment = alignment self.add_uint32(Keys.General.ALIGNMENT, alignment) + def add_vocab_size(self, size: int) -> None: + self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size) + def add_context_length(self, length: int) -> None: self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) diff --git a/llama.cpp b/llama.cpp index eb48b1e90c8d1..10fd53469eb6f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -258,6 +258,7 @@ enum llm_kv { LLM_KV_GENERAL_SOURCE_URL, LLM_KV_GENERAL_SOURCE_HF_REPO, + LLM_KV_VOCAB_SIZE, LLM_KV_CONTEXT_LENGTH, LLM_KV_EMBEDDING_LENGTH, LLM_KV_BLOCK_COUNT, @@ -321,6 +322,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, + { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, { LLM_KV_BLOCK_COUNT, "%s.block_count" }, @@ -3242,10 +3244,11 @@ static const char * llama_model_type_name(e_model type) { static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ switch (type) { - case LLAMA_VOCAB_TYPE_SPM: return "SPM"; - case LLAMA_VOCAB_TYPE_BPE: return "BPE"; - case LLAMA_VOCAB_TYPE_WPM: return "WPM"; - default: return "unknown"; + case LLAMA_VOCAB_TYPE_NONE: return "no vocab"; + case LLAMA_VOCAB_TYPE_SPM: return "SPM"; + case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + case LLAMA_VOCAB_TYPE_WPM: return "WPM"; + default: return "unknown"; } } @@ -3277,14 +3280,14 @@ static void llm_load_hparams( ml.get_key(LLM_KV_GENERAL_NAME, model.name, false); // get hparams kv - ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); - ml.get_key (LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); - ml.get_key (LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); - ml.get_key (LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff); - ml.get_key (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head); - ml.get_key (LLM_KV_BLOCK_COUNT, hparams.n_layer); - ml.get_key (LLM_KV_EXPERT_COUNT, hparams.n_expert, false); - ml.get_key (LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); + ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); + ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); + ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); + ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff); + ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head); + ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer); + ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); + ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS); GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert); @@ -3645,30 +3648,25 @@ static void llm_load_vocab( const auto kv = LLM_KV(model.arch); - const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str()); - if (token_idx == -1) { - throw std::runtime_error("cannot find tokenizer vocab in model file\n"); - } - - const float * scores = nullptr; - const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str()); - if (score_idx != -1) { - scores = (const float * ) gguf_get_arr_data(ctx, score_idx); - } - - const int * toktypes = nullptr; - const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str()); - if (toktype_idx != -1) { - toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); - } - // determine vocab type { std::string tokenizer_name; ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name); - if (tokenizer_name == "llama") { + if (tokenizer_name == "no_vocab") { + vocab.type = LLAMA_VOCAB_TYPE_NONE; + + // default special tokens + vocab.special_bos_id = -1; + vocab.special_eos_id = -1; + vocab.special_unk_id = -1; + vocab.special_sep_id = -1; + vocab.special_pad_id = -1; + vocab.linefeed_id = -1; + + return; + } else if (tokenizer_name == "llama") { vocab.type = LLAMA_VOCAB_TYPE_SPM; // default special tokens @@ -3734,6 +3732,23 @@ static void llm_load_vocab( } } + const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str()); + if (token_idx == -1) { + throw std::runtime_error("cannot find tokenizer vocab in model file\n"); + } + + const float * scores = nullptr; + const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str()); + if (score_idx != -1) { + scores = (const float * ) gguf_get_arr_data(ctx, score_idx); + } + + const int * toktypes = nullptr; + const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str()); + if (toktype_idx != -1) { + toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); + } + const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx); vocab.id_to_token.resize(n_vocab); @@ -5023,7 +5038,8 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam llm_load_print_meta(ml, model); - if (model.hparams.n_vocab != model.vocab.id_to_token.size()) { + if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE && + model.hparams.n_vocab != model.vocab.id_to_token.size()) { throw std::runtime_error("vocab size mismatch"); } @@ -9361,26 +9377,32 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) { } static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) { + GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL; } static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) { + GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN; } static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) { + GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL; } static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) { + GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE; } static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) { + GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED; } static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { + GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE); GGML_ASSERT(llama_is_byte_token(vocab, id)); const auto& token_data = vocab.id_to_token.at(id); switch (llama_vocab_get_type(vocab)) { @@ -9401,6 +9423,7 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { } static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { + GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE); static const char * hex = "0123456789ABCDEF"; switch (llama_vocab_get_type(vocab)) { case LLAMA_VOCAB_TYPE_SPM: { @@ -10232,6 +10255,8 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } } break; + case LLAMA_VOCAB_TYPE_NONE: + GGML_ASSERT(false); } return output; @@ -13138,7 +13163,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { } int32_t llama_n_vocab(const struct llama_model * model) { - return model->vocab.id_to_token.size(); + return model->hparams.n_vocab; } int32_t llama_n_ctx_train(const struct llama_model * model) { @@ -13962,14 +13987,17 @@ float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id } const char * llama_token_get_text(const struct llama_model * model, llama_token token) { + GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE); return model->vocab.id_to_token[token].text.c_str(); } float llama_token_get_score(const struct llama_model * model, llama_token token) { + GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE); return model->vocab.id_to_token[token].score; } llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) { + GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE); return model->vocab.id_to_token[token].type; } diff --git a/llama.h b/llama.h index 2d16cc9b9fa2c..90aa5372e740b 100644 --- a/llama.h +++ b/llama.h @@ -59,9 +59,10 @@ extern "C" { typedef int32_t llama_seq_id; enum llama_vocab_type { - LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece - LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding - LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece + LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab + LLAMA_VOCAB_TYPE_SPM = 1, // SentencePiece + LLAMA_VOCAB_TYPE_BPE = 2, // Byte Pair Encoding + LLAMA_VOCAB_TYPE_WPM = 3, // WordPiece }; // note: these values should be synchronized with ggml_rope From 727107707a73b3dc8a497cf9fc9405722c16dd2b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ond=C5=99ej=20=C4=8Cert=C3=ADk?= Date: Thu, 14 Mar 2024 11:57:31 -0600 Subject: [PATCH 263/401] gguf-py : bump version to 0.8.0 (#6060) --- gguf-py/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 9789c2c877165..96396e04e8f3f 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.7.0" +version = "0.8.0" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 6e0438da3cc95b89cdbf55f45fa4e324d9076792 Mon Sep 17 00:00:00 2001 From: Steve Grubb Date: Thu, 14 Mar 2024 14:29:32 -0400 Subject: [PATCH 264/401] gguf : fix resource leaks (#6061) There several places where a gguf context is allocated. A call to gguf_free is missing in some error paths. Also on linux, llama-bench was missing a fclose. --- examples/gguf/gguf.cpp | 1 + examples/llama-bench/llama-bench.cpp | 1 + examples/llava/clip.cpp | 4 ++++ examples/train-text-from-scratch/train-text-from-scratch.cpp | 1 + 4 files changed, 7 insertions(+) diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index e67be4fb2995e..5444503a5d218 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -211,6 +211,7 @@ static bool gguf_ex_read_1(const std::string & fname) { for (int j = 0; j < ggml_nelements(cur); ++j) { if (data[j] != 100 + i) { fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]); + gguf_free(ctx); return false; } } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index bf94e7e7a60a4..d6e5e0497dc3a 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -103,6 +103,7 @@ static std::string get_cpu_info() { } } } + fclose(f); } #endif // TODO: other platforms diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 6653b815d93a1..2035554ea8741 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -995,6 +995,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { if (!new_clip->ctx_data) { fprintf(stderr, "%s: ggml_init() failed\n", __func__); clip_free(new_clip); + gguf_free(ctx); return nullptr; } @@ -1002,6 +1003,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { if (!fin) { printf("cannot open model file for loading tensors\n"); clip_free(new_clip); + gguf_free(ctx); return nullptr; } @@ -1023,6 +1025,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { if (!fin) { printf("%s: failed to seek for tensor %s\n", __func__, name); clip_free(new_clip); + gguf_free(ctx); return nullptr; } int num_bytes = ggml_nbytes(cur); @@ -1908,6 +1911,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i break; default: printf("Please use an input file in f32 or f16\n"); + gguf_free(ctx_out); return false; } diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 7eafe8515a943..7d06e401b462b 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -711,6 +711,7 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model * load_checkpoint_gguf(fctx, f_ggml_ctx, model, train); + gguf_free(fctx); return true; } From 4755afd1cbd40d93c017e5b98c39796f52345314 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 14 Mar 2024 22:58:41 +0200 Subject: [PATCH 265/401] llama : fix integer overflow during quantization (#6063) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 10fd53469eb6f..2c384197492e8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11977,7 +11977,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n return new_type; } -static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector & workers, const int nthread) { +static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector & workers, const int nthread) { std::mutex mutex; int counter = 0; size_t new_size = 0; From b0bc9f4a9da7c19f4779106ea83b23feca747566 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 15 Mar 2024 09:22:24 +0100 Subject: [PATCH 266/401] llama-bench : use random tokens to improve accuracy with mixtral (#6069) --- examples/llama-bench/llama-bench.cpp | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index d6e5e0497dc3a..32eea786919f6 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -8,6 +8,7 @@ #include #include #include +#include #include #include #include @@ -1123,15 +1124,19 @@ struct sql_printer : public printer { static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - //std::vector tokens(n_prompt, llama_token_bos(llama_get_model(ctx))); - //llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt, n_past, 0)); - //GGML_UNUSED(n_batch); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); + + std::vector tokens(n_batch); - std::vector tokens(n_batch, llama_token_bos(llama_get_model(ctx))); int n_processed = 0; while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); + tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; + for (int i = 1; i < n_tokens; i++) { + tokens[i] = std::rand() % n_vocab; + } llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); n_processed += n_tokens; } @@ -1142,11 +1147,15 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - llama_token token = llama_token_bos(llama_get_model(ctx)); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); + + llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 0; i < n_gen; i++) { llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); llama_synchronize(ctx); + token = std::rand() % n_vocab; } } From aab606a11fc0a9740a7f297521c3eef851dfb351 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 15 Mar 2024 09:44:57 +0100 Subject: [PATCH 267/401] llama : add Orion chat template (#6066) --- llama.cpp | 20 ++++++++++++++++++++ tests/test-chat-template.cpp | 4 ++++ 2 files changed, 24 insertions(+) diff --git a/llama.cpp b/llama.cpp index 2c384197492e8..b8a8d2723ae7d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -14242,6 +14242,26 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "model\n"; } + } else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) { + // OrionStarAI/Orion-14B-Chat + std::string system_prompt = ""; + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + // there is no system message support, we will merge it with user prompt + system_prompt = message->content; + continue; + } else if (role == "user") { + ss << "Human: "; + if (!system_prompt.empty()) { + ss << system_prompt << "\n\n"; + system_prompt = ""; + } + ss << message->content << "\n\nAssistant: "; + } else { + ss << message->content << ""; + } + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index fa2eb577b6e42..6e9e4bd1ea2cc 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -31,6 +31,8 @@ int main(void) { "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}", // google/gemma-7b-it "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\\n' + message['content'] | trim + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\\n'}}{% endif %}", + // OrionStarAI/Orion-14B-Chat + "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -45,6 +47,8 @@ int main(void) { "system\nYou are a helpful assistant\nuser\nHello\nassistant\nHi there\nuser\nWho are you\nassistant\n I am an assistant \nuser\nAnother question\nassistant\n", // google/gemma-7b-it "user\nYou are a helpful assistant\n\nHello\nmodel\nHi there\nuser\nWho are you\nmodel\nI am an assistant\nuser\nAnother question\nmodel\n", + // OrionStarAI/Orion-14B-Chat + "Human: You are a helpful assistant\n\nHello\n\nAssistant: Hi thereHuman: Who are you\n\nAssistant: I am an assistant Human: Another question\n\nAssistant: ", }; std::vector formatted_chat(1024); int32_t res; From 7ce2c77f88e1ca66ec48417e56f91746bac018c2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ond=C5=99ej=20=C4=8Cert=C3=ADk?= Date: Fri, 15 Mar 2024 02:46:51 -0600 Subject: [PATCH 268/401] gguf : add support for I64 and F64 arrays (#6062) * gguf : add support for I64 and F64 arrays GGML currently does not support I64 or F64 arrays and they are not often used in machine learning, however if in the future the need arises, it would be nice to add them now, so that the types are next to the other types I8, I16, I32 in the enums, and it also reserves their type number. Furthermore, with this addition the GGUF format becomes very usable for most computational applications of NumPy (being compatible with the most common NumPy dtypes: i8, i16, i32, i64, f32, f64), providing a faster, and more versatile alternative to the `npz` format, and a simpler alternative to the `hdf5` format. The change in this PR seems small, not significantly increasing the maintenance burden. I tested this from Python using GGUFWriter/Reader and `gguf-dump`, as well as from C, everything seems to work. * Fix compiler warnings --- ggml.c | 17 +++++++++++++++++ ggml.h | 2 ++ gguf-py/gguf/constants.py | 4 ++++ gguf-py/gguf/gguf_reader.py | 12 +++++++++--- gguf-py/gguf/gguf_writer.py | 12 ++++++++---- 5 files changed, 40 insertions(+), 7 deletions(-) diff --git a/ggml.c b/ggml.c index fbc66f65b1052..c94006e51a092 100644 --- a/ggml.c +++ b/ggml.c @@ -470,6 +470,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(int32_t), .is_quantized = false, }, + [GGML_TYPE_I64] = { + .type_name = "i64", + .blck_size = 1, + .type_size = sizeof(int64_t), + .is_quantized = false, + }, + [GGML_TYPE_F64] = { + .type_name = "f64", + .blck_size = 1, + .type_size = sizeof(double), + .is_quantized = false, + .nrows = 1, + }, [GGML_TYPE_F32] = { .type_name = "f32", .blck_size = 1, @@ -12418,6 +12431,8 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: + case GGML_TYPE_I64: + case GGML_TYPE_F64: case GGML_TYPE_COUNT: { GGML_ASSERT(false); @@ -12504,6 +12519,8 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: + case GGML_TYPE_I64: + case GGML_TYPE_F64: case GGML_TYPE_COUNT: { GGML_ASSERT(false); diff --git a/ggml.h b/ggml.h index ab26c8f5908c7..c937d4a535adb 100644 --- a/ggml.h +++ b/ggml.h @@ -366,6 +366,8 @@ extern "C" { GGML_TYPE_I8 = 24, GGML_TYPE_I16 = 25, GGML_TYPE_I32 = 26, + GGML_TYPE_I64 = 27, + GGML_TYPE_F64 = 28, GGML_TYPE_COUNT, }; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2d7cf16c14ed1..458a641dcd229 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -665,6 +665,8 @@ class GGMLQuantizationType(IntEnum): I8 = 24 I16 = 25 I32 = 26 + I64 = 27 + F64 = 28 class GGUFEndian(IntEnum): @@ -734,6 +736,8 @@ def get_type(val: Any) -> GGUFValueType: GGMLQuantizationType.I8: (1, 1), GGMLQuantizationType.I16: (1, 2), GGMLQuantizationType.I32: (1, 4), + GGMLQuantizationType.I64: (1, 8), + GGMLQuantizationType.F64: (1, 8), } diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 1c10f57538992..33afac552ca75 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -242,12 +242,15 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: n_bytes = n_elems * type_size // block_size data_offs = int(start_offs + offset_tensor[0]) item_type: npt.DTypeLike - if ggml_type == GGMLQuantizationType.F32: + if ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + elif ggml_type == GGMLQuantizationType.F32: item_count = n_elems item_type = np.float32 - elif ggml_type == GGMLQuantizationType.F16: + elif ggml_type == GGMLQuantizationType.F64: item_count = n_elems - item_type = np.float16 + item_type = np.float64 elif ggml_type == GGMLQuantizationType.I8: item_count = n_elems item_type = np.int8 @@ -257,6 +260,9 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: elif ggml_type == GGMLQuantizationType.I32: item_count = n_elems item_type = np.int32 + elif ggml_type == GGMLQuantizationType.I64: + item_count = n_elems + item_type = np.int64 else: item_count = n_bytes item_type = np.uint8 diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 81b2eb884d485..1967b633ce261 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -204,18 +204,22 @@ def add_tensor_info( for i in range(n_dims): self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: - if tensor_dtype == np.float32: - dtype = GGMLQuantizationType.F32 - elif tensor_dtype == np.float16: + if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 + elif tensor_dtype == np.float32: + dtype = GGMLQuantizationType.F32 + elif tensor_dtype == np.float64: + dtype = GGMLQuantizationType.F64 elif tensor_dtype == np.int8: dtype = GGMLQuantizationType.I8 elif tensor_dtype == np.int16: dtype = GGMLQuantizationType.I16 elif tensor_dtype == np.int32: dtype = GGMLQuantizationType.I32 + elif tensor_dtype == np.int64: + dtype = GGMLQuantizationType.I64 else: - raise ValueError("Only F32, F16, I8, I16, I32 tensors are supported for now") + raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") else: dtype = raw_dtype self.ti_data += self._pack("I", dtype) From 753e36f650fa2a5869f89188d9ee745dc74cf14b Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 15 Mar 2024 09:26:20 +0000 Subject: [PATCH 269/401] [SYCL] Fix non-intel device selection (#6042) * Fix non-intel device selection * Update ggml-sycl.cpp Co-authored-by: Neo Zhang Jianyu * Update ggml-sycl.cpp Co-authored-by: Neo Zhang Jianyu --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Co-authored-by: Neo Zhang Jianyu --- ggml-sycl.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 9f6506383cc0d..a1ca6aba5951d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3451,7 +3451,7 @@ class sycl_gpu_mgr { dpct::device_info prop; dpct::get_device_info(prop, device); if (max_compute_units == prop.get_max_compute_units() && - prop.get_major_version() == 1) { + is_ext_oneapi_device(device)) { gpus.push_back(id); devices.push_back(device); work_group_size = prop.get_max_work_group_size(); @@ -3484,6 +3484,15 @@ class sycl_gpu_mgr { assert(false); return -1; } + + bool is_ext_oneapi_device(const sycl::device &dev) { + sycl::backend dev_backend = dev.get_backend(); + if (dev_backend == sycl::backend::ext_oneapi_level_zero || + dev_backend == sycl::backend::ext_oneapi_cuda || + dev_backend == sycl::backend::ext_oneapi_hip) + return true; + return false; + } }; static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL; From 131b0584096ee9df4d07cb28759dfea6efe6475f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 15 Mar 2024 11:36:50 +0200 Subject: [PATCH 270/401] make : ggml-metal.o depends on ggml.h --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index cb597b209181b..c0f1250366a64 100644 --- a/Makefile +++ b/Makefile @@ -553,7 +553,7 @@ endif endif # LLAMA_METAL ifdef LLAMA_METAL -ggml-metal.o: ggml-metal.m ggml-metal.h +ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h $(CC) $(CFLAGS) -c $< -o $@ ifdef LLAMA_METAL_EMBED_LIBRARY From 46acb3676718b983157058aecf729a2064fc7d34 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 15 Mar 2024 18:53:53 +0800 Subject: [PATCH 271/401] fix set main gpu error (#6073) --- examples/sycl/build.sh | 5 +- examples/sycl/run-llama2.sh | 16 +- ggml-sycl.cpp | 312 +++++++++++++++++++++++++++--------- ggml-sycl.h | 5 + llama.cpp | 23 ++- 5 files changed, 272 insertions(+), 89 deletions(-) diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh index 26ad2f7da754e..f20391d7a0000 100755 --- a/examples/sycl/build.sh +++ b/examples/sycl/build.sh @@ -13,8 +13,11 @@ source /opt/intel/oneapi/setvars.sh #for FP32 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -#build example/main only +#build example/main #cmake --build . --config Release --target main +#build example/llama-bench +#cmake --build . --config Release --target llama-bench + #build all binary cmake --build . --config Release -v diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index 52f7c01a4bd90..c979a52f6f5a0 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -9,18 +9,28 @@ source /opt/intel/oneapi/setvars.sh if [ $# -gt 0 ]; then GGML_SYCL_DEVICE=$1 + GGML_SYCL_SINGLE_GPU=1 else GGML_SYCL_DEVICE=0 fi -echo "use $GGML_SYCL_DEVICE as main GPU" + #export GGML_SYCL_DEBUG=1 #ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. -#use all GPUs with same max compute units -ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 +if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then + echo "use $GGML_SYCL_DEVICE as main GPU" + #use signle GPU only + ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none +else + #use multiple GPUs with same max compute units + ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 +fi #use main GPU only #ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none +#use multiple GPUs with same max compute units +#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 + diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a1ca6aba5951d..6dc5eb20c5a63 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #include #include #include @@ -24,10 +25,9 @@ #include #include #include - #include #include - +#include #include #include @@ -82,6 +82,30 @@ Following definition copied from DPCT head files, which are used by ggml-sycl.cp #define __dpct_noinline__ __attribute__((noinline)) #endif + +std::string get_device_type_name(const sycl::device &Device) { + auto DeviceType = Device.get_info(); + switch (DeviceType) { + case sycl::info::device_type::cpu: + return "cpu"; + case sycl::info::device_type::gpu: + return "gpu"; + case sycl::info::device_type::host: + return "host"; + case sycl::info::device_type::accelerator: + return "acc"; + default: + return "unknown"; + } +} + +std::string get_device_backend_and_type(const sycl::device &device) { + std::stringstream device_type; + sycl::backend backend = device.get_backend(); + device_type << backend << ":" << get_device_type_name(device); + return device_type.str(); +} + namespace dpct { typedef sycl::queue *queue_ptr; @@ -942,17 +966,65 @@ namespace dpct private: mutable std::recursive_mutex m_mutex; + static bool compare_dev(sycl::device &device1, sycl::device &device2) + { + dpct::device_info prop1; + dpct::get_device_info(prop1, device1); + dpct::device_info prop2; + dpct::get_device_info(prop2, device2); + return prop1.get_max_compute_units() > prop2.get_max_compute_units(); + } + static int convert_backend_index(std::string & backend) { + if (backend == "ext_oneapi_level_zero:gpu") return 0; + if (backend == "opencl:gpu") return 1; + if (backend == "opencl:cpu") return 2; + if (backend == "opencl:acc") return 3; + printf("convert_backend_index: can't handle backend=%s\n", backend.c_str()); + GGML_ASSERT(false); + } + static bool compare_backend(std::string &backend1, std::string &backend2) { + return convert_backend_index(backend1) < convert_backend_index(backend2); + } dev_mgr() { sycl::device default_device = sycl::device(sycl::default_selector_v); _devs.push_back(std::make_shared(default_device)); - std::vector sycl_all_devs = - sycl::device::get_devices(sycl::info::device_type::all); + std::vector sycl_all_devs; // Collect other devices except for the default device. if (default_device.is_cpu()) _cpu_device = 0; + + auto Platforms = sycl::platform::get_platforms(); + // Keep track of the number of devices per backend + std::map DeviceNums; + std::map> backend_devices; + + while (!Platforms.empty()) { + auto Platform = Platforms.back(); + Platforms.pop_back(); + auto devices = Platform.get_devices(); + std::string backend_type = get_device_backend_and_type(devices[0]); + for (const auto &device : devices) { + backend_devices[backend_type].push_back(device); + } + } + + std::vector keys; + for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) { + keys.push_back(it->first); + } + std::sort(keys.begin(), keys.end(), compare_backend); + + for (auto &key : keys) { + std::vector devs = backend_devices[key]; + std::sort(devs.begin(), devs.end(), compare_dev); + for (const auto &dev : devs) { + sycl_all_devs.push_back(dev); + } + } + for (auto &dev : sycl_all_devs) { if (dev == default_device) @@ -3202,6 +3274,11 @@ static int g_work_group_size = 0; #define GGML_SYCL_MMV_Y 1 #endif +enum ggml_sycl_backend_gpu_mode { + SYCL_UNSET_GPU_MODE = -1, + SYCL_SINGLE_GPU_MODE = 0, + SYCL_MUL_GPU_MODE +}; static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); @@ -3401,12 +3478,31 @@ class sycl_gpu_mgr { int work_group_size = 0; std::string gpus_list = ""; + /* + Use all GPUs with same top max compute units + */ sycl_gpu_mgr() { detect_sycl_gpu_list_with_max_cu(); get_allow_gpus(); create_context_with_gpus(); } + /* + Only use the assigned GPU + */ + sycl_gpu_mgr(int main_gpu_id) { + sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); + dpct::device_info prop; + dpct::get_device_info(prop, device); + gpus.push_back(main_gpu_id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + max_compute_units = prop.get_max_compute_units(); + + get_allow_gpus(); + create_context_with_gpus(); + } + void create_context_with_gpus() { sycl::context ctx = sycl::context(devices); assert(gpus.size() > 0); @@ -3422,7 +3518,7 @@ class sycl_gpu_mgr { gpus_list += std::to_string(gpus[i]); gpus_list += ","; } - if (gpus_list.length() > 2) { + if (gpus_list.length() > 1) { gpus_list.pop_back(); } } @@ -3471,8 +3567,8 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + printf("miss to get device index by id=%d\n", id); + GGML_ASSERT(false); } int get_next_index(int id) { @@ -3481,8 +3577,7 @@ class sycl_gpu_mgr { if (gpus[i] == id) return i; } - assert(false); - return -1; + GGML_ASSERT(false); } bool is_ext_oneapi_device(const sycl::device &dev) { @@ -3500,11 +3595,14 @@ static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; static int g_main_device_id = -1; +static bool g_ggml_backend_sycl_buffer_type_initialized = false; static std::array g_default_tensor_split = {}; static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; +static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = SYCL_UNSET_GPU_MODE; + struct sycl_device_capabilities { int cc; // compute capability bool vmm; // virtual memory support @@ -13008,17 +13106,20 @@ bool ggml_sycl_loaded(void) { return g_sycl_loaded; } -void print_device_detail(int id) { +void print_device_detail(int id, sycl::device &device, std::string device_type) { + dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_device_info(prop, dpct::dev_mgr::instance().get_device(id)))); - sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); + dpct::get_device_info(prop, device))); + std::string version; version += std::to_string(prop.get_major_version()); version += "."; version += std::to_string(prop.get_minor_version()); - fprintf(stderr, "|%2d|%45s|%18s|%17d|%14d|%13d|%15lu|\n", id, + device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); + + fprintf(stderr, "|%2d|%18s|%45s|%10s|%11d|%8d|%7d|%15lu|\n", id, device_type.c_str(), prop.get_name(), version.c_str(), prop.get_max_compute_units(), prop.get_max_work_group_size(), prop.get_max_sub_group_size(), prop.get_global_mem_size()); @@ -13026,19 +13127,35 @@ void print_device_detail(int id) { void ggml_backend_sycl_print_sycl_devices() { int device_count = dpct::dev_mgr::instance().device_count(); + std::map DeviceNums; fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "|ID| Name |compute capability|Max compute units|Max work group|Max sub group|Global mem size|\n"); - fprintf(stderr, "|--|---------------------------------------------|------------------|-----------------|--------------|-------------|---------------|\n"); + fprintf(stderr, "| | | |Compute |Max compute|Max work|Max sub| |\n"); + fprintf(stderr, "|ID| Device Type| Name|capability|units |group |group |Global mem size|\n"); + fprintf(stderr, "|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|\n"); for (int id = 0; id < device_count; ++id) { - print_device_detail(id); + sycl::device device = dpct::dev_mgr::instance().get_device(id); + sycl::backend backend = device.get_backend(); + std::string backend_type = get_device_backend_and_type(device); + int type_id=DeviceNums[backend_type]++; + std::stringstream device_type; + device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; + print_device_detail(id, device, device_type.str()); } } void print_gpu_device_list() { - fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", - g_sycl_gpu_mgr->get_gpu_count(), - g_sycl_gpu_mgr->gpus_list.c_str(), - g_sycl_gpu_mgr->max_compute_units); + GGML_ASSERT(g_sycl_gpu_mgr); + + char* hint=NULL; + if (g_ggml_sycl_backend_gpu_mode == SYCL_SINGLE_GPU_MODE) { + hint = "use %d SYCL GPUs: [%s] with Max compute units:%d\n"; + } else { + hint = "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n"; + } + fprintf(stderr, hint, + g_sycl_gpu_mgr->get_gpu_count(), + g_sycl_gpu_mgr->gpus_list.c_str(), + g_sycl_gpu_mgr->max_compute_units); } int get_sycl_env(const char *env_name, int default_val) { @@ -13074,6 +13191,15 @@ void ggml_init_sycl() try { #else fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif + +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + if (CHECK_TRY_ERROR(g_all_sycl_device_count = dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; @@ -13082,68 +13208,65 @@ void ggml_init_sycl() try { } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); ggml_backend_sycl_print_sycl_devices(); + initialized = true; + g_sycl_loaded = true; + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); - - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); - g_work_group_size = g_sycl_gpu_mgr->work_group_size; - - print_gpu_device_list(); +void ggml_init_by_gpus(int device_count) try { + g_device_count = device_count; + g_work_group_size = g_sycl_gpu_mgr->work_group_size; - int64_t total_vram = 0; + int64_t total_vram = 0; -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; - } + print_gpu_device_list(); - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; + } - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; - g_default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device_id)))); - g_device_caps[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } + g_default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; - } + g_device_caps[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + } - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; + } - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); - } + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); } - initialized = true; - g_sycl_loaded = true; + const dpct::queue_ptr stream = g_syclStreams[i][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } } catch (sycl::exception const &exc) { @@ -16551,22 +16674,24 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { + if (device_index>=g_device_count or device_index<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device_index, g_device_count-1); + GGML_ASSERT(device_indexgpus[i])}, }; } - ggml_backend_sycl_buffer_type_initialized = true; + g_ggml_backend_sycl_buffer_type_initialized = true; } - - return &ggml_backend_sycl_buffer_types[device]; + return &ggml_backend_sycl_buffer_types[device_index]; } // sycl split buffer type @@ -17319,11 +17444,42 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { return g_sycl_gpu_mgr->get_index(device_id); } +GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) { + return g_sycl_gpu_mgr->gpus[device_index]; +} + +GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) { + GGML_ASSERT(main_gpu_idget_gpu_count()); + g_ggml_backend_sycl_buffer_type_initialized = false; +} + +GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() { + if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) { + return; + } + + fprintf(stderr, "ggml_backend_sycl_set_mul_device_mode: true\n"); + + if (g_sycl_gpu_mgr) { + delete g_sycl_gpu_mgr; + } + g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_ggml_sycl_backend_gpu_mode = SYCL_MUL_GPU_MODE; + ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count()); + g_ggml_backend_sycl_buffer_type_initialized = false; +} + extern "C" int ggml_backend_sycl_reg_devices(); int ggml_backend_sycl_reg_devices() { - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); - g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + ggml_backend_sycl_set_mul_device_mode(); assert(g_device_count>0); for (int i = 0; i < g_device_count; i++) { int id = g_sycl_gpu_mgr->gpus[i]; diff --git a/ggml-sycl.h b/ggml-sycl.h index bf5b11b369d19..c549a64a1f44c 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -29,6 +29,11 @@ GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_typ GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); +// TODO: these are temporary +// ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670 +GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index); +GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id); +GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode(); #ifdef __cplusplus } #endif diff --git a/llama.cpp b/llama.cpp index b8a8d2723ae7d..8e185d4bfe773 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5064,6 +5064,16 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam } #endif +#ifdef GGML_USE_SYCL + if (params.split_mode == LLAMA_SPLIT_MODE_NONE) { + ggml_backend_sycl_set_single_device_mode(params.main_gpu); + //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index. + params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu); + } else { + ggml_backend_sycl_set_mul_device_mode(); + } +#endif + if (!llm_load_tensors( ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data @@ -12921,23 +12931,22 @@ struct llama_context * llama_new_context_with_model( if (model->n_gpu_layers > 0) { // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - int main_gpu_index = ggml_backend_sycl_get_device_index(model->main_gpu); - ggml_backend_t backend = ggml_backend_sycl_init(main_gpu_index); + ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, model->main_gpu, main_gpu_index); + int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu); llama_free(ctx); return nullptr; } ctx->backends.push_back(backend); } else { // LLAMA_SPLIT_LAYER requires a backend for each GPU - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - int device_id = id_list[i]; ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i); + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i); llama_free(ctx); return nullptr; } From 3020327f6cd6d2ce50528dd65f4b199d2ea8b1ae Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 15 Mar 2024 13:24:03 +0100 Subject: [PATCH 272/401] cuda : disable unused cudaLaunchHostFunc code (#6078) --- ggml-cuda.cu | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d1b5e52ba9011..db595409aa735 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -11541,6 +11541,7 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev if (ggml_backend_is_cuda(event->backend)) { CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx->device][0], (cudaEvent_t)event->context, 0)); } else { +#if 0 // untested auto wait_fn = [](void * user_data) { ggml_backend_event_t event = (ggml_backend_event_t)user_data; @@ -11548,6 +11549,8 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev }; CUDA_CHECK(cudaLaunchHostFunc(g_cudaStreams[cuda_ctx->device][0], wait_fn, event)); +#endif + GGML_ASSERT(false); } } From 4e9a7f7f7fb6acbddd1462909c8d696e38edbfcc Mon Sep 17 00:00:00 2001 From: Ting Lou Date: Fri, 15 Mar 2024 22:31:05 +0800 Subject: [PATCH 273/401] llava : change API to pure C style for Rust FFI bindgen (#6079) Co-authored-by: Lou Ting --- examples/llava/clip.cpp | 36 ++++++++++++++++++------------------ examples/llava/clip.h | 6 +++--- examples/llava/llava.cpp | 2 +- examples/llava/llava.h | 4 ++-- 4 files changed, 24 insertions(+), 24 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 2035554ea8741..a0ed82d7e2328 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1235,16 +1235,16 @@ struct clip_image_f32 * clip_image_f32_init() { void clip_image_u8_free(struct clip_image_u8 * img) { delete img; } void clip_image_f32_free(struct clip_image_f32 * img) { delete img; } -void clip_image_u8_batch_free(struct clip_image_u8_batch & batch) { - if (batch.size > 0) { - delete[] batch.data; - batch.size = 0; +void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { + if (batch->size > 0) { + delete[] batch->data; + batch->size = 0; } } -void clip_image_f32_batch_free(struct clip_image_f32_batch & batch) { - if (batch.size > 0) { - delete[] batch.data; - batch.size = 0; +void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { + if (batch->size > 0) { + delete[] batch->data; + batch->size = 0; } } @@ -1497,7 +1497,7 @@ static std::vector divide_to_patches_u8(const clip_image_u8 & im // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector // res_imgs memory is being allocated here, previous allocations will be freed if found -bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) { +bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) { bool pad_to_square = true; if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); @@ -1509,11 +1509,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli pad_to_square = false; } // free the previous res_imgs if any set - if (res_imgs.size > 0) { + if (res_imgs->size > 0) { clip_image_f32_batch_free(res_imgs); } - res_imgs.data = nullptr; - res_imgs.size = 0; + res_imgs->data = nullptr; + res_imgs->size = 0; // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 @@ -1568,11 +1568,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square patches.insert(patches.begin(), image_original_resize); // clip_image_f32_batch_init(patches.size()); - res_imgs.size = patches.size(); - res_imgs.data = new clip_image_f32[res_imgs.size]; + res_imgs->size = patches.size(); + res_imgs->data = new clip_image_f32[res_imgs->size]; int num=0; for (auto& patch : patches) { - normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std); + normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std); num++; } @@ -1660,9 +1660,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli // } // res_imgs.push_back(res); - res_imgs.size = 1; - res_imgs.data = new clip_image_f32[res_imgs.size]; - res_imgs.data[0] = *res; + res_imgs->size = 1; + res_imgs->data = new clip_image_f32[res_imgs->size]; + res_imgs->data[0] = *res; clip_image_f32_free(res); return true; diff --git a/examples/llava/clip.h b/examples/llava/clip.h index e5bd54924a9c8..45bdad6897658 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -60,8 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init(); CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); -CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch & batch); -CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch); +CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch); +CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch); CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); @@ -69,7 +69,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); /** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ -CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs ); +CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 9801281661b25..29764757aab5d 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -223,7 +223,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli clip_image_f32_batch img_res_v; img_res_v.size = 0; img_res_v.data = nullptr; - if (!clip_image_preprocess(ctx_clip, img, img_res_v)) { + if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) { fprintf(stderr, "%s: unable to preprocess image\n", __func__); delete[] img_res_v.data; return false; diff --git a/examples/llava/llava.h b/examples/llava/llava.h index 2d40f3f1d5f84..19212f6e9e9c5 100644 --- a/examples/llava/llava.h +++ b/examples/llava/llava.h @@ -29,9 +29,9 @@ struct llava_image_embed { }; /** sanity check for clip <-> llava embed size match */ -LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip); +LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip); -LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); +LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); /** build an image embed from image file bytes */ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); From 12247f4c69a173b9482f68aaa174ec37fc909ccf Mon Sep 17 00:00:00 2001 From: Andrew Canis Date: Fri, 15 Mar 2024 16:41:22 -0400 Subject: [PATCH 274/401] llama : add Command-R support (#6033) Information about the Command-R 35B model (128k context) can be found at: https://huggingface.co/CohereForAI/c4ai-command-r-v01 Based on the llama2 model with a few changes: 1) New hyper parameter to scale output logits (logit_scale) 2) Uses LayerNorm instead of RMSNorm 3) Transfomer layers have a single shared LayerNorm that feeds into both the self-attention and FFN layers in parallel. There is no post-attention LayerNorm. 4) No support for Rotary Position Embeddings (RoPE) scaling 5) No biases used Find GGUF files here: https://huggingface.co/andrewcanis/c4ai-command-r-v01-GGUF To convert model to GGUF format yourself: 1) Download Command-R Hugging Face safetensors: git lfs install git clone https://huggingface.co/CohereForAI/c4ai-command-r-v01 2) Run: python3 convert-hf-to-gguf.py --outtype f16 ./c4ai-command-r-v01 --- README.md | 1 + convert-hf-to-gguf.py | 17 ++++ gguf-py/gguf/constants.py | 15 +++ gguf-py/gguf/gguf_writer.py | 3 + llama.cpp | 183 ++++++++++++++++++++++++++++++++++++ 5 files changed, 219 insertions(+) diff --git a/README.md b/README.md index 61bedc3f86309..5cbdf7e476b09 100644 --- a/README.md +++ b/README.md @@ -112,6 +112,7 @@ Typically finetunes of the base models below are supported as well. - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) - [x] [Mamba](https://github.com/state-spaces/mamba) +- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01) **Multimodal models:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5eee320163d29..cf1f98d660633 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1965,6 +1965,23 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("CohereForCausalLM") +class CommandR2Model(Model): + model_arch = gguf.MODEL_ARCH.COMMAND_R + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # max_position_embeddings = 8192 in config.json but model was actually + # trained on 128k context length + self.hparams["max_position_embeddings"] = self.hparams["model_max_length"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 458a641dcd229..4a4facb06ea14 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -42,6 +42,7 @@ class LLM: EXPERT_COUNT = "{arch}.expert_count" EXPERT_USED_COUNT = "{arch}.expert_used_count" POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -121,6 +122,7 @@ class MODEL_ARCH(IntEnum): GEMMA = auto() STARCODER2 = auto() MAMBA = auto() + COMMAND_R = auto() class MODEL_TENSOR(IntEnum): @@ -187,6 +189,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.GEMMA: "gemma", MODEL_ARCH.STARCODER2: "starcoder2", MODEL_ARCH.MAMBA: "mamba", + MODEL_ARCH.COMMAND_R: "command-r", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -579,6 +582,18 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.SSM_D, MODEL_TENSOR.SSM_OUT, ], + MODEL_ARCH.COMMAND_R: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 1967b633ce261..2ae6c814b52de 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -361,6 +361,9 @@ def add_max_alibi_bias(self, bias: float) -> None: def add_clamp_kqv(self, value: float) -> None: self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + def add_logit_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value) + def add_expert_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count) diff --git a/llama.cpp b/llama.cpp index 8e185d4bfe773..fc5dd5cb43a7e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -214,6 +214,7 @@ enum llm_arch { LLM_ARCH_GEMMA, LLM_ARCH_STARCODER2, LLM_ARCH_MAMBA, + LLM_ARCH_COMMAND_R, LLM_ARCH_UNKNOWN, }; @@ -243,6 +244,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_GEMMA, "gemma" }, { LLM_ARCH_STARCODER2, "starcoder2" }, { LLM_ARCH_MAMBA, "mamba" }, + { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -268,6 +270,7 @@ enum llm_kv { LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, LLM_KV_POOLING_TYPE, + LLM_KV_LOGIT_SCALE, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, @@ -332,6 +335,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, + { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -838,6 +842,21 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" }, }, }, + { + LLM_ARCH_COMMAND_R, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1597,6 +1616,7 @@ enum e_model { MODEL_20B, MODEL_30B, MODEL_34B, + MODEL_35B, MODEL_40B, MODEL_65B, MODEL_70B, @@ -1643,6 +1663,7 @@ struct llama_hparams { float f_clamp_kqv = 0.0f; float f_max_alibi_bias = 0.0f; + float f_logit_scale = 0.0f; bool causal_attn = true; bool need_kq_pos = false; @@ -3231,6 +3252,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; + case MODEL_35B: return "35B"; case MODEL_40B: return "40B"; case MODEL_65B: return "65B"; case MODEL_70B: return "70B"; @@ -3623,6 +3645,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_COMMAND_R: + { + ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_35B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3944,6 +3975,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps); LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias); + LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale); LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); @@ -4918,6 +4950,37 @@ static bool llm_load_tensors( layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); } } break; + case LLM_ARCH_COMMAND_R: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + // init output from the input tok embed + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -8315,6 +8378,121 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_command_r() { + + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const float f_logit_scale = hparams.f_logit_scale; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + struct ggml_tensor * ffn_inp = cur; + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + struct ggml_tensor * attn_out = cur; + + // feed-forward network + { + cur = llm_build_ffn(ctx0, ffn_inp, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + // add together residual + FFN + self-attention + cur = ggml_add(ctx0, cur, inpL); + cur = ggml_add(ctx0, cur, attn_out); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + + if (f_logit_scale) { + cur = ggml_scale(ctx0, cur, f_logit_scale); + } + + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -8497,6 +8675,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_mamba(); } break; + case LLM_ARCH_COMMAND_R: + { + result = llm.build_command_r(); + } break; default: GGML_ASSERT(false); } @@ -13147,6 +13329,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_ORION: case LLM_ARCH_INTERNLM2: case LLM_ARCH_MINICPM: + case LLM_ARCH_COMMAND_R: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 From 877b4d0c628cc70dddb5df72ed8fc14d126ca7e8 Mon Sep 17 00:00:00 2001 From: Theia Vogel Date: Fri, 15 Mar 2024 13:43:02 -0700 Subject: [PATCH 275/401] llama : add support for control vectors (#5970) * control vector api and implementation * control-vectors : minor code style updates * disable control vector when data == nullptr use -1 for disabled range (also on init) in case we ever support controlling layer 0 (embeddings) --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 215 ++++++++++++++++++++++++++++++++++++++++++++++ common/common.h | 31 ++++++- llama.cpp | 128 +++++++++++++++++++++++++++ llama.h | 23 ++++- 4 files changed, 392 insertions(+), 5 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 58fbd05aa3516..4912237e0d0f1 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -568,6 +568,34 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.lora_base = argv[i]; + } else if (arg == "--control-vector") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.control_vectors.push_back({ 1.0f, argv[i], }); + } else if (arg == "--control-vector-scaled") { + if (++i >= argc) { + invalid_param = true; + break; + } + const char * fname = argv[i]; + if (++i >= argc) { + invalid_param = true; + break; + } + params.control_vectors.push_back({ std::stof(argv[i]), fname, }); + } else if (arg == "--control-vector-layer-range") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.control_vector_layer_start = std::stoi(argv[i]); + if (++i >= argc) { + invalid_param = true; + break; + } + params.control_vector_layer_end = std::stoi(argv[i]); } else if (arg == "--mmproj") { if (++i >= argc) { invalid_param = true; @@ -1095,6 +1123,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" --control-vector FNAME\n"); + printf(" add a control vector\n"); + printf(" --control-vector-scaled FNAME S\n"); + printf(" add a control vector with user defined scaling S\n"); + printf(" --control-vector-layer-range START END\n"); + printf(" layer range to apply the control vector(s) to, start and end inclusive\n"); printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -md FNAME, --model-draft FNAME\n"); @@ -1360,6 +1394,30 @@ std::tuple llama_init_from_gpt_par return std::make_tuple(nullptr, nullptr); } + if (!params.control_vectors.empty()) { + if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); + + const auto cvec = llama_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + int err = llama_control_vector_apply(lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); float lora_scale = std::get<1>(params.lora_adapter[i]); @@ -1890,3 +1948,160 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n) return sum / (sqrt(sum1) * sqrt(sum2)); } + +// +// Control vector utils +// + +static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) { + int32_t n_tensors; + + size_t n_bytes = 0; + + uint32_t max_direction_layer = 0; + + llama_control_vector_data result = { -1, {} }; + + // calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer + { + struct ggml_init_params meta_params = { + /* .mem_size = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(), + /* .mem_buffer = */ nullptr, + /* .no_alloc = */ true, + }; + ggml_context * meta_ctx = ggml_init(meta_params); + struct gguf_init_params meta_gguf_params = { + /* .no_alloc = */ true, + /* .ctx = */ &meta_ctx, + }; + struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params); + if (!meta_ctx_gguf) { + fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str()); + ggml_free(meta_ctx); + return result; + } + + n_tensors = gguf_get_n_tensors(meta_ctx_gguf); + for (int i = 0; i < n_tensors; i++) { + std::string name = gguf_get_tensor_name(meta_ctx_gguf, i); + + // split on '.' + size_t dotpos = name.find('.'); + if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") { + try { + uint32_t layer = std::stoi(name.substr(dotpos + 1)); + if (layer == 0) { + fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str()); + ggml_free(meta_ctx); + gguf_free(meta_ctx_gguf); + return result; + } + if (layer > max_direction_layer) { + max_direction_layer = layer; + } + } catch (...) { + fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str()); + ggml_free(meta_ctx); + gguf_free(meta_ctx_gguf); + return result; + } + } + + struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str()); + if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) { + fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str()); + ggml_free(meta_ctx); + gguf_free(meta_ctx_gguf); + return result; + } + if (result.n_embd == -1) { + result.n_embd = ggml_nelements(tensor_meta); + } else if (ggml_nelements(tensor_meta) != result.n_embd) { + fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str()); + ggml_free(meta_ctx); + gguf_free(meta_ctx_gguf); + return result; + } + n_bytes += ggml_nbytes(tensor_meta); + } + ggml_free(meta_ctx); + gguf_free(meta_ctx_gguf); + } + + if (n_tensors == 0) { + fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str()); + return result; + } + + // load and scale tensors into final control vector context + struct ggml_init_params ggml_params = { + /* .mem_size = */ ggml_tensor_overhead() * n_tensors + n_bytes, + /* .mem_buffer = */ nullptr, + /* .no_alloc = */ false, + }; + struct ggml_context * ctx = ggml_init(ggml_params); + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params); + if (!ctx_gguf) { + fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str()); + ggml_free(ctx); + return result; + } + + // do not store data for layer 0 (it's not used) + result.data.resize(result.n_embd * max_direction_layer); + + for (uint32_t il = 1; il <= max_direction_layer; il++) { + const std::string name = "direction." + std::to_string(il); + const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str()); + + float * dst = result.data.data() + result.n_embd * (il - 1); + + if (tensor) { + const float * src = (const float *) tensor->data; + for (int j = 0; j < result.n_embd; j++) { + dst[j] = src[j] * load_info.strength; + } + } else { + for (int j = 0; j < result.n_embd; j++) { + dst[j] = 0.0f; + } + } + } + + return result; +} + +llama_control_vector_data llama_control_vector_load(const std::vector & load_infos) { + llama_control_vector_data result = { -1, {} }; + + for (const auto & info : load_infos) { + auto cur = llama_control_vector_load_one(info); + + if (cur.n_embd == -1) { + return result; + } + if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) { + fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str()); + return result; + } + + if (result.n_embd == -1) { + result = std::move(cur); + } else { + for (size_t i = 0; i < cur.data.size(); i++) { + result.data[i] += cur.data[i]; + } + } + } + + if (result.n_embd == -1) { + fprintf(stderr, "%s: no vectors passed\n", __func__); + } + + return result; +} diff --git a/common/common.h b/common/common.h index d250eef8b2b6b..687f3425e8544 100644 --- a/common/common.h +++ b/common/common.h @@ -37,10 +37,13 @@ extern char const *LLAMA_COMMIT; extern char const *LLAMA_COMPILER; extern char const *LLAMA_BUILD_TARGET; +struct llama_control_vector_load_info; + +int32_t get_num_physical_cores(); + // // CLI argument parsing // -int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed @@ -103,6 +106,11 @@ struct gpt_params { std::vector> lora_adapter; // lora adapter path with user defined scale std::string lora_base = ""; // base model path for the lora adapter + std::vector control_vectors; // control vector with user defined scale + + int32_t control_vector_layer_start = -1; // layer range for control vector + int32_t control_vector_layer_end = -1; // layer range for control vector + int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line // (which is more convenient to use for plotting) @@ -269,3 +277,24 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40 void llama_embd_normalize(const float * inp, float * out, int n); float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n); + +// +// Control vector utils +// + +struct llama_control_vector_data { + int n_embd; + + // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd + std::vector data; +}; + +struct llama_control_vector_load_info { + float strength; + + std::string fname; +}; + +// Load control vectors, scale each by strength, and add them together. +// On error, returns {-1, empty} +llama_control_vector_data llama_control_vector_load(const std::vector & load_infos); diff --git a/llama.cpp b/llama.cpp index fc5dd5cb43a7e..52bd718ba89a5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1894,6 +1894,31 @@ struct llama_kv_cache { } }; +struct llama_control_vector { + std::vector tensors; // per layer + std::vector ctxs; + std::vector bufs; + + int32_t layer_start = -1; + int32_t layer_end = -1; + + ggml_tensor * tensor_for(int il) const { + if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) { + return nullptr; + } + return tensors[il]; + } + + ~llama_control_vector() { + for (struct ggml_context * ctx : ctxs) { + ggml_free(ctx); + } + for (ggml_backend_buffer_t buf : bufs) { + ggml_backend_buffer_free(buf); + } + } +}; + struct llama_vocab { using id = int32_t; using token = std::string; @@ -2108,6 +2133,9 @@ struct llama_context { struct ggml_tensor * inp_s_mask; // F32 [1, kv_size] struct ggml_tensor * inp_s_seq; // I32 [kv_size, n_batch] + // control vectors + struct llama_control_vector cvec; + #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; #endif @@ -5931,6 +5959,12 @@ struct llm_build_context { } cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } cb(cur, "l_out", il); // input for next layer @@ -13366,6 +13400,10 @@ int32_t llama_n_embd(const struct llama_model * model) { return model->hparams.n_embd; } +int32_t llama_n_layer(const struct llama_model * model) { + return model->hparams.n_layer; +} + float llama_rope_freq_scale_train(const struct llama_model * model) { return model->hparams.rope_freq_scale_train; } @@ -13465,6 +13503,96 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const } } +static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) { + GGML_ASSERT(cvec.tensors.empty()); + GGML_ASSERT(cvec.ctxs.empty()); + GGML_ASSERT(cvec.bufs.empty()); + + // count layer buffer types + std::map buft_layer_count; + for (int64_t i = 0; i < model.hparams.n_layer; i++) { + buft_layer_count[model.buft_layer[i].buft]++; + } + + // allocate contexts + std::map ctx_map; + for (auto & it : buft_layer_count) { + int n_layers = it.second; + struct ggml_init_params params = { + /*.mem_size =*/ n_layers * ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__); + return 1; + } + ctx_map[it.first] = ctx; + } + + // make tensors + cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 + for (size_t il = 1; il < model.hparams.n_layer; il++) { + struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft); + ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); + cvec.tensors.push_back(tensor); + } + + // allocate tensors / buffers and zero + for (auto it : ctx_map) { + ggml_backend_buffer_type_t buft = it.first; + ggml_context * ctx = it.second; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (!buf) { + LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__); + return false; + } + ggml_backend_buffer_clear(buf, 0); + cvec.ctxs.push_back(ctx); + cvec.bufs.push_back(buf); + } + + return true; +} + +int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) { + const llama_model & model = lctx->model; + llama_control_vector & cvec = lctx->cvec; + + if (data == nullptr) { + // disable the current control vector (but leave allocated for later) + cvec.layer_start = -1; + cvec.layer_end = -1; + return 0; + } + + if (n_embd != (int) model.hparams.n_embd) { + LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__); + return 1; + } + + if (cvec.tensors.empty()) { + if (!llama_control_vector_init(cvec, model)) { + return 1; + } + } + + cvec.layer_start = il_start; + cvec.layer_end = il_end; + + for (size_t il = 1; il < model.hparams.n_layer; il++) { + assert(cvec.tensors[il] != nullptr); + + const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present + if (off + n_embd <= len) { + ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il])); + } + } + + return 0; +} + struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) { struct llama_kv_cache_view result = { /*.n_cells = */ 0, diff --git a/llama.h b/llama.h index 90aa5372e740b..40dcf54e394f8 100644 --- a/llama.h +++ b/llama.h @@ -388,6 +388,7 @@ extern "C" { LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); LLAMA_API int32_t llama_n_embd (const struct llama_model * model); + LLAMA_API int32_t llama_n_layer (const struct llama_model * model); // Get the model's RoPE frequency scaling factor LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model); @@ -435,10 +436,24 @@ extern "C" { // Returns 0 on success LLAMA_API int32_t llama_model_apply_lora_from_file( const struct llama_model * model, - const char * path_lora, - float scale, - const char * path_base_model, - int32_t n_threads); + const char * path_lora, + float scale, + const char * path_base_model, + int32_t n_threads); + + // Apply a loaded control vector to a llama_context, or if data is NULL, clear + // the currently loaded vector. + // n_embd should be the size of a single layer's control, and data should point + // to an n_embd x n_layers buffer starting from layer 1. + // il_start and il_end are the layer range the vector should apply to (both inclusive) + // See llama_control_vector_load in common to load a control vector. + LLAMA_API int32_t llama_control_vector_apply( + struct llama_context * lctx, + const float * data, + size_t len, + int32_t n_embd, + int32_t il_start, + int32_t il_end); // // KV cache From d84c48505f60bcd358b82a751d40418c4d235643 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 15 Mar 2024 22:14:16 +0100 Subject: [PATCH 276/401] llama : fix Baichuan2 13B (#6092) --- llama.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 52bd718ba89a5..e4db288ddd907 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6000,7 +6000,7 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = build_inp_pos(); + struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr; // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -6050,7 +6050,6 @@ struct llm_build_context { cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, NULL, Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); From a56d09a4407f29c21e149b44fd5308f83aa1cb09 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 16 Mar 2024 13:20:53 +0100 Subject: [PATCH 277/401] ci : close inactive issue with workflow (#6053) * issues: ci - close inactive issue with workflow * ci: close issue, change workflow schedule time --- .github/workflows/close-issue.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 .github/workflows/close-issue.yml diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml new file mode 100644 index 0000000000000..bc08a72d03166 --- /dev/null +++ b/.github/workflows/close-issue.yml @@ -0,0 +1,22 @@ +name: Close inactive issues +on: + schedule: + - cron: "42 0 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 30 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + repo-token: ${{ secrets.GITHUB_TOKEN }} From 15961ec04dbd59d21d8984d42e4c0f7e7e7d320a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Sat, 16 Mar 2024 11:39:15 -0400 Subject: [PATCH 278/401] common : refactor nested if causing error C1061 on MSVC (#6101) * Refactor nested if causing error C1061 on MSVC. * Revert back and remove else's. * Add flag to track found arguments. --- common/common.cpp | 475 ++++++++++++++++++++++++++++++++++------------ 1 file changed, 356 insertions(+), 119 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 4912237e0d0f1..1b0ba849398de 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -151,13 +151,17 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { std::replace(arg.begin(), arg.end(), '_', '-'); } + bool arg_found = false; if (arg == "-s" || arg == "--seed") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.seed = std::stoul(argv[i]); - } else if (arg == "-t" || arg == "--threads") { + } + if (arg == "-t" || arg == "--threads") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -166,7 +170,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads <= 0) { params.n_threads = std::thread::hardware_concurrency(); } - } else if (arg == "-tb" || arg == "--threads-batch") { + } + if (arg == "-tb" || arg == "--threads-batch") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -175,7 +181,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads_batch <= 0) { params.n_threads_batch = std::thread::hardware_concurrency(); } - } else if (arg == "-td" || arg == "--threads-draft") { + } + if (arg == "-td" || arg == "--threads-draft") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -184,7 +192,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads_draft <= 0) { params.n_threads_draft = std::thread::hardware_concurrency(); } - } else if (arg == "-tbd" || arg == "--threads-batch-draft") { + } + if (arg == "-tbd" || arg == "--threads-batch-draft") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -193,25 +203,37 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads_batch_draft <= 0) { params.n_threads_batch_draft = std::thread::hardware_concurrency(); } - } else if (arg == "-p" || arg == "--prompt") { + } + if (arg == "-p" || arg == "--prompt") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.prompt = argv[i]; - } else if (arg == "-e" || arg == "--escape") { + } + if (arg == "-e" || arg == "--escape") { + arg_found = true; params.escape = true; - } else if (arg == "--prompt-cache") { + } + if (arg == "--prompt-cache") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.path_prompt_cache = argv[i]; - } else if (arg == "--prompt-cache-all") { + } + if (arg == "--prompt-cache-all") { + arg_found = true; params.prompt_cache_all = true; - } else if (arg == "--prompt-cache-ro") { + } + if (arg == "--prompt-cache-ro") { + arg_found = true; params.prompt_cache_ro = true; - } else if (arg == "-bf" || arg == "--binary-file") { + } + if (arg == "-bf" || arg == "--binary-file") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -228,7 +250,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { ss << file.rdbuf(); params.prompt = ss.str(); fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]); - } else if (arg == "-f" || arg == "--file") { + } + if (arg == "-f" || arg == "--file") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -245,51 +269,67 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (!params.prompt.empty() && params.prompt.back() == '\n') { params.prompt.pop_back(); } - } else if (arg == "-n" || arg == "--n-predict") { + } + if (arg == "-n" || arg == "--n-predict") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_predict = std::stoi(argv[i]); - } else if (arg == "--top-k") { + } + if (arg == "--top-k") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.top_k = std::stoi(argv[i]); - } else if (arg == "-c" || arg == "--ctx-size") { + } + if (arg == "-c" || arg == "--ctx-size") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); - } else if (arg == "--grp-attn-n" || arg == "-gan") { + } + if (arg == "--grp-attn-n" || arg == "-gan") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.grp_attn_n = std::stoi(argv[i]); - } else if (arg == "--grp-attn-w" || arg == "-gaw") { + } + if (arg == "--grp-attn-w" || arg == "-gaw") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.grp_attn_w = std::stoi(argv[i]); - } else if (arg == "--rope-freq-base") { + } + if (arg == "--rope-freq-base") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.rope_freq_base = std::stof(argv[i]); - } else if (arg == "--rope-freq-scale") { + } + if (arg == "--rope-freq-scale") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = std::stof(argv[i]); - } else if (arg == "--rope-scaling") { + } + if (arg == "--rope-scaling") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -299,43 +339,57 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } - } else if (arg == "--rope-scale") { + } + if (arg == "--rope-scale") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = 1.0f/std::stof(argv[i]); - } else if (arg == "--yarn-orig-ctx") { + } + if (arg == "--yarn-orig-ctx") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.yarn_orig_ctx = std::stoi(argv[i]); - } else if (arg == "--yarn-ext-factor") { + } + if (arg == "--yarn-ext-factor") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.yarn_ext_factor = std::stof(argv[i]); - } else if (arg == "--yarn-attn-factor") { + } + if (arg == "--yarn-attn-factor") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.yarn_attn_factor = std::stof(argv[i]); - } else if (arg == "--yarn-beta-fast") { + } + if (arg == "--yarn-beta-fast") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_fast = std::stof(argv[i]); - } else if (arg == "--yarn-beta-slow") { + } + if (arg == "--yarn-beta-slow") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_slow = std::stof(argv[i]); - } else if (arg == "--pooling") { + } + if (arg == "--pooling") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -345,118 +399,156 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } - } else if (arg == "--defrag-thold" || arg == "-dt") { + } + if (arg == "--defrag-thold" || arg == "-dt") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.defrag_thold = std::stof(argv[i]); - } else if (arg == "--samplers") { + } + if (arg == "--samplers") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } const auto sampler_names = string_split(argv[i], ';'); sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); - } else if (arg == "--sampling-seq") { + } + if (arg == "--sampling-seq") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.samplers_sequence = sampler_types_from_chars(argv[i]); - } else if (arg == "--top-p") { + } + if (arg == "--top-p") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.top_p = std::stof(argv[i]); - } else if (arg == "--min-p") { + } + if (arg == "--min-p") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.min_p = std::stof(argv[i]); - } else if (arg == "--temp") { + } + if (arg == "--temp") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.temp = std::stof(argv[i]); sparams.temp = std::max(sparams.temp, 0.0f); - } else if (arg == "--tfs") { + } + if (arg == "--tfs") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.tfs_z = std::stof(argv[i]); - } else if (arg == "--typical") { + } + if (arg == "--typical") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.typical_p = std::stof(argv[i]); - } else if (arg == "--repeat-last-n") { + } + if (arg == "--repeat-last-n") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.penalty_last_n = std::stoi(argv[i]); sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n); - } else if (arg == "--repeat-penalty") { + } + if (arg == "--repeat-penalty") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.penalty_repeat = std::stof(argv[i]); - } else if (arg == "--frequency-penalty") { + } + if (arg == "--frequency-penalty") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.penalty_freq = std::stof(argv[i]); - } else if (arg == "--presence-penalty") { + } + if (arg == "--presence-penalty") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.penalty_present = std::stof(argv[i]); - } else if (arg == "--dynatemp-range") { + } + if (arg == "--dynatemp-range") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.dynatemp_range = std::stof(argv[i]); - } else if (arg == "--dynatemp-exp") { + } + if (arg == "--dynatemp-exp") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.dynatemp_exponent = std::stof(argv[i]); - } else if (arg == "--mirostat") { + } + if (arg == "--mirostat") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.mirostat = std::stoi(argv[i]); - } else if (arg == "--mirostat-lr") { + } + if (arg == "--mirostat-lr") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.mirostat_eta = std::stof(argv[i]); - } else if (arg == "--mirostat-ent") { + } + if (arg == "--mirostat-ent") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.mirostat_tau = std::stof(argv[i]); - } else if (arg == "--cfg-negative-prompt") { + } + if (arg == "--cfg-negative-prompt") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.cfg_negative_prompt = argv[i]; - } else if (arg == "--cfg-negative-prompt-file") { + } + if (arg == "--cfg-negative-prompt-file") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -471,86 +563,114 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') { sparams.cfg_negative_prompt.pop_back(); } - } else if (arg == "--cfg-scale") { + } + if (arg == "--cfg-scale") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.cfg_scale = std::stof(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { + } + if (arg == "-b" || arg == "--batch-size") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); - } else if (arg == "-ub" || arg == "--ubatch-size") { + } + if (arg == "-ub" || arg == "--ubatch-size") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_ubatch = std::stoi(argv[i]); - } else if (arg == "--keep") { + } + if (arg == "--keep") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_keep = std::stoi(argv[i]); - } else if (arg == "--draft") { + } + if (arg == "--draft") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_draft = std::stoi(argv[i]); - } else if (arg == "--chunks") { + } + if (arg == "--chunks") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_chunks = std::stoi(argv[i]); - } else if (arg == "-np" || arg == "--parallel") { + } + if (arg == "-np" || arg == "--parallel") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-ns" || arg == "--sequences") { + } + if (arg == "-ns" || arg == "--sequences") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_sequences = std::stoi(argv[i]); - } else if (arg == "--p-split" || arg == "-ps") { + } + if (arg == "--p-split" || arg == "-ps") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.p_split = std::stof(argv[i]); - } else if (arg == "-m" || arg == "--model") { + } + if (arg == "-m" || arg == "--model") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.model = argv[i]; - } else if (arg == "-md" || arg == "--model-draft") { + } + if (arg == "-md" || arg == "--model-draft") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.model_draft = argv[i]; - } else if (arg == "-a" || arg == "--alias") { + } + if (arg == "-a" || arg == "--alias") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.model_alias = argv[i]; - } else if (arg == "--lora") { + } + if (arg == "--lora") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; - } else if (arg == "--lora-scaled") { + } + if (arg == "--lora-scaled") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -562,19 +682,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; - } else if (arg == "--lora-base") { + } + if (arg == "--lora-base") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.lora_base = argv[i]; - } else if (arg == "--control-vector") { + } + if (arg == "--control-vector") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.control_vectors.push_back({ 1.0f, argv[i], }); - } else if (arg == "--control-vector-scaled") { + } + if (arg == "--control-vector-scaled") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -585,7 +711,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.control_vectors.push_back({ std::stof(argv[i]), fname, }); - } else if (arg == "--control-vector-layer-range") { + } + if (arg == "--control-vector-layer-range") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -596,49 +724,85 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.control_vector_layer_end = std::stoi(argv[i]); - } else if (arg == "--mmproj") { + } + if (arg == "--mmproj") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.mmproj = argv[i]; - } else if (arg == "--image") { + } + if (arg == "--image") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.image = argv[i]; - } else if (arg == "-i" || arg == "--interactive") { + } + if (arg == "-i" || arg == "--interactive") { + arg_found = true; params.interactive = true; - } else if (arg == "--embedding") { + } + if (arg == "--embedding") { + arg_found = true; params.embedding = true; - } else if (arg == "--interactive-first") { + } + if (arg == "--interactive-first") { + arg_found = true; params.interactive_first = true; - } else if (arg == "-ins" || arg == "--instruct") { + } + if (arg == "-ins" || arg == "--instruct") { + arg_found = true; params.instruct = true; - } else if (arg == "-cml" || arg == "--chatml") { + } + if (arg == "-cml" || arg == "--chatml") { + arg_found = true; params.chatml = true; - } else if (arg == "--infill") { + } + if (arg == "--infill") { + arg_found = true; params.infill = true; - } else if (arg == "-dkvc" || arg == "--dump-kv-cache") { + } + if (arg == "-dkvc" || arg == "--dump-kv-cache") { + arg_found = true; params.dump_kv_cache = true; - } else if (arg == "-nkvo" || arg == "--no-kv-offload") { + } + if (arg == "-nkvo" || arg == "--no-kv-offload") { + arg_found = true; params.no_kv_offload = true; - } else if (arg == "-ctk" || arg == "--cache-type-k") { + } + if (arg == "-ctk" || arg == "--cache-type-k") { + arg_found = true; params.cache_type_k = argv[++i]; - } else if (arg == "-ctv" || arg == "--cache-type-v") { + } + if (arg == "-ctv" || arg == "--cache-type-v") { + arg_found = true; params.cache_type_v = argv[++i]; - } else if (arg == "--multiline-input") { + } + if (arg == "--multiline-input") { + arg_found = true; params.multiline_input = true; - } else if (arg == "--simple-io") { + } + if (arg == "--simple-io") { + arg_found = true; params.simple_io = true; - } else if (arg == "-cb" || arg == "--cont-batching") { + } + if (arg == "-cb" || arg == "--cont-batching") { + arg_found = true; params.cont_batching = true; - } else if (arg == "--color") { + } + if (arg == "--color") { + arg_found = true; params.use_color = true; - } else if (arg == "--mlock") { + } + if (arg == "--mlock") { + arg_found = true; params.use_mlock = true; - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + } + if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -648,7 +812,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } - } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + } + if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -658,7 +824,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } - } else if (arg == "--main-gpu" || arg == "-mg") { + } + if (arg == "--main-gpu" || arg == "-mg") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -667,7 +835,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { #ifndef GGML_USE_CUBLAS_SYCL fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL - } else if (arg == "--split-mode" || arg == "-sm") { + } + if (arg == "--split-mode" || arg == "-sm") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -691,7 +861,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL - } else if (arg == "--tensor-split" || arg == "-ts") { + } + if (arg == "--tensor-split" || arg == "-ts") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -716,9 +888,13 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { #ifndef GGML_USE_CUBLAS_SYCL_VULKAN fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL - } else if (arg == "--no-mmap") { + } + if (arg == "--no-mmap") { + arg_found = true; params.use_mmap = false; - } else if (arg == "--numa") { + } + if (arg == "--numa") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -728,17 +904,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } else { invalid_param = true; break; } - } else if (arg == "--verbose-prompt") { + } + if (arg == "--verbose-prompt") { + arg_found = true; params.verbose_prompt = true; - } else if (arg == "--no-display-prompt") { + } + if (arg == "--no-display-prompt") { + arg_found = true; params.display_prompt = false; - } else if (arg == "-r" || arg == "--reverse-prompt") { + } + if (arg == "-r" || arg == "--reverse-prompt") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.antiprompt.emplace_back(argv[i]); - } else if (arg == "-ld" || arg == "--logdir") { + } + if (arg == "-ld" || arg == "--logdir") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -748,63 +932,93 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.logdir.back() != DIRECTORY_SEPARATOR) { params.logdir += DIRECTORY_SEPARATOR; } - } else if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { + } + if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.logits_file = argv[i]; - } else if (arg == "--perplexity" || arg == "--all-logits") { + } + if (arg == "--perplexity" || arg == "--all-logits") { + arg_found = true; params.logits_all = true; - } else if (arg == "--ppl-stride") { + } + if (arg == "--ppl-stride") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.ppl_stride = std::stoi(argv[i]); - } else if (arg == "-ptc" || arg == "--print-token-count") { + } + if (arg == "-ptc" || arg == "--print-token-count") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.n_print = std::stoi(argv[i]); - } else if (arg == "--ppl-output-type") { + } + if (arg == "--ppl-output-type") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.ppl_output_type = std::stoi(argv[i]); - } else if (arg == "--hellaswag") { + } + if (arg == "--hellaswag") { + arg_found = true; params.hellaswag = true; - } else if (arg == "--hellaswag-tasks") { + } + if (arg == "--hellaswag-tasks") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.hellaswag_tasks = std::stoi(argv[i]); - } else if (arg == "--winogrande") { + } + if (arg == "--winogrande") { + arg_found = true; params.winogrande = true; - } else if (arg == "--winogrande-tasks") { + } + if (arg == "--winogrande-tasks") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.winogrande_tasks = std::stoi(argv[i]); - } else if (arg == "--multiple-choice") { + } + if (arg == "--multiple-choice") { + arg_found = true; params.multiple_choice = true; - } else if (arg == "--multiple-choice-tasks") { + } + if (arg == "--multiple-choice-tasks") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.multiple_choice_tasks = std::stoi(argv[i]); - } else if (arg == "--kl-divergence") { + } + if (arg == "--kl-divergence") { + arg_found = true; params.kl_divergence = true; - } else if (arg == "--ignore-eos") { + } + if (arg == "--ignore-eos") { + arg_found = true; params.ignore_eos = true; - } else if (arg == "--no-penalize-nl") { + } + if (arg == "--no-penalize-nl") { + arg_found = true; sparams.penalize_nl = false; - } else if (arg == "-l" || arg == "--logit-bias") { + } + if (arg == "-l" || arg == "--logit-bias") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -823,36 +1037,51 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - } else if (arg == "-h" || arg == "--help") { + } + if (arg == "-h" || arg == "--help") { + arg_found = true; return false; - - } else if (arg == "--version") { + } + if (arg == "--version") { + arg_found = true; fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); exit(0); - } else if (arg == "--random-prompt") { + } + if (arg == "--random-prompt") { + arg_found = true; params.random_prompt = true; - } else if (arg == "--in-prefix-bos") { + } + if (arg == "--in-prefix-bos") { + arg_found = true; params.input_prefix_bos = true; - } else if (arg == "--in-prefix") { + } + if (arg == "--in-prefix") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.input_prefix = argv[i]; - } else if (arg == "--in-suffix") { + } + if (arg == "--in-suffix") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } params.input_suffix = argv[i]; - } else if (arg == "--grammar") { + } + if (arg == "--grammar") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; } sparams.grammar = argv[i]; - } else if (arg == "--grammar-file") { + } + if (arg == "--grammar-file") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -868,7 +1097,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { std::istreambuf_iterator(), std::back_inserter(sparams.grammar) ); - } else if (arg == "--override-kv") { + } + if (arg == "--override-kv") { + arg_found = true; if (++i >= argc) { invalid_param = true; break; @@ -911,10 +1142,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.kv_overrides.push_back(kvo); #ifndef LOG_DISABLE_LOGS // Parse args for logging parameters - } else if ( log_param_single_parse( argv[i] ) ) { + } + if ( log_param_single_parse( argv[i] ) ) { + arg_found = true; // Do nothing, log_param_single_parse automatically does it's thing // and returns if a match was found and parsed. - } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) { + } + if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) { + arg_found = true; // We have a matching known parameter requiring an argument, // now we need to check if there is anything after this argv // and flag invalid_param or parse it. @@ -928,7 +1163,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } // End of Parse args for logging parameters #endif // LOG_DISABLE_LOGS - } else { + } + + if (!arg_found) { throw std::invalid_argument("error: unknown argument: " + arg); } } From dfbfdd60f90207404039c6578d709231496831d9 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 16 Mar 2024 16:42:08 +0100 Subject: [PATCH 279/401] readme : add wllama as a wasm binding (#6100) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5cbdf7e476b09..c2f3342f0ff42 100644 --- a/README.md +++ b/README.md @@ -134,6 +134,7 @@ Typically finetunes of the base models below are supported as well. - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm) +- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) From b5f4ae09c3244ae1644b67c03ed9f4227ab25ad2 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sat, 16 Mar 2024 16:46:29 +0100 Subject: [PATCH 280/401] gritlm : add initial README.md (#6086) * gritlm: add initial README.md to examples/gritlm This commit adds a suggestion for an initial README.md for the gritlm example. Signed-off-by: Daniel Bevenius * squash! gritlm: add initial README.md to examples/gritlm Use the `scripts/hf.sh` script to download the model file. Signed-off-by: Daniel Bevenius * squash! gritlm: add initial README.md to examples/gritlm Fix editorconfig-checker error in examples/gritlm/README.md. Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/gritlm/README.md | 62 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 examples/gritlm/README.md diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md new file mode 100644 index 0000000000000..64cc192040c43 --- /dev/null +++ b/examples/gritlm/README.md @@ -0,0 +1,62 @@ +## Generative Representational Instruction Tuning (GRIT) Example +[gritlm] a model which can generate embeddings as well as "normal" text +generation depending on the instructions in the prompt. + +* Paper: https://arxiv.org/pdf/2402.09906.pdf + +### Retrieval-Augmented Generation (RAG) use case +One use case for `gritlm` is to use it with RAG. If we recall how RAG works is +that we take documents that we want to use as context, to ground the large +language model (LLM), and we create token embeddings for them. We then store +these token embeddings in a vector database. + +When we perform a query, prompt the LLM, we will first create token embeddings +for the query and then search the vector database to retrieve the most +similar vectors, and return those documents so they can be passed to the LLM as +context. Then the query and the context will be passed to the LLM which will +have to _again_ create token embeddings for the query. But because gritlm is used +the first query can be cached and the second query tokenization generation does +not have to be performed at all. + +### Running the example +Download a Grit model: +```console +$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf +``` + +Run the example using the downloaded model: +```console +$ ./gritlm -m gritlm-7b_q4_1.gguf + +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605 +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103 +Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112 +Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547 + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +When shadows lurk and ghosts do roam, +And darkness reigns, a fearsome sight. + +Thou didst set out, with heart aglow, +To conquer this mountain, so high, +And reach the summit, where the stars do glow, +And the moon shines bright, up in the sky. + +Through the mist and fog, thou didst press on, +With steadfast courage, and a steadfast will, +Through the darkness, thou didst not be gone, +But didst climb on, with a steadfast skill. + +At last, thou didst reach the summit's crest, +And gazed upon the world below, +And saw the beauty of the night's best, +And felt the peace, that only nature knows. + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +Thou art a hero, in the eyes of all, +For thou didst conquer this mountain, so bright. +``` + +[gritlm]: https://github.com/ContextualAI/gritlm From c47cf414efafb8f60596edc7edb5a2d68065e992 Mon Sep 17 00:00:00 2001 From: AmirAli Mirian <37371367+amiralimi@users.noreply.github.com> Date: Sat, 16 Mar 2024 11:52:02 -0400 Subject: [PATCH 281/401] ggml : add AVX512F SIMD (#6088) --- ggml.c | 95 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) diff --git a/ggml.c b/ggml.c index c94006e51a092..fa23cb3c44f87 100644 --- a/ggml.c +++ b/ggml.c @@ -931,6 +931,101 @@ inline static float vaddvq_f32(float32x4_t v) { #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif +#elif defined(__AVX512F__) + +#define GGML_SIMD + +// F32 AVX512 + +#define GGML_F32_STEP 64 +#define GGML_F32_EPR 16 + +#define GGML_F32x16 __m512 +#define GGML_F32x16_ZERO _mm512_setzero_ps() +#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) +#define GGML_F32x16_LOAD _mm512_loadu_ps +#define GGML_F32x16_STORE _mm512_storeu_ps +// _mm512_fmadd_ps is defined in AVX512F so no guard is required +#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32x16_ADD _mm512_add_ps +#define GGML_F32x16_MUL _mm512_mul_ps +#define GGML_F32x16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = _mm512_reduce_add_ps(x[0]); \ +} while (0) + +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x16 +#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD +#define GGML_F32_VEC_STORE GGML_F32x16_STORE +#define GGML_F32_VEC_FMA GGML_F32x16_FMA +#define GGML_F32_VEC_ADD GGML_F32x16_ADD +#define GGML_F32_VEC_MUL GGML_F32x16_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE + +// F16 AVX512 + +// F16 AVX + +#define GGML_F16_STEP 64 +#define GGML_F16_EPR 16 + +// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead + +#define GGML_F32Cx16 __m512 +#define GGML_F32Cx16_ZERO _mm512_setzero_ps() +#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) + +// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F +// so F16C guard isn't required +#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x))) +#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) + +#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32Cx16_ADD _mm512_add_ps +#define GGML_F32Cx16_MUL _mm512_mul_ps +#define GGML_F32Cx16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = _mm512_reduce_add_ps(x[0]); \ +} while (0) + +#define GGML_F16_VEC GGML_F32Cx16 +#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE + #elif defined(__AVX__) #define GGML_SIMD From dc0f6125487dcfbff913360f9d877bc0ccf6aa57 Mon Sep 17 00:00:00 2001 From: GainLee Date: Mon, 18 Mar 2024 01:12:22 +0800 Subject: [PATCH 282/401] ggml:fix finding transfer queue family index error (#6094) Co-authored-by: GainLee --- ggml-vulkan.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 7cce616ba714f..698b31496f417 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -710,6 +710,12 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector= 0) { + return compute_index; + } + std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl; for(auto &q_family : queue_family_props) { From cd776c37c945bf58efc8fe44b370456680cb1b59 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 17 Mar 2024 19:51:57 +0200 Subject: [PATCH 283/401] ci : close all stale issues at once (#6115) --- .github/workflows/close-issue.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index bc08a72d03166..2682f308cb46e 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -19,4 +19,5 @@ jobs: close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." days-before-pr-stale: -1 days-before-pr-close: -1 + operations-per-run: 1000 repo-token: ${{ secrets.GITHUB_TOKEN }} From d01b3c4c32357567f3531d4e6ceffc5d23e87583 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sun, 17 Mar 2024 19:12:37 +0100 Subject: [PATCH 284/401] common: llama_load_model_from_url using --model-url (#6098) * common: llama_load_model_from_url with libcurl dependency Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 22 ++ .github/workflows/server.yml | 20 +- CMakeLists.txt | 1 + Makefile | 5 + common/CMakeLists.txt | 13 +- common/common.cpp | 238 +++++++++++++++++- common/common.h | 4 + examples/main/README.md | 1 + examples/server/README.md | 1 + examples/server/server.cpp | 8 + examples/server/tests/README.md | 2 +- .../server/tests/features/embeddings.feature | 3 +- examples/server/tests/features/environment.py | 97 +++---- examples/server/tests/features/server.feature | 3 +- examples/server/tests/features/steps/steps.py | 37 ++- examples/server/tests/requirements.txt | 1 + 16 files changed, 399 insertions(+), 57 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0da01d5ba6ead..945df42f886a6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -48,6 +48,28 @@ jobs: CC=gcc-8 make tests -j $(nproc) make test -j $(nproc) + ubuntu-focal-make-curl: + runs-on: ubuntu-20.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev + + - name: Build + id: make_build + env: + LLAMA_FATAL_WARNINGS: 1 + LLAMA_CURL: 1 + run: | + CC=gcc-8 make -j $(nproc) + ubuntu-latest-cmake: runs-on: ubuntu-latest diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 5e38b3547c659..4ea09115a3c44 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -57,7 +57,8 @@ jobs: cmake \ python3-pip \ wget \ - language-pack-en + language-pack-en \ + libcurl4-openssl-dev - name: Build id: cmake_build @@ -67,6 +68,7 @@ jobs: cmake .. \ -DLLAMA_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server @@ -101,12 +103,21 @@ jobs: with: fetch-depth: 0 + - name: libCURL + id: get_libcurl + env: + CURL_VERSION: 8.6.0_6 + run: | + curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip" + mkdir $env:RUNNER_TEMP/libcurl + tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl + - name: Build id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_BUILD_SERVER=ON -DCMAKE_BUILD_TYPE=Release ; + cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server - name: Python setup @@ -120,6 +131,11 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt + - name: Copy Libcurl + id: prepare_libcurl + run: | + cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll + - name: Tests id: server_integration_tests if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} diff --git a/CMakeLists.txt b/CMakeLists.txt index 3ac2804a6881a..fc4cff28f44ac 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -99,6 +99,7 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") +option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) diff --git a/Makefile b/Makefile index c0f1250366a64..838daf5c02acd 100644 --- a/Makefile +++ b/Makefile @@ -595,6 +595,11 @@ include scripts/get-flags.mk CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic endif +ifdef LLAMA_CURL +override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL +override LDFLAGS := $(LDFLAGS) -lcurl +endif + # # Print build information # diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 350bbdf7f7b1b..af2629a460b93 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -68,6 +68,17 @@ if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() +set(LLAMA_COMMON_EXTRA_LIBS build_info) + +# Use curl to download model url +if (LLAMA_CURL) + find_package(CURL REQUIRED) + add_definitions(-DLLAMA_USE_CURL) + include_directories(${CURL_INCLUDE_DIRS}) + find_library(CURL_LIBRARY curl REQUIRED) + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY}) +endif () + target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama) +target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama) diff --git a/common/common.cpp b/common/common.cpp index 1b0ba849398de..2f5d965d6511c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -37,6 +37,9 @@ #include #include #endif +#if defined(LLAMA_USE_CURL) +#include +#endif #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -50,6 +53,18 @@ #define GGML_USE_CUBLAS_SYCL_VULKAN #endif +#if defined(LLAMA_USE_CURL) +#ifdef __linux__ +#include +#elif defined(_WIN32) +#define PATH_MAX MAX_PATH +#else +#include +#endif +#define LLAMA_CURL_MAX_PATH_LENGTH PATH_MAX +#define LLAMA_CURL_MAX_HEADER_LENGTH 256 +#endif // LLAMA_USE_CURL + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -644,6 +659,13 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } params.model = argv[i]; } + if (arg == "-mu" || arg == "--model-url") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_url = argv[i]; + } if (arg == "-md" || arg == "--model-draft") { arg_found = true; if (++i >= argc) { @@ -1368,6 +1390,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" layer range to apply the control vector(s) to, start and end inclusive\n"); printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); + printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); + printf(" model download url (default: %s)\n", params.model_url.c_str()); printf(" -md FNAME, --model-draft FNAME\n"); printf(" draft model for speculative decoding\n"); printf(" -ld LOGDIR, --logdir LOGDIR\n"); @@ -1613,10 +1637,222 @@ void llama_batch_add( batch.n_tokens++; } +#ifdef LLAMA_USE_CURL + +struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, + struct llama_model_params params) { + // Basic validation of the model_url + if (!model_url || strlen(model_url) == 0) { + fprintf(stderr, "%s: invalid model_url\n", __func__); + return NULL; + } + + // Initialize libcurl globally + auto curl = curl_easy_init(); + + if (!curl) { + fprintf(stderr, "%s: error initializing libcurl\n", __func__); + return NULL; + } + + // Set the URL, allow to follow http redirection + curl_easy_setopt(curl, CURLOPT_URL, model_url); + curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); +#if defined(_WIN32) + // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of + // operating system. Currently implemented under MS-Windows. + curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + + // Check if the file already exists locally + struct stat model_file_info; + auto file_exists = (stat(path_model, &model_file_info) == 0); + + // If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files + char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; + char etag_path[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; + snprintf(etag_path, sizeof(etag_path), "%s.etag", path_model); + + char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; + char last_modified_path[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; + snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path_model); + + if (file_exists) { + auto * f_etag = fopen(etag_path, "r"); + if (f_etag) { + if (!fgets(etag, sizeof(etag), f_etag)) { + fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path); + } else { + fprintf(stderr, "%s: previous model file found %s: %s\n", __func__, etag_path, etag); + } + fclose(f_etag); + } + + auto * f_last_modified = fopen(last_modified_path, "r"); + if (f_last_modified) { + if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) { + fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path); + } else { + fprintf(stderr, "%s: previous model file found %s: %s\n", __func__, last_modified_path, + last_modified); + } + fclose(f_last_modified); + } + } + + // Send a HEAD request to retrieve the etag and last-modified headers + struct llama_load_model_from_url_headers { + char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; + char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; + }; + llama_load_model_from_url_headers headers; + { + typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); + auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { + llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata; + + const char * etag_prefix = "etag: "; + if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) { + strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF + } + + const char * last_modified_prefix = "last-modified: "; + if (strncmp(buffer, last_modified_prefix, strlen(last_modified_prefix)) == 0) { + strncpy(headers->last_modified, buffer + strlen(last_modified_prefix), + n_items - strlen(last_modified_prefix) - 2); // Remove CRLF + } + return n_items; + }; + + curl_easy_setopt(curl, CURLOPT_NOBODY, 1L); // will trigger the HEAD verb + curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L); // hide head request progress + curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, static_cast(header_callback)); + curl_easy_setopt(curl, CURLOPT_HEADERDATA, &headers); + + CURLcode res = curl_easy_perform(curl); + if (res != CURLE_OK) { + curl_easy_cleanup(curl); + fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); + return NULL; + } + + long http_code = 0; + curl_easy_getinfo(curl, CURLINFO_RESPONSE_CODE, &http_code); + if (http_code != 200) { + // HEAD not supported, we don't know if the file has changed + // force trigger downloading + file_exists = false; + fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code); + } + } + + // If the ETag or the Last-Modified headers are different: trigger a new download + if (!file_exists || strcmp(etag, headers.etag) != 0 || strcmp(last_modified, headers.last_modified) != 0) { + char path_model_temporary[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; + snprintf(path_model_temporary, sizeof(path_model_temporary), "%s.downloadInProgress", path_model); + if (file_exists) { + fprintf(stderr, "%s: deleting previous downloaded model file: %s\n", __func__, path_model); + if (remove(path_model) != 0) { + curl_easy_cleanup(curl); + fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path_model); + return NULL; + } + } + + // Set the output file + auto * outfile = fopen(path_model_temporary, "wb"); + if (!outfile) { + curl_easy_cleanup(curl); + fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path_model); + return NULL; + } + + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd); + auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t { + return fwrite(data, size, nmemb, (FILE *)fd); + }; + curl_easy_setopt(curl, CURLOPT_NOBODY, 0L); + curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile); + + // display download progress + curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); + + // start the download + fprintf(stderr, "%s: downloading model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, + model_url, path_model, headers.etag, headers.last_modified); + auto res = curl_easy_perform(curl); + if (res != CURLE_OK) { + fclose(outfile); + curl_easy_cleanup(curl); + fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); + return NULL; + } + + long http_code = 0; + curl_easy_getinfo (curl, CURLINFO_RESPONSE_CODE, &http_code); + if (http_code < 200 || http_code >= 400) { + fclose(outfile); + curl_easy_cleanup(curl); + fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code); + return NULL; + } + + // Clean up + fclose(outfile); + + // Write the new ETag to the .etag file + if (strlen(headers.etag) > 0) { + auto * etag_file = fopen(etag_path, "w"); + if (etag_file) { + fputs(headers.etag, etag_file); + fclose(etag_file); + fprintf(stderr, "%s: model etag saved %s: %s\n", __func__, etag_path, headers.etag); + } + } + + // Write the new lastModified to the .etag file + if (strlen(headers.last_modified) > 0) { + auto * last_modified_file = fopen(last_modified_path, "w"); + if (last_modified_file) { + fputs(headers.last_modified, last_modified_file); + fclose(last_modified_file); + fprintf(stderr, "%s: model last modified saved %s: %s\n", __func__, last_modified_path, + headers.last_modified); + } + } + + if (rename(path_model_temporary, path_model) != 0) { + curl_easy_cleanup(curl); + fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_model_temporary, path_model); + return NULL; + } + } + + curl_easy_cleanup(curl); + + return llama_load_model_from_file(path_model, params); +} + +#else + +struct llama_model * llama_load_model_from_url(const char * /*model_url*/, const char * /*path_model*/, + struct llama_model_params /*params*/) { + fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); + return nullptr; +} + +#endif // LLAMA_USE_CURL + std::tuple llama_init_from_gpt_params(gpt_params & params) { auto mparams = llama_model_params_from_gpt_params(params); - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); + llama_model * model = nullptr; + if (!params.model_url.empty()) { + model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); + } else { + model = llama_load_model_from_file(params.model.c_str(), mparams); + } if (model == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); return std::make_tuple(nullptr, nullptr); diff --git a/common/common.h b/common/common.h index 687f3425e8544..8dd8a3edc9c94 100644 --- a/common/common.h +++ b/common/common.h @@ -89,6 +89,7 @@ struct gpt_params { struct llama_sampling_params sparams; std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_url = ""; // model url to download std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; @@ -191,6 +192,9 @@ std::tuple llama_init_from_gpt_par struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); +struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, + struct llama_model_params params); + // Batch utils void llama_batch_clear(struct llama_batch & batch); diff --git a/examples/main/README.md b/examples/main/README.md index 7f84e42623274..6a8d1e1c50cbb 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -67,6 +67,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt In this section, we cover the most commonly used options for running the `main` program with the LLaMA models: - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). +- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. diff --git a/examples/server/README.md b/examples/server/README.md index 8f8454affaecd..755e1d5384f55 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -20,6 +20,7 @@ The project is under active development, and we are [looking for feedback and co - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. - `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). +- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 895d608fdcc06..d2a8e541d3305 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2195,6 +2195,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co } printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); + printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); + printf(" model download url (default: %s)\n", params.model_url.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); printf(" set an alias for the model, will be added as `model` field in completion response\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); @@ -2317,6 +2319,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } params.model = argv[i]; + } else if (arg == "-mu" || arg == "--model-url") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_url = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 95a0353b6a9c5..feb2b1d6cf5de 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -57,7 +57,7 @@ Feature or Scenario must be annotated with `@llama.cpp` to be included in the de To run a scenario annotated with `@bug`, start: ```shell -DEBUG=ON ./tests.sh --no-skipped --tags bug +DEBUG=ON ./tests.sh --no-skipped --tags bug --stop ``` After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature index 57359b267a668..dcf1434f97124 100644 --- a/examples/server/tests/features/embeddings.feature +++ b/examples/server/tests/features/embeddings.feature @@ -4,7 +4,8 @@ Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models + And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf + And a model file ggml-model-f16.gguf And a model alias bert-bge-small And 42 as server seed And 2 slots diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 8ad987e1bb618..82104e9202e5e 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -1,10 +1,12 @@ -import errno import os +import signal import socket -import subprocess +import sys import time +import traceback from contextlib import closing -import signal + +import psutil def before_scenario(context, scenario): @@ -20,33 +22,40 @@ def before_scenario(context, scenario): def after_scenario(context, scenario): - if context.server_process is None: - return - if scenario.status == "failed": - if 'GITHUB_ACTIONS' in os.environ: - print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n") - if os.path.isfile('llama.log'): - with closing(open('llama.log', 'r')) as f: - for line in f: - print(line) - if not is_server_listening(context.server_fqdn, context.server_port): - print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n") - - if not pid_exists(context.server_process.pid): - assert False, f"Server not running pid={context.server_process.pid} ..." - - server_graceful_shutdown(context) - - # Wait few for socket to free up - time.sleep(0.05) - - attempts = 0 - while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port): - server_kill(context) - time.sleep(0.1) - attempts += 1 - if attempts > 5: - server_kill_hard(context) + try: + if 'server_process' not in context or context.server_process is None: + return + if scenario.status == "failed": + if 'GITHUB_ACTIONS' in os.environ: + print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n") + if os.path.isfile('llama.log'): + with closing(open('llama.log', 'r')) as f: + for line in f: + print(line) + if not is_server_listening(context.server_fqdn, context.server_port): + print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n") + + if not pid_exists(context.server_process.pid): + assert False, f"Server not running pid={context.server_process.pid} ..." + + server_graceful_shutdown(context) + + # Wait few for socket to free up + time.sleep(0.05) + + attempts = 0 + while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port): + server_kill(context) + time.sleep(0.1) + attempts += 1 + if attempts > 5: + server_kill_hard(context) + except: + exc = sys.exception() + print("error in after scenario: \n") + print(exc) + print("*** print_tb: \n") + traceback.print_tb(exc.__traceback__, file=sys.stdout) def server_graceful_shutdown(context): @@ -67,11 +76,11 @@ def server_kill_hard(context): path = context.server_path print(f"Server dangling exits, hard killing force {pid}={path}...\n") - if os.name == 'nt': - process = subprocess.check_output(['taskkill', '/F', '/pid', str(pid)]).decode() - print(process) - else: - os.kill(-pid, signal.SIGKILL) + try: + psutil.Process(pid).kill() + except psutil.NoSuchProcess: + return False + return True def is_server_listening(server_fqdn, server_port): @@ -84,17 +93,9 @@ def is_server_listening(server_fqdn, server_port): def pid_exists(pid): - """Check whether pid exists in the current process table.""" - if pid < 0: + try: + psutil.Process(pid) + except psutil.NoSuchProcess: return False - if os.name == 'nt': - output = subprocess.check_output(['TASKLIST', '/FI', f'pid eq {pid}']).decode() - print(output) - return "No tasks are running" not in output - else: - try: - os.kill(pid, 0) - except OSError as e: - return e.errno == errno.EPERM - else: - return True + return True + diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 5014f326dc050..7448986e75a49 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -4,7 +4,8 @@ Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models + And a model url https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf + And a model file stories260K.gguf And a model alias tinyllama-2 And 42 as server seed # KV Cache corresponds to the total amount of tokens diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index a59a52d21748a..9e348d5fc4c37 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -5,6 +5,8 @@ import re import socket import subprocess +import sys +import threading import time from contextlib import closing from re import RegexFlag @@ -32,6 +34,8 @@ def step_server_config(context, server_fqdn, server_port): context.base_url = f'http://{context.server_fqdn}:{context.server_port}' context.model_alias = None + context.model_file = None + context.model_url = None context.n_batch = None context.n_ubatch = None context.n_ctx = None @@ -65,6 +69,16 @@ def step_download_hf_model(context, hf_file, hf_repo): print(f"model file: {context.model_file}\n") +@step('a model file {model_file}') +def step_model_file(context, model_file): + context.model_file = model_file + + +@step('a model url {model_url}') +def step_model_url(context, model_url): + context.model_url = model_url + + @step('a model alias {model_alias}') def step_model_alias(context, model_alias): context.model_alias = model_alias @@ -141,7 +155,8 @@ def step_start_server(context): async def step_wait_for_the_server_to_be_started(context, expecting_status): match expecting_status: case 'healthy': - await wait_for_health_status(context, context.base_url, 200, 'ok') + await wait_for_health_status(context, context.base_url, 200, 'ok', + timeout=30) case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', @@ -1038,8 +1053,11 @@ def start_server_background(context): server_args = [ '--host', server_listen_addr, '--port', context.server_port, - '--model', context.model_file ] + if context.model_file: + server_args.extend(['--model', context.model_file]) + if context.model_url: + server_args.extend(['--model-url', context.model_url]) if context.n_batch: server_args.extend(['--batch-size', context.n_batch]) if context.n_ubatch: @@ -1079,8 +1097,23 @@ def start_server_background(context): pkwargs = { 'creationflags': flags, + 'stdout': subprocess.PIPE, + 'stderr': subprocess.PIPE } context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], **pkwargs) + + def log_stdout(process): + for line in iter(process.stdout.readline, b''): + print(line.decode('utf-8'), end='') + thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,)) + thread_stdout.start() + + def log_stderr(process): + for line in iter(process.stderr.readline, b''): + print(line.decode('utf-8'), end='', file=sys.stderr) + thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,)) + thread_stderr.start() + print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}") diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 2e4f42ad28c23..c2c960102b523 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -3,4 +3,5 @@ behave~=1.2.6 huggingface_hub~=0.20.3 numpy~=1.24.4 openai~=0.25.0 +psutil~=5.9.8 prometheus-client~=0.20.0 From 3a6efdd03c46c5ba08e43880d34260c02dd9999b Mon Sep 17 00:00:00 2001 From: Romain D <90720+Artefact2@users.noreply.github.com> Date: Mon, 18 Mar 2024 09:04:41 +0100 Subject: [PATCH 285/401] convert : use f32 outtype for bf16 tensors (#6106) The old behaviour is to use f16, but bf16 to f16 is not a lossless conversion. Change the outtype to f32 to default to a lossless conversion. --- convert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index 161430f3e717e..817cb66123a8f 100755 --- a/convert.py +++ b/convert.py @@ -1167,9 +1167,9 @@ def write_all( def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type - if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): + if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)): return GGMLFileType.AllF32 - if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)): + if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): return GGMLFileType.MostlyF16 if output_type_str == "q8_0": return GGMLFileType.MostlyQ8_0 From 9b03719ad712e2dc36c5c0c20f352bf3e4bda332 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9rence?= <13496987+Royalphax@users.noreply.github.com> Date: Mon, 18 Mar 2024 09:17:00 +0100 Subject: [PATCH 286/401] convert : add support for CamembertModel architecture (#6119) Adding support for CamembertModel architecture used by : https://huggingface.co/dangvantuan/sentence-camembert-large --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index cf1f98d660633..1e49d56c19514 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1634,7 +1634,7 @@ def write_tensors(self): self.post_write_tensors(tensor_map, name, data_torch) -@Model.register("BertModel") +@Model.register("BertModel", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT From 496bc79bc2b79bfd6124b8687a8dbd6a646e9b06 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Mon, 18 Mar 2024 04:27:44 -0400 Subject: [PATCH 287/401] common : tidy-up argument parsing (#6105) * Tidy-up argument parsing. * Missing ref. * common : minor * common : add static classifier --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 1829 +++++++++++++++++++++++---------------------- 1 file changed, 921 insertions(+), 908 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 2f5d965d6511c..919182862cb5c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -154,1040 +154,1053 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { return result; } -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { - bool invalid_param = false; - std::string arg; - const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sparams; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } +static bool gpt_params_find_arg(int argc, char ** argv, gpt_params & params, int & i, bool & invalid_param) { + std::string arg = argv[i]; + llama_sampling_params& sparams = params.sparams; - bool arg_found = false; - if (arg == "-s" || arg == "--seed") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.seed = std::stoul(argv[i]); + if (arg == "-s" || arg == "--seed") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-t" || arg == "--threads") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - if (params.n_threads <= 0) { - params.n_threads = std::thread::hardware_concurrency(); - } - } - if (arg == "-tb" || arg == "--threads-batch") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - if (params.n_threads_batch <= 0) { - params.n_threads_batch = std::thread::hardware_concurrency(); - } - } - if (arg == "-td" || arg == "--threads-draft") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_draft = std::stoi(argv[i]); - if (params.n_threads_draft <= 0) { - params.n_threads_draft = std::thread::hardware_concurrency(); - } + params.seed = std::stoul(argv[i]); + return true; + } + if (arg == "-t" || arg == "--threads") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-tbd" || arg == "--threads-batch-draft") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch_draft = std::stoi(argv[i]); - if (params.n_threads_batch_draft <= 0) { - params.n_threads_batch_draft = std::thread::hardware_concurrency(); - } + params.n_threads = std::stoi(argv[i]); + if (params.n_threads <= 0) { + params.n_threads = std::thread::hardware_concurrency(); } - if (arg == "-p" || arg == "--prompt") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.prompt = argv[i]; + return true; + } + if (arg == "-tb" || arg == "--threads-batch") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-e" || arg == "--escape") { - arg_found = true; - params.escape = true; + params.n_threads_batch = std::stoi(argv[i]); + if (params.n_threads_batch <= 0) { + params.n_threads_batch = std::thread::hardware_concurrency(); } - if (arg == "--prompt-cache") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.path_prompt_cache = argv[i]; + return true; + } + if (arg == "-td" || arg == "--threads-draft") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--prompt-cache-all") { - arg_found = true; - params.prompt_cache_all = true; + params.n_threads_draft = std::stoi(argv[i]); + if (params.n_threads_draft <= 0) { + params.n_threads_draft = std::thread::hardware_concurrency(); } - if (arg == "--prompt-cache-ro") { - arg_found = true; - params.prompt_cache_ro = true; + return true; + } + if (arg == "-tbd" || arg == "--threads-batch-draft") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-bf" || arg == "--binary-file") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i], std::ios::binary); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - // store the external file name in params - params.prompt_file = argv[i]; - std::ostringstream ss; - ss << file.rdbuf(); - params.prompt = ss.str(); - fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]); - } - if (arg == "-f" || arg == "--file") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - // store the external file name in params - params.prompt_file = argv[i]; - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); - if (!params.prompt.empty() && params.prompt.back() == '\n') { - params.prompt.pop_back(); - } + params.n_threads_batch_draft = std::stoi(argv[i]); + if (params.n_threads_batch_draft <= 0) { + params.n_threads_batch_draft = std::thread::hardware_concurrency(); } - if (arg == "-n" || arg == "--n-predict") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_predict = std::stoi(argv[i]); + return true; + } + if (arg == "-p" || arg == "--prompt") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--top-k") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.top_k = std::stoi(argv[i]); + params.prompt = argv[i]; + return true; + } + if (arg == "-e" || arg == "--escape") { + params.escape = true; + return true; + } + if (arg == "--prompt-cache") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-c" || arg == "--ctx-size") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ctx = std::stoi(argv[i]); + params.path_prompt_cache = argv[i]; + return true; + } + if (arg == "--prompt-cache-all") { + params.prompt_cache_all = true; + return true; + } + if (arg == "--prompt-cache-ro") { + params.prompt_cache_ro = true; + return true; + } + if (arg == "-bf" || arg == "--binary-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + std::ifstream file(argv[i], std::ios::binary); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + // store the external file name in params + params.prompt_file = argv[i]; + std::ostringstream ss; + ss << file.rdbuf(); + params.prompt = ss.str(); + fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]); + return true; + } + if (arg == "-f" || arg == "--file") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--grp-attn-n" || arg == "-gan") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_n = std::stoi(argv[i]); + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; } - if (arg == "--grp-attn-w" || arg == "-gaw") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_w = std::stoi(argv[i]); + // store the external file name in params + params.prompt_file = argv[i]; + std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); + if (!params.prompt.empty() && params.prompt.back() == '\n') { + params.prompt.pop_back(); } - if (arg == "--rope-freq-base") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_base = std::stof(argv[i]); + return true; + } + if (arg == "-n" || arg == "--n-predict") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--rope-freq-scale") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = std::stof(argv[i]); + params.n_predict = std::stoi(argv[i]); + return true; + } + if (arg == "--top-k") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--rope-scaling") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } - else { invalid_param = true; break; } - } - if (arg == "--rope-scale") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = 1.0f/std::stof(argv[i]); + sparams.top_k = std::stoi(argv[i]); + return true; + } + if (arg == "-c" || arg == "--ctx-size") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--yarn-orig-ctx") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_orig_ctx = std::stoi(argv[i]); + params.n_ctx = std::stoi(argv[i]); + return true; + } + if (arg == "--grp-attn-n" || arg == "-gan") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--yarn-ext-factor") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_ext_factor = std::stof(argv[i]); + params.grp_attn_n = std::stoi(argv[i]); + return true; + } + if (arg == "--grp-attn-w" || arg == "-gaw") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--yarn-attn-factor") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_attn_factor = std::stof(argv[i]); + params.grp_attn_w = std::stoi(argv[i]); + return true; + } + if (arg == "--rope-freq-base") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--yarn-beta-fast") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_fast = std::stof(argv[i]); + params.rope_freq_base = std::stof(argv[i]); + return true; + } + if (arg == "--rope-freq-scale") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--yarn-beta-slow") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_slow = std::stof(argv[i]); + params.rope_freq_scale = std::stof(argv[i]); + return true; + } + if (arg == "--rope-scaling") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--pooling") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } - else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } - else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } - else { invalid_param = true; break; } - } - if (arg == "--defrag-thold" || arg == "-dt") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.defrag_thold = std::stof(argv[i]); + std::string value(argv[i]); + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } + else { invalid_param = true; } + return true; + } + if (arg == "--rope-scale") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--samplers") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); + params.rope_freq_scale = 1.0f / std::stof(argv[i]); + return true; + } + if (arg == "--yarn-orig-ctx") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--sampling-seq") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.samplers_sequence = sampler_types_from_chars(argv[i]); + params.yarn_orig_ctx = std::stoi(argv[i]); + return true; + } + if (arg == "--yarn-ext-factor") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--top-p") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.top_p = std::stof(argv[i]); + params.yarn_ext_factor = std::stof(argv[i]); + return true; + } + if (arg == "--yarn-attn-factor") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--min-p") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.min_p = std::stof(argv[i]); + params.yarn_attn_factor = std::stof(argv[i]); + return true; + } + if (arg == "--yarn-beta-fast") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--temp") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.temp = std::stof(argv[i]); - sparams.temp = std::max(sparams.temp, 0.0f); + params.yarn_beta_fast = std::stof(argv[i]); + return true; + } + if (arg == "--yarn-beta-slow") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--tfs") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.tfs_z = std::stof(argv[i]); + params.yarn_beta_slow = std::stof(argv[i]); + return true; + } + if (arg == "--pooling") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--typical") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.typical_p = std::stof(argv[i]); + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; } + return true; + } + if (arg == "--defrag-thold" || arg == "-dt") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--repeat-last-n") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_last_n = std::stoi(argv[i]); - sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n); + params.defrag_thold = std::stof(argv[i]); + return true; + } + if (arg == "--samplers") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--repeat-penalty") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_repeat = std::stof(argv[i]); + const auto sampler_names = string_split(argv[i], ';'); + sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); + return true; + } + if (arg == "--sampling-seq") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--frequency-penalty") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_freq = std::stof(argv[i]); + sparams.samplers_sequence = sampler_types_from_chars(argv[i]); + return true; + } + if (arg == "--top-p") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--presence-penalty") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_present = std::stof(argv[i]); + sparams.top_p = std::stof(argv[i]); + return true; + } + if (arg == "--min-p") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--dynatemp-range") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.dynatemp_range = std::stof(argv[i]); + sparams.min_p = std::stof(argv[i]); + return true; + } + if (arg == "--temp") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--dynatemp-exp") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.dynatemp_exponent = std::stof(argv[i]); + sparams.temp = std::stof(argv[i]); + sparams.temp = std::max(sparams.temp, 0.0f); + return true; + } + if (arg == "--tfs") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--mirostat") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat = std::stoi(argv[i]); + sparams.tfs_z = std::stof(argv[i]); + return true; + } + if (arg == "--typical") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--mirostat-lr") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat_eta = std::stof(argv[i]); + sparams.typical_p = std::stof(argv[i]); + return true; + } + if (arg == "--repeat-last-n") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--mirostat-ent") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat_tau = std::stof(argv[i]); + sparams.penalty_last_n = std::stoi(argv[i]); + sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n); + return true; + } + if (arg == "--repeat-penalty") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--cfg-negative-prompt") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.cfg_negative_prompt = argv[i]; + sparams.penalty_repeat = std::stof(argv[i]); + return true; + } + if (arg == "--frequency-penalty") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--cfg-negative-prompt-file") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(sparams.cfg_negative_prompt)); - if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') { - sparams.cfg_negative_prompt.pop_back(); - } + sparams.penalty_freq = std::stof(argv[i]); + return true; + } + if (arg == "--presence-penalty") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--cfg-scale") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.cfg_scale = std::stof(argv[i]); + sparams.penalty_present = std::stof(argv[i]); + return true; + } + if (arg == "--dynatemp-range") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-b" || arg == "--batch-size") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_batch = std::stoi(argv[i]); + sparams.dynatemp_range = std::stof(argv[i]); + return true; + } + if (arg == "--dynatemp-exp") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ub" || arg == "--ubatch-size") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ubatch = std::stoi(argv[i]); + sparams.dynatemp_exponent = std::stof(argv[i]); + return true; + } + if (arg == "--mirostat") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--keep") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_keep = std::stoi(argv[i]); + sparams.mirostat = std::stoi(argv[i]); + return true; + } + if (arg == "--mirostat-lr") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--draft") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_draft = std::stoi(argv[i]); + sparams.mirostat_eta = std::stof(argv[i]); + return true; + } + if (arg == "--mirostat-ent") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--chunks") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_chunks = std::stoi(argv[i]); + sparams.mirostat_tau = std::stof(argv[i]); + return true; + } + if (arg == "--cfg-negative-prompt") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-np" || arg == "--parallel") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_parallel = std::stoi(argv[i]); + sparams.cfg_negative_prompt = argv[i]; + return true; + } + if (arg == "--cfg-negative-prompt-file") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ns" || arg == "--sequences") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_sequences = std::stoi(argv[i]); + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; } - if (arg == "--p-split" || arg == "-ps") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_split = std::stof(argv[i]); + std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(sparams.cfg_negative_prompt)); + if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') { + sparams.cfg_negative_prompt.pop_back(); } - if (arg == "-m" || arg == "--model") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.model = argv[i]; + return true; + } + if (arg == "--cfg-scale") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-mu" || arg == "--model-url") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_url = argv[i]; + sparams.cfg_scale = std::stof(argv[i]); + return true; + } + if (arg == "-b" || arg == "--batch-size") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-md" || arg == "--model-draft") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_draft = argv[i]; + params.n_batch = std::stoi(argv[i]); + return true; + } + if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-a" || arg == "--alias") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_alias = argv[i]; + params.n_ubatch = std::stoi(argv[i]); + return true; + } + if (arg == "--keep") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--lora") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(argv[i], 1.0f); - params.use_mmap = false; + params.n_keep = std::stoi(argv[i]); + return true; + } + if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--lora-scaled") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - const char * lora_adapter = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); - params.use_mmap = false; + params.n_draft = std::stoi(argv[i]); + return true; + } + if (arg == "--chunks") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--lora-base") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_base = argv[i]; + params.n_chunks = std::stoi(argv[i]); + return true; + } + if (arg == "-np" || arg == "--parallel") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--control-vector") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.control_vectors.push_back({ 1.0f, argv[i], }); + params.n_parallel = std::stoi(argv[i]); + return true; + } + if (arg == "-ns" || arg == "--sequences") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--control-vector-scaled") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - const char * fname = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - params.control_vectors.push_back({ std::stof(argv[i]), fname, }); + params.n_sequences = std::stoi(argv[i]); + return true; + } + if (arg == "--p-split" || arg == "-ps") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--control-vector-layer-range") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.control_vector_layer_start = std::stoi(argv[i]); - if (++i >= argc) { - invalid_param = true; - break; - } - params.control_vector_layer_end = std::stoi(argv[i]); + params.p_split = std::stof(argv[i]); + return true; + } + if (arg == "-m" || arg == "--model") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--mmproj") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.mmproj = argv[i]; + params.model = argv[i]; + return true; + } + if (arg == "-mu" || arg == "--model-url") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--image") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.image = argv[i]; + params.model_url = argv[i]; + return true; + } + if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-i" || arg == "--interactive") { - arg_found = true; - params.interactive = true; + params.model_draft = argv[i]; + return true; + } + if (arg == "-a" || arg == "--alias") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--embedding") { - arg_found = true; - params.embedding = true; + params.model_alias = argv[i]; + return true; + } + if (arg == "--lora") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--interactive-first") { - arg_found = true; - params.interactive_first = true; + params.lora_adapter.emplace_back(argv[i], 1.0f); + params.use_mmap = false; + return true; + } + if (arg == "--lora-scaled") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ins" || arg == "--instruct") { - arg_found = true; - params.instruct = true; + const char* lora_adapter = argv[i]; + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-cml" || arg == "--chatml") { - arg_found = true; - params.chatml = true; + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); + params.use_mmap = false; + return true; + } + if (arg == "--lora-base") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--infill") { - arg_found = true; - params.infill = true; + params.lora_base = argv[i]; + return true; + } + if (arg == "--control-vector") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-dkvc" || arg == "--dump-kv-cache") { - arg_found = true; - params.dump_kv_cache = true; + params.control_vectors.push_back({ 1.0f, argv[i], }); + return true; + } + if (arg == "--control-vector-scaled") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-nkvo" || arg == "--no-kv-offload") { - arg_found = true; - params.no_kv_offload = true; + const char* fname = argv[i]; + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ctk" || arg == "--cache-type-k") { - arg_found = true; - params.cache_type_k = argv[++i]; + params.control_vectors.push_back({ std::stof(argv[i]), fname, }); + return true; + } + if (arg == "--control-vector-layer-range") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ctv" || arg == "--cache-type-v") { - arg_found = true; - params.cache_type_v = argv[++i]; + params.control_vector_layer_start = std::stoi(argv[i]); + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--multiline-input") { - arg_found = true; - params.multiline_input = true; + params.control_vector_layer_end = std::stoi(argv[i]); + return true; + } + if (arg == "--mmproj") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--simple-io") { - arg_found = true; - params.simple_io = true; + params.mmproj = argv[i]; + return true; + } + if (arg == "--image") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-cb" || arg == "--cont-batching") { - arg_found = true; - params.cont_batching = true; + params.image = argv[i]; + return true; + } + if (arg == "-i" || arg == "--interactive") { + params.interactive = true; + return true; + } + if (arg == "--embedding") { + params.embedding = true; + return true; + } + if (arg == "--interactive-first") { + params.interactive_first = true; + return true; + } + if (arg == "-ins" || arg == "--instruct") { + params.instruct = true; + return true; + } + if (arg == "-cml" || arg == "--chatml") { + params.chatml = true; + return true; + } + if (arg == "--infill") { + params.infill = true; + return true; + } + if (arg == "-dkvc" || arg == "--dump-kv-cache") { + params.dump_kv_cache = true; + return true; + } + if (arg == "-nkvo" || arg == "--no-kv-offload") { + params.no_kv_offload = true; + return true; + } + if (arg == "-ctk" || arg == "--cache-type-k") { + params.cache_type_k = argv[++i]; + return true; + } + if (arg == "-ctv" || arg == "--cache-type-v") { + params.cache_type_v = argv[++i]; + return true; + } + if (arg == "--multiline-input") { + params.multiline_input = true; + return true; + } + if (arg == "--simple-io") { + params.simple_io = true; + return true; + } + if (arg == "-cb" || arg == "--cont-batching") { + params.cont_batching = true; + return true; + } + if (arg == "--color") { + params.use_color = true; + return true; + } + if (arg == "--mlock") { + params.use_mlock = true; + return true; + } + if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--color") { - arg_found = true; - params.use_color = true; + params.n_gpu_layers = std::stoi(argv[i]); + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } - if (arg == "--mlock") { - arg_found = true; - params.use_mlock = true; + return true; + } + if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_gpu_layers = std::stoi(argv[i]); - if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } + params.n_gpu_layers_draft = std::stoi(argv[i]); + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } - if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_gpu_layers_draft = std::stoi(argv[i]); - if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } + return true; + } + if (arg == "--main-gpu" || arg == "-mg") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--main-gpu" || arg == "-mg") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.main_gpu = std::stoi(argv[i]); + params.main_gpu = std::stoi(argv[i]); #ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL + return true; + } + if (arg == "--split-mode" || arg == "-sm") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--split-mode" || arg == "-sm") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_MODE_NONE; - } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } else if (arg_next == "row") { + std::string arg_next = argv[i]; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_MODE_NONE; + } + else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_MODE_LAYER; + } + else if (arg_next == "row") { #ifdef GGML_USE_SYCL - fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); - exit(1); + fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); + exit(1); #endif // GGML_USE_SYCL - params.split_mode = LLAMA_SPLIT_MODE_ROW; - } else { - invalid_param = true; - break; - } + params.split_mode = LLAMA_SPLIT_MODE_ROW; + } + else { + invalid_param = true; + return true; + } #ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL - + return true; + } + if (arg == "--tensor-split" || arg == "-ts") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--tensor-split" || arg == "-ts") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; + std::string arg_next = argv[i]; - // split string by , and / - const std::regex regex{R"([,/]+)"}; - std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; - std::vector split_arg{it, {}}; - if (split_arg.size() >= llama_max_devices()) { - invalid_param = true; - break; + // split string by , and / + const std::regex regex{ R"([,/]+)" }; + std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; + std::vector split_arg{ it, {} }; + if (split_arg.size() >= llama_max_devices()) { + invalid_param = true; + return true; + } + for (size_t i = 0; i < llama_max_devices(); ++i) { + if (i < split_arg.size()) { + params.tensor_split[i] = std::stof(split_arg[i]); } - for (size_t i = 0; i < llama_max_devices(); ++i) { - if (i < split_arg.size()) { - params.tensor_split[i] = std::stof(split_arg[i]); - } else { - params.tensor_split[i] = 0.0f; - } + else { + params.tensor_split[i] = 0.0f; } + } #ifndef GGML_USE_CUBLAS_SYCL_VULKAN - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL + return true; + } + if (arg == "--no-mmap") { + params.use_mmap = false; + return true; + } + if (arg == "--numa") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--no-mmap") { - arg_found = true; - params.use_mmap = false; + std::string value(argv[i]); + /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { invalid_param = true; } + return true; + } + if (arg == "--verbose-prompt") { + params.verbose_prompt = true; + return true; + } + if (arg == "--no-display-prompt") { + params.display_prompt = false; + return true; + } + if (arg == "-r" || arg == "--reverse-prompt") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--numa") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } - else { invalid_param = true; break; } - } - if (arg == "--verbose-prompt") { - arg_found = true; - params.verbose_prompt = true; - } - if (arg == "--no-display-prompt") { - arg_found = true; - params.display_prompt = false; - } - if (arg == "-r" || arg == "--reverse-prompt") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.antiprompt.emplace_back(argv[i]); + params.antiprompt.emplace_back(argv[i]); + return true; + } + if (arg == "-ld" || arg == "--logdir") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ld" || arg == "--logdir") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.logdir = argv[i]; + params.logdir = argv[i]; - if (params.logdir.back() != DIRECTORY_SEPARATOR) { - params.logdir += DIRECTORY_SEPARATOR; - } + if (params.logdir.back() != DIRECTORY_SEPARATOR) { + params.logdir += DIRECTORY_SEPARATOR; } - if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.logits_file = argv[i]; + return true; + } + if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--perplexity" || arg == "--all-logits") { - arg_found = true; - params.logits_all = true; + params.logits_file = argv[i]; + return true; + } + if (arg == "--perplexity" || arg == "--all-logits") { + params.logits_all = true; + return true; + } + if (arg == "--ppl-stride") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--ppl-stride") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.ppl_stride = std::stoi(argv[i]); + params.ppl_stride = std::stoi(argv[i]); + return true; + } + if (arg == "-ptc" || arg == "--print-token-count") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-ptc" || arg == "--print-token-count") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_print = std::stoi(argv[i]); + params.n_print = std::stoi(argv[i]); + return true; + } + if (arg == "--ppl-output-type") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--ppl-output-type") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.ppl_output_type = std::stoi(argv[i]); + params.ppl_output_type = std::stoi(argv[i]); + return true; + } + if (arg == "--hellaswag") { + params.hellaswag = true; + return true; + } + if (arg == "--hellaswag-tasks") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--hellaswag") { - arg_found = true; - params.hellaswag = true; + params.hellaswag_tasks = std::stoi(argv[i]); + return true; + } + if (arg == "--winogrande") { + params.winogrande = true; + return true; + } + if (arg == "--winogrande-tasks") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--hellaswag-tasks") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - params.hellaswag_tasks = std::stoi(argv[i]); + params.winogrande_tasks = std::stoi(argv[i]); + return true; + } + if (arg == "--multiple-choice") { + params.multiple_choice = true; + return true; + } + if (arg == "--multiple-choice-tasks") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--winogrande") { - arg_found = true; - params.winogrande = true; + params.multiple_choice_tasks = std::stoi(argv[i]); + return true; + } + if (arg == "--kl-divergence") { + params.kl_divergence = true; + return true; + } + if (arg == "--ignore-eos") { + params.ignore_eos = true; + return true; + } + if (arg == "--no-penalize-nl") { + sparams.penalize_nl = false; + return true; + } + if (arg == "-l" || arg == "--logit-bias") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--winogrande-tasks") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; + std::stringstream ss(argv[i]); + llama_token key; + char sign; + std::string value_str; + try { + if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { + sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); } - params.winogrande_tasks = std::stoi(argv[i]); - } - if (arg == "--multiple-choice") { - arg_found = true; - params.multiple_choice = true; - } - if (arg == "--multiple-choice-tasks") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; + else { + throw std::exception(); } - params.multiple_choice_tasks = std::stoi(argv[i]); } - if (arg == "--kl-divergence") { - arg_found = true; - params.kl_divergence = true; + catch (const std::exception&) { + invalid_param = true; + return true; } - if (arg == "--ignore-eos") { - arg_found = true; - params.ignore_eos = true; + return true; + } + if (arg == "-h" || arg == "--help") { + return false; + } + if (arg == "--version") { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } + if (arg == "--random-prompt") { + params.random_prompt = true; + return true; + } + if (arg == "--in-prefix-bos") { + params.input_prefix_bos = true; + return true; + } + if (arg == "--in-prefix") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--no-penalize-nl") { - arg_found = true; - sparams.penalize_nl = false; + params.input_prefix = argv[i]; + return true; + } + if (arg == "--in-suffix") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-l" || arg == "--logit-bias") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::stringstream ss(argv[i]); - llama_token key; - char sign; - std::string value_str; - try { - if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { - sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); - } else { - throw std::exception(); - } - } catch (const std::exception&) { - invalid_param = true; - break; - } + params.input_suffix = argv[i]; + return true; + } + if (arg == "--grammar") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "-h" || arg == "--help") { - arg_found = true; - return false; + sparams.grammar = argv[i]; + return true; + } + if (arg == "--grammar-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(sparams.grammar) + ); + return true; + } + if (arg == "--override-kv") { + if (++i >= argc) { + invalid_param = true; + return true; } - if (arg == "--version") { - arg_found = true; - fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); - fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); - exit(0); + char* sep = strchr(argv[i], '='); + if (sep == nullptr || sep - argv[i] >= 128) { + fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); + invalid_param = true; + return true; } - if (arg == "--random-prompt") { - arg_found = true; - params.random_prompt = true; + struct llama_model_kv_override kvo; + std::strncpy(kvo.key, argv[i], sep - argv[i]); + kvo.key[sep - argv[i]] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.int_value = std::atol(sep); } - if (arg == "--in-prefix-bos") { - arg_found = true; - params.input_prefix_bos = true; + else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.float_value = std::atof(sep); } - if (arg == "--in-prefix") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; + else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.bool_value = true; } - params.input_prefix = argv[i]; - } - if (arg == "--in-suffix") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; + else if (std::strcmp(sep, "false") == 0) { + kvo.bool_value = false; } - params.input_suffix = argv[i]; - } - if (arg == "--grammar") { - arg_found = true; - if (++i >= argc) { + else { + fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); invalid_param = true; - break; + return true; } - sparams.grammar = argv[i]; } - if (arg == "--grammar-file") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(sparams.grammar) - ); - } - if (arg == "--override-kv") { - arg_found = true; - if (++i >= argc) { - invalid_param = true; - break; - } - char * sep = strchr(argv[i], '='); - if (sep == nullptr || sep - argv[i] >= 128) { - fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - struct llama_model_kv_override kvo; - std::strncpy(kvo.key, argv[i], sep - argv[i]); - kvo.key[sep - argv[i]] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } else { - fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - params.kv_overrides.push_back(kvo); + else { + fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); + invalid_param = true; + return true; + } + params.kv_overrides.push_back(kvo); + return true; + } #ifndef LOG_DISABLE_LOGS - // Parse args for logging parameters - } - if ( log_param_single_parse( argv[i] ) ) { - arg_found = true; - // Do nothing, log_param_single_parse automatically does it's thing - // and returns if a match was found and parsed. - } - if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) { - arg_found = true; - // We have a matching known parameter requiring an argument, - // now we need to check if there is anything after this argv - // and flag invalid_param or parse it. - if (++i >= argc) { - invalid_param = true; - break; - } - if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) { - invalid_param = true; - break; - } - // End of Parse args for logging parameters + // Parse args for logging parameters + if (log_param_single_parse(argv[i])) { + // Do nothing, log_param_single_parse automatically does it's thing + // and returns if a match was found and parsed. + return true; + } + if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) { + // We have a matching known parameter requiring an argument, + // now we need to check if there is anything after this argv + // and flag invalid_param or parse it. + if (++i >= argc) { + invalid_param = true; + return true; + } + if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) { + invalid_param = true; + return true; + } + return true; + } + // End of Parse args for logging parameters #endif // LOG_DISABLE_LOGS + + return false; +} + +bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { + bool invalid_param = false; + std::string arg; + const std::string arg_prefix = "--"; + llama_sampling_params & sparams = params.sparams; + + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); } - if (!arg_found) { + if (!gpt_params_find_arg(argc, argv, params, i, invalid_param)) { throw std::invalid_argument("error: unknown argument: " + arg); } } From 2bf8d0f7c4cc1235755ad06961ca761e458c5e55 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 18 Mar 2024 11:03:04 +0100 Subject: [PATCH 288/401] backend : offload large batches to GPU (#6083) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * backend : offload large batches to GPU * fix hip * code cleanup * fix CUDA split buffers * Update ggml-backend-impl.h Co-authored-by: Johannes Gäßler * cuda : fix memset without set_device * imatrix : remove sched affix from weight names * sched : add a new split if the current one has too many inputs reduce max inputs per split more cleanup * update backends ggml-ci --------- Co-authored-by: Johannes Gäßler --- examples/imatrix/imatrix.cpp | 32 ++- examples/llama-bench/llama-bench.cpp | 4 +- ggml-alloc.c | 10 +- ggml-backend-impl.h | 5 + ggml-backend.c | 278 ++++++++++++++----------- ggml-backend.h | 8 +- ggml-cuda.cu | 297 +++++++++------------------ ggml-cuda.h | 21 +- ggml-kompute.cpp | 1 + ggml-metal.m | 1 + ggml-sycl.cpp | 1 + ggml-vulkan.cpp | 1 + ggml.c | 19 +- llama.cpp | 67 +++--- 14 files changed, 349 insertions(+), 396 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index f21bc48f3b466..ea79b9062ddad 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -56,13 +56,31 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const struct ggml_tensor * src0 = t->src[0]; const struct ggml_tensor * src1 = t->src[1]; + std::string wname; + { + // remove any prefix and suffixes from the name + // CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight + const char * p = strchr(src0->name, '#'); + if (p != NULL) { + p = p + 1; + const char * q = strchr(p, '#'); + if (q != NULL) { + wname = std::string(p, q - p); + } else { + wname = p; + } + } else { + wname = src0->name; + } + } + // when ask is true, the scheduler wants to know if we are interested in data from this tensor // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection if (ask) { if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications if (t->op != GGML_OP_MUL_MAT) return false; if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false; - if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false; + if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false; return true; } @@ -94,12 +112,12 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * // this is necessary to guarantee equal number of "ncall" for each tensor for (int ex = 0; ex < n_as; ++ex) { src0 = t->src[2 + ex]; - auto& e = m_stats[src0->name]; + auto& e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); } else if (e.values.size() != (size_t)src1->ne[0]) { - fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]); + fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); exit(1); //GGML_ASSERT(false); } // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger @@ -107,7 +125,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * //if (idx == t->src[0]->ne[0] - 1) ++e.ncall; ++e.ncall; if (m_params.verbosity > 1) { - printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); + printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); } for (int row = 0; row < (int)src1->ne[1]; ++row) { const int excur = m_ids[row*n_as + idx]; @@ -129,17 +147,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * } } } else { - auto& e = m_stats[src0->name]; + auto& e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); } else if (e.values.size() != (size_t)src1->ne[0]) { - fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]); + fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); exit(1); //GGML_ASSERT(false); } ++e.ncall; if (m_params.verbosity > 1) { - printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); + printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); } for (int row = 0; row < (int)src1->ne[1]; ++row) { const float * x = data + row * src1->ne[0]; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 32eea786919f6..4cb2308044732 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -114,10 +114,10 @@ static std::string get_cpu_info() { static std::string get_gpu_info() { std::string id; #ifdef GGML_USE_CUBLAS - int count = ggml_cuda_get_device_count(); + int count = ggml_backend_cuda_get_device_count(); for (int i = 0; i < count; i++) { char buf[128]; - ggml_cuda_get_device_description(i, buf, sizeof(buf)); + ggml_backend_cuda_get_device_description(i, buf, sizeof(buf)); id += buf; if (i < count - 1) { id += "/"; diff --git a/ggml-alloc.c b/ggml-alloc.c index 8ac1d3e51470c..643b2e55f2d2a 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -548,7 +548,11 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - if (ggml_is_view(node)) { + // TODO: better way to add external dependencies + // GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to + // control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node + // itself is never used and should not be considered a dependency + if (ggml_is_view(node) && node->op != GGML_OP_NONE) { struct ggml_tensor * view_src = node->view_src; ggml_gallocr_hash_get(galloc, view_src)->n_views += 1; } @@ -565,8 +569,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr ggml_gallocr_hash_get(galloc, src)->n_children += 1; - // allocate explicit inputs and leafs - if (src->flags & GGML_TENSOR_FLAG_INPUT || src->op == GGML_OP_NONE) { + // allocate explicit inputs + if (src->flags & GGML_TENSOR_FLAG_INPUT) { ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i)); } } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index e475e20e5f46a..f121e1de420fa 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -103,6 +103,11 @@ extern "C" { // check if the backend supports an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer + // these should be expensive operations with large batch sizes that may benefit from running on this backend + // even if the weight has to be copied from the CPU temporarily + bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op); + // (optional) event synchronization ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend); void (*GGML_CALL event_free) (ggml_backend_event_t event); diff --git a/ggml-backend.c b/ggml-backend.c index 31f8d5a6dd30b..9f0084df78942 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -278,7 +278,7 @@ enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_ return err; } -bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -286,6 +286,13 @@ bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * return backend->iface.supports_op(backend, op); } +bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) { + if (backend->iface.offload_op != NULL) { + return backend->iface.offload_op(backend, op); + } + return false; +} + // backend copy static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { @@ -761,6 +768,10 @@ GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(gg if (cpu_plan->cplan.work_size > 0) { cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size); + if (cpu_plan->cplan.work_data == NULL) { + free(cpu_plan); + return NULL; + } } cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; @@ -834,6 +845,7 @@ static struct ggml_backend_i cpu_backend_i = { /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, /* .graph_compute = */ ggml_backend_cpu_graph_compute, /* .supports_op = */ ggml_backend_cpu_supports_op, + /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, @@ -999,11 +1011,11 @@ static bool ggml_is_view_op(enum ggml_op op) { #endif #ifndef GGML_SCHED_MAX_SPLITS -#define GGML_SCHED_MAX_SPLITS 256 +#define GGML_SCHED_MAX_SPLITS 2048 #endif #ifndef GGML_SCHED_MAX_SPLIT_INPUTS -#define GGML_SCHED_MAX_SPLIT_INPUTS 16 +#define GGML_SCHED_MAX_SPLIT_INPUTS 4 #endif #ifndef GGML_SCHED_MAX_COPIES @@ -1043,8 +1055,9 @@ struct ggml_backend_sched { struct ggml_cgraph * graph; // graph splits - struct ggml_backend_sched_split splits[GGML_SCHED_MAX_SPLITS]; + struct ggml_backend_sched_split * splits; int n_splits; + int splits_capacity; // pipeline parallelism support int n_copies; @@ -1114,40 +1127,48 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st // TODO: use supports_op to check if the backend supports the op // assign pre-allocated nodes to their backend - // dst - int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor); - if (cur_backend != -1) { + int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor); + if (cur_backend_id != -1) { SET_CAUSE(tensor, "1.dst"); - return cur_backend; + return cur_backend_id; } // view_src if (tensor->view_src != NULL) { - cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src); - if (cur_backend != -1) { + cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src); + if (cur_backend_id != -1) { SET_CAUSE(tensor, "1.vsrc"); - return cur_backend; + return cur_backend_id; } } - // input + // graph input if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { - cur_backend = sched->n_backends - 1; // last backend (assumed CPU) + cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU) SET_CAUSE(tensor, "1.inp"); - return cur_backend; + return cur_backend_id; } // assign nodes that use weights to the backend of the weights + // operations with weights are preferably run on the same backend as the weights for (int i = 0; i < GGML_MAX_SRC; i++) { const struct ggml_tensor * src = tensor->src[i]; if (src == NULL) { continue; } if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - int src_backend = ggml_backend_sched_backend_from_buffer(sched, src); - // operations with weights are always run on the same backend as the weights + int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src); + // check if a backend with higher prio wants to offload the op + if (src_backend_id == sched->n_backends - 1) { + for (int b = 0; b < src_backend_id; b++) { + if (ggml_backend_offload_op(sched->backends[b], tensor)) { + SET_CAUSE(tensor, "1.off"); + return b; + } + } + } SET_CAUSE(tensor, "1.wgt%d", i); - return src_backend; + return src_backend_id; } } @@ -1227,28 +1248,31 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg // pass 1: assign backends to ops with pre-allocated inputs for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; - if (tensor_backend_id(leaf) != -1) { + int * leaf_backend_id = &tensor_backend_id(leaf); + if (*leaf_backend_id != -1) { // do not overwrite user assignments continue; } - tensor_backend_id(leaf) = ggml_backend_sched_backend_id_from_cur(sched, leaf); + *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf); } for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - if (tensor_backend_id(node) != -1) { + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { // do not overwrite user assignments continue; } - tensor_backend_id(node) = ggml_backend_sched_backend_id_from_cur(sched, node); + *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node); // src for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { continue; } - if (tensor_backend_id(src) == -1) { - tensor_backend_id(src) = ggml_backend_sched_backend_id_from_cur(sched, src); + int * src_backend_id = &tensor_backend_id(src); + if (*src_backend_id == -1) { + *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src); } } } @@ -1270,21 +1294,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (ggml_is_view_op(node->op)) { continue; } - int tensor_backend_id = tensor_backend_id(node); - if (tensor_backend_id != -1) { - if (tensor_backend_id == sched->n_backends - 1) { + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + if (*node_backend_id == sched->n_backends - 1) { // skip cpu (lowest prio backend) cur_backend_id = -1; } else { - cur_backend_id = tensor_backend_id; + cur_backend_id = *node_backend_id; } } else { - tensor_backend_id(node) = cur_backend_id; + *node_backend_id = cur_backend_id; SET_CAUSE(node, "2.2"); } } } - // pass 2.1 expand gpu up { int cur_backend_id = -1; @@ -1293,22 +1316,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (ggml_is_view_op(node->op)) { continue; } - int tensor_backend_id = tensor_backend_id(node); - if (tensor_backend_id != -1) { - if (tensor_backend_id == sched->n_backends - 1) { + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + if (*node_backend_id == sched->n_backends - 1) { // skip cpu (lowest prio backend) cur_backend_id = -1; } else { - cur_backend_id = tensor_backend_id; + cur_backend_id = *node_backend_id; } } else { - tensor_backend_id(node) = cur_backend_id; + *node_backend_id = cur_backend_id; SET_CAUSE(node, "2.1"); } } } - - // pass 2.4 expand rest down { int cur_backend_id = -1; @@ -1317,16 +1338,16 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (ggml_is_view_op(node->op)) { continue; } - int tensor_backend_id = tensor_backend_id(node); - if (tensor_backend_id != -1) { - cur_backend_id = tensor_backend_id; + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + cur_backend_id = *node_backend_id; } else { - tensor_backend_id(node) = cur_backend_id; + *node_backend_id = cur_backend_id; SET_CAUSE(node, "2.4"); } } } - // pass 2.3 expand rest up + // pass 2.3 expand rest up { int cur_backend_id = -1; for (int i = graph->n_nodes - 1; i >= 0; i--) { @@ -1334,11 +1355,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (ggml_is_view_op(node->op)) { continue; } - int tensor_backend_id = tensor_backend_id(node); - if (tensor_backend_id != -1) { - cur_backend_id = tensor_backend_id; + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + cur_backend_id = *node_backend_id; } else { - tensor_backend_id(node) = cur_backend_id; + *node_backend_id = cur_backend_id; SET_CAUSE(node, "2.3"); } } @@ -1351,9 +1372,9 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg // pass 3: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - int cur_backend_id = tensor_backend_id(node); - if (node->view_src != NULL && cur_backend_id == -1) { - cur_backend_id = tensor_backend_id(node) = tensor_backend_id(node->view_src); + int * cur_backend_id = &tensor_backend_id(node); + if (node->view_src != NULL && *cur_backend_id == -1) { + *cur_backend_id = tensor_backend_id(node->view_src); SET_CAUSE(node, "3.vsrc"); } for (int j = 0; j < GGML_MAX_SRC; j++) { @@ -1361,14 +1382,14 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (src == NULL) { continue; } - int src_backend_id = tensor_backend_id(src); - if (src_backend_id == -1) { + int * src_backend_id = &tensor_backend_id(src); + if (*src_backend_id == -1) { if (src->view_src != NULL) { // views are always on the same backend as the source - tensor_backend_id(src) = tensor_backend_id(src->view_src); + *src_backend_id = tensor_backend_id(src->view_src); SET_CAUSE(src, "3.vsrc"); } else { - tensor_backend_id(src) = cur_backend_id; + *src_backend_id = *cur_backend_id; SET_CAUSE(src, "3.cur"); } } @@ -1380,19 +1401,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg // pass 4: split graph, find tensors that need to be copied { - int cur_split = 0; + int i_split = 0; + struct ggml_backend_sched_split * split = &sched->splits[0]; // find the backend of the first split, skipping view ops for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (!ggml_is_view_op(node->op)) { - sched->splits[0].backend_id = tensor_backend_id(node); + split->backend_id = tensor_backend_id(node); break; } } - sched->splits[0].i_start = 0; - sched->splits[0].n_inputs = 0; - memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK - int cur_backend_id = sched->splits[0].backend_id; + split->i_start = 0; + split->n_inputs = 0; + memset(split->inputs, 0, sizeof(split->inputs)); //HACK + int cur_backend_id = split->backend_id; for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1400,18 +1422,54 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg continue; } - int tensor_backend_id = tensor_backend_id(node); + const int node_backend_id = tensor_backend_id(node); - GGML_ASSERT(tensor_backend_id != -1); // all nodes should be assigned by now + GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now - if (tensor_backend_id != cur_backend_id) { - sched->splits[cur_split].i_end = i; - cur_split++; - GGML_ASSERT(cur_split < GGML_SCHED_MAX_SPLITS); - sched->splits[cur_split].backend_id = tensor_backend_id; - sched->splits[cur_split].i_start = i; - sched->splits[cur_split].n_inputs = 0; - cur_backend_id = tensor_backend_id; + // check if we should start a new split based on the sources of the current node + bool need_new_split = false; + if (node_backend_id == cur_backend_id && split->n_inputs > 0) { + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + // check if a weight is on a different backend + // by starting a new split, the memory of the previously offloaded weights can be reused + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + int src_backend_id = tensor_backend_id(src); + if (src_backend_id != -1 && src_backend_id != cur_backend_id) { + need_new_split = true; + break; + } + } + // check if the split has too many inputs + if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) { + const size_t id = hash_id(src); + int src_backend_id = sched->tensor_backend_id[id]; + if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) { + //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name); + need_new_split = true; + break; + } + } + } + } + + if (node_backend_id != cur_backend_id || need_new_split) { + split->i_end = i; + i_split++; + if (i_split >= sched->splits_capacity) { + sched->splits_capacity *= 2; + sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split)); + GGML_ASSERT(sched->splits != NULL); + } + GGML_ASSERT(i_split < GGML_SCHED_MAX_SPLITS); + split = &sched->splits[i_split]; + split->backend_id = node_backend_id; + split->i_start = i; + split->n_inputs = 0; + cur_backend_id = node_backend_id; } // find inputs that are not on the same backend @@ -1421,10 +1479,10 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg continue; } - int src_backend_id = tensor_backend_id(src); + const int src_backend_id = tensor_backend_id(src); assert(src_backend_id != -1); // all inputs should be assigned by now - if (src->flags & GGML_TENSOR_FLAG_INPUT) { + if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) { size_t id = hash_id(src); if (sched->tensor_copies[id][src_backend_id][0] == NULL) { ggml_backend_t backend = sched->backends[src_backend_id]; @@ -1441,7 +1499,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor } sched->tensor_copies[id][src_backend_id][c] = tensor_copy; - tensor_backend_id(tensor_copy) = src_backend_id; SET_CAUSE(tensor_copy, "4.cpy"); } int n_graph_inputs = sched->n_graph_inputs++; @@ -1450,9 +1507,9 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } - if (src_backend_id != tensor_backend_id) { + if (src_backend_id != node_backend_id) { // create a copy of the input in the split's backend - size_t id = hash_id(src); + const size_t id = hash_id(src); if (sched->tensor_copies[id][cur_backend_id][0] == NULL) { ggml_backend_t backend = sched->backends[cur_backend_id]; for (int c = 0; c < sched->n_copies; c++) { @@ -1463,76 +1520,42 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor } sched->tensor_copies[id][cur_backend_id][c] = tensor_copy; - tensor_backend_id(tensor_copy) = cur_backend_id; SET_CAUSE(tensor_copy, "4.cpy"); } - int n_inputs = sched->splits[cur_split].n_inputs++; + int n_inputs = split->n_inputs++; GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); - sched->splits[cur_split].inputs[n_inputs] = src; + split->inputs[n_inputs] = src; } node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy]; } } } - sched->splits[cur_split].i_end = graph->n_nodes; - sched->n_splits = cur_split + 1; + split->i_end = graph->n_nodes; + sched->n_splits = i_split + 1; } #ifdef DEBUG_PASS4 fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif -#ifndef NDEBUG - // sanity check: all sources should have the same backend as the node - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); - if (tensor_backend == NULL) { - fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); - } - if (node->view_src != NULL && tensor_backend != ggml_backend_sched_get_tensor_backend(sched, node->view_src)) { - fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n", - node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", - node->view_src->name, ggml_backend_sched_get_tensor_backend(sched, node->view_src) ? - ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, node->view_src)) : "NULL"); - } - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - continue; - } - ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); - if (src_backend != tensor_backend /* && src_backend != NULL */) { - fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", - node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", - j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL"); - } - if (src->view_src != NULL && src_backend != ggml_backend_sched_get_tensor_backend(sched, src->view_src)) { - fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n", - src->name, src_backend ? ggml_backend_name(src_backend) : "NULL", - src->view_src->name, ggml_backend_sched_get_tensor_backend(sched, src->view_src) ? - ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, src->view_src)) : "NULL"); - } - } - } - fflush(stderr); -#endif - // create copies of the graph for each split // TODO: avoid this copy - struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS, false); + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2, false); for (int i = 0; i < sched->n_splits; i++) { struct ggml_backend_sched_split * split = &sched->splits[i]; split->graph = ggml_graph_view(graph, split->i_start, split->i_end); // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split for (int j = 0; j < split->n_inputs; j++) { + assert(graph_copy->size > (graph_copy->n_nodes + 1)); + struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id][sched->cur_copy]; + const size_t input_id = hash_id(input); + struct ggml_tensor * input_cpy = sched->tensor_copies[input_id][split->backend_id][sched->cur_copy]; // add a dependency to the input source so that it is not freed before the copy is done struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input); input_dep->src[0] = input; - sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input); + sched->node_backend_ids[graph_copy->n_nodes] = sched->tensor_backend_id[input_id]; graph_copy->nodes[graph_copy->n_nodes++] = input_dep; // add a dependency to the input copy so that it is allocated at the start of the split @@ -1541,6 +1564,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } for (int j = split->i_start; j < split->i_end; j++) { + assert(graph_copy->size > graph_copy->n_nodes); sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]); graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; } @@ -1625,13 +1649,12 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s } ggml_backend_tensor_copy(input, input_cpy); } else { + // wait for the split backend to finish using the input before overwriting it if (sched->events[split_backend_id][sched->cur_copy] != NULL) { ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]); } else { ggml_backend_synchronize(split_backend); - ggml_backend_synchronize(input_backend); } - ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy); } } @@ -1701,17 +1724,21 @@ ggml_backend_sched_t ggml_backend_sched_new( struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); // initialize hash table - sched->hash_set = ggml_hash_set_new(graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS); + sched->hash_set = ggml_hash_set_new(graph_size); sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size); sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size); - sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), graph_size); - sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), graph_size); + + const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2; + sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), nodes_size); + sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size); sched->n_backends = n_backends; sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; - GGML_ASSERT(sched->n_copies <= GGML_SCHED_MAX_COPIES); + const int initial_splits_capacity = 16; + sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity); + sched->splits_capacity = initial_splits_capacity; for (int b = 0; b < n_backends; b++) { sched->backends[b] = backends[b]; @@ -1742,6 +1769,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { } ggml_gallocr_free(sched->galloc); ggml_free(sched->ctx); + free(sched->splits); free(sched->hash_set.keys); free(sched->tensor_backend_id); free(sched->tensor_copies); @@ -1762,6 +1790,8 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) { } bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { + GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes); + ggml_backend_sched_split_graph(sched, measure_graph); // TODO: extract this to a separate function @@ -1776,7 +1806,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * } bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS); + GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes); ggml_backend_sched_split_graph(sched, graph); diff --git a/ggml-backend.h b/ggml-backend.h index 099d9c258794e..422457ab6fc50 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -70,11 +70,11 @@ extern "C" { GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); - - GGML_API bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); + GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index db595409aa735..139025588ed61 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -82,6 +82,10 @@ #define cudaGetDeviceProperties hipGetDeviceProperties #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError +#define cudaHostRegister hipHostRegister +#define cudaHostRegisterPortable hipHostRegisterPortable +#define cudaHostRegisterReadOnly hipHostRegisterReadOnly +#define cudaHostUnregister hipHostUnregister #define cudaLaunchHostFunc hipLaunchHostFunc #ifdef GGML_HIP_UMA #define cudaMalloc hipMallocManaged @@ -7787,11 +7791,7 @@ struct cuda_pool_alloc { static bool g_cublas_loaded = false; -GGML_CALL bool ggml_cublas_loaded(void) { - return g_cublas_loaded; -} - -GGML_CALL void ggml_init_cublas() { +static void ggml_init_cublas() { static bool initialized = false; if (!initialized) { @@ -7880,7 +7880,7 @@ GGML_CALL void ggml_init_cublas() { } } -GGML_CALL void * ggml_cuda_host_malloc(size_t size) { +static void * ggml_cuda_host_malloc(size_t size) { if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { return nullptr; } @@ -7890,7 +7890,7 @@ GGML_CALL void * ggml_cuda_host_malloc(size_t size) { if (err != cudaSuccess) { // clear the error cudaGetLastError(); - fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", + fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, size/1024.0/1024.0, cudaGetErrorString(err)); return nullptr; } @@ -7898,7 +7898,7 @@ GGML_CALL void * ggml_cuda_host_malloc(size_t size) { return ptr; } -GGML_CALL void ggml_cuda_host_free(void * ptr) { +static void ggml_cuda_host_free(void * ptr) { CUDA_CHECK(cudaFreeHost(ptr)); } @@ -9036,21 +9036,13 @@ static void ggml_cuda_op_soft_max( // positions tensor float * src2_dd = nullptr; - cuda_pool_alloc src2_f; ggml_tensor * src2 = dst->src[2]; const bool use_src2 = src2 != nullptr; if (use_src2) { - const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; - - if (src2_on_device) { - ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; - src2_dd = (float *) src2_extra->data_device[g_main_device]; - } else { - src2_dd = src2_f.alloc(ggml_nelements(src2)); - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); - } + ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; + src2_dd = (float *) src2_extra->data_device[g_main_device]; } soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); @@ -9107,55 +9099,24 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; - // dd = data device float * src0_ddf = nullptr; float * src1_ddf = nullptr; float * dst_ddf = nullptr; - cuda_pool_alloc src0_f; - cuda_pool_alloc src1_f; - cuda_pool_alloc dst_f; - ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; - if (src0_on_device) { - src0_ddf = (float *) src0_extra->data_device[g_main_device]; - } else { - src0_ddf = src0_f.alloc(ggml_nelements(src0)); - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream)); - } + src0_ddf = (float *) src0_extra->data_device[g_main_device]; if (use_src1) { - if (src1_on_device) { - src1_ddf = (float *) src1_extra->data_device[g_main_device]; - } else { - src1_ddf = src1_f.alloc(ggml_nelements(src1)); - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); - } - } - if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device]; - } else { - dst_ddf = dst_f.alloc(ggml_nelements(dst)); + src1_ddf = (float *) src1_extra->data_device[g_main_device]; } + dst_ddf = (float *) dst_extra->data_device[g_main_device]; // do the computation op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); CUDA_CHECK(cudaGetLastError()); - - // copy dst to host if necessary - if (!dst_on_device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); - } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - CUDA_CHECK(cudaDeviceSynchronize()); - } } static void ggml_cuda_set_peer_access(const int n_tokens) { @@ -9251,7 +9212,6 @@ static void ggml_cuda_op_mul_mat( ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; const bool src0_is_contiguous = ggml_is_contiguous(src0); const bool src1_is_contiguous = ggml_is_contiguous(src1); @@ -9322,13 +9282,13 @@ static void ggml_cuda_op_mul_mat( used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; + const bool src1_on_device = id == g_main_device; // TODO: check from buffer + const bool dst_on_device = id == g_main_device; ggml_cuda_set_device(id); cudaStream_t stream = g_cudaStreams[id][0]; - if (src0_on_device && src0_is_contiguous) { + if (src0_is_contiguous) { dev[id].src0_dd = (char *) src0_extra->data_device[id]; } else { dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ggml_nbytes(src0)); @@ -9374,8 +9334,8 @@ static void ggml_cuda_op_mul_mat( continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device; + const bool src1_on_device = id == g_main_device; // TODO: check from buffer + const bool dst_on_device = id == g_main_device; const int64_t row_diff = dev[id].row_high - dev[id].row_low; ggml_cuda_set_device(id); @@ -9400,12 +9360,12 @@ static void ggml_cuda_op_mul_mat( // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device) { + if (id == g_main_device) { dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary - if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { + if (src1_is_contiguous) { if (id != g_main_device) { if (convert_src1_to_q8_1) { char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; @@ -9418,19 +9378,19 @@ static void ggml_cuda_op_mul_mat( src1_ncols*ne10*sizeof(float), stream)); } } - } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) { + } else if (src1_on_device && !src1_is_contiguous) { CUDA_CHECK(ggml_cuda_cpy_tensor_2d( src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); } else { GGML_ASSERT(false); } - if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) { + if (convert_src1_to_q8_1 && !src1_is_contiguous) { quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); CUDA_CHECK(cudaGetLastError()); } - if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { + if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream)); } @@ -9441,17 +9401,7 @@ static void ggml_cuda_op_mul_mat( // copy dst to host or other device if necessary if (!dst_on_device) { - void * dst_off_device; - cudaMemcpyKind kind; - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - dst_off_device = dst->data; - kind = cudaMemcpyDeviceToHost; - } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { - dst_off_device = dst_extra->data_device[g_main_device]; - kind = cudaMemcpyDeviceToDevice; - } else { - GGML_ASSERT(false); - } + void * dst_off_device = dst_extra->data_device[g_main_device]; if (split) { // src0 = weight matrix is saved as a transposed matrix for better memory layout. // dst is NOT transposed. @@ -9462,28 +9412,26 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; #if !defined(GGML_USE_HIPBLAS) - if (kind == cudaMemcpyDeviceToDevice) { - // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices - cudaMemcpy3DPeerParms p = {}; - p.dstDevice = g_main_device; - p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); - p.srcDevice = id; - p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); - p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); - CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); - } else + // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices + cudaMemcpy3DPeerParms p = {}; + p.dstDevice = g_main_device; + p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); + p.srcDevice = id; + p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); + p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); + CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); +#else + // HIP does not support cudaMemcpy3DPeerAsync or vmm pools + CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), + dst_dd_i, row_diff*sizeof(float), + row_diff*sizeof(float), src1_ncols, + cudaMemcpyDeviceToDevice, stream)); #endif - { - CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), - dst_dd_i, row_diff*sizeof(float), - row_diff*sizeof(float), src1_ncols, - kind, stream)); - } } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); dhf_dst_i += src1_col_0*ne0; - CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), kind, stream)); + CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream)); } } @@ -9510,11 +9458,6 @@ static void ggml_cuda_op_mul_mat( } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaDeviceSynchronize()); - } } static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -9599,36 +9542,19 @@ static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, gg static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; - // dd = data device float * src0_ddf = nullptr; float * src1_ddf = nullptr; float * dst_ddf = nullptr; - cuda_pool_alloc dst_f; - ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; - if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device]; - } else { - dst_ddf = dst_f.alloc(ggml_nelements(dst)); - } + dst_ddf = (float *) dst_extra->data_device[g_main_device]; // do the computation ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); CUDA_CHECK(cudaGetLastError()); - - // copy dst to host if necessary - if (!dst_on_device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); - } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - CUDA_CHECK(cudaDeviceSynchronize()); - } } static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -9639,21 +9565,6 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } -GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (!g_cublas_loaded) return false; - - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - src1->type == GGML_TYPE_F32 && - dst->type == GGML_TYPE_F32 && - (ne0 >= 32 && ne1 >= 32 && ne10 >= 32); -} - static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); @@ -9891,11 +9802,6 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool all_on_device = - (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && - (src1->backend == GGML_BACKEND_TYPE_GPU) && - ( dst->backend == GGML_BACKEND_TYPE_GPU); - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; @@ -9972,13 +9878,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + } else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch ggml_cuda_mul_mat_batched_cublas(src0, src1, dst); } else if (use_dequantize_mul_mat_vec) { @@ -10178,6 +10084,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s ggml_cuda_mul_mat_id_cublas(dst); // TODO: mmq/mmv support #endif + cudaStream_t stream = g_cudaStreams[g_main_device][0]; const size_t nb11 = src1->nb[1]; const size_t nb1 = dst->nb[1]; @@ -10187,16 +10094,9 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s const int32_t n_as = ((int32_t *) dst->op_params)[1]; std::vector ids_host(ggml_nbytes(ids)); - - cudaStream_t stream = g_cudaStreams[g_main_device][0]; - - if (ids->backend == GGML_BACKEND_TYPE_GPU) { - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; - CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); - CUDA_CHECK(cudaStreamSynchronize(stream)); - } else { - memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); - } + const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; + CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra; const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra; @@ -10213,20 +10113,11 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; - char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? - (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; - char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? - (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; + char * src1_original = (char *) src1_extra->data_device[g_main_device]; + char * dst_original = (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - //int32_t row_id; - //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0])); - //CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0])); - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); GGML_ASSERT(row_id >= 0 && row_id < n_as); @@ -10248,11 +10139,6 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); - const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_TYPE_CPU ? - cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice; - const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_TYPE_CPU ? - cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice; - for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; @@ -10267,7 +10153,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11, - nb11, src1_kind, stream)); + nb11, cudaMemcpyDeviceToDevice, stream)); num_src1_rows++; } @@ -10299,15 +10185,11 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s GGML_ASSERT(row_id >= 0 && row_id < n_as); CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1, - nb1, dst_kind, stream)); + nb1, cudaMemcpyDeviceToDevice, stream)); num_src1_rows++; } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - CUDA_CHECK(cudaStreamSynchronize(stream)); - } } static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -10435,7 +10317,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -GGML_CALL static void ggml_cuda_set_main_device(const int main_device) { +static void ggml_cuda_set_main_device(const int main_device) { if (main_device >= g_device_count) { fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", main_device, g_device_count, g_main_device); @@ -10450,18 +10332,9 @@ GGML_CALL static void ggml_cuda_set_main_device(const int main_device) { } } -GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +static bool ggml_cuda_compute_forward(struct ggml_tensor * tensor) { if (!g_cublas_loaded) return false; - ggml_cuda_func_t func; - const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU - || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) - || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); - - if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { - return false; - } - if (tensor->op == GGML_OP_MUL_MAT) { if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { #ifndef NDEBUG @@ -10471,6 +10344,8 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st } } + ggml_cuda_func_t func; + switch (tensor->op) { case GGML_OP_REPEAT: func = ggml_cuda_repeat; @@ -10548,15 +10423,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st func = ggml_cuda_rms_norm; break; case GGML_OP_MUL_MAT: - if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { - return false; - } func = ggml_cuda_mul_mat; break; case GGML_OP_MUL_MAT_ID: - if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) { - return false; - } func = ggml_cuda_mul_mat_id; break; case GGML_OP_SCALE: @@ -10613,17 +10482,11 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st ggml_cuda_set_peer_access(tensor->src[1]->ne[1]); } - if (params->ith != 0) { - return true; - } - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return true; - } func(tensor->src[0], tensor->src[1], tensor); return true; } -GGML_CALL int ggml_cuda_get_device_count() { +static int ggml_cuda_get_device_count() { int device_count; if (cudaGetDeviceCount(&device_count) != cudaSuccess) { return 0; @@ -10631,7 +10494,7 @@ GGML_CALL int ggml_cuda_get_device_count() { return device_count; } -GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { +static void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); snprintf(description, description_size, "%s", prop.name); @@ -10736,6 +10599,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { + ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size)); } } @@ -10873,6 +10737,8 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { + ggml_init_cublas(); + // FIXME: this is not thread safe if (device >= ggml_backend_cuda_get_device_count()) { return nullptr; @@ -11157,6 +11023,8 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { + ggml_init_cublas(); + // FIXME: this is not thread safe static std::map, struct ggml_backend_buffer_type> buft_map; @@ -11348,9 +11216,6 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t ggml_cuda_set_main_device(cuda_ctx->device); - ggml_compute_params params = {}; - params.type = GGML_TASK_TYPE_COMPUTE; - params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -11372,7 +11237,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } #endif - bool ok = ggml_cuda_compute_forward(¶ms, node); + bool ok = ggml_cuda_compute_forward(node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } @@ -11509,6 +11374,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons UNUSED(backend); } +GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) { + const int min_batch_size = 32; + + return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; + + UNUSED(backend); +} + static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; @@ -11571,6 +11444,7 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, /* .supports_op = */ ggml_backend_cuda_supports_op, + /* .offload_op = */ ggml_backend_cuda_offload_op, /* .event_new = */ ggml_backend_cuda_event_new, /* .event_free = */ ggml_backend_cuda_event_free, /* .event_record = */ ggml_backend_cuda_event_record, @@ -11584,7 +11458,7 @@ static ggml_guid_t ggml_backend_cuda_guid() { } GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { - ggml_init_cublas(); // TODO: remove from ggml.c + ggml_init_cublas(); if (device < 0 || device >= ggml_cuda_get_device_count()) { fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); @@ -11627,6 +11501,31 @@ GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, si CUDA_CHECK(cudaMemGetInfo(free, total)); } +GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) { + if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { + return false; + } + + cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly); + if (err != cudaSuccess) { + // clear the error + cudaGetLastError(); + + fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__, + size/1024.0/1024.0, cudaGetErrorString(err)); + return false; + } + return true; +} + +GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) { + cudaError_t err = cudaHostUnregister(buffer); + if (err != cudaSuccess) { + // clear the error + cudaGetLastError(); + } +} + // backend registry GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); diff --git a/ggml-cuda.h b/ggml-cuda.h index b1ebd61d7fb66..5eb4af40f4d1f 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -17,29 +17,17 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 -// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`. -GGML_API GGML_CALL void ggml_init_cublas(void); - -// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`. -GGML_API GGML_CALL bool ggml_cublas_loaded(void); - -GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size); -GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr); - -GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); - -GGML_API GGML_CALL int ggml_cuda_get_device_count(void); -GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size); - // backend API GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device); GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend); +// device buffer GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); + // split tensor buffer that splits matrices by rows across multiple devices GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); + // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); @@ -47,6 +35,9 @@ GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void); GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); +GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); +GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer); + #ifdef __cplusplus } #endif diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 4caf2c9e78b02..81dd5067864ce 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1951,6 +1951,7 @@ static struct ggml_backend_i kompute_backend_i = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_kompute_graph_compute, /* .supports_op = */ ggml_backend_kompute_supports_op, + /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, diff --git a/ggml-metal.m b/ggml-metal.m index c3451a79b103f..109e5fe6ba13d 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2837,6 +2837,7 @@ GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, con /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, /* .supports_op = */ ggml_backend_metal_supports_op, + /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 6dc5eb20c5a63..d51f23b410595 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -17390,6 +17390,7 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, + /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 698b31496f417..cbceaa19fbacd 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5699,6 +5699,7 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_vk_graph_compute, /* .supports_op = */ ggml_backend_vk_supports_op, + /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, diff --git a/ggml.c b/ggml.c index fa23cb3c44f87..1d5854960a51b 100644 --- a/ggml.c +++ b/ggml.c @@ -282,8 +282,6 @@ inline static void * ggml_calloc(size_t num, size_t size) { #else #include #endif -#elif defined(GGML_USE_CUBLAS) -#include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) #include "ggml-opencl.h" #elif defined(GGML_USE_VULKAN) @@ -2640,9 +2638,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } -#if defined(GGML_USE_CUBLAS) - ggml_init_cublas(); -#elif defined(GGML_USE_CLBLAST) +#if defined(GGML_USE_CLBLAST) ggml_cl_init(); #elif defined(GGML_USE_VULKAN) ggml_vk_init_cpu_assist(); @@ -11105,7 +11101,6 @@ static void ggml_compute_forward_out_prod_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod // TODO: #if defined(GGML_USE_CLBLAST) #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) @@ -11305,7 +11300,6 @@ static void ggml_compute_forward_out_prod_q_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) if (params->type == GGML_TASK_TYPE_INIT) { @@ -16051,14 +16045,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm return; } -#ifdef GGML_USE_CUBLAS - bool skip_cpu = ggml_cuda_compute_forward(params, tensor); - if (skip_cpu) { - return; - } - GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU); - GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU); -#elif defined(GGML_USE_VULKAN) +#if defined(GGML_USE_VULKAN) const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor); #ifdef GGML_VULKAN_CHECK_RESULTS if (skip_cpu) { @@ -16070,7 +16057,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU); -#endif // GGML_USE_CUBLAS +#endif // GGML_USE_VULKAN #ifdef GGML_USE_SYCL bool skip_cpu = ggml_sycl_compute_forward(params, tensor); diff --git a/llama.cpp b/llama.cpp index e4db288ddd907..b8bef6dafe735 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2040,6 +2040,11 @@ struct llama_model { ggml_free(ctx); } for (ggml_backend_buffer_t buf : bufs) { +#ifdef GGML_USE_CUBLAS + if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) { + ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf)); + } +#endif ggml_backend_buffer_free(buf); } } @@ -5033,6 +5038,13 @@ static bool llm_load_tensors( size_t first, last; ml.get_mapping_range(&first, &last, ctx); buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first); +#ifdef GGML_USE_CUBLAS + if (n_layer >= n_gpu_layers) { + ggml_backend_cuda_register_host_buffer( + ggml_backend_buffer_get_base(buf), + ggml_backend_buffer_get_size(buf)); + } +#endif } #ifdef GGML_USE_METAL else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) { @@ -8231,7 +8243,6 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); } struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); @@ -8601,12 +8612,15 @@ static struct ggml_cgraph * llama_build_graph( } // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends - // to fix this, we assign the norm layer manually to the backend of its layer - if (il != -1 && strcmp(name, "norm") == 0) { - for (auto * backend : lctx.backends) { - if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) { - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); - break; + // FIXME: fix in ggml_backend_sched + const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer; + if (batch.n_tokens < 32 || full_offload) { + if (il != -1 && strcmp(name, "norm") == 0) { + for (auto * backend : lctx.backends) { + if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) { + ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); + break; + } } } } @@ -13107,27 +13121,25 @@ struct llama_context * llama_new_context_with_model( ctx->backends.push_back(ctx->backend_metal); } #elif defined(GGML_USE_CUBLAS) - if (model->n_gpu_layers > 0) { + if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); + ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU + for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_cuda_init(device); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu); + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device); llama_free(ctx); return nullptr; } ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU - for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) { - ggml_backend_t backend = ggml_backend_cuda_init(device); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } } } #elif defined(GGML_USE_VULKAN) @@ -13285,14 +13297,17 @@ struct llama_context * llama_new_context_with_model( ggml_backend_t backend = ctx->backends[i]; ggml_backend_buffer_type_t buft = backend_buft[i]; size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend); - LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, - ggml_backend_buft_name(buft), - size / 1024.0 / 1024.0); + if (size > 1) { + LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, + ggml_backend_buft_name(buft), + size / 1024.0 / 1024.0); + } } // note: the number of splits during measure is higher than during inference due to the kv shift int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); - LLAMA_LOG_INFO("%s: graph splits: %d\n", __func__, n_splits); + LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes); + LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits); } } From 4f6d1337ca5a409dc74aca8c479b7c34408a69c0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 18 Mar 2024 13:45:27 +0200 Subject: [PATCH 289/401] ci : temporary disable sanitizer builds (#6128) --- .github/workflows/build.yml | 68 ++++++++++++++++++------------------ .github/workflows/server.yml | 6 ++-- 2 files changed, 37 insertions(+), 37 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 945df42f886a6..992c34a0302cc 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -98,40 +98,40 @@ jobs: cd build ctest -L main --verbose --timeout 900 - ubuntu-latest-cmake-sanitizer: - runs-on: ubuntu-latest - - continue-on-error: true - - strategy: - matrix: - sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug, Release] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v3 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential - - - name: Build - id: cmake_build - run: | - mkdir build - cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 +# ubuntu-latest-cmake-sanitizer: +# runs-on: ubuntu-latest +# +# continue-on-error: true +# +# strategy: +# matrix: +# sanitizer: [ADDRESS, THREAD, UNDEFINED] +# build_type: [Debug, Release] +# +# steps: +# - name: Clone +# id: checkout +# uses: actions/checkout@v3 +# +# - name: Dependencies +# id: depends +# run: | +# sudo apt-get update +# sudo apt-get install build-essential +# +# - name: Build +# id: cmake_build +# run: | +# mkdir build +# cd build +# cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} +# cmake --build . --config ${{ matrix.build_type }} -j $(nproc) +# +# - name: Test +# id: cmake_test +# run: | +# cd build +# ctest -L main --verbose --timeout 900 ubuntu-latest-cmake-mpi: runs-on: ubuntu-latest diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 4ea09115a3c44..65ca7d9ca3ae9 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -24,13 +24,13 @@ jobs: strategy: matrix: - sanitizer: [ADDRESS, THREAD, UNDEFINED] + # TODO: temporary disabled due to linux kernel issues + #sanitizer: [ADDRESS, THREAD, UNDEFINED] + sanitizer: [UNDEFINED] build_type: [Debug] include: - build_type: Release sanitizer: "" - - build_type: Debug - sanitizer: THREAD disabled_on_pr: true fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken From ac9ee6a4ad740bc1ee484ede43e9f92b5af244c1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 18 Mar 2024 13:45:38 +0200 Subject: [PATCH 290/401] ci : disable stale issue messages (#6126) --- .github/workflows/close-issue.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index 2682f308cb46e..eaffd074de0aa 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -15,7 +15,6 @@ jobs: days-before-issue-stale: 30 days-before-issue-close: 14 stale-issue-label: "stale" - stale-issue-message: "This issue is stale because it has been open for 30 days with no activity." close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." days-before-pr-stale: -1 days-before-pr-close: -1 From 5e1b7f94a03e0b3b8e4578625bbdadc7bbd2b93c Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 18 Mar 2024 16:33:44 +0100 Subject: [PATCH 291/401] backend : set max split inputs to GGML_MAX_SRC (#6137) --- ggml-backend.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-backend.c b/ggml-backend.c index 9f0084df78942..6026570ae95aa 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1015,7 +1015,7 @@ static bool ggml_is_view_op(enum ggml_op op) { #endif #ifndef GGML_SCHED_MAX_SPLIT_INPUTS -#define GGML_SCHED_MAX_SPLIT_INPUTS 4 +#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC #endif #ifndef GGML_SCHED_MAX_COPIES From 104f5e0fc156d48476258295457cafeec2a2af10 Mon Sep 17 00:00:00 2001 From: Felix Date: Mon, 18 Mar 2024 16:40:22 +0100 Subject: [PATCH 292/401] clip : fix memory leak (#6138) --- examples/llava/clip.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index a0ed82d7e2328..690bca2eb7732 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -497,7 +497,6 @@ struct clip_ctx { // memory buffers to evaluate the model ggml_backend_buffer_t params_buffer = NULL; - ggml_backend_buffer_t compute_buffer = NULL; ggml_backend_t backend = NULL; ggml_gallocr_t compute_alloc = NULL; @@ -1676,6 +1675,9 @@ void clip_free(clip_ctx * ctx) { ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf); + ggml_backend_buffer_free(ctx->params_buffer); + ggml_backend_free(ctx->backend); + ggml_gallocr_free(ctx->compute_alloc); delete ctx; } From d199ca79f279e84ebe27caafe0aa59c461d88969 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Mon, 18 Mar 2024 12:49:02 -0400 Subject: [PATCH 293/401] mpt : implement backwards compatiblity with duped output tensor (#6139) --- llama.cpp | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/llama.cpp b/llama.cpp index b8bef6dafe735..1a9fe0c4d2cea 100644 --- a/llama.cpp +++ b/llama.cpp @@ -540,6 +540,7 @@ static const std::map> LLM_TENSOR_NA { { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output"}, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, @@ -4300,9 +4301,9 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_OUTPUT, "weight").c_str()) >= 0) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } else { + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + if (!model.output) { model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU ml.n_created--; // artificial tensor ml.size_data += ggml_nbytes(model.output); @@ -4507,10 +4508,12 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false); - // same as tok_embd, duplicated to allow offloading - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + if (!model.output) { + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } } for (int i = 0; i < n_layer; ++i) { From 2d15886bb092c3b780c676b5cc57ff3337af9c83 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 17 Mar 2024 06:37:44 +0000 Subject: [PATCH 294/401] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/9df3e30ce24fd28c7b3e2de0d986769db5d6225d' (2024-03-06) → 'github:NixOS/nixpkgs/d691274a972b3165335d261cc4671335f5c67de9' (2024-03-14) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index f9865d5e464cd..80de76dbf3ed6 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1709703039, - "narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=", + "lastModified": 1710451336, + "narHash": "sha256-pP86Pcfu3BrAvRO7R64x7hs+GaQrjFes+mEPowCfkxY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d", + "rev": "d691274a972b3165335d261cc4671335f5c67de9", "type": "github" }, "original": { From 4c28b8252907561165827125d2d1a4bad6926ac6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Tue, 19 Mar 2024 01:59:36 -0400 Subject: [PATCH 295/401] common : print usage on '-h' and '--help' (#6145) --- common/common.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 919182862cb5c..5f10718ece90c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1056,7 +1056,8 @@ static bool gpt_params_find_arg(int argc, char ** argv, gpt_params & params, int return true; } if (arg == "-h" || arg == "--help") { - return false; + gpt_print_usage(argc, argv, gpt_params()); + exit(0); } if (arg == "--version") { fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); From 970a48060ab9a6cc67aa063870323781c2a7bd7d Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 19 Mar 2024 09:06:54 +0100 Subject: [PATCH 296/401] ci : exempt some labels from being tagged as stale (#6140) --- .github/workflows/close-issue.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index eaffd074de0aa..a151c6780aaa5 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -12,6 +12,7 @@ jobs: steps: - uses: actions/stale@v5 with: + exempt-issue-labels: "refactor,help wanted,good first issue,research" days-before-issue-stale: 30 days-before-issue-close: 14 stale-issue-label: "stale" From b80cf3b2d1dee0ad325f7a794fecc66befce7336 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 19 Mar 2024 10:21:54 +0200 Subject: [PATCH 297/401] common : disable repeat penalties by default (#6127) --- common/sampling.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/common/sampling.h b/common/sampling.h index 48b2459d1f944..79a998be8e408 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -32,13 +32,13 @@ typedef struct llama_sampling_params { float dynatemp_range = 0.00f; // 0.0 = disabled float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float penalty_repeat = 1.10f; // 1.0 = disabled + float penalty_repeat = 1.00f; // 1.0 = disabled float penalty_freq = 0.00f; // 0.0 = disabled float penalty_present = 0.00f; // 0.0 = disabled int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = true; // consider newlines as a repeatable token + bool penalize_nl = false; // consider newlines as a repeatable token std::vector samplers_sequence = { llama_sampler_type::TOP_K, From d0d5de42e5a65865b5fddb6f5c785083539b74c3 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Tue, 19 Mar 2024 12:05:44 +0100 Subject: [PATCH 298/401] gguf-split: split and merge gguf per batch of tensors (#6135) * gguf-split: split and merge gguf files per tensor * gguf-split: build with make toolchain * gguf-split: rename `--split-tensors-size` to `--split-max-tensors`. Set general.split_count KV to all split * split : minor style + fix compile warnings * gguf-split: remove --upload not implemented --------- Co-authored-by: Georgi Gerganov --- Makefile | 4 + examples/CMakeLists.txt | 1 + examples/gguf-split/CMakeLists.txt | 5 + examples/gguf-split/README.md | 9 + examples/gguf-split/gguf-split.cpp | 491 +++++++++++++++++++++++++++++ 5 files changed, 510 insertions(+) create mode 100644 examples/gguf-split/CMakeLists.txt create mode 100644 examples/gguf-split/README.md create mode 100644 examples/gguf-split/gguf-split.cpp diff --git a/Makefile b/Makefile index 838daf5c02acd..1daad45ed8e4a 100644 --- a/Makefile +++ b/Makefile @@ -753,6 +753,10 @@ gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index e762cf8b9238b..b59cc65bfedb0 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -21,6 +21,7 @@ else() add_subdirectory(embedding) add_subdirectory(finetune) add_subdirectory(gritlm) + add_subdirectory(gguf-split) add_subdirectory(infill) add_subdirectory(llama-bench) add_subdirectory(llava) diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt new file mode 100644 index 0000000000000..828e624352c8d --- /dev/null +++ b/examples/gguf-split/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET gguf-split) +add_executable(${TARGET} gguf-split.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md new file mode 100644 index 0000000000000..ddb1f76497aed --- /dev/null +++ b/examples/gguf-split/README.md @@ -0,0 +1,9 @@ +## GGUF split Example + +CLI to split / merge GGUF files. + +**Command line options:** + +- `--split`: split GGUF to multiple GGUF, default operation. +- `--split-max-tensors`: maximum tensors in each split: default(128) +- `--merge`: merge multiple GGUF to a single GGUF. diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp new file mode 100644 index 0000000000000..5d7040ab5cbb3 --- /dev/null +++ b/examples/gguf-split/gguf-split.cpp @@ -0,0 +1,491 @@ +#include "llama.h" +#include "ggml.h" +#include "common.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +enum split_operation : uint8_t { + SPLIT_OP_SPLIT, + SPLIT_OP_MERGE, +}; + +static const char * const LLM_KV_GENERAL_SPLIT_I_SPLIT = "general.split"; +static const char * const LLM_KV_GENERAL_SPLIT_N_SPLIT = "general.split_count"; + +static const int SPLIT_FILENAME_MAX = 256; + +static const char * const SPLIT_FILENAME_FORMAT = "%s-%05d-of-%05d.gguf"; + +struct split_params { + split_operation operation = SPLIT_OP_SPLIT; + int n_split_tensors = 128; + std::string input; + std::string output; +}; + +static void split_print_usage(const char * executable) { + const split_params default_params; + printf("\n"); + printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable); + printf("\n"); + printf("Apply a GGUF operation on IN to OUT."); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --version show version and build info\n"); + printf(" --split split GGUF to multiple GGUF (default)\n"); + printf(" --split-max-tensors max tensors in each split: default(%d)\n", default_params.n_split_tensors); + printf(" --merge merge multiple GGUF to a single GGUF\n"); + printf("\n"); +} + +static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) { + std::string arg; + const std::string arg_prefix = "--"; + bool invalid_param = false; + + int arg_idx = 1; + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + arg = argv[arg_idx]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + bool arg_found = false; + if (arg == "-h" || arg == "--help") { + split_print_usage(argv[0]); + exit(0); + } + if (arg == "--version") { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } + + if (arg == "--merge") { + arg_found = true; + params.operation = SPLIT_OP_MERGE; + } + if (arg == "--split") { + arg_found = true; + params.operation = SPLIT_OP_SPLIT; + } + if (arg == "--split-max-tensors") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + params.n_split_tensors = atoi(argv[arg_idx]); + } + + if (!arg_found) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + } + + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument: " + arg); + } + + if (argc - arg_idx < 2) { + printf("%s: bad arguments\n", argv[0]); + split_print_usage(argv[0]); + return false; + } + + params.input = argv[arg_idx++]; + params.output = argv[arg_idx++]; + + return true; +} + +static bool split_params_parse(int argc, const char ** argv, split_params & params) { + bool result = true; + try { + if (!split_params_parse_ex(argc, argv, params)) { + split_print_usage(argv[0]); + exit(1); + } + } + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + split_print_usage(argv[0]); + exit(1); + } + return result; +} + +static void zeros(std::ofstream & file, size_t n) { + char zero = 0; + for (size_t i = 0; i < n; ++i) { + file.write(&zero, 1); + } +} + +static std::string split_file_name(const std::string & path, int i_split, int n_split) { + char f_split[SPLIT_FILENAME_MAX] = {0}; + snprintf(f_split, sizeof(f_split), SPLIT_FILENAME_FORMAT, path.c_str(), i_split + 1, n_split); + return std::string(f_split); +} + +struct split_strategy { + const split_params params; + std::ifstream & f_input; + struct gguf_context * ctx_gguf; + struct ggml_context * ctx_meta = NULL; + const int n_tensors; + + const int n_split; + int i_split = 0; + + int i_tensor = 0; + + std::vector read_data; + + struct gguf_context * ctx_out; + std::ofstream fout; + + split_strategy(const split_params & params, + std::ifstream & f_input, + struct gguf_context * ctx_gguf, + struct ggml_context * ctx_meta) : + params(params), + f_input(f_input), + ctx_gguf(ctx_gguf), + ctx_meta(ctx_meta), + n_tensors(gguf_get_n_tensors(ctx_gguf)), + n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) { + } + + bool should_split() const { + return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0; + } + + void split_start() { + ctx_out = gguf_init_empty(); + + // Save all metadata in first split only + if (i_split == 0) { + gguf_set_kv(ctx_out, ctx_gguf); + } + gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_I_SPLIT, i_split); + gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, n_split); + + // populate the original tensors, so we get an initial metadata + for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) { + struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + gguf_add_tensor(ctx_out, meta); + } + + auto split_name = split_file_name(params.output, i_split, n_split); + + fprintf(stderr, "%s: %s ...", __func__, split_name.c_str()); + fout = std::ofstream(split_name, std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + + auto meta_size = gguf_get_meta_size(ctx_out); + + // placeholder for the meta data + ::zeros(fout, meta_size); + + i_split++; + } + + void next_tensor() { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + auto n_bytes = ggml_nbytes(t); + + if (read_data.size() < n_bytes) { + read_data.resize(n_bytes); + } + + auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor); + f_input.seekg(offset); + f_input.read((char *)read_data.data(), n_bytes); + + t->data = read_data.data(); + + // write tensor data + padding + fout.write((const char *)t->data, n_bytes); + zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + + i_tensor++; + } + + void split_end() { + // go back to beginning of file and write the updated metadata + fout.seekp(0); + std::vector data(gguf_get_meta_size(ctx_out)); + gguf_get_meta_data(ctx_out, data.data()); + fout.write((const char *)data.data(), data.size()); + + fout.close(); + gguf_free(ctx_out); + + fprintf(stderr, "\033[3Ddone\n"); + } +}; + +static void gguf_split(const split_params & split_params) { + struct ggml_context * ctx_meta = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx_meta, + }; + + std::ifstream f_input(split_params.input.c_str(), std::ios::binary); + if (!f_input.is_open()) { + fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(1); + } + + auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params); + if (!ctx_gguf) { + fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(1); + } + + split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta); + fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n", + __func__, split_params.input.c_str(), + split_file_name(split_params.output, strategy.i_split, strategy.n_split).c_str(), + split_params.n_split_tensors); + + strategy.split_start(); + + while (strategy.i_tensor < strategy.n_tensors) { + strategy.next_tensor(); + if (strategy.should_split()) { + strategy.split_end(); + strategy.split_start(); + } + } + strategy.split_end(); + + gguf_free(ctx_gguf); + f_input.close(); + + fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n", + __func__, strategy.n_split, strategy.n_tensors); +} + +static void gguf_merge(const split_params & split_params) { + fprintf(stderr, "%s: %s -> %s\n", + __func__, split_params.input.c_str(), + split_params.output.c_str()); + int n_split = 1; + int total_tensors = 0; + + auto * ctx_out = gguf_init_empty(); + std::ofstream fout(split_params.output.c_str(), std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + + std::vector read_data; + std::vector ctx_metas; + std::vector ctx_ggufs; + + std::string split_prefix; + + // First pass to find KV and tensors metadata + for (int i_split = 0; i_split < n_split; i_split++) { + struct ggml_context * ctx_meta = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx_meta, + }; + + auto split_name = split_params.input; + if (i_split > 0) { + split_name = split_file_name(split_prefix, i_split, n_split); + } + fprintf(stderr, "%s: reading metadata %s ...", __func__, split_name.c_str()); + + auto * ctx_gguf = gguf_init_from_file(split_name.c_str(), params); + if (!ctx_gguf) { + fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(1); + } + ctx_ggufs.push_back(ctx_gguf); + ctx_metas.push_back(ctx_meta); + + if (i_split == 0) { + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_GENERAL_SPLIT_N_SPLIT); + if (key_n_split < 0) { + fprintf(stderr, + "\n%s: input file does not contain %s metadata\n", + __func__, + LLM_KV_GENERAL_SPLIT_N_SPLIT); + gguf_free(ctx_gguf); + gguf_free(ctx_out); + fout.close(); + exit(1); + } + + n_split = gguf_get_val_u8(ctx_gguf, key_n_split); + if (n_split < 1) { + fprintf(stderr, + "\n%s: input file does not contain a valid split count %d\n", + __func__, + n_split); + gguf_free(ctx_gguf); + gguf_free(ctx_out); + fout.close(); + exit(1); + } + + // Do not trigger merge if we try to merge again the output + gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, 0); + + // Set metadata from the first split + gguf_set_kv(ctx_out, ctx_gguf); + } + + // Verify the file naming + { + int i_split_file = 0; + int n_split_file = 0; + const char * i_split_format = "-00000-of-00000.gguf"; + + if (split_name.size() < strlen(i_split_format)) { + fprintf(stderr, "\n%s: unexpected input file name: %s\n", __func__, split_params.input.c_str()); + for (auto * _ctx_gguf : ctx_ggufs) { + gguf_free(_ctx_gguf); + } + gguf_free(ctx_out); + fout.close(); + exit(1); + } + + split_prefix = split_name.substr(0, split_name.size() - strlen(i_split_format)); + + const char * split_name_c_str = split_name.c_str(); + int n_part = sscanf(&split_name_c_str[0] + split_prefix.size(), "-%d-of-%d", &i_split_file, &n_split_file); + + if (n_part != 2 || i_split_file - 1 != i_split || n_split_file != n_split) { + fprintf(stderr, "\n%s: unexpected input file name: %s" + " i_split=%d i_split_file=%d" + " n_split=%d n_split_file=%d\n", __func__, + split_params.input.c_str(), + i_split, i_split_file, + n_split, n_split_file); + for (auto * _ctx_gguf : ctx_ggufs) { + gguf_free(_ctx_gguf); + } + gguf_free(ctx_out); + fout.close(); + exit(1); + } + } + + auto n_tensors = gguf_get_n_tensors(ctx_gguf); + for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + gguf_add_tensor(ctx_out, t); + } + total_tensors += n_tensors; + + fprintf(stderr, "\033[3Ddone\n"); + } + + // placeholder for the meta data + { + auto meta_size = gguf_get_meta_size(ctx_out); + ::zeros(fout, meta_size); + } + + // Write tensors data + for (int i_split = 0; i_split < n_split; i_split++) { + auto split_name = split_file_name(split_prefix, i_split, n_split); + std::ifstream f_input(split_name.c_str(), std::ios::binary); + if (!f_input.is_open()) { + fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_name.c_str()); + for (auto * _ctx_gguf : ctx_ggufs) { + gguf_free(_ctx_gguf); + } + gguf_free(ctx_out); + fout.close(); + exit(1); + } + fprintf(stderr, "%s: writing tensors %s ...", __func__, split_name.c_str()); + + auto * ctx_gguf = ctx_ggufs[i_split]; + auto * ctx_meta = ctx_metas[i_split]; + + auto n_tensors = gguf_get_n_tensors(ctx_gguf); + for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + + auto n_bytes = ggml_nbytes(t); + + if (read_data.size() < n_bytes) { + read_data.resize(n_bytes); + } + + auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor); + f_input.seekg(offset); + f_input.read((char *)read_data.data(), n_bytes); + + // write tensor data + padding + fout.write((const char *)read_data.data(), n_bytes); + zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + } + + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + f_input.close(); + fprintf(stderr, "\033[3Ddone\n"); + } + + { + // go back to beginning of file and write the updated metadata + fout.seekp(0); + std::vector data(gguf_get_meta_size(ctx_out)); + gguf_get_meta_data(ctx_out, data.data()); + fout.write((const char *)data.data(), data.size()); + + fout.close(); + gguf_free(ctx_out); + } + + fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n", + __func__, split_params.output.c_str(), n_split, total_tensors); +} + +int main(int argc, const char ** argv) { + if (argc < 3) { + split_print_usage(argv[0]); + } + + split_params params; + split_params_parse(argc, argv, params); + + switch (params.operation) { + case SPLIT_OP_SPLIT: gguf_split(params); + break; + case SPLIT_OP_MERGE: gguf_merge(params); + break; + default:split_print_usage(argv[0]); + exit(1); + } + + return 0; +} From d8b009a9456bf5284376149f3deb09300a37701a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Tue, 19 Mar 2024 12:16:09 -0400 Subject: [PATCH 299/401] Remove undeed header file. (#6158) --- examples/gguf-split/gguf-split.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 5d7040ab5cbb3..8e12e64937bd7 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -13,9 +13,7 @@ #include #include -#include #include -#include enum split_operation : uint8_t { SPLIT_OP_SPLIT, From d26e8b669dbf1f5f5a0afe4d2d885e86cf566302 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Wed, 20 Mar 2024 08:28:49 +0530 Subject: [PATCH 300/401] increase igpu cluster limit (#6159) --- ggml-sycl.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-sycl.h b/ggml-sycl.h index c549a64a1f44c..1c9d52115222b 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -13,7 +13,7 @@ extern "C" { #endif -#define GGML_SYCL_MAX_DEVICES 16 +#define GGML_SYCL_MAX_DEVICES 48 #define GGML_SYCL_NAME "SYCL" GGML_API void ggml_init_sycl(void); From 6c0b287748327741b113d7d6018b68c63039b1c5 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 20 Mar 2024 11:21:41 +0800 Subject: [PATCH 301/401] update readme sycl for new update (#6151) * update readme sycl for new update * Update README-sycl.md Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> * Update README-sycl.md Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> * Update README-sycl.md Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> * Update README-sycl.md Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> * Update README-sycl.md Co-authored-by: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> * Update README-sycl.md Co-authored-by: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> * update by review comments * update w64devkit link * update for verify device id part * Update README-sycl.md Co-authored-by: Meng, Hengyu --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Co-authored-by: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Co-authored-by: Meng, Hengyu --- README-sycl.md | 129 ++++++++++++++++++++++--------- examples/sycl/win-run-llama2.bat | 2 - 2 files changed, 93 insertions(+), 38 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 9359a94901677..32adfda47769c 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -29,6 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). ## News - 2024.3 + - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437). - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. - Support detecting all GPUs with level-zero and same top **Max compute units**. @@ -81,7 +82,7 @@ For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, rec |-|-|-| |Ampere Series| Support| A100| -### oneMKL +### oneMKL for CUDA The current oneMKL release does not contain the oneMKL cuBlas backend. As a result for Nvidia GPU's oneMKL must be built from source. @@ -254,16 +255,16 @@ Run without parameter: Check the ID in startup log, like: ``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - +found 6 SYCL devices: +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| +| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136| +| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216| +| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616| +| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616| ``` |Attribute|Note| @@ -271,12 +272,35 @@ found 4 SYCL devices: |compute capability 1.3|Level-zero running time, recommended | |compute capability 3.0|OpenCL running time, slower than level-zero in most cases| -4. Set device ID and execute llama.cpp +4. Device selection and execution of llama.cpp + +There are two device selection modes: + +- Single device: Use one device assigned by user. +- Multiple devices: Automatically choose the devices with the same biggest Max compute units. + +|Device selection|Parameter| +|-|-| +|Single device|--split-mode none --main-gpu DEVICE_ID | +|Multiple devices|--split-mode layer (default)| -Set device ID = 0 by **GGML_SYCL_DEVICE=0** +Examples: + +- Use device 0: ```sh -GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 +``` +or run by script: + +```sh +./examples/sycl/run_llama2.sh 0 +``` + +- Use multiple devices: + +```sh +ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer ``` or run by script: @@ -289,13 +313,19 @@ Note: - By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. -5. Check the device ID in output +5. Verify the device ID in output + +Verify to see if the selected GPU is shown in the output, like: -Like: ``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` +Or +``` +use 1 SYCL GPUs: [0] with Max compute units:512 ``` + ## Windows ### Setup Environment @@ -355,7 +385,7 @@ a. Download & install cmake for Windows: https://cmake.org/download/ b. Download & install mingw-w64 make for Windows provided by w64devkit -- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). +- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip). - Extract `w64devkit` on your pc. @@ -430,15 +460,16 @@ build\bin\main.exe Check the ID in startup log, like: ``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 +found 6 SYCL devices: +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| +| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136| +| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216| +| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616| +| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616| ``` @@ -447,13 +478,31 @@ found 4 SYCL devices: |compute capability 1.3|Level-zero running time, recommended | |compute capability 3.0|OpenCL running time, slower than level-zero in most cases| -4. Set device ID and execute llama.cpp -Set device ID = 0 by **set GGML_SYCL_DEVICE=0** +4. Device selection and execution of llama.cpp + +There are two device selection modes: + +- Single device: Use one device assigned by user. +- Multiple devices: Automatically choose the devices with the same biggest Max compute units. + +|Device selection|Parameter| +|-|-| +|Single device|--split-mode none --main-gpu DEVICE_ID | +|Multiple devices|--split-mode layer (default)| + +Examples: + +- Use device 0: ``` -set GGML_SYCL_DEVICE=0 -build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 +build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0 +``` + +- Use multiple devices: + +``` +build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer ``` or run by script: @@ -466,11 +515,17 @@ Note: - By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. -5. Check the device ID in output -Like: +5. Verify the device ID in output + +Verify to see if the selected GPU is shown in the output, like: + ``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` +Or +``` +use 1 SYCL GPUs: [0] with Max compute units:512 ``` ## Environment Variable @@ -489,7 +544,6 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |Name|Value|Function| |-|-|-| -|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| |GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| |ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer| @@ -507,6 +561,9 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ## Q&A +Note: please add prefix **[SYCL]** in issue title, so that we will check it as soon as possible. + + - Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. Miss to enable oneAPI running environment. @@ -538,4 +595,4 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ## Todo -- Support multiple cards. +- Support row layer split for multiple card runs. diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index cf621c6759314..1d4d7d2cdcb6f 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -6,8 +6,6 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force -set GGML_SYCL_DEVICE=0 -rem set GGML_SYCL_DEBUG=1 .\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0 From bd60d82d0cc8b6852ec535495a5042dbdf05de24 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Wed, 20 Mar 2024 01:33:49 -0400 Subject: [PATCH 302/401] server tests : more pythonic process management; fix bare `except:` (#6146) * server tests : remove seemingly redundant newlines in print() * server tests : use built-in subprocess features, not os.kill and psutil * server tests : do not catch e.g. SystemExit; use print_exc * server tests: handle TimeoutExpired exception * server tests: fix connect on dual-stack systems * server: tests: add new tokens regex on windows generated following new repeat penalties default changed in (#6127) * server: tests: remove the hack on windows since now we get the good socket family * server: tests: add new tokens regex following new repeat penalties default changed in (#6127) * server: tests: add new tokens regex following new repeat penalties default changed in (#6127) --------- Co-authored-by: Pierrick HYMBERT --- examples/server/tests/features/environment.py | 74 ++++++------------- examples/server/tests/features/server.feature | 14 ++-- examples/server/tests/features/steps/steps.py | 33 +++++---- examples/server/tests/requirements.txt | 1 - 4 files changed, 46 insertions(+), 76 deletions(-) diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 82104e9202e5e..e7845dc2f51fc 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -5,15 +5,14 @@ import time import traceback from contextlib import closing - -import psutil +from subprocess import TimeoutExpired def before_scenario(context, scenario): context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' if context.debug: - print("DEBUG=ON\n") - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n") + print("DEBUG=ON") + print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") port = 8080 if 'PORT' in os.environ: port = int(os.environ['PORT']) @@ -27,60 +26,40 @@ def after_scenario(context, scenario): return if scenario.status == "failed": if 'GITHUB_ACTIONS' in os.environ: - print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n") + print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n") if os.path.isfile('llama.log'): with closing(open('llama.log', 'r')) as f: for line in f: print(line) if not is_server_listening(context.server_fqdn, context.server_port): - print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n") + print("\x1b[33;101mERROR: Server stopped listening\x1b[0m") - if not pid_exists(context.server_process.pid): + if context.server_process.poll() is not None: assert False, f"Server not running pid={context.server_process.pid} ..." - server_graceful_shutdown(context) + server_graceful_shutdown(context) # SIGINT - # Wait few for socket to free up - time.sleep(0.05) + try: + context.server_process.wait(0.5) + except TimeoutExpired: + print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...") + context.server_process.kill() # SIGKILL + context.server_process.wait() - attempts = 0 - while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port): - server_kill(context) + while is_server_listening(context.server_fqdn, context.server_port): time.sleep(0.1) - attempts += 1 - if attempts > 5: - server_kill_hard(context) - except: - exc = sys.exception() - print("error in after scenario: \n") - print(exc) - print("*** print_tb: \n") - traceback.print_tb(exc.__traceback__, file=sys.stdout) + except Exception: + print("ignoring error in after_scenario:") + traceback.print_exc(file=sys.stdout) def server_graceful_shutdown(context): - print(f"shutting down server pid={context.server_process.pid} ...\n") + print(f"shutting down server pid={context.server_process.pid} ...") if os.name == 'nt': - os.kill(context.server_process.pid, signal.CTRL_C_EVENT) + interrupt = signal.CTRL_C_EVENT else: - os.kill(context.server_process.pid, signal.SIGINT) - - -def server_kill(context): - print(f"killing server pid={context.server_process.pid} ...\n") - context.server_process.kill() - - -def server_kill_hard(context): - pid = context.server_process.pid - path = context.server_path - - print(f"Server dangling exits, hard killing force {pid}={path}...\n") - try: - psutil.Process(pid).kill() - except psutil.NoSuchProcess: - return False - return True + interrupt = signal.SIGINT + context.server_process.send_signal(interrupt) def is_server_listening(server_fqdn, server_port): @@ -88,14 +67,5 @@ def is_server_listening(server_fqdn, server_port): result = sock.connect_ex((server_fqdn, server_port)) _is_server_listening = result == 0 if _is_server_listening: - print(f"server is listening on {server_fqdn}:{server_port}...\n") + print(f"server is listening on {server_fqdn}:{server_port}...") return _is_server_listening - - -def pid_exists(pid): - try: - psutil.Process(pid) - except psutil.NoSuchProcess: - return False - return True - diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 7448986e75a49..45a988db67f5f 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -35,9 +35,9 @@ Feature: llama.cpp server And metric llamacpp:tokens_predicted is Examples: Prompts - | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | - | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | - | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not | + | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | + | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | + | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not | Scenario: Completion prompt truncated Given a prompt: @@ -48,7 +48,7 @@ Feature: llama.cpp server Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. """ And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry + Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl And the completion is truncated And 109 prompt tokens are processed @@ -65,9 +65,9 @@ Feature: llama.cpp server And the completion is truncated Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | - | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | | + | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | + | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | Scenario: Tokenize / Detokenize diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 9e348d5fc4c37..40c97001ac5d7 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -66,7 +66,7 @@ def step_server_config(context, server_fqdn, server_port): def step_download_hf_model(context, hf_file, hf_repo): context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) if context.debug: - print(f"model file: {context.model_file}\n") + print(f"model file: {context.model_file}") @step('a model file {model_file}') @@ -137,9 +137,12 @@ def step_start_server(context): if 'GITHUB_ACTIONS' in os.environ: max_attempts *= 2 + addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM) + family, typ, proto, _, sockaddr = addrs[0] + while True: - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: - result = sock.connect_ex((context.server_fqdn, context.server_port)) + with closing(socket.socket(family, typ, proto)) as sock: + result = sock.connect_ex(sockaddr) if result == 0: print("\x1b[33;46mserver started!\x1b[0m") return @@ -209,7 +212,7 @@ async def step_request_completion(context, api_error): user_api_key=context.user_api_key) context.tasks_result.append(completion) if context.debug: - print(f"Completion response: {completion}\n") + print(f"Completion response: {completion}") if expect_api_error: assert completion == 401, f"completion must be an 401 status code: {completion}" @@ -354,7 +357,7 @@ def step_prompt_passkey(context, passkey, i_pos): prompt += context.prompt_junk_suffix if context.debug: passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" - print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") + print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```") context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) context.n_prompts = len(context.prompts) @@ -363,7 +366,7 @@ def step_prompt_passkey(context, passkey, i_pos): @async_run_until_complete async def step_oai_chat_completions(context, api_error): if context.debug: - print(f"Submitting OAI compatible completions request...\n") + print(f"Submitting OAI compatible completions request...") expect_api_error = api_error == 'raised' completion = await oai_chat_completions(context.prompts.pop(), context.system_prompt, @@ -508,12 +511,12 @@ async def step_all_embeddings_are_the_same(context): embedding1 = np.array(embeddings[i]) embedding2 = np.array(embeddings[j]) if context.debug: - print(f"embedding1: {embedding1[-8:]}\n") - print(f"embedding2: {embedding2[-8:]}\n") + print(f"embedding1: {embedding1[-8:]}") + print(f"embedding2: {embedding2[-8:]}") similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) msg = f"Similarity between {i} and {j}: {similarity:.10f}" if context.debug: - print(f"{msg}\n") + print(f"{msg}") assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg @@ -630,7 +633,7 @@ async def step_prometheus_metrics_exported(context): metrics_raw = await metrics_response.text() metric_exported = False if context.debug: - print(f"/metrics answer:\n{metrics_raw}\n") + print(f"/metrics answer:\n{metrics_raw}") context.metrics = {} for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: @@ -932,7 +935,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re last_match = end highlighted += content[last_match:] if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - print(f"Checking completion response: {highlighted}\n") + print(f"Checking completion response: {highlighted}") assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' if expected_predicted_n and expected_predicted_n > 0: assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' @@ -942,7 +945,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: - print(f"Waiting for all {n_tasks} tasks results...\n") + print(f"Waiting for all {n_tasks} tasks results...") for task_no in range(n_tasks): context.tasks_result.append(await context.concurrent_tasks.pop()) n_completions = len(context.tasks_result) @@ -959,7 +962,7 @@ async def wait_for_health_status(context, slots_processing=None, expected_slots=None): if context.debug: - print(f"Starting checking for health for expected_health_status={expected_health_status}\n") + print(f"Starting checking for health for expected_health_status={expected_health_status}") interval = 0.5 counter = 0 if 'GITHUB_ACTIONS' in os.environ: @@ -1048,8 +1051,6 @@ def start_server_background(context): if 'LLAMA_SERVER_BIN_PATH' in os.environ: context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] server_listen_addr = context.server_fqdn - if os.name == 'nt': - server_listen_addr = '0.0.0.0' server_args = [ '--host', server_listen_addr, '--port', context.server_port, @@ -1088,7 +1089,7 @@ def start_server_background(context): server_args.append('--verbose') if 'SERVER_LOG_FORMAT_JSON' not in os.environ: server_args.extend(['--log-format', "text"]) - print(f"starting server with: {context.server_path} {server_args}\n") + print(f"starting server with: {context.server_path} {server_args}") flags = 0 if 'nt' == os.name: flags |= subprocess.DETACHED_PROCESS diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index c2c960102b523..2e4f42ad28c23 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -3,5 +3,4 @@ behave~=1.2.6 huggingface_hub~=0.20.3 numpy~=1.24.4 openai~=0.25.0 -psutil~=5.9.8 prometheus-client~=0.20.0 From 47cc7a7bf9793f81a6dfe7c2096c499403f64dd6 Mon Sep 17 00:00:00 2001 From: Karthick Date: Wed, 20 Mar 2024 16:32:34 +0530 Subject: [PATCH 303/401] Server: Handle n_keep parameter in the request (#6174) --- examples/server/utils.hpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 2ddb2cd21f8d6..cc5fce6918b07 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -371,6 +371,7 @@ static json oaicompat_completion_params_parse( llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); + llama_params["n_keep"] = json_value(body, "n_keep", 0); if (body.count("grammar") != 0) { llama_params["grammar"] = json_value(body, "grammar", json::object()); From f8c4e745e1e728204ab26dbadf52853545e6789c Mon Sep 17 00:00:00 2001 From: Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> Date: Wed, 20 Mar 2024 19:20:37 +0800 Subject: [PATCH 304/401] llava : add a MobileVLM_V2-1.7B backup (#6152) * Add MobileVLM_V2 backup * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov * Update examples/llava/convert-image-encoder-to-gguf.py Co-authored-by: Georgi Gerganov * clip : fix whitespace --------- Co-authored-by: Georgi Gerganov --- examples/llava/MobileVLM-README.md | 14 ++++++- examples/llava/clip.cpp | 42 ++++++++++++++++++- .../llava/convert-image-encoder-to-gguf.py | 9 ++-- 3 files changed, 59 insertions(+), 6 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 9eba791dadfef..c1f361d1704f4 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -1,11 +1,13 @@ # MobileVLM -Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants. +Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants. for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM) The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. +Notice: The overall process of model inference for both **MobilVLM** and **MobilVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown. + ## Usage Build with cmake or run `make llava-cli` to build it. @@ -34,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B ``` -3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF: +3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh python ./examples/llava/convert-image-encoder-to-gguf \ @@ -44,6 +46,14 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldp ``` +```sh +python ./examples/llava/convert-image-encoder-to-gguf \ + -m path/to/clip-vit-large-patch14-336 \ + --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ + --output-dir path/to/MobileVLM-1.7B_V2 \ + --projector-type ldpv2 +``` + 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 690bca2eb7732..6d7b4950f5c70 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -119,6 +119,7 @@ static std::string format(const char * fmt, ...) { #define TN_LLAVA_PROJ "mm.%d.%s" #define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" +#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" #define TN_IMAGE_NEWLINE "model.image_newline" @@ -126,12 +127,14 @@ enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, + PROJECTOR_TYPE_LDPV2, PROJECTOR_TYPE_UNKNOWN, }; static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MLP, "mlp" }, { PROJECTOR_TYPE_LDP, "ldp" }, + { PROJECTOR_TYPE_LDPV2, "ldpv2"}, }; @@ -807,6 +810,29 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } embeddings = block_1; } + else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } else { GGML_ASSERT(false); } @@ -1177,7 +1203,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } else { + } + else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2) + { + // MobilVLM_V2 projection + vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight")); + vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias")); + vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight")); + vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias")); + vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight")); + vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias")); + } + else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } @@ -1966,6 +2003,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { if (ctx->proj_type == PROJECTOR_TYPE_LDP) { return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0]; } + if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) { + return ctx->vision_model.mm_model_peg_0_b->ne[0]; + } if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; } diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index c69f89ac2b60e..b00bf7c6d0b59 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -1,6 +1,7 @@ import argparse import os import json +import re import torch import numpy as np @@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b def get_tensor_name(name: str) -> str: if "projection" in name: return name - if "mm_projector" in name: - return name.replace("model.mm_projector", "mm") + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") @@ -83,7 +86,7 @@ def bytes_to_unicode(): ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, help="The clip model is from openclip (for ViT-SO400M type))") ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") -ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 From d795988d9e09f75412efe2134512914c42780ad5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 20 Mar 2024 13:29:49 +0200 Subject: [PATCH 305/401] Revert "llava : add a MobileVLM_V2-1.7B backup (#6152)" This reverts commit f8c4e745e1e728204ab26dbadf52853545e6789c. --- examples/llava/MobileVLM-README.md | 14 +------ examples/llava/clip.cpp | 42 +------------------ .../llava/convert-image-encoder-to-gguf.py | 9 ++-- 3 files changed, 6 insertions(+), 59 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index c1f361d1704f4..9eba791dadfef 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -1,13 +1,11 @@ # MobileVLM -Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants. +Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants. for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM) The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. -Notice: The overall process of model inference for both **MobilVLM** and **MobilVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown. - ## Usage Build with cmake or run `make llava-cli` to build it. @@ -36,7 +34,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B ``` -3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: +3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF: ```sh python ./examples/llava/convert-image-encoder-to-gguf \ @@ -46,14 +44,6 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldp ``` -```sh -python ./examples/llava/convert-image-encoder-to-gguf \ - -m path/to/clip-vit-large-patch14-336 \ - --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ - --output-dir path/to/MobileVLM-1.7B_V2 \ - --projector-type ldpv2 -``` - 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 6d7b4950f5c70..690bca2eb7732 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -119,7 +119,6 @@ static std::string format(const char * fmt, ...) { #define TN_LLAVA_PROJ "mm.%d.%s" #define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" -#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" #define TN_IMAGE_NEWLINE "model.image_newline" @@ -127,14 +126,12 @@ enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, - PROJECTOR_TYPE_LDPV2, PROJECTOR_TYPE_UNKNOWN, }; static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MLP, "mlp" }, { PROJECTOR_TYPE_LDP, "ldp" }, - { PROJECTOR_TYPE_LDPV2, "ldpv2"}, }; @@ -810,29 +807,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } embeddings = block_1; } - else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) - { - int n_patch = 24; - struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); - mlp_0 = ggml_gelu(ctx0, mlp_0); - struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); - mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); - // mlp_2 ne = [2048, 576, 1, 1] - // // AVG Pool Layer 2*2, strides = 2 - mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); - // mlp_2 ne = [576, 2048, 1, 1] - mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); - // mlp_2 ne [24, 24, 2048, 1] - mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); - // weight ne = [3, 3, 2048, 1] - struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); - peg_0 = ggml_add(ctx0, peg_0, mlp_2); - peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); - peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); - embeddings = peg_0; - } else { GGML_ASSERT(false); } @@ -1203,18 +1177,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2) - { - // MobilVLM_V2 projection - vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight")); - vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias")); - vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight")); - vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias")); - vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight")); - vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias")); - } - else { + } else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } @@ -2003,9 +1966,6 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { if (ctx->proj_type == PROJECTOR_TYPE_LDP) { return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0]; } - if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) { - return ctx->vision_model.mm_model_peg_0_b->ne[0]; - } if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; } diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index b00bf7c6d0b59..c69f89ac2b60e 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -1,7 +1,6 @@ import argparse import os import json -import re import torch import numpy as np @@ -39,11 +38,9 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b def get_tensor_name(name: str) -> str: if "projection" in name: return name + if "mm_projector" in name: - name = name.replace("model.mm_projector", "mm") - name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) - name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) - return name + return name.replace("model.mm_projector", "mm") return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") @@ -86,7 +83,7 @@ def bytes_to_unicode(): ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, help="The clip model is from openclip (for ViT-SO400M type))") ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") -ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 From bc0baab2ea8f806961dd3fb9f534cfeb59a2e1fc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 20 Mar 2024 14:14:32 +0200 Subject: [PATCH 306/401] server : allow to override -ngl in tests (#6170) --- examples/server/tests/features/steps/steps.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 40c97001ac5d7..42cdd89850398 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -24,12 +24,16 @@ def step_server_config(context, server_fqdn, server_port): context.server_fqdn = server_fqdn context.server_port = int(server_port) + context.n_gpu_layer = None if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) print(f"$PORT set, overriding server port with to {context.server_port}") if 'FQDN' in os.environ: context.server_fqdn = os.environ['FQDN'] print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}") + if 'N_GPU_LAYERS' in os.environ: + context.n_gpu_layer = int(os.environ['N_GPU_LAYERS']) + print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}") context.base_url = f'http://{context.server_fqdn}:{context.server_port}' @@ -41,7 +45,6 @@ def step_server_config(context, server_fqdn, server_port): context.n_ctx = None context.n_ga = None context.n_ga_w = None - context.n_gpu_layer = None context.n_predict = None context.n_prompts = 0 context.n_server_predict = None From 6b7e76d28cdf4361740054140708c71828453522 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 20 Mar 2024 14:17:34 +0200 Subject: [PATCH 307/401] gitignore : ignore curl-related files --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 1ad8d929b87e8..fbeb9223c203a 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,8 @@ *.dot *.bat *.metallib +*.etag +*.lastModified .DS_Store .build/ .cache/ From 91f8ad167dcd24b54615b468d9dd764ebe1d37ad Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Wed, 20 Mar 2024 13:30:36 +0100 Subject: [PATCH 308/401] Server: version bump for httplib and json (#6169) * server: version bump for httplib and json * fix build * bring back content_length --- examples/server/httplib.h | 1797 +++++++++++++++++++++++++------------ examples/server/json.hpp | 1664 +++++++++++++++++++--------------- 2 files changed, 2151 insertions(+), 1310 deletions(-) diff --git a/examples/server/httplib.h b/examples/server/httplib.h index 28746000cddce..f360bd93ea098 100644 --- a/examples/server/httplib.h +++ b/examples/server/httplib.h @@ -1,14 +1,14 @@ // // httplib.h // -// Copyright (c) 2023 Yuji Hirose. All rights reserved. +// Copyright (c) 2024 Yuji Hirose. All rights reserved. // MIT License // #ifndef CPPHTTPLIB_HTTPLIB_H #define CPPHTTPLIB_HTTPLIB_H -#define CPPHTTPLIB_VERSION "0.12.2" +#define CPPHTTPLIB_VERSION "0.15.3" /* * Configuration @@ -82,6 +82,10 @@ #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 #endif +#ifndef CPPHTTPLIB_RANGE_MAX_COUNT +#define CPPHTTPLIB_RANGE_MAX_COUNT 1024 +#endif + #ifndef CPPHTTPLIB_TCP_NODELAY #define CPPHTTPLIB_TCP_NODELAY false #endif @@ -160,10 +164,6 @@ using ssize_t = long; #define WSA_FLAG_NO_HANDLE_INHERIT 0x80 #endif -#ifndef strcasecmp -#define strcasecmp _stricmp -#endif // strcasecmp - using socket_t = SOCKET; #ifdef CPPHTTPLIB_USE_POLL #define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout) @@ -172,9 +172,15 @@ using socket_t = SOCKET; #else // not _WIN32 #include -#ifndef _AIX +#if !defined(_AIX) && !defined(__MVS__) #include #endif +#ifdef __MVS__ +#include +#ifndef NI_MAXHOST +#define NI_MAXHOST 1025 +#endif +#endif #include #include #include @@ -187,6 +193,7 @@ using socket_t = SOCKET; #endif #include #include +#include #include #include #include @@ -207,6 +214,7 @@ using socket_t = int; #include #include #include +#include #include #include #include @@ -223,6 +231,9 @@ using socket_t = int; #include #include #include +#include +#include +#include #ifdef CPPHTTPLIB_OPENSSL_SUPPORT #ifdef _WIN32 @@ -237,7 +248,6 @@ using socket_t = int; #ifdef _MSC_VER #pragma comment(lib, "crypt32.lib") -#pragma comment(lib, "cryptui.lib") #endif #elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__) #include @@ -259,10 +269,8 @@ using socket_t = int; #include #include -#if OPENSSL_VERSION_NUMBER < 0x1010100fL -#error Sorry, OpenSSL versions prior to 1.1.1 are not supported -#elif OPENSSL_VERSION_NUMBER < 0x30000000L -#define SSL_get1_peer_certificate SSL_get_peer_certificate +#if OPENSSL_VERSION_NUMBER < 0x30000000L +#error Sorry, OpenSSL versions prior to 3.0.0 are not supported #endif #endif @@ -321,7 +329,7 @@ struct scope_exit { explicit scope_exit(std::function &&f) : exit_function(std::move(f)), execute_on_destruction{true} {} - scope_exit(scope_exit &&rhs) + scope_exit(scope_exit &&rhs) noexcept : exit_function(std::move(rhs.exit_function)), execute_on_destruction{rhs.execute_on_destruction} { rhs.release(); @@ -344,6 +352,81 @@ struct scope_exit { } // namespace detail +enum StatusCode { + // Information responses + Continue_100 = 100, + SwitchingProtocol_101 = 101, + Processing_102 = 102, + EarlyHints_103 = 103, + + // Successful responses + OK_200 = 200, + Created_201 = 201, + Accepted_202 = 202, + NonAuthoritativeInformation_203 = 203, + NoContent_204 = 204, + ResetContent_205 = 205, + PartialContent_206 = 206, + MultiStatus_207 = 207, + AlreadyReported_208 = 208, + IMUsed_226 = 226, + + // Redirection messages + MultipleChoices_300 = 300, + MovedPermanently_301 = 301, + Found_302 = 302, + SeeOther_303 = 303, + NotModified_304 = 304, + UseProxy_305 = 305, + unused_306 = 306, + TemporaryRedirect_307 = 307, + PermanentRedirect_308 = 308, + + // Client error responses + BadRequest_400 = 400, + Unauthorized_401 = 401, + PaymentRequired_402 = 402, + Forbidden_403 = 403, + NotFound_404 = 404, + MethodNotAllowed_405 = 405, + NotAcceptable_406 = 406, + ProxyAuthenticationRequired_407 = 407, + RequestTimeout_408 = 408, + Conflict_409 = 409, + Gone_410 = 410, + LengthRequired_411 = 411, + PreconditionFailed_412 = 412, + PayloadTooLarge_413 = 413, + UriTooLong_414 = 414, + UnsupportedMediaType_415 = 415, + RangeNotSatisfiable_416 = 416, + ExpectationFailed_417 = 417, + ImATeapot_418 = 418, + MisdirectedRequest_421 = 421, + UnprocessableContent_422 = 422, + Locked_423 = 423, + FailedDependency_424 = 424, + TooEarly_425 = 425, + UpgradeRequired_426 = 426, + PreconditionRequired_428 = 428, + TooManyRequests_429 = 429, + RequestHeaderFieldsTooLarge_431 = 431, + UnavailableForLegalReasons_451 = 451, + + // Server error responses + InternalServerError_500 = 500, + NotImplemented_501 = 501, + BadGateway_502 = 502, + ServiceUnavailable_503 = 503, + GatewayTimeout_504 = 504, + HttpVersionNotSupported_505 = 505, + VariantAlsoNegotiates_506 = 506, + InsufficientStorage_507 = 507, + LoopDetected_508 = 508, + NotExtended_510 = 510, + NetworkAuthenticationRequired_511 = 511, +}; + using Headers = std::multimap; using Params = std::multimap; @@ -373,17 +456,18 @@ class DataSink { DataSink &operator=(DataSink &&) = delete; std::function write; + std::function is_writable; std::function done; std::function done_with_trailer; std::ostream os; private: - class data_sink_streambuf : public std::streambuf { + class data_sink_streambuf final : public std::streambuf { public: explicit data_sink_streambuf(DataSink &sink) : sink_(sink) {} protected: - std::streamsize xsputn(const char *s, std::streamsize n) { + std::streamsize xsputn(const char *s, std::streamsize n) override { sink_.write(s, static_cast(n)); return n; } @@ -465,6 +549,7 @@ struct Request { MultipartFormDataMap files; Ranges ranges; Match matches; + std::unordered_map path_params; // for client ResponseHandler response_handler; @@ -476,8 +561,7 @@ struct Request { bool has_header(const std::string &key) const; std::string get_header_value(const std::string &key, size_t id = 0) const; - template - T get_header_value(const std::string &key, size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); @@ -509,14 +593,14 @@ struct Response { bool has_header(const std::string &key) const; std::string get_header_value(const std::string &key, size_t id = 0) const; - template - T get_header_value(const std::string &key, size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); - void set_redirect(const std::string &url, int status = 302); + void set_redirect(const std::string &url, int status = StatusCode::Found_302); void set_content(const char *s, size_t n, const std::string &content_type); void set_content(const std::string &s, const std::string &content_type); + void set_content(std::string &&s, const std::string &content_type); void set_content_provider( size_t length, const std::string &content_type, ContentProvider provider, @@ -573,15 +657,16 @@ class TaskQueue { TaskQueue() = default; virtual ~TaskQueue() = default; - virtual void enqueue(std::function fn) = 0; + virtual bool enqueue(std::function fn) = 0; virtual void shutdown() = 0; virtual void on_idle() {} }; -class ThreadPool : public TaskQueue { +class ThreadPool final : public TaskQueue { public: - explicit ThreadPool(size_t n) : shutdown_(false) { + explicit ThreadPool(size_t n, size_t mqr = 0) + : shutdown_(false), max_queued_requests_(mqr) { while (n) { threads_.emplace_back(worker(*this)); n--; @@ -591,13 +676,17 @@ class ThreadPool : public TaskQueue { ThreadPool(const ThreadPool &) = delete; ~ThreadPool() override = default; - void enqueue(std::function fn) override { + bool enqueue(std::function fn) override { { std::unique_lock lock(mutex_); + if (max_queued_requests_ > 0 && jobs_.size() >= max_queued_requests_) { + return false; + } jobs_.push_back(std::move(fn)); } cond_.notify_one(); + return true; } void shutdown() override { @@ -647,6 +736,7 @@ class ThreadPool : public TaskQueue { std::list> jobs_; bool shutdown_; + size_t max_queued_requests_ = 0; std::condition_variable cond_; std::mutex mutex_; @@ -658,6 +748,82 @@ using SocketOptions = std::function; void default_socket_options(socket_t sock); +const char *status_message(int status); + +std::string get_bearer_token_auth(const Request &req); + +namespace detail { + +class MatcherBase { +public: + virtual ~MatcherBase() = default; + + // Match request path and populate its matches and + virtual bool match(Request &request) const = 0; +}; + +/** + * Captures parameters in request path and stores them in Request::path_params + * + * Capture name is a substring of a pattern from : to /. + * The rest of the pattern is matched agains the request path directly + * Parameters are captured starting from the next character after + * the end of the last matched static pattern fragment until the next /. + * + * Example pattern: + * "/path/fragments/:capture/more/fragments/:second_capture" + * Static fragments: + * "/path/fragments/", "more/fragments/" + * + * Given the following request path: + * "/path/fragments/:1/more/fragments/:2" + * the resulting capture will be + * {{"capture", "1"}, {"second_capture", "2"}} + */ +class PathParamsMatcher final : public MatcherBase { +public: + PathParamsMatcher(const std::string &pattern); + + bool match(Request &request) const override; + +private: + static constexpr char marker = ':'; + // Treat segment separators as the end of path parameter capture + // Does not need to handle query parameters as they are parsed before path + // matching + static constexpr char separator = '/'; + + // Contains static path fragments to match against, excluding the '/' after + // path params + // Fragments are separated by path params + std::vector static_fragments_; + // Stores the names of the path parameters to be used as keys in the + // Request::path_params map + std::vector param_names_; +}; + +/** + * Performs std::regex_match on request path + * and stores the result in Request::matches + * + * Note that regex match is performed directly on the whole request. + * This means that wildcard patterns may match multiple path segments with /: + * "/begin/(.*)/end" will match both "/begin/middle/end" and "/begin/1/2/end". + */ +class RegexMatcher final : public MatcherBase { +public: + RegexMatcher(const std::string &pattern) : regex_(pattern) {} + + bool match(Request &request) const override; + +private: + std::regex regex_; +}; + +ssize_t write_headers(Stream &strm, const Headers &headers); + +} // namespace detail + class Server { public: using Handler = std::function; @@ -702,6 +868,7 @@ class Server { bool remove_mount_point(const std::string &mount_point); Server &set_file_extension_and_mimetype_mapping(const std::string &ext, const std::string &mime); + Server &set_default_file_mimetype(const std::string &mime); Server &set_file_request_handler(Handler handler); Server &set_error_handler(HandlerWithResponse handler); @@ -718,6 +885,8 @@ class Server { Server &set_socket_options(SocketOptions socket_options); Server &set_default_headers(Headers headers); + Server & + set_header_writer(std::function const &writer); Server &set_keep_alive_max_count(size_t count); Server &set_keep_alive_timeout(time_t sec); @@ -765,9 +934,14 @@ class Server { size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH; private: - using Handlers = std::vector>; + using Handlers = + std::vector, Handler>>; using HandlersForContentReader = - std::vector>; + std::vector, + HandlerWithContentReader>>; + + static std::unique_ptr + make_matcher(const std::string &pattern); socket_t create_server_socket(const std::string &host, int port, int socket_flags, @@ -778,16 +952,16 @@ class Server { bool routing(Request &req, Response &res, Stream &strm); bool handle_file_request(const Request &req, Response &res, bool head = false); - bool dispatch_request(Request &req, Response &res, const Handlers &handlers); - bool - dispatch_request_for_content_reader(Request &req, Response &res, - ContentReader content_reader, - const HandlersForContentReader &handlers); + bool dispatch_request(Request &req, Response &res, + const Handlers &handlers) const; + bool dispatch_request_for_content_reader( + Request &req, Response &res, ContentReader content_reader, + const HandlersForContentReader &handlers) const; - bool parse_request_line(const char *s, Request &req); + bool parse_request_line(const char *s, Request &req) const; void apply_ranges(const Request &req, Response &res, - std::string &content_type, std::string &boundary); - bool write_response(Stream &strm, bool close_connection, const Request &req, + std::string &content_type, std::string &boundary) const; + bool write_response(Stream &strm, bool close_connection, Request &req, Response &res); bool write_response_with_content(Stream &strm, bool close_connection, const Request &req, Response &res); @@ -806,21 +980,23 @@ class Server { bool read_content_core(Stream &strm, Request &req, Response &res, ContentReceiver receiver, MultipartContentHeader multipart_header, - ContentReceiver multipart_receiver); + ContentReceiver multipart_receiver) const; virtual bool process_and_close_socket(socket_t sock); + std::atomic is_running_{false}; + std::atomic done_{false}; + struct MountPointEntry { std::string mount_point; std::string base_dir; Headers headers; }; std::vector base_dirs_; - - std::atomic is_running_{false}; - std::atomic done_{false}; std::map file_extension_and_mimetype_map_; + std::string default_file_mimetype_ = "application/octet-stream"; Handler file_request_handler_; + Handlers get_handlers_; Handlers post_handlers_; HandlersForContentReader post_handlers_for_content_reader_; @@ -831,18 +1007,22 @@ class Server { Handlers delete_handlers_; HandlersForContentReader delete_handlers_for_content_reader_; Handlers options_handlers_; + HandlerWithResponse error_handler_; ExceptionHandler exception_handler_; HandlerWithResponse pre_routing_handler_; Handler post_routing_handler_; - Logger logger_; Expect100ContinueHandler expect_100_continue_handler_; + Logger logger_; + int address_family_ = AF_UNSPEC; bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY; SocketOptions socket_options_ = default_socket_options; Headers default_headers_; + std::function header_writer_ = + detail::write_headers; }; enum class Error { @@ -860,17 +1040,19 @@ enum class Error { UnsupportedMultipartBoundaryChars, Compression, ConnectionTimeout, + ProxyConnection, // For internal use only SSLPeerCouldBeClosed_, }; -std::string to_string(const Error error); +std::string to_string(Error error); std::ostream &operator<<(std::ostream &os, const Error &obj); class Result { public: + Result() = default; Result(std::unique_ptr &&res, Error err, Headers &&request_headers = Headers{}) : res_(std::move(res)), err_(err), @@ -893,13 +1075,13 @@ class Result { bool has_request_header(const std::string &key) const; std::string get_request_header_value(const std::string &key, size_t id = 0) const; - template - T get_request_header_value(const std::string &key, size_t id = 0) const; + uint64_t get_request_header_value_u64(const std::string &key, + size_t id = 0) const; size_t get_request_header_value_count(const std::string &key) const; private: std::unique_ptr res_; - Error err_; + Error err_ = Error::Unknown; Headers request_headers_; }; @@ -1059,16 +1241,21 @@ class ClientImpl { bool send(Request &req, Response &res, Error &error); Result send(const Request &req); - size_t is_socket_open() const; + void stop(); - socket_t socket() const; + std::string host() const; + int port() const; - void stop(); + size_t is_socket_open() const; + socket_t socket() const; void set_hostname_addr_map(std::map addr_map); void set_default_headers(Headers headers); + void + set_header_writer(std::function const &writer); + void set_address_family(int family); void set_tcp_nodelay(bool on); void set_socket_options(SocketOptions socket_options); @@ -1117,6 +1304,7 @@ class ClientImpl { void set_ca_cert_path(const std::string &ca_cert_file_path, const std::string &ca_cert_dir_path = std::string()); void set_ca_cert_store(X509_STORE *ca_cert_store); + X509_STORE *create_ca_cert_store(const char *ca_cert, std::size_t size) const; #endif #ifdef CPPHTTPLIB_OPENSSL_SUPPORT @@ -1145,14 +1333,14 @@ class ClientImpl { // Also, shutdown_ssl and close_socket should also NOT be called concurrently // with a DIFFERENT thread sending requests using that socket. virtual void shutdown_ssl(Socket &socket, bool shutdown_gracefully); - void shutdown_socket(Socket &socket); + void shutdown_socket(Socket &socket) const; void close_socket(Socket &socket); bool process_request(Stream &strm, Request &req, Response &res, bool close_connection, Error &error); bool write_content_with_provider(Stream &strm, const Request &req, - Error &error); + Error &error) const; void copy_settings(const ClientImpl &rhs); @@ -1177,6 +1365,10 @@ class ClientImpl { // Default headers Headers default_headers_; + // Header writer + std::function header_writer_ = + detail::write_headers; + // Settings std::string client_cert_path_; std::string client_key_path_; @@ -1239,7 +1431,8 @@ class ClientImpl { Result send_(Request &&req); socket_t create_client_socket(Error &error) const; - bool read_response_line(Stream &strm, const Request &req, Response &res); + bool read_response_line(Stream &strm, const Request &req, + Response &res) const; bool write_request(Stream &strm, Request &req, bool close_connection, Error &error); bool redirect(Request &req, Response &res, Error &error); @@ -1258,7 +1451,7 @@ class ClientImpl { const std::string &content_type); ContentProviderWithoutLength get_multipart_content_provider( const std::string &boundary, const MultipartFormDataItems &items, - const MultipartFormDataProviderItems &provider_items); + const MultipartFormDataProviderItems &provider_items) const; std::string adjust_host_string(const std::string &host) const; @@ -1431,16 +1624,21 @@ class Client { bool send(Request &req, Response &res, Error &error); Result send(const Request &req); - size_t is_socket_open() const; + void stop(); - socket_t socket() const; + std::string host() const; + int port() const; - void stop(); + size_t is_socket_open() const; + socket_t socket() const; void set_hostname_addr_map(std::map addr_map); void set_default_headers(Headers headers); + void + set_header_writer(std::function const &writer); + void set_address_family(int family); void set_tcp_nodelay(bool on); void set_socket_options(SocketOptions socket_options); @@ -1497,6 +1695,7 @@ class Client { const std::string &ca_cert_dir_path = std::string()); void set_ca_cert_store(X509_STORE *ca_cert_store); + void load_ca_cert_store(const char *ca_cert, std::size_t size); long get_openssl_verify_result() const; @@ -1538,7 +1737,7 @@ class SSLServer : public Server { std::mutex ctx_mutex_; }; -class SSLClient : public ClientImpl { +class SSLClient final : public ClientImpl { public: explicit SSLClient(const std::string &host); @@ -1546,16 +1745,19 @@ class SSLClient : public ClientImpl { explicit SSLClient(const std::string &host, int port, const std::string &client_cert_path, - const std::string &client_key_path); + const std::string &client_key_path, + const std::string &private_key_password = std::string()); explicit SSLClient(const std::string &host, int port, X509 *client_cert, - EVP_PKEY *client_key); + EVP_PKEY *client_key, + const std::string &private_key_password = std::string()); ~SSLClient() override; bool is_valid() const override; void set_ca_cert_store(X509_STORE *ca_cert_store); + void load_ca_cert_store(const char *ca_cert, std::size_t size); long get_openssl_verify_result() const; @@ -1564,7 +1766,7 @@ class SSLClient : public ClientImpl { private: bool create_and_connect_socket(Socket &socket, Error &error) override; void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override; - void shutdown_ssl_impl(Socket &socket, bool shutdown_socket); + void shutdown_ssl_impl(Socket &socket, bool shutdown_gracefully); bool process_socket(const Socket &socket, std::function callback) override; @@ -1608,15 +1810,9 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) { callback(static_cast(sec), static_cast(usec)); } -template -inline T get_header_value(const Headers & /*headers*/, - const std::string & /*key*/, size_t /*id*/ = 0, - uint64_t /*def*/ = 0) {} - -template <> -inline uint64_t get_header_value(const Headers &headers, - const std::string &key, size_t id, - uint64_t def) { +inline uint64_t get_header_value_u64(const Headers &headers, + const std::string &key, size_t id, + uint64_t def) { auto rng = headers.equal_range(key); auto it = rng.first; std::advance(it, static_cast(id)); @@ -1628,14 +1824,14 @@ inline uint64_t get_header_value(const Headers &headers, } // namespace detail -template -inline T Request::get_header_value(const std::string &key, size_t id) const { - return detail::get_header_value(headers, key, id, 0); +inline uint64_t Request::get_header_value_u64(const std::string &key, + size_t id) const { + return detail::get_header_value_u64(headers, key, id, 0); } -template -inline T Response::get_header_value(const std::string &key, size_t id) const { - return detail::get_header_value(headers, key, id, 0); +inline uint64_t Response::get_header_value_u64(const std::string &key, + size_t id) const { + return detail::get_header_value_u64(headers, key, id, 0); } template @@ -1665,21 +1861,106 @@ inline ssize_t Stream::write_format(const char *fmt, const Args &...args) { inline void default_socket_options(socket_t sock) { int yes = 1; #ifdef _WIN32 - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes), - sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, + reinterpret_cast(&yes), sizeof(yes)); setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE, - reinterpret_cast(&yes), sizeof(yes)); + reinterpret_cast(&yes), sizeof(yes)); #else #ifdef SO_REUSEPORT - setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast(&yes), - sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, + reinterpret_cast(&yes), sizeof(yes)); #else - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes), - sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, + reinterpret_cast(&yes), sizeof(yes)); #endif #endif } +inline const char *status_message(int status) { + switch (status) { + case StatusCode::Continue_100: return "Continue"; + case StatusCode::SwitchingProtocol_101: return "Switching Protocol"; + case StatusCode::Processing_102: return "Processing"; + case StatusCode::EarlyHints_103: return "Early Hints"; + case StatusCode::OK_200: return "OK"; + case StatusCode::Created_201: return "Created"; + case StatusCode::Accepted_202: return "Accepted"; + case StatusCode::NonAuthoritativeInformation_203: + return "Non-Authoritative Information"; + case StatusCode::NoContent_204: return "No Content"; + case StatusCode::ResetContent_205: return "Reset Content"; + case StatusCode::PartialContent_206: return "Partial Content"; + case StatusCode::MultiStatus_207: return "Multi-Status"; + case StatusCode::AlreadyReported_208: return "Already Reported"; + case StatusCode::IMUsed_226: return "IM Used"; + case StatusCode::MultipleChoices_300: return "Multiple Choices"; + case StatusCode::MovedPermanently_301: return "Moved Permanently"; + case StatusCode::Found_302: return "Found"; + case StatusCode::SeeOther_303: return "See Other"; + case StatusCode::NotModified_304: return "Not Modified"; + case StatusCode::UseProxy_305: return "Use Proxy"; + case StatusCode::unused_306: return "unused"; + case StatusCode::TemporaryRedirect_307: return "Temporary Redirect"; + case StatusCode::PermanentRedirect_308: return "Permanent Redirect"; + case StatusCode::BadRequest_400: return "Bad Request"; + case StatusCode::Unauthorized_401: return "Unauthorized"; + case StatusCode::PaymentRequired_402: return "Payment Required"; + case StatusCode::Forbidden_403: return "Forbidden"; + case StatusCode::NotFound_404: return "Not Found"; + case StatusCode::MethodNotAllowed_405: return "Method Not Allowed"; + case StatusCode::NotAcceptable_406: return "Not Acceptable"; + case StatusCode::ProxyAuthenticationRequired_407: + return "Proxy Authentication Required"; + case StatusCode::RequestTimeout_408: return "Request Timeout"; + case StatusCode::Conflict_409: return "Conflict"; + case StatusCode::Gone_410: return "Gone"; + case StatusCode::LengthRequired_411: return "Length Required"; + case StatusCode::PreconditionFailed_412: return "Precondition Failed"; + case StatusCode::PayloadTooLarge_413: return "Payload Too Large"; + case StatusCode::UriTooLong_414: return "URI Too Long"; + case StatusCode::UnsupportedMediaType_415: return "Unsupported Media Type"; + case StatusCode::RangeNotSatisfiable_416: return "Range Not Satisfiable"; + case StatusCode::ExpectationFailed_417: return "Expectation Failed"; + case StatusCode::ImATeapot_418: return "I'm a teapot"; + case StatusCode::MisdirectedRequest_421: return "Misdirected Request"; + case StatusCode::UnprocessableContent_422: return "Unprocessable Content"; + case StatusCode::Locked_423: return "Locked"; + case StatusCode::FailedDependency_424: return "Failed Dependency"; + case StatusCode::TooEarly_425: return "Too Early"; + case StatusCode::UpgradeRequired_426: return "Upgrade Required"; + case StatusCode::PreconditionRequired_428: return "Precondition Required"; + case StatusCode::TooManyRequests_429: return "Too Many Requests"; + case StatusCode::RequestHeaderFieldsTooLarge_431: + return "Request Header Fields Too Large"; + case StatusCode::UnavailableForLegalReasons_451: + return "Unavailable For Legal Reasons"; + case StatusCode::NotImplemented_501: return "Not Implemented"; + case StatusCode::BadGateway_502: return "Bad Gateway"; + case StatusCode::ServiceUnavailable_503: return "Service Unavailable"; + case StatusCode::GatewayTimeout_504: return "Gateway Timeout"; + case StatusCode::HttpVersionNotSupported_505: + return "HTTP Version Not Supported"; + case StatusCode::VariantAlsoNegotiates_506: return "Variant Also Negotiates"; + case StatusCode::InsufficientStorage_507: return "Insufficient Storage"; + case StatusCode::LoopDetected_508: return "Loop Detected"; + case StatusCode::NotExtended_510: return "Not Extended"; + case StatusCode::NetworkAuthenticationRequired_511: + return "Network Authentication Required"; + + default: + case StatusCode::InternalServerError_500: return "Internal Server Error"; + } +} + +inline std::string get_bearer_token_auth(const Request &req) { + if (req.has_header("Authorization")) { + static std::string BearerHeaderPrefix = "Bearer "; + return req.get_header_value("Authorization") + .substr(BearerHeaderPrefix.length()); + } + return ""; +} + template inline Server & Server::set_read_timeout(const std::chrono::duration &duration) { @@ -1720,6 +2001,7 @@ inline std::string to_string(const Error error) { return "Unsupported HTTP multipart boundary characters"; case Error::Compression: return "Compression failed"; case Error::ConnectionTimeout: return "Connection timed out"; + case Error::ProxyConnection: return "Proxy connection failed"; case Error::Unknown: return "Unknown"; default: break; } @@ -1733,10 +2015,9 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) { return os; } -template -inline T Result::get_request_header_value(const std::string &key, - size_t id) const { - return detail::get_header_value(request_headers_, key, id, 0); +inline uint64_t Result::get_request_header_value_u64(const std::string &key, + size_t id) const { + return detail::get_header_value_u64(request_headers_, key, id, 0); } template @@ -1790,7 +2071,7 @@ void hosted_at(const std::string &hostname, std::vector &addrs); std::string append_query_params(const std::string &path, const Params ¶ms); -std::pair make_range_header(Ranges ranges); +std::pair make_range_header(const Ranges &ranges); std::pair make_basic_authentication_header(const std::string &username, @@ -1810,6 +2091,9 @@ std::string trim_copy(const std::string &s); void split(const char *b, const char *e, char d, std::function fn); +void split(const char *b, const char *e, char d, size_t m, + std::function fn); + bool process_client_socket(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec, @@ -1844,7 +2128,7 @@ enum class EncodingType { None = 0, Gzip, Brotli }; EncodingType encoding_type(const Request &req, const Response &res); -class BufferStream : public Stream { +class BufferStream final : public Stream { public: BufferStream() = default; ~BufferStream() override = default; @@ -1884,19 +2168,19 @@ class decompressor { Callback callback) = 0; }; -class nocompressor : public compressor { +class nocompressor final : public compressor { public: - virtual ~nocompressor() = default; + ~nocompressor() override = default; bool compress(const char *data, size_t data_length, bool /*last*/, Callback callback) override; }; #ifdef CPPHTTPLIB_ZLIB_SUPPORT -class gzip_compressor : public compressor { +class gzip_compressor final : public compressor { public: gzip_compressor(); - ~gzip_compressor(); + ~gzip_compressor() override; bool compress(const char *data, size_t data_length, bool last, Callback callback) override; @@ -1906,10 +2190,10 @@ class gzip_compressor : public compressor { z_stream strm_; }; -class gzip_decompressor : public decompressor { +class gzip_decompressor final : public decompressor { public: gzip_decompressor(); - ~gzip_decompressor(); + ~gzip_decompressor() override; bool is_valid() const override; @@ -1923,7 +2207,7 @@ class gzip_decompressor : public decompressor { #endif #ifdef CPPHTTPLIB_BROTLI_SUPPORT -class brotli_compressor : public compressor { +class brotli_compressor final : public compressor { public: brotli_compressor(); ~brotli_compressor(); @@ -1935,7 +2219,7 @@ class brotli_compressor : public compressor { BrotliEncoderState *state_ = nullptr; }; -class brotli_decompressor : public decompressor { +class brotli_decompressor final : public decompressor { public: brotli_decompressor(); ~brotli_decompressor(); @@ -1972,6 +2256,29 @@ class stream_line_reader { std::string glowable_buffer_; }; +class mmap { +public: + mmap(const char *path); + ~mmap(); + + bool open(const char *path); + void close(); + + bool is_open() const; + size_t size() const; + const char *data() const; + +private: +#if defined(_WIN32) + HANDLE hFile_; + HANDLE hMapping_; +#else + int fd_; +#endif + size_t size_; + void *addr_; +}; + } // namespace detail // ---------------------------------------------------------------------------- @@ -2003,7 +2310,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt, val = 0; for (; cnt; i++, cnt--) { if (!s[i]) { return false; } - int v = 0; + auto v = 0; if (is_hex(s[i], v)) { val = val * 16 + v; } else { @@ -2014,7 +2321,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt, } inline std::string from_i_to_hex(size_t n) { - const char *charset = "0123456789abcdef"; + static const auto charset = "0123456789abcdef"; std::string ret; do { ret = charset[n & 15] + ret; @@ -2025,7 +2332,7 @@ inline std::string from_i_to_hex(size_t n) { inline size_t to_utf8(int code, char *buff) { if (code < 0x0080) { - buff[0] = (code & 0x7F); + buff[0] = static_cast(code & 0x7F); return 1; } else if (code < 0x0800) { buff[0] = static_cast(0xC0 | ((code >> 6) & 0x1F)); @@ -2064,8 +2371,8 @@ inline std::string base64_encode(const std::string &in) { std::string out; out.reserve(in.size()); - int val = 0; - int valb = -6; + auto val = 0; + auto valb = -6; for (auto c : in) { val = (val << 8) + static_cast(c); @@ -2112,6 +2419,11 @@ inline bool is_valid_path(const std::string &path) { // Read component auto beg = i; while (i < path.size() && path[i] != '/') { + if (path[i] == '\0') { + return false; + } else if (path[i] == '\\') { + return false; + } i++; } @@ -2196,7 +2508,7 @@ inline std::string decode_url(const std::string &s, for (size_t i = 0; i < s.size(); i++) { if (s[i] == '%' && i + 1 < s.size()) { if (s[i + 1] == 'u') { - int val = 0; + auto val = 0; if (from_hex_to_i(s, i + 2, 4, val)) { // 4 digits Unicode codes char buff[4]; @@ -2207,7 +2519,7 @@ inline std::string decode_url(const std::string &s, result += s[i]; } } else { - int val = 0; + auto val = 0; if (from_hex_to_i(s, i + 1, 2, val)) { // 2 digits hex codes result += static_cast(val); @@ -2260,16 +2572,30 @@ inline std::string trim_copy(const std::string &s) { return s.substr(r.first, r.second - r.first); } +inline std::string trim_double_quotes_copy(const std::string &s) { + if (s.length() >= 2 && s.front() == '"' && s.back() == '"') { + return s.substr(1, s.size() - 2); + } + return s; +} + inline void split(const char *b, const char *e, char d, std::function fn) { + return split(b, e, d, (std::numeric_limits::max)(), std::move(fn)); +} + +inline void split(const char *b, const char *e, char d, size_t m, + std::function fn) { size_t i = 0; size_t beg = 0; + size_t count = 1; while (e ? (b + i < e) : (b[i] != '\0')) { - if (b[i] == d) { + if (b[i] == d && count < m) { auto r = trim(b, e, beg, i); if (r.first < r.second) { fn(&b[r.first], &b[r.second]); } beg = i + 1; + count++; } i++; } @@ -2345,6 +2671,105 @@ inline void stream_line_reader::append(char c) { } } +inline mmap::mmap(const char *path) +#if defined(_WIN32) + : hFile_(NULL), hMapping_(NULL) +#else + : fd_(-1) +#endif + , + size_(0), addr_(nullptr) { + open(path); +} + +inline mmap::~mmap() { close(); } + +inline bool mmap::open(const char *path) { + close(); + +#if defined(_WIN32) + std::wstring wpath; + for (size_t i = 0; i < strlen(path); i++) { + wpath += path[i]; + } + + hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, + OPEN_EXISTING, NULL); + + if (hFile_ == INVALID_HANDLE_VALUE) { return false; } + + LARGE_INTEGER size{}; + if (!::GetFileSizeEx(hFile_, &size)) { return false; } + size_ = static_cast(size.QuadPart); + + hMapping_ = + ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL); + + if (hMapping_ == NULL) { + close(); + return false; + } + + addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0); +#else + fd_ = ::open(path, O_RDONLY); + if (fd_ == -1) { return false; } + + struct stat sb; + if (fstat(fd_, &sb) == -1) { + close(); + return false; + } + size_ = static_cast(sb.st_size); + + addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0); +#endif + + if (addr_ == nullptr) { + close(); + return false; + } + + return true; +} + +inline bool mmap::is_open() const { return addr_ != nullptr; } + +inline size_t mmap::size() const { return size_; } + +inline const char *mmap::data() const { + return static_cast(addr_); +} + +inline void mmap::close() { +#if defined(_WIN32) + if (addr_) { + ::UnmapViewOfFile(addr_); + addr_ = nullptr; + } + + if (hMapping_) { + ::CloseHandle(hMapping_); + hMapping_ = NULL; + } + + if (hFile_ != INVALID_HANDLE_VALUE) { + ::CloseHandle(hFile_); + hFile_ = INVALID_HANDLE_VALUE; + } +#else + if (addr_ != nullptr) { + munmap(addr_, size_); + addr_ = nullptr; + } + + if (fd_ != -1) { + ::close(fd_); + fd_ = -1; + } +#endif + size_ = 0; +} inline int close_socket(socket_t sock) { #ifdef _WIN32 return closesocket(sock); @@ -2354,7 +2779,7 @@ inline int close_socket(socket_t sock) { } template inline ssize_t handle_EINTR(T fn) { - ssize_t res = false; + ssize_t res = 0; while (true) { res = fn(); if (res < 0 && errno == EINTR) { continue; } @@ -2399,7 +2824,7 @@ inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) { return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); #else #ifndef _WIN32 - if (sock >= FD_SETSIZE) { return 1; } + if (sock >= FD_SETSIZE) { return -1; } #endif fd_set fds; @@ -2427,7 +2852,7 @@ inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) { return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); #else #ifndef _WIN32 - if (sock >= FD_SETSIZE) { return 1; } + if (sock >= FD_SETSIZE) { return -1; } #endif fd_set fds; @@ -2458,7 +2883,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec, if (poll_res == 0) { return Error::ConnectionTimeout; } if (poll_res > 0 && pfd_read.revents & (POLLIN | POLLOUT)) { - int error = 0; + auto error = 0; socklen_t len = sizeof(error); auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len); @@ -2490,7 +2915,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec, if (ret == 0) { return Error::ConnectionTimeout; } if (ret > 0 && (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) { - int error = 0; + auto error = 0; socklen_t len = sizeof(error); auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len); @@ -2512,7 +2937,7 @@ inline bool is_socket_alive(socket_t sock) { return detail::read_socket(sock, &buf[0], sizeof(buf), MSG_PEEK) > 0; } -class SocketStream : public Stream { +class SocketStream final : public Stream { public: SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec); @@ -2537,11 +2962,11 @@ class SocketStream : public Stream { size_t read_buff_off_ = 0; size_t read_buff_content_size_ = 0; - static const size_t read_buff_size_ = 1024 * 4; + static const size_t read_buff_size_ = 1024l * 4; }; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT -class SSLSocketStream : public Stream { +class SSLSocketStream final : public Stream { public: SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, @@ -2666,7 +3091,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #ifndef _WIN32 if (hints.ai_family == AF_UNIX) { const auto addrlen = host.length(); - if (addrlen > sizeof(sockaddr_un::sun_path)) return INVALID_SOCKET; + if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; } auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol); if (sock != INVALID_SOCKET) { @@ -2735,17 +3160,27 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #endif if (tcp_nodelay) { - int yes = 1; - setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&yes), - sizeof(yes)); + auto yes = 1; +#ifdef _WIN32 + setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, + reinterpret_cast(&yes), sizeof(yes)); +#else + setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, + reinterpret_cast(&yes), sizeof(yes)); +#endif } if (socket_options) { socket_options(sock); } if (rp->ai_family == AF_INET6) { - int no = 0; - setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, reinterpret_cast(&no), - sizeof(no)); + auto no = 0; +#ifdef _WIN32 + setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, + reinterpret_cast(&no), sizeof(no)); +#else + setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, + reinterpret_cast(&no), sizeof(no)); +#endif } // bind or connect @@ -2804,7 +3239,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { return ret; } -#if !defined _WIN32 && !defined ANDROID && !defined _AIX +#if !defined _WIN32 && !defined ANDROID && !defined _AIX && !defined __MVS__ #define USE_IF2IP #endif @@ -2860,7 +3295,7 @@ inline socket_t create_client_socket( #ifdef USE_IF2IP auto ip_from_if = if2ip(address_family, intf); if (ip_from_if.empty()) { ip_from_if = intf; } - if (!bind_ip_address(sock2, ip_from_if.c_str())) { + if (!bind_ip_address(sock2, ip_from_if)) { error = Error::BindIPAddress; return false; } @@ -2888,13 +3323,14 @@ inline socket_t create_client_socket( #ifdef _WIN32 auto timeout = static_cast(read_timeout_sec * 1000 + read_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(read_timeout_sec); tv.tv_usec = static_cast(read_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } { @@ -2902,13 +3338,14 @@ inline socket_t create_client_socket( #ifdef _WIN32 auto timeout = static_cast(write_timeout_sec * 1000 + write_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(write_timeout_sec); tv.tv_usec = static_cast(write_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } @@ -3008,18 +3445,20 @@ inline constexpr unsigned int operator"" _t(const char *s, size_t l) { } // namespace udl -inline const char * +inline std::string find_content_type(const std::string &path, - const std::map &user_data) { + const std::map &user_data, + const std::string &default_content_type) { auto ext = file_extension(path); auto it = user_data.find(ext); - if (it != user_data.end()) { return it->second.c_str(); } + if (it != user_data.end()) { return it->second; } using udl::operator""_t; switch (str2tag(ext)) { - default: return nullptr; + default: return default_content_type; + case "css"_t: return "text/css"; case "csv"_t: return "text/csv"; case "htm"_t: @@ -3072,76 +3511,6 @@ find_content_type(const std::string &path, } } -inline const char *status_message(int status) { - switch (status) { - case 100: return "Continue"; - case 101: return "Switching Protocol"; - case 102: return "Processing"; - case 103: return "Early Hints"; - case 200: return "OK"; - case 201: return "Created"; - case 202: return "Accepted"; - case 203: return "Non-Authoritative Information"; - case 204: return "No Content"; - case 205: return "Reset Content"; - case 206: return "Partial Content"; - case 207: return "Multi-Status"; - case 208: return "Already Reported"; - case 226: return "IM Used"; - case 300: return "Multiple Choice"; - case 301: return "Moved Permanently"; - case 302: return "Found"; - case 303: return "See Other"; - case 304: return "Not Modified"; - case 305: return "Use Proxy"; - case 306: return "unused"; - case 307: return "Temporary Redirect"; - case 308: return "Permanent Redirect"; - case 400: return "Bad Request"; - case 401: return "Unauthorized"; - case 402: return "Payment Required"; - case 403: return "Forbidden"; - case 404: return "Not Found"; - case 405: return "Method Not Allowed"; - case 406: return "Not Acceptable"; - case 407: return "Proxy Authentication Required"; - case 408: return "Request Timeout"; - case 409: return "Conflict"; - case 410: return "Gone"; - case 411: return "Length Required"; - case 412: return "Precondition Failed"; - case 413: return "Payload Too Large"; - case 414: return "URI Too Long"; - case 415: return "Unsupported Media Type"; - case 416: return "Range Not Satisfiable"; - case 417: return "Expectation Failed"; - case 418: return "I'm a teapot"; - case 421: return "Misdirected Request"; - case 422: return "Unprocessable Entity"; - case 423: return "Locked"; - case 424: return "Failed Dependency"; - case 425: return "Too Early"; - case 426: return "Upgrade Required"; - case 428: return "Precondition Required"; - case 429: return "Too Many Requests"; - case 431: return "Request Header Fields Too Large"; - case 451: return "Unavailable For Legal Reasons"; - case 501: return "Not Implemented"; - case 502: return "Bad Gateway"; - case 503: return "Service Unavailable"; - case 504: return "Gateway Timeout"; - case 505: return "HTTP Version Not Supported"; - case 506: return "Variant Also Negotiates"; - case 507: return "Insufficient Storage"; - case 508: return "Loop Detected"; - case 510: return "Not Extended"; - case 511: return "Network Authentication Required"; - - default: - case 500: return "Internal Server Error"; - } -} - inline bool can_compress_content_type(const std::string &content_type) { using udl::operator""_t; @@ -3218,7 +3587,7 @@ inline bool gzip_compressor::compress(const char *data, size_t data_length, data += strm_.avail_in; auto flush = (last && data_length == 0) ? Z_FINISH : Z_NO_FLUSH; - int ret = Z_OK; + auto ret = Z_OK; std::array buff{}; do { @@ -3262,7 +3631,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, Callback callback) { assert(is_valid_); - int ret = Z_OK; + auto ret = Z_OK; do { constexpr size_t max_avail_in = @@ -3276,16 +3645,12 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, data += strm_.avail_in; std::array buff{}; - while (strm_.avail_in > 0) { + while (strm_.avail_in > 0 && ret == Z_OK) { strm_.avail_out = static_cast(buff.size()); strm_.next_out = reinterpret_cast(buff.data()); - auto prev_avail_in = strm_.avail_in; - ret = inflate(&strm_, Z_NO_FLUSH); - if (prev_avail_in - strm_.avail_in == 0) { return false; } - assert(ret != Z_STREAM_ERROR); switch (ret) { case Z_NEED_DICT: @@ -3298,7 +3663,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, } } - if (ret != Z_OK && ret != Z_STREAM_END) return false; + if (ret != Z_OK && ret != Z_STREAM_END) { return false; } } while (data_length > 0); @@ -3367,7 +3732,7 @@ inline bool brotli_decompressor::decompress(const char *data, return 0; } - const uint8_t *next_in = (const uint8_t *)data; + auto next_in = reinterpret_cast(data); size_t avail_in = data_length; size_t total_out; @@ -3437,6 +3802,9 @@ inline bool parse_header(const char *beg, const char *end, T fn) { } if (p < end) { + auto key_len = key_end - beg; + if (!key_len) { return false; } + auto key = std::string(beg, key_end); auto val = compare_case_ignore(key, "Location") ? std::string(p, end) @@ -3526,11 +3894,7 @@ inline bool read_content_without_length(Stream &strm, uint64_t r = 0; for (;;) { auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ); - if (n < 0) { - return false; - } else if (n == 0) { - return true; - } + if (n <= 0) { return true; } if (!out(buf, static_cast(n), r, 0)) { return false; } r += static_cast(n); @@ -3566,7 +3930,7 @@ inline bool read_content_chunked(Stream &strm, T &x, if (!line_reader.getline()) { return false; } - if (strcmp(line_reader.ptr(), "\r\n")) { return false; } + if (strcmp(line_reader.ptr(), "\r\n") != 0) { return false; } if (!line_reader.getline()) { return false; } } @@ -3576,7 +3940,7 @@ inline bool read_content_chunked(Stream &strm, T &x, // Trailer if (!line_reader.getline()) { return false; } - while (strcmp(line_reader.ptr(), "\r\n")) { + while (strcmp(line_reader.ptr(), "\r\n") != 0) { if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; } // Exclude line terminator @@ -3595,8 +3959,8 @@ inline bool read_content_chunked(Stream &strm, T &x, } inline bool is_chunked_transfer_encoding(const Headers &headers) { - return !strcasecmp(get_header_value(headers, "Transfer-Encoding", 0, ""), - "chunked"); + return compare_case_ignore( + get_header_value(headers, "Transfer-Encoding", 0, ""), "chunked"); } template @@ -3611,14 +3975,14 @@ bool prepare_content_receiver(T &x, int &status, #ifdef CPPHTTPLIB_ZLIB_SUPPORT decompressor = detail::make_unique(); #else - status = 415; + status = StatusCode::UnsupportedMediaType_415; return false; #endif } else if (encoding.find("br") != std::string::npos) { #ifdef CPPHTTPLIB_BROTLI_SUPPORT decompressor = detail::make_unique(); #else - status = 415; + status = StatusCode::UnsupportedMediaType_415; return false; #endif } @@ -3634,7 +3998,7 @@ bool prepare_content_receiver(T &x, int &status, }; return callback(std::move(out)); } else { - status = 500; + status = StatusCode::InternalServerError_500; return false; } } @@ -3662,7 +4026,7 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } else if (!has_header(x.headers, "Content-Length")) { ret = read_content_without_length(strm, out); } else { - auto len = get_header_value(x.headers, "Content-Length"); + auto len = get_header_value_u64(x.headers, "Content-Length", 0, 0); if (len > payload_max_length) { exceed_payload_max_length = true; skip_content_with_length(strm, len); @@ -3672,7 +4036,10 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } } - if (!ret) { status = exceed_payload_max_length ? 413 : 400; } + if (!ret) { + status = exceed_payload_max_length ? StatusCode::PayloadTooLarge_413 + : StatusCode::BadRequest_400; + } return ret; }); } // namespace detail @@ -3720,6 +4087,8 @@ inline bool write_content(Stream &strm, const ContentProvider &content_provider, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + while (offset < end_offset && !is_shutting_down()) { if (!strm.is_writable()) { error = Error::Write; @@ -3764,6 +4133,8 @@ write_content_without_length(Stream &strm, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + data_sink.done = [&](void) { data_available = false; }; while (data_available && !is_shutting_down()) { @@ -3814,6 +4185,8 @@ write_content_chunked(Stream &strm, const ContentProvider &content_provider, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + auto done_with_trailer = [&](const Headers *trailer) { if (!ok) { return; } @@ -3898,7 +4271,8 @@ inline bool redirect(T &cli, Request &req, Response &res, new_req.path = path; new_req.redirect_count_ -= 1; - if (res.status == 303 && (req.method != "GET" && req.method != "HEAD")) { + if (res.status == StatusCode::SeeOther_303 && + (req.method != "GET" && req.method != "HEAD")) { new_req.method = "GET"; new_req.body.clear(); new_req.headers.clear(); @@ -3910,7 +4284,8 @@ inline bool redirect(T &cli, Request &req, Response &res, if (ret) { req = new_req; res = new_res; - res.location = location; + + if (res.location.empty()) { res.location = location; } } return ret; } @@ -3957,14 +4332,34 @@ inline bool parse_multipart_boundary(const std::string &content_type, if (pos == std::string::npos) { return false; } auto end = content_type.find(';', pos); auto beg = pos + strlen(boundary_keyword); - boundary = content_type.substr(beg, end - beg); - if (boundary.length() >= 2 && boundary.front() == '"' && - boundary.back() == '"') { - boundary = boundary.substr(1, boundary.size() - 2); - } + boundary = trim_double_quotes_copy(content_type.substr(beg, end - beg)); return !boundary.empty(); } +inline void parse_disposition_params(const std::string &s, Params ¶ms) { + std::set cache; + split(s.data(), s.data() + s.size(), ';', [&](const char *b, const char *e) { + std::string kv(b, e); + if (cache.find(kv) != cache.end()) { return; } + cache.insert(kv); + + std::string key; + std::string val; + split(b, e, '=', [&](const char *b2, const char *e2) { + if (key.empty()) { + key.assign(b2, e2); + } else { + val.assign(b2, e2); + } + }); + + if (!key.empty()) { + params.emplace(trim_double_quotes_copy((key)), + trim_double_quotes_copy((val))); + } + }); +} + #ifdef CPPHTTPLIB_NO_EXCEPTIONS inline bool parse_range_header(const std::string &s, Ranges &ranges) { #else @@ -3975,9 +4370,9 @@ inline bool parse_range_header(const std::string &s, Ranges &ranges) try { if (std::regex_match(s, m, re_first_range)) { auto pos = static_cast(m.position(1)); auto len = static_cast(m.length(1)); - bool all_valid_ranges = true; + auto all_valid_ranges = true; split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) { - if (!all_valid_ranges) return; + if (!all_valid_ranges) { return; } static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))"); std::cmatch cm; if (std::regex_match(b, e, cm, re_another_range)) { @@ -4022,11 +4417,6 @@ class MultipartFormDataParser { bool parse(const char *buf, size_t n, const ContentReceiver &content_callback, const MultipartContentHeader &header_callback) { - // TODO: support 'filename*' - static const std::regex re_content_disposition( - R"~(^Content-Disposition:\s*form-data;\s*name="(.*?)"(?:;\s*filename="(.*?)")?(?:;\s*filename\*=\S+)?\s*$)~", - std::regex_constants::icase); - buf_append(buf, n); while (buf_size() > 0) { @@ -4059,18 +4449,54 @@ class MultipartFormDataParser { break; } - static const std::string header_name = "content-type:"; const auto header = buf_head(pos); - if (start_with_case_ignore(header, header_name)) { - file_.content_type = trim_copy(header.substr(header_name.size())); + + if (!parse_header(header.data(), header.data() + header.size(), + [&](std::string &&, std::string &&) {})) { + is_valid_ = false; + return false; + } + + static const std::string header_content_type = "Content-Type:"; + + if (start_with_case_ignore(header, header_content_type)) { + file_.content_type = + trim_copy(header.substr(header_content_type.size())); } else { + static const std::regex re_content_disposition( + R"~(^Content-Disposition:\s*form-data;\s*(.*)$)~", + std::regex_constants::icase); + std::smatch m; if (std::regex_match(header, m, re_content_disposition)) { - file_.name = m[1]; - file_.filename = m[2]; - } else { - is_valid_ = false; - return false; + Params params; + parse_disposition_params(m[1], params); + + auto it = params.find("name"); + if (it != params.end()) { + file_.name = it->second; + } else { + is_valid_ = false; + return false; + } + + it = params.find("filename"); + if (it != params.end()) { file_.filename = it->second; } + + it = params.find("filename*"); + if (it != params.end()) { + // Only allow UTF-8 enconnding... + static const std::regex re_rfc5987_encoding( + R"~(^UTF-8''(.+?)$)~", std::regex_constants::icase); + + std::smatch m2; + if (std::regex_match(it->second, m2, re_rfc5987_encoding)) { + file_.filename = decode_url(m2[1], false); // override... + } else { + is_valid_ = false; + return false; + } + } } } buf_erase(pos + crlf_.size()); @@ -4108,9 +4534,9 @@ class MultipartFormDataParser { buf_erase(crlf_.size()); state_ = 1; } else { - if (dash_crlf_.size() > buf_size()) { return true; } - if (buf_start_with(dash_crlf_)) { - buf_erase(dash_crlf_.size()); + if (dash_.size() > buf_size()) { return true; } + if (buf_start_with(dash_)) { + buf_erase(dash_.size()); is_valid_ = true; buf_erase(buf_size()); // Remove epilogue } else { @@ -4143,7 +4569,6 @@ class MultipartFormDataParser { const std::string dash_ = "--"; const std::string crlf_ = "\r\n"; - const std::string dash_crlf_ = "--\r\n"; std::string boundary_; std::string dash_boundary_crlf_; std::string crlf_dash_boundary_; @@ -4230,28 +4655,32 @@ inline std::string to_lower(const char *beg, const char *end) { return out; } -inline std::string make_multipart_data_boundary() { +inline std::string random_string(size_t length) { static const char data[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; // std::random_device might actually be deterministic on some // platforms, but due to lack of support in the c++ standard library, // doing better requires either some ugly hacks or breaking portability. - std::random_device seed_gen; + static std::random_device seed_gen; // Request 128 bits of entropy for initialization - std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), seed_gen()}; - std::mt19937 engine(seed_sequence); + static std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), + seed_gen()}; - std::string result = "--cpp-httplib-multipart-data-"; + static std::mt19937 engine(seed_sequence); - for (auto i = 0; i < 16; i++) { + std::string result; + for (size_t i = 0; i < length; i++) { result += data[engine() % (sizeof(data) - 1)]; } - return result; } +inline std::string make_multipart_data_boundary() { + return "--cpp-httplib-multipart-data-" + detail::random_string(16); +} + inline bool is_multipart_boundary_chars_valid(const std::string &boundary) { auto valid = true; for (size_t i = 0; i < boundary.size(); i++) { @@ -4304,48 +4733,97 @@ serialize_multipart_formdata(const MultipartFormDataItems &items, body += item.content + serialize_multipart_formdata_item_end(); } - if (finish) body += serialize_multipart_formdata_finish(boundary); + if (finish) { body += serialize_multipart_formdata_finish(boundary); } return body; } -inline std::pair -get_range_offset_and_length(const Request &req, size_t content_length, - size_t index) { - auto r = req.ranges[index]; +inline bool range_error(Request &req, Response &res) { + if (!req.ranges.empty() && 200 <= res.status && res.status < 300) { + ssize_t contant_len = static_cast( + res.content_length_ ? res.content_length_ : res.body.size()); - if (r.first == -1 && r.second == -1) { - return std::make_pair(0, content_length); - } + ssize_t prev_first_pos = -1; + ssize_t prev_last_pos = -1; + size_t overwrapping_count = 0; - auto slen = static_cast(content_length); + // NOTE: The following Range check is based on '14.2. Range' in RFC 9110 + // 'HTTP Semantics' to avoid potential denial-of-service attacks. + // https://www.rfc-editor.org/rfc/rfc9110#section-14.2 - if (r.first == -1) { - r.first = (std::max)(static_cast(0), slen - r.second); - r.second = slen - 1; + // Too many ranges + if (req.ranges.size() > CPPHTTPLIB_RANGE_MAX_COUNT) { return true; } + + for (auto &r : req.ranges) { + auto &first_pos = r.first; + auto &last_pos = r.second; + + if (first_pos == -1 && last_pos == -1) { + first_pos = 0; + last_pos = contant_len; + } + + if (first_pos == -1) { + first_pos = contant_len - last_pos; + last_pos = contant_len - 1; + } + + if (last_pos == -1) { last_pos = contant_len - 1; } + + // Range must be within content length + if (!(0 <= first_pos && first_pos <= last_pos && + last_pos <= contant_len - 1)) { + return true; + } + + // Ranges must be in ascending order + if (first_pos <= prev_first_pos) { return true; } + + // Request must not have more than two overlapping ranges + if (first_pos <= prev_last_pos) { + overwrapping_count++; + if (overwrapping_count > 2) { return true; } + } + + prev_first_pos = (std::max)(prev_first_pos, first_pos); + prev_last_pos = (std::max)(prev_last_pos, last_pos); + } } - if (r.second == -1) { r.second = slen - 1; } + return false; +} + +inline std::pair +get_range_offset_and_length(Range r, size_t content_length) { + (void)(content_length); // patch to get rid of "unused parameter" on release build + assert(r.first != -1 && r.second != -1); + assert(0 <= r.first && r.first < static_cast(content_length)); + assert(r.first <= r.second && + r.second < static_cast(content_length)); + return std::make_pair(r.first, static_cast(r.second - r.first) + 1); } -inline std::string make_content_range_header_field(size_t offset, size_t length, - size_t content_length) { +inline std::string make_content_range_header_field( + const std::pair &offset_and_length, size_t content_length) { + auto st = offset_and_length.first; + auto ed = st + offset_and_length.second - 1; + std::string field = "bytes "; - field += std::to_string(offset); + field += std::to_string(st); field += "-"; - field += std::to_string(offset + length - 1); + field += std::to_string(ed); field += "/"; field += std::to_string(content_length); return field; } template -bool process_multipart_ranges_data(const Request &req, Response &res, +bool process_multipart_ranges_data(const Request &req, const std::string &boundary, const std::string &content_type, - SToken stoken, CToken ctoken, - Content content) { + size_t content_length, SToken stoken, + CToken ctoken, Content content) { for (size_t i = 0; i < req.ranges.size(); i++) { ctoken("--"); stoken(boundary); @@ -4356,50 +4834,51 @@ bool process_multipart_ranges_data(const Request &req, Response &res, ctoken("\r\n"); } - auto offsets = get_range_offset_and_length(req, res.body.size(), i); - auto offset = offsets.first; - auto length = offsets.second; + auto offset_and_length = + get_range_offset_and_length(req.ranges[i], content_length); ctoken("Content-Range: "); - stoken(make_content_range_header_field(offset, length, res.body.size())); + stoken(make_content_range_header_field(offset_and_length, content_length)); ctoken("\r\n"); ctoken("\r\n"); - if (!content(offset, length)) { return false; } + + if (!content(offset_and_length.first, offset_and_length.second)) { + return false; + } ctoken("\r\n"); } ctoken("--"); stoken(boundary); - ctoken("--\r\n"); + ctoken("--"); return true; } -inline bool make_multipart_ranges_data(const Request &req, Response &res, +inline void make_multipart_ranges_data(const Request &req, Response &res, const std::string &boundary, const std::string &content_type, + size_t content_length, std::string &data) { - return process_multipart_ranges_data( - req, res, boundary, content_type, + process_multipart_ranges_data( + req, boundary, content_type, content_length, [&](const std::string &token) { data += token; }, [&](const std::string &token) { data += token; }, [&](size_t offset, size_t length) { - if (offset < res.body.size()) { - data += res.body.substr(offset, length); - return true; - } - return false; + assert(offset + length <= content_length); + data += res.body.substr(offset, length); + return true; }); } -inline size_t -get_multipart_ranges_data_length(const Request &req, Response &res, - const std::string &boundary, - const std::string &content_type) { +inline size_t get_multipart_ranges_data_length(const Request &req, + const std::string &boundary, + const std::string &content_type, + size_t content_length) { size_t data_length = 0; process_multipart_ranges_data( - req, res, boundary, content_type, + req, boundary, content_type, content_length, [&](const std::string &token) { data_length += token.size(); }, [&](const std::string &token) { data_length += token.size(); }, [&](size_t /*offset*/, size_t length) { @@ -4411,13 +4890,13 @@ get_multipart_ranges_data_length(const Request &req, Response &res, } template -inline bool write_multipart_ranges_data(Stream &strm, const Request &req, - Response &res, - const std::string &boundary, - const std::string &content_type, - const T &is_shutting_down) { +inline bool +write_multipart_ranges_data(Stream &strm, const Request &req, Response &res, + const std::string &boundary, + const std::string &content_type, + size_t content_length, const T &is_shutting_down) { return process_multipart_ranges_data( - req, res, boundary, content_type, + req, boundary, content_type, content_length, [&](const std::string &token) { strm.write(token); }, [&](const std::string &token) { strm.write(token); }, [&](size_t offset, size_t length) { @@ -4426,18 +4905,6 @@ inline bool write_multipart_ranges_data(Stream &strm, const Request &req, }); } -inline std::pair -get_range_offset_and_length(const Request &req, const Response &res, - size_t index) { - auto r = req.ranges[index]; - - if (r.second == -1) { - r.second = static_cast(res.content_length_) - 1; - } - - return std::make_pair(r.first, r.second - r.first + 1); -} - inline bool expect_content(const Request &req) { if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" || req.method == "PRI" || req.method == "DELETE") { @@ -4471,7 +4938,7 @@ inline std::string message_digest(const std::string &s, const EVP_MD *algo) { std::stringstream ss; for (auto i = 0u; i < hash_length; ++i) { ss << std::hex << std::setw(2) << std::setfill('0') - << (unsigned int)hash[i]; + << static_cast(hash[i]); } return ss.str(); @@ -4564,7 +5031,7 @@ inline bool retrieve_root_certs_from_keychain(CFObjectPtr &certs) { inline bool add_certs_to_x509_store(CFArrayRef certs, X509_STORE *store) { auto result = false; - for (int i = 0; i < CFArrayGetCount(certs); ++i) { + for (auto i = 0; i < CFArrayGetCount(certs); ++i) { const auto cert = reinterpret_cast( CFArrayGetValueAtIndex(certs, i)); @@ -4707,7 +5174,7 @@ inline bool parse_www_authenticate(const Response &res, s = s.substr(pos + 1); auto beg = std::sregex_iterator(s.begin(), s.end(), re); for (auto i = beg; i != std::sregex_iterator(); ++i) { - auto m = *i; + const auto &m = *i; auto key = s.substr(static_cast(m.position(1)), static_cast(m.length(1))); auto val = m.length(2) > 0 @@ -4724,20 +5191,6 @@ inline bool parse_www_authenticate(const Response &res, return false; } -// https://stackoverflow.com/questions/440133/how-do-i-create-a-random-alpha-numeric-string-in-c/440240#answer-440240 -inline std::string random_string(size_t length) { - auto randchar = []() -> char { - const char charset[] = "0123456789" - "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - "abcdefghijklmnopqrstuvwxyz"; - const size_t max_index = (sizeof(charset) - 1); - return charset[static_cast(std::rand()) % max_index]; - }; - std::string str(length, 0); - std::generate_n(str.begin(), length, randchar); - return str; -} - class ContentProviderAdapter { public: explicit ContentProviderAdapter( @@ -4782,7 +5235,7 @@ inline void hosted_at(const std::string &hostname, const auto &addr = *reinterpret_cast(rp->ai_addr); std::string ip; - int dummy = -1; + auto dummy = -1; if (detail::get_ip_and_port(addr, sizeof(struct sockaddr_storage), ip, dummy)) { addrs.push_back(ip); @@ -4802,10 +5255,11 @@ inline std::string append_query_params(const std::string &path, } // Header utilities -inline std::pair make_range_header(Ranges ranges) { +inline std::pair +make_range_header(const Ranges &ranges) { std::string field = "bytes="; auto i = 0; - for (auto r : ranges) { + for (const auto &r : ranges) { if (i != 0) { field += ", "; } if (r.first != -1) { field += std::to_string(r.first); } field += '-'; @@ -4924,7 +5378,7 @@ inline void Response::set_redirect(const std::string &url, int stat) { if (300 <= stat && stat < 400) { this->status = stat; } else { - this->status = 302; + this->status = StatusCode::Found_302; } } } @@ -4943,13 +5397,22 @@ inline void Response::set_content(const std::string &s, set_content(s.data(), s.size(), content_type); } +inline void Response::set_content(std::string &&s, + const std::string &content_type) { + body = std::move(s); + + auto rng = headers.equal_range("Content-Type"); + headers.erase(rng.first, rng.second); + set_header("Content-Type", content_type); +} + inline void Response::set_content_provider( size_t in_length, const std::string &content_type, ContentProvider provider, ContentProviderResourceReleaser resource_releaser) { set_header("Content-Type", content_type); content_length_ = in_length; if (in_length > 0) { content_provider_ = std::move(provider); } - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = false; } @@ -4959,7 +5422,7 @@ inline void Response::set_content_provider( set_header("Content-Type", content_type); content_length_ = 0; content_provider_ = detail::ContentProviderAdapter(std::move(provider)); - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = false; } @@ -4969,7 +5432,7 @@ inline void Response::set_chunked_content_provider( set_header("Content-Type", content_type); content_length_ = 0; content_provider_ = detail::ContentProviderAdapter(std::move(provider)); - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = true; } @@ -5010,7 +5473,7 @@ inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec, write_timeout_sec_(write_timeout_sec), write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {} -inline SocketStream::~SocketStream() {} +inline SocketStream::~SocketStream() = default; inline bool SocketStream::is_readable() const { return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; @@ -5120,6 +5583,99 @@ inline socket_t BufferStream::socket() const { return 0; } inline const std::string &BufferStream::get_buffer() const { return buffer; } +inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) { + // One past the last ending position of a path param substring + std::size_t last_param_end = 0; + +#ifndef CPPHTTPLIB_NO_EXCEPTIONS + // Needed to ensure that parameter names are unique during matcher + // construction + // If exceptions are disabled, only last duplicate path + // parameter will be set + std::unordered_set param_name_set; +#endif + + while (true) { + const auto marker_pos = pattern.find(marker, last_param_end); + if (marker_pos == std::string::npos) { break; } + + static_fragments_.push_back( + pattern.substr(last_param_end, marker_pos - last_param_end)); + + const auto param_name_start = marker_pos + 1; + + auto sep_pos = pattern.find(separator, param_name_start); + if (sep_pos == std::string::npos) { sep_pos = pattern.length(); } + + auto param_name = + pattern.substr(param_name_start, sep_pos - param_name_start); + +#ifndef CPPHTTPLIB_NO_EXCEPTIONS + if (param_name_set.find(param_name) != param_name_set.cend()) { + std::string msg = "Encountered path parameter '" + param_name + + "' multiple times in route pattern '" + pattern + "'."; + throw std::invalid_argument(msg); + } +#endif + + param_names_.push_back(std::move(param_name)); + + last_param_end = sep_pos + 1; + } + + if (last_param_end < pattern.length()) { + static_fragments_.push_back(pattern.substr(last_param_end)); + } +} + +inline bool PathParamsMatcher::match(Request &request) const { + request.matches = std::smatch(); + request.path_params.clear(); + request.path_params.reserve(param_names_.size()); + + // One past the position at which the path matched the pattern last time + std::size_t starting_pos = 0; + for (size_t i = 0; i < static_fragments_.size(); ++i) { + const auto &fragment = static_fragments_[i]; + + if (starting_pos + fragment.length() > request.path.length()) { + return false; + } + + // Avoid unnecessary allocation by using strncmp instead of substr + + // comparison + if (std::strncmp(request.path.c_str() + starting_pos, fragment.c_str(), + fragment.length()) != 0) { + return false; + } + + starting_pos += fragment.length(); + + // Should only happen when we have a static fragment after a param + // Example: '/users/:id/subscriptions' + // The 'subscriptions' fragment here does not have a corresponding param + if (i >= param_names_.size()) { continue; } + + auto sep_pos = request.path.find(separator, starting_pos); + if (sep_pos == std::string::npos) { sep_pos = request.path.length(); } + + const auto ¶m_name = param_names_[i]; + + request.path_params.emplace( + param_name, request.path.substr(starting_pos, sep_pos - starting_pos)); + + // Mark everythin up to '/' as matched + starting_pos = sep_pos + 1; + } + // Returns false if the path is longer than the pattern + return starting_pos >= request.path.length(); +} + +inline bool RegexMatcher::match(Request &request) const { + request.path_params.clear(); + return std::regex_match(request.path, request.matches, regex_); +} + } // namespace detail // HTTP server implementation @@ -5131,69 +5687,72 @@ inline Server::Server() #endif } -inline Server::~Server() {} +inline Server::~Server() = default; + +inline std::unique_ptr +Server::make_matcher(const std::string &pattern) { + if (pattern.find("/:") != std::string::npos) { + return detail::make_unique(pattern); + } else { + return detail::make_unique(pattern); + } +} inline Server &Server::Get(const std::string &pattern, Handler handler) { - get_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + get_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Post(const std::string &pattern, Handler handler) { - post_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + post_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Post(const std::string &pattern, HandlerWithContentReader handler) { - post_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + post_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Put(const std::string &pattern, Handler handler) { - put_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + put_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Put(const std::string &pattern, HandlerWithContentReader handler) { - put_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + put_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Patch(const std::string &pattern, Handler handler) { - patch_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + patch_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Patch(const std::string &pattern, HandlerWithContentReader handler) { - patch_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + patch_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Delete(const std::string &pattern, Handler handler) { - delete_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + delete_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Delete(const std::string &pattern, HandlerWithContentReader handler) { - delete_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + delete_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Options(const std::string &pattern, Handler handler) { - options_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + options_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } @@ -5231,6 +5790,11 @@ Server::set_file_extension_and_mimetype_mapping(const std::string &ext, return *this; } +inline Server &Server::set_default_file_mimetype(const std::string &mime) { + default_file_mimetype_ = mime; + return *this; +} + inline Server &Server::set_file_request_handler(Handler handler) { file_request_handler_ = std::move(handler); return *this; @@ -5272,7 +5836,6 @@ inline Server &Server::set_logger(Logger logger) { inline Server & Server::set_expect_100_continue_handler(Expect100ContinueHandler handler) { expect_100_continue_handler_ = std::move(handler); - return *this; } @@ -5296,6 +5859,12 @@ inline Server &Server::set_default_headers(Headers headers) { return *this; } +inline Server &Server::set_header_writer( + std::function const &writer) { + header_writer_ = writer; + return *this; +} + inline Server &Server::set_keep_alive_max_count(size_t count) { keep_alive_max_count_ = count; return *this; @@ -5331,8 +5900,7 @@ inline Server &Server::set_payload_max_length(size_t length) { inline bool Server::bind_to_port(const std::string &host, int port, int socket_flags) { - if (bind_internal(host, port, socket_flags) < 0) return false; - return true; + return bind_internal(host, port, socket_flags) >= 0; } inline int Server::bind_to_any_port(const std::string &host, int socket_flags) { return bind_internal(host, 0, socket_flags); @@ -5366,7 +5934,7 @@ inline void Server::stop() { } } -inline bool Server::parse_request_line(const char *s, Request &req) { +inline bool Server::parse_request_line(const char *s, Request &req) const { auto len = strlen(s); if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; } len -= 2; @@ -5407,7 +5975,7 @@ inline bool Server::parse_request_line(const char *s, Request &req) { size_t count = 0; detail::split(req.target.data(), req.target.data() + req.target.size(), '?', - [&](const char *b, const char *e) { + 2, [&](const char *b, const char *e) { switch (count) { case 0: req.path = detail::decode_url(std::string(b, e), false); @@ -5430,7 +5998,10 @@ inline bool Server::parse_request_line(const char *s, Request &req) { } inline bool Server::write_response(Stream &strm, bool close_connection, - const Request &req, Response &res) { + Request &req, Response &res) { + // NOTE: `req.ranges` should be empty, otherwise it will be applied + // incorrectly to the error content. + req.ranges.clear(); return write_response_core(strm, close_connection, req, res, false); } @@ -5486,11 +6057,11 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, detail::BufferStream bstrm; if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status, - detail::status_message(res.status))) { + status_message(res.status))) { return false; } - if (!detail::write_headers(bstrm, res.headers)) { return false; } + if (!header_writer_(bstrm, res.headers)) { return false; } // Flush buffer auto &data = bstrm.get_buffer(); @@ -5508,7 +6079,6 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, if (write_content_with_provider(strm, req, res, boundary, content_type)) { res.content_provider_success_ = true; } else { - res.content_provider_success_ = false; ret = false; } } @@ -5533,15 +6103,16 @@ Server::write_content_with_provider(Stream &strm, const Request &req, return detail::write_content(strm, res.content_provider_, 0, res.content_length_, is_shutting_down); } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.content_length_, 0); - auto offset = offsets.first; - auto length = offsets.second; - return detail::write_content(strm, res.content_provider_, offset, length, - is_shutting_down); + auto offset_and_length = detail::get_range_offset_and_length( + req.ranges[0], res.content_length_); + + return detail::write_content(strm, res.content_provider_, + offset_and_length.first, + offset_and_length.second, is_shutting_down); } else { return detail::write_multipart_ranges_data( - strm, req, res, boundary, content_type, is_shutting_down); + strm, req, res, boundary, content_type, res.content_length_, + is_shutting_down); } } else { if (res.is_chunked_content_provider_) { @@ -5598,7 +6169,7 @@ inline bool Server::read_content(Stream &strm, Request &req, Response &res) { const auto &content_type = req.get_header_value("Content-Type"); if (!content_type.find("application/x-www-form-urlencoded")) { if (req.body.size() > CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH) { - res.status = 413; // NOTE: should be 414? + res.status = StatusCode::PayloadTooLarge_413; // NOTE: should be 414? return false; } detail::parse_query_text(req.body, req.params); @@ -5617,10 +6188,11 @@ inline bool Server::read_content_with_content_receiver( std::move(multipart_receiver)); } -inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, - ContentReceiver receiver, - MultipartContentHeader multipart_header, - ContentReceiver multipart_receiver) { +inline bool +Server::read_content_core(Stream &strm, Request &req, Response &res, + ContentReceiver receiver, + MultipartContentHeader multipart_header, + ContentReceiver multipart_receiver) const { detail::MultipartFormDataParser multipart_form_data_parser; ContentReceiverWithProgress out; @@ -5628,7 +6200,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, const auto &content_type = req.get_header_value("Content-Type"); std::string boundary; if (!detail::parse_multipart_boundary(content_type, boundary)) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } @@ -5664,7 +6236,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, if (req.is_multipart_form_data()) { if (!multipart_form_data_parser.is_valid()) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } } @@ -5683,17 +6255,26 @@ inline bool Server::handle_file_request(const Request &req, Response &res, if (path.back() == '/') { path += "index.html"; } if (detail::is_file(path)) { - detail::read_file(path, res.body); - auto type = - detail::find_content_type(path, file_extension_and_mimetype_map_); - if (type) { res.set_header("Content-Type", type); } for (const auto &kv : entry.headers) { - res.set_header(kv.first.c_str(), kv.second); + res.set_header(kv.first, kv.second); } - res.status = req.has_header("Range") ? 206 : 200; + + auto mm = std::make_shared(path.c_str()); + if (!mm->is_open()) { return false; } + + res.set_content_provider( + mm->size(), + detail::find_content_type(path, file_extension_and_mimetype_map_, + default_file_mimetype_), + [mm](size_t offset, size_t length, DataSink &sink) -> bool { + sink.write(mm->data() + offset, length); + return true; + }); + if (!head && file_request_handler_) { file_request_handler_(req, res); } + return true; } } @@ -5789,13 +6370,14 @@ inline bool Server::listen_internal() { #ifdef _WIN32 auto timeout = static_cast(read_timeout_sec_ * 1000 + read_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(read_timeout_sec_); tv.tv_usec = static_cast(read_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } { @@ -5803,17 +6385,22 @@ inline bool Server::listen_internal() { #ifdef _WIN32 auto timeout = static_cast(write_timeout_sec_ * 1000 + write_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(write_timeout_sec_); tv.tv_usec = static_cast(write_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } - task_queue->enqueue([this, sock]() { process_and_close_socket(sock); }); + if (!task_queue->enqueue( + [this, sock]() { process_and_close_socket(sock); })) { + detail::shutdown_socket(sock); + detail::close_socket(sock); + } } task_queue->shutdown(); @@ -5829,7 +6416,7 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) { } // File handler - bool is_head_request = req.method == "HEAD"; + auto is_head_request = req.method == "HEAD"; if ((req.method == "GET" || is_head_request) && handle_file_request(req, res, is_head_request)) { return true; @@ -5895,17 +6482,17 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) { return dispatch_request(req, res, patch_handlers_); } - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } inline bool Server::dispatch_request(Request &req, Response &res, - const Handlers &handlers) { + const Handlers &handlers) const { for (const auto &x : handlers) { - const auto &pattern = x.first; + const auto &matcher = x.first; const auto &handler = x.second; - if (std::regex_match(req.path, req.matches, pattern)) { + if (matcher->match(req)) { handler(req, res); return true; } @@ -5915,18 +6502,18 @@ inline bool Server::dispatch_request(Request &req, Response &res, inline void Server::apply_ranges(const Request &req, Response &res, std::string &content_type, - std::string &boundary) { + std::string &boundary) const { if (req.ranges.size() > 1) { - boundary = detail::make_multipart_data_boundary(); - auto it = res.headers.find("Content-Type"); if (it != res.headers.end()) { content_type = it->second; res.headers.erase(it); } - res.headers.emplace("Content-Type", - "multipart/byteranges; boundary=" + boundary); + boundary = detail::make_multipart_data_boundary(); + + res.set_header("Content-Type", + "multipart/byteranges; boundary=" + boundary); } auto type = detail::encoding_type(req, res); @@ -5937,16 +6524,17 @@ inline void Server::apply_ranges(const Request &req, Response &res, if (req.ranges.empty()) { length = res.content_length_; } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.content_length_, 0); - auto offset = offsets.first; - length = offsets.second; + auto offset_and_length = detail::get_range_offset_and_length( + req.ranges[0], res.content_length_); + + length = offset_and_length.second; + auto content_range = detail::make_content_range_header_field( - offset, length, res.content_length_); + offset_and_length, res.content_length_); res.set_header("Content-Range", content_range); } else { - length = detail::get_multipart_ranges_data_length(req, res, boundary, - content_type); + length = detail::get_multipart_ranges_data_length( + req, boundary, content_type, res.content_length_); } res.set_header("Content-Length", std::to_string(length)); } else { @@ -5965,28 +6553,22 @@ inline void Server::apply_ranges(const Request &req, Response &res, if (req.ranges.empty()) { ; } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.body.size(), 0); - auto offset = offsets.first; - auto length = offsets.second; + auto offset_and_length = + detail::get_range_offset_and_length(req.ranges[0], res.body.size()); + auto offset = offset_and_length.first; + auto length = offset_and_length.second; + auto content_range = detail::make_content_range_header_field( - offset, length, res.body.size()); + offset_and_length, res.body.size()); res.set_header("Content-Range", content_range); - if (offset < res.body.size()) { - res.body = res.body.substr(offset, length); - } else { - res.body.clear(); - res.status = 416; - } + + assert(offset + length <= res.body.size()); + res.body = res.body.substr(offset, length); } else { std::string data; - if (detail::make_multipart_ranges_data(req, res, boundary, content_type, - data)) { - res.body.swap(data); - } else { - res.body.clear(); - res.status = 416; - } + detail::make_multipart_ranges_data(req, res, boundary, content_type, + res.body.size(), data); + res.body.swap(data); } if (type != detail::EncodingType::None) { @@ -6025,12 +6607,12 @@ inline void Server::apply_ranges(const Request &req, Response &res, inline bool Server::dispatch_request_for_content_reader( Request &req, Response &res, ContentReader content_reader, - const HandlersForContentReader &handlers) { + const HandlersForContentReader &handlers) const { for (const auto &x : handlers) { - const auto &pattern = x.first; + const auto &matcher = x.first; const auto &handler = x.second; - if (std::regex_match(req.path, req.matches, pattern)) { + if (matcher->match(req)) { handler(req, res, content_reader); return true; } @@ -6050,15 +6632,10 @@ Server::process_request(Stream &strm, bool close_connection, if (!line_reader.getline()) { return false; } Request req; - Response res; + Response res; res.version = "HTTP/1.1"; - - for (const auto &header : default_headers_) { - if (res.headers.find(header.first) == res.headers.end()) { - res.headers.insert(header); - } - } + res.headers = default_headers_; #ifdef _WIN32 // TODO: Increase FD_SETSIZE statically (libzmq), dynamically (MySQL). @@ -6068,7 +6645,7 @@ Server::process_request(Stream &strm, bool close_connection, if (strm.socket() >= FD_SETSIZE) { Headers dummy; detail::read_headers(strm, dummy); - res.status = 500; + res.status = StatusCode::InternalServerError_500; return write_response(strm, close_connection, req, res); } #endif @@ -6078,14 +6655,14 @@ Server::process_request(Stream &strm, bool close_connection, if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) { Headers dummy; detail::read_headers(strm, dummy); - res.status = 414; + res.status = StatusCode::UriTooLong_414; return write_response(strm, close_connection, req, res); } // Request line and headers if (!parse_request_line(line_reader.ptr(), req) || !detail::read_headers(strm, req.headers)) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return write_response(strm, close_connection, req, res); } @@ -6109,7 +6686,7 @@ Server::process_request(Stream &strm, bool close_connection, if (req.has_header("Range")) { const auto &range_header_value = req.get_header_value("Range"); if (!detail::parse_range_header(range_header_value, req.ranges)) { - res.status = 416; + res.status = StatusCode::RangeNotSatisfiable_416; return write_response(strm, close_connection, req, res); } } @@ -6117,22 +6694,22 @@ Server::process_request(Stream &strm, bool close_connection, if (setup_request) { setup_request(req); } if (req.get_header_value("Expect") == "100-continue") { - auto status = 100; + int status = StatusCode::Continue_100; if (expect_100_continue_handler_) { status = expect_100_continue_handler_(req, res); } switch (status) { - case 100: - case 417: + case StatusCode::Continue_100: + case StatusCode::ExpectationFailed_417: strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status, - detail::status_message(status)); + status_message(status)); break; default: return write_response(strm, close_connection, req, res); } } - // Rounting - bool routed = false; + // Routing + auto routed = false; #ifdef CPPHTTPLIB_NO_EXCEPTIONS routed = routing(req, res, strm); #else @@ -6144,7 +6721,7 @@ Server::process_request(Stream &strm, bool close_connection, exception_handler_(req, res, ep); routed = true; } else { - res.status = 500; + res.status = StatusCode::InternalServerError_500; std::string val; auto s = e.what(); for (size_t i = 0; s[i]; i++) { @@ -6162,17 +6739,29 @@ Server::process_request(Stream &strm, bool close_connection, exception_handler_(req, res, ep); routed = true; } else { - res.status = 500; + res.status = StatusCode::InternalServerError_500; res.set_header("EXCEPTION_WHAT", "UNKNOWN"); } } #endif - if (routed) { - if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; } + if (res.status == -1) { + res.status = req.ranges.empty() ? StatusCode::OK_200 + : StatusCode::PartialContent_206; + } + + if (detail::range_error(req, res)) { + res.body.clear(); + res.content_length_ = 0; + res.content_provider_ = nullptr; + res.status = StatusCode::RangeNotSatisfiable_416; + return write_response(strm, close_connection, req, res); + } + return write_response_with_content(strm, close_connection, req, res); } else { - if (res.status == -1) { res.status = 404; } + if (res.status == -1) { res.status = StatusCode::NotFound_404; } + return write_response(strm, close_connection, req, res); } } @@ -6272,7 +6861,7 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const { // Check is custom IP specified for host_ std::string ip; auto it = addr_map_.find(host_); - if (it != addr_map_.end()) ip = it->second; + if (it != addr_map_.end()) { ip = it->second; } return detail::create_client_socket( host_, ip, port_, address_family_, tcp_nodelay_, socket_options_, @@ -6297,7 +6886,7 @@ inline void ClientImpl::shutdown_ssl(Socket & /*socket*/, socket_requests_are_from_thread_ == std::this_thread::get_id()); } -inline void ClientImpl::shutdown_socket(Socket &socket) { +inline void ClientImpl::shutdown_socket(Socket &socket) const { if (socket.sock == INVALID_SOCKET) { return; } detail::shutdown_socket(socket.sock); } @@ -6322,7 +6911,7 @@ inline void ClientImpl::close_socket(Socket &socket) { } inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, - Response &res) { + Response &res) const { std::array buf{}; detail::stream_line_reader line_reader(strm, buf.data(), buf.size()); @@ -6330,9 +6919,9 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, if (!line_reader.getline()) { return false; } #ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR - const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n"); -#else const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r?\n"); +#else + const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n"); #endif std::cmatch m; @@ -6344,7 +6933,7 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, res.reason = std::string(m[3]); // Ignore '100 Continue' - while (res.status == 100) { + while (res.status == StatusCode::Continue_100) { if (!line_reader.getline()) { return false; } // CRLF if (!line_reader.getline()) { return false; } // next response line @@ -6492,15 +7081,31 @@ inline bool ClientImpl::handle_request(Stream &strm, Request &req, if (!ret) { return false; } + if (res.get_header_value("Connection") == "close" || + (res.version == "HTTP/1.0" && res.reason != "Connection established")) { + // TODO this requires a not-entirely-obvious chain of calls to be correct + // for this to be safe. + + // This is safe to call because handle_request is only called by send_ + // which locks the request mutex during the process. It would be a bug + // to call it from a different thread since it's a thread-safety issue + // to do these things to the socket if another thread is using the socket. + std::lock_guard guard(socket_mutex_); + shutdown_ssl(socket_, true); + shutdown_socket(socket_); + close_socket(socket_); + } + if (300 < res.status && res.status < 400 && follow_location_) { req = req_save; ret = redirect(req, res, error); } #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - if ((res.status == 401 || res.status == 407) && + if ((res.status == StatusCode::Unauthorized_401 || + res.status == StatusCode::ProxyAuthenticationRequired_407) && req.authorization_count_ < 5) { - auto is_proxy = res.status == 407; + auto is_proxy = res.status == StatusCode::ProxyAuthenticationRequired_407; const auto &username = is_proxy ? proxy_digest_auth_username_ : digest_auth_username_; const auto &password = @@ -6571,7 +7176,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { } else { if (next_scheme == "https") { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - SSLClient cli(next_host.c_str(), next_port); + SSLClient cli(next_host, next_port); cli.copy_settings(*this); if (ca_cert_store_) { cli.set_ca_cert_store(ca_cert_store_); } return detail::redirect(cli, req, res, path, location, error); @@ -6579,7 +7184,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { return false; #endif } else { - ClientImpl cli(next_host.c_str(), next_port); + ClientImpl cli(next_host, next_port); cli.copy_settings(*this); return detail::redirect(cli, req, res, path, location, error); } @@ -6588,7 +7193,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { inline bool ClientImpl::write_content_with_provider(Stream &strm, const Request &req, - Error &error) { + Error &error) const { auto is_shutting_down = []() { return false; }; if (req.is_chunked_content_provider_) { @@ -6616,32 +7221,32 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, // Prepare additional headers if (close_connection) { if (!req.has_header("Connection")) { - req.headers.emplace("Connection", "close"); + req.set_header("Connection", "close"); } } if (!req.has_header("Host")) { if (is_ssl()) { if (port_ == 443) { - req.headers.emplace("Host", host_); + req.set_header("Host", host_); } else { - req.headers.emplace("Host", host_and_port_); + req.set_header("Host", host_and_port_); } } else { if (port_ == 80) { - req.headers.emplace("Host", host_); + req.set_header("Host", host_); } else { - req.headers.emplace("Host", host_and_port_); + req.set_header("Host", host_and_port_); } } } - if (!req.has_header("Accept")) { req.headers.emplace("Accept", "*/*"); } + if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); } #ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT if (!req.has_header("User-Agent")) { auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION; - req.headers.emplace("User-Agent", agent); + req.set_header("User-Agent", agent); } #endif @@ -6650,23 +7255,23 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, if (!req.is_chunked_content_provider_) { if (!req.has_header("Content-Length")) { auto length = std::to_string(req.content_length_); - req.headers.emplace("Content-Length", length); + req.set_header("Content-Length", length); } } } else { if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH") { - req.headers.emplace("Content-Length", "0"); + req.set_header("Content-Length", "0"); } } } else { if (!req.has_header("Content-Type")) { - req.headers.emplace("Content-Type", "text/plain"); + req.set_header("Content-Type", "text/plain"); } if (!req.has_header("Content-Length")) { auto length = std::to_string(req.body.size()); - req.headers.emplace("Content-Length", length); + req.set_header("Content-Length", length); } } @@ -6706,7 +7311,7 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path; bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str()); - detail::write_headers(bstrm, req.headers); + header_writer_(bstrm, req.headers); // Flush buffer auto &data = bstrm.get_buffer(); @@ -6734,12 +7339,10 @@ inline std::unique_ptr ClientImpl::send_with_content_provider( ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, const std::string &content_type, Error &error) { - if (!content_type.empty()) { - req.headers.emplace("Content-Type", content_type); - } + if (!content_type.empty()) { req.set_header("Content-Type", content_type); } #ifdef CPPHTTPLIB_ZLIB_SUPPORT - if (compress_) { req.headers.emplace("Content-Encoding", "gzip"); } + if (compress_) { req.set_header("Content-Encoding", "gzip"); } #endif #ifdef CPPHTTPLIB_ZLIB_SUPPORT @@ -6800,10 +7403,9 @@ inline std::unique_ptr ClientImpl::send_with_content_provider( req.content_provider_ = detail::ContentProviderAdapter( std::move(content_provider_without_length)); req.is_chunked_content_provider_ = true; - req.headers.emplace("Transfer-Encoding", "chunked"); + req.set_header("Transfer-Encoding", "chunked"); } else { req.body.assign(body, content_length); - ; } } @@ -6864,7 +7466,8 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, } // Body - if ((res.status != 204) && req.method != "HEAD" && req.method != "CONNECT") { + if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" && + req.method != "CONNECT") { auto redirect = 300 < res.status && res.status < 400 && follow_location_; if (req.response_handler && !redirect) { @@ -6909,24 +7512,6 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, } } - if (res.get_header_value("Connection") == "close" || - (res.version == "HTTP/1.0" && res.reason != "Connection established")) { - // TODO this requires a not-entirely-obvious chain of calls to be correct - // for this to be safe. Maybe a code refactor (such as moving this out to - // the send function and getting rid of the recursiveness of the mutex) - // could make this more obvious. - - // This is safe to call because process_request is only called by - // handle_request which is only called by send, which locks the request - // mutex during the process. It would be a bug to call it from a different - // thread since it's a thread-safety issue to do these things to the socket - // if another thread is using the socket. - std::lock_guard guard(socket_mutex_); - shutdown_ssl(socket_, true); - shutdown_socket(socket_); - close_socket(socket_); - } - // Log if (logger_) { logger_(req, res); } @@ -6935,13 +7520,14 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider( const std::string &boundary, const MultipartFormDataItems &items, - const MultipartFormDataProviderItems &provider_items) { - size_t cur_item = 0, cur_start = 0; + const MultipartFormDataProviderItems &provider_items) const { + size_t cur_item = 0; + size_t cur_start = 0; // cur_item and cur_start are copied to within the std::function and maintain // state between successive calls return [&, cur_item, cur_start](size_t offset, DataSink &sink) mutable -> bool { - if (!offset && items.size()) { + if (!offset && !items.empty()) { sink.os << detail::serialize_multipart_formdata(items, boundary, false); return true; } else if (cur_item < provider_items.size()) { @@ -6954,12 +7540,13 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider( } DataSink cur_sink; - bool has_data = true; + auto has_data = true; cur_sink.write = sink.write; cur_sink.done = [&]() { has_data = false; }; - if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) + if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) { return false; + } if (!has_data) { sink.os << detail::serialize_multipart_formdata_item_end(); @@ -7078,14 +7665,15 @@ inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, if (params.empty()) { return Get(path, headers); } std::string path_with_query = append_query_params(path, params); - return Get(path_with_query.c_str(), headers, progress); + return Get(path_with_query, headers, std::move(progress)); } inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, const Headers &headers, ContentReceiver content_receiver, Progress progress) { - return Get(path, params, headers, nullptr, content_receiver, progress); + return Get(path, params, headers, nullptr, std::move(content_receiver), + std::move(progress)); } inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, @@ -7094,12 +7682,13 @@ inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, ContentReceiver content_receiver, Progress progress) { if (params.empty()) { - return Get(path, headers, response_handler, content_receiver, progress); + return Get(path, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } std::string path_with_query = append_query_params(path, params); - return Get(path_with_query.c_str(), headers, response_handler, - content_receiver, progress); + return Get(path_with_query, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } inline Result ClientImpl::Head(const std::string &path) { @@ -7201,7 +7790,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const auto &content_type = detail::serialize_multipart_formdata_get_content_type(boundary); const auto &body = detail::serialize_multipart_formdata(items, boundary); - return Post(path, headers, body, content_type.c_str()); + return Post(path, headers, body, content_type); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, @@ -7214,7 +7803,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const auto &content_type = detail::serialize_multipart_formdata_get_content_type(boundary); const auto &body = detail::serialize_multipart_formdata(items, boundary); - return Post(path, headers, body, content_type.c_str()); + return Post(path, headers, body, content_type); } inline Result @@ -7423,9 +8012,7 @@ inline Result ClientImpl::Delete(const std::string &path, req.headers = headers; req.path = path; - if (!content_type.empty()) { - req.headers.emplace("Content-Type", content_type); - } + if (!content_type.empty()) { req.set_header("Content-Type", content_type); } req.body.assign(body, content_length); return send_(std::move(req)); @@ -7458,13 +8045,6 @@ inline Result ClientImpl::Options(const std::string &path, return send_(std::move(req)); } -inline size_t ClientImpl::is_socket_open() const { - std::lock_guard guard(socket_mutex_); - return socket_.is_open(); -} - -inline socket_t ClientImpl::socket() const { return socket_.sock; } - inline void ClientImpl::stop() { std::lock_guard guard(socket_mutex_); @@ -7488,6 +8068,17 @@ inline void ClientImpl::stop() { close_socket(socket_); } +inline std::string ClientImpl::host() const { return host_; } + +inline int ClientImpl::port() const { return port_; } + +inline size_t ClientImpl::is_socket_open() const { + std::lock_guard guard(socket_mutex_); + return socket_.is_open(); +} + +inline socket_t ClientImpl::socket() const { return socket_.sock; } + inline void ClientImpl::set_connection_timeout(time_t sec, time_t usec) { connection_timeout_sec_ = sec; connection_timeout_usec_ = usec; @@ -7536,6 +8127,11 @@ inline void ClientImpl::set_default_headers(Headers headers) { default_headers_ = std::move(headers); } +inline void ClientImpl::set_header_writer( + std::function const &writer) { + header_writer_ = writer; +} + inline void ClientImpl::set_address_family(int family) { address_family_ = family; } @@ -7575,9 +8171,7 @@ inline void ClientImpl::set_proxy_digest_auth(const std::string &username, proxy_digest_auth_username_ = username; proxy_digest_auth_password_ = password; } -#endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT inline void ClientImpl::set_ca_cert_path(const std::string &ca_cert_file_path, const std::string &ca_cert_dir_path) { ca_cert_file_path_ = ca_cert_file_path; @@ -7589,9 +8183,34 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) { ca_cert_store_ = ca_cert_store; } } -#endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert, + std::size_t size) const { + auto mem = BIO_new_mem_buf(ca_cert, static_cast(size)); + if (!mem) { return nullptr; } + + auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr); + if (!inf) { + BIO_free_all(mem); + return nullptr; + } + + auto cts = X509_STORE_new(); + if (cts) { + for (auto i = 0; i < static_cast(sk_X509_INFO_num(inf)); i++) { + auto itmp = sk_X509_INFO_value(inf, i); + if (!itmp) { continue; } + + if (itmp->x509) { X509_STORE_add_cert(cts, itmp->x509); } + if (itmp->crl) { X509_STORE_add_crl(cts, itmp->crl); } + } + } + + sk_X509_INFO_pop_free(inf, X509_INFO_free); + BIO_free_all(mem); + return cts; +} + inline void ClientImpl::enable_server_certificate_verification(bool enabled) { server_certificate_verification_ = enabled; } @@ -7655,7 +8274,7 @@ bool ssl_connect_or_accept_nonblocking(socket_t sock, SSL *ssl, U ssl_connect_or_accept, time_t timeout_sec, time_t timeout_usec) { - int res = 0; + auto res = 0; while ((res = ssl_connect_or_accept(ssl)) != 1) { auto err = SSL_get_error(ssl, res); switch (err) { @@ -7718,7 +8337,7 @@ inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl, SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY); } -inline SSLSocketStream::~SSLSocketStream() {} +inline SSLSocketStream::~SSLSocketStream() = default; inline bool SSLSocketStream::is_readable() const { return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; @@ -7736,7 +8355,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { auto ret = SSL_read(ssl_, ptr, static_cast(size)); if (ret < 0) { auto err = SSL_get_error(ssl_, ret); - int n = 1000; + auto n = 1000; #ifdef _WIN32 while (--n >= 0 && (err == SSL_ERROR_WANT_READ || (err == SSL_ERROR_SYSCALL && @@ -7769,7 +8388,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) { auto ret = SSL_write(ssl_, ptr, static_cast(handle_size)); if (ret < 0) { auto err = SSL_get_error(ssl_, ret); - int n = 1000; + auto n = 1000; #ifdef _WIN32 while (--n >= 0 && (err == SSL_ERROR_WANT_WRITE || (err == SSL_ERROR_SYSCALL && @@ -7822,10 +8441,10 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path, SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION); - // add default password callback before opening encrypted private key if (private_key_password != nullptr && (private_key_password[0] != '\0')) { - SSL_CTX_set_default_passwd_cb_userdata(ctx_, - (char *)private_key_password); + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, + reinterpret_cast(const_cast(private_key_password))); } if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 || @@ -7927,16 +8546,23 @@ inline SSLClient::SSLClient(const std::string &host, int port) inline SSLClient::SSLClient(const std::string &host, int port, const std::string &client_cert_path, - const std::string &client_key_path) + const std::string &client_key_path, + const std::string &private_key_password) : ClientImpl(host, port, client_cert_path, client_key_path) { ctx_ = SSL_CTX_new(TLS_client_method()); detail::split(&host_[0], &host_[host_.size()], '.', [&](const char *b, const char *e) { - host_components_.emplace_back(std::string(b, e)); + host_components_.emplace_back(b, e); }); if (!client_cert_path.empty() && !client_key_path.empty()) { + if (!private_key_password.empty()) { + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, reinterpret_cast( + const_cast(private_key_password.c_str()))); + } + if (SSL_CTX_use_certificate_file(ctx_, client_cert_path.c_str(), SSL_FILETYPE_PEM) != 1 || SSL_CTX_use_PrivateKey_file(ctx_, client_key_path.c_str(), @@ -7948,16 +8574,23 @@ inline SSLClient::SSLClient(const std::string &host, int port, } inline SSLClient::SSLClient(const std::string &host, int port, - X509 *client_cert, EVP_PKEY *client_key) + X509 *client_cert, EVP_PKEY *client_key, + const std::string &private_key_password) : ClientImpl(host, port) { ctx_ = SSL_CTX_new(TLS_client_method()); detail::split(&host_[0], &host_[host_.size()], '.', [&](const char *b, const char *e) { - host_components_.emplace_back(std::string(b, e)); + host_components_.emplace_back(b, e); }); if (client_cert != nullptr && client_key != nullptr) { + if (!private_key_password.empty()) { + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, reinterpret_cast( + const_cast(private_key_password.c_str()))); + } + if (SSL_CTX_use_certificate(ctx_, client_cert) != 1 || SSL_CTX_use_PrivateKey(ctx_, client_key) != 1) { SSL_CTX_free(ctx_); @@ -7989,6 +8622,11 @@ inline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) { } } +inline void SSLClient::load_ca_cert_store(const char *ca_cert, + std::size_t size) { + set_ca_cert_store(ClientImpl::create_ca_cert_store(ca_cert, size)); +} + inline long SSLClient::get_openssl_verify_result() const { return verify_result_; } @@ -8003,14 +8641,14 @@ inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) { inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, bool &success, Error &error) { success = true; - Response res2; + Response proxy_res; if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { Request req2; req2.method = "CONNECT"; req2.path = host_and_port_; - return process_request(strm, req2, res2, false, error); + return process_request(strm, req2, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are no // requests in flight @@ -8021,12 +8659,12 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, return false; } - if (res2.status == 407) { + if (proxy_res.status == StatusCode::ProxyAuthenticationRequired_407) { if (!proxy_digest_auth_username_.empty() && !proxy_digest_auth_password_.empty()) { std::map auth; - if (detail::parse_www_authenticate(res2, auth, true)) { - Response res3; + if (detail::parse_www_authenticate(proxy_res, auth, true)) { + proxy_res = Response(); if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { @@ -8037,7 +8675,7 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, req3, auth, 1, detail::random_string(10), proxy_digest_auth_username_, proxy_digest_auth_password_, true)); - return process_request(strm, req3, res3, false, error); + return process_request(strm, req3, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are // no requests in flight @@ -8048,17 +8686,28 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, return false; } } - } else { - res = res2; - return false; } } + // If status code is not 200, proxy request is failed. + // Set error to ProxyConnection and return proxy response + // as the response of the request + if (proxy_res.status != StatusCode::OK_200) { + error = Error::ProxyConnection; + res = std::move(proxy_res); + // Thread-safe to close everything because we are assuming there are + // no requests in flight + shutdown_ssl(socket, true); + shutdown_socket(socket); + close_socket(socket); + return false; + } + return true; } inline bool SSLClient::load_certs() { - bool ret = true; + auto ret = true; std::call_once(initialize_cert_, [&]() { std::lock_guard guard(ctx_mutex_); @@ -8134,7 +8783,11 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) { return true; }, [&](SSL *ssl2) { - SSL_set_tlsext_host_name(ssl2, host_.c_str()); + // NOTE: Direct call instead of using the OpenSSL macro to suppress + // -Wold-style-cast warning + // SSL_set_tlsext_host_name(ssl2, host_.c_str()); + SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name, + static_cast(const_cast(host_.c_str()))); return true; }); @@ -8208,8 +8861,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { auto type = GEN_DNS; - struct in6_addr addr6; - struct in_addr addr; + struct in6_addr addr6 {}; + struct in_addr addr {}; size_t addr_len = 0; #ifndef __MINGW32__ @@ -8234,8 +8887,9 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { for (decltype(count) i = 0; i < count && !dsn_matched; i++) { auto val = sk_GENERAL_NAME_value(alt_names, i); if (val->type == type) { - auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5); - auto name_len = (size_t)ASN1_STRING_length(val->d.ia5); + auto name = + reinterpret_cast(ASN1_STRING_get0_data(val->d.ia5)); + auto name_len = static_cast(ASN1_STRING_length(val->d.ia5)); switch (type) { case GEN_DNS: dsn_matched = check_host_name(name, name_len); break; @@ -8253,7 +8907,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { if (dsn_matched || ip_matched) { ret = true; } } - GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names); + GENERAL_NAMES_free(const_cast( + reinterpret_cast(alt_names))); return ret; } @@ -8282,7 +8937,7 @@ inline bool SSLClient::check_host_name(const char *pattern, std::vector pattern_components; detail::split(&pattern[0], &pattern[pattern_len], '.', [&](const char *b, const char *e) { - pattern_components.emplace_back(std::string(b, e)); + pattern_components.emplace_back(b, e); }); if (host_components_.size() != pattern_components.size()) { return false; } @@ -8361,7 +9016,7 @@ inline Client::Client(const std::string &host, int port, : cli_(detail::make_unique(host, port, client_cert_path, client_key_path)) {} -inline Client::~Client() {} +inline Client::~Client() = default; inline bool Client::is_valid() const { return cli_ != nullptr && cli_->is_valid(); @@ -8421,19 +9076,20 @@ inline Result Client::Get(const std::string &path, const Headers &headers, } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, Progress progress) { - return cli_->Get(path, params, headers, progress); + return cli_->Get(path, params, headers, std::move(progress)); } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, ContentReceiver content_receiver, Progress progress) { - return cli_->Get(path, params, headers, content_receiver, progress); + return cli_->Get(path, params, headers, std::move(content_receiver), + std::move(progress)); } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, ResponseHandler response_handler, ContentReceiver content_receiver, Progress progress) { - return cli_->Get(path, params, headers, response_handler, content_receiver, - progress); + return cli_->Get(path, params, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } inline Result Client::Head(const std::string &path) { return cli_->Head(path); } @@ -8665,12 +9321,16 @@ inline bool Client::send(Request &req, Response &res, Error &error) { inline Result Client::send(const Request &req) { return cli_->send(req); } +inline void Client::stop() { cli_->stop(); } + +inline std::string Client::host() const { return cli_->host(); } + +inline int Client::port() const { return cli_->port(); } + inline size_t Client::is_socket_open() const { return cli_->is_socket_open(); } inline socket_t Client::socket() const { return cli_->socket(); } -inline void Client::stop() { cli_->stop(); } - inline void Client::set_hostname_addr_map(std::map addr_map) { cli_->set_hostname_addr_map(std::move(addr_map)); @@ -8680,6 +9340,11 @@ inline void Client::set_default_headers(Headers headers) { cli_->set_default_headers(std::move(headers)); } +inline void Client::set_header_writer( + std::function const &writer) { + cli_->set_header_writer(writer); +} + inline void Client::set_address_family(int family) { cli_->set_address_family(family); } @@ -8754,7 +9419,9 @@ inline void Client::enable_server_certificate_verification(bool enabled) { } #endif -inline void Client::set_logger(Logger logger) { cli_->set_logger(logger); } +inline void Client::set_logger(Logger logger) { + cli_->set_logger(std::move(logger)); +} #ifdef CPPHTTPLIB_OPENSSL_SUPPORT inline void Client::set_ca_cert_path(const std::string &ca_cert_file_path, @@ -8770,6 +9437,10 @@ inline void Client::set_ca_cert_store(X509_STORE *ca_cert_store) { } } +inline void Client::load_ca_cert_store(const char *ca_cert, std::size_t size) { + set_ca_cert_store(cli_->create_ca_cert_store(ca_cert, size)); +} + inline long Client::get_openssl_verify_result() const { if (is_ssl_) { return static_cast(*cli_).get_openssl_verify_result(); diff --git a/examples/server/json.hpp b/examples/server/json.hpp index ea945f346d67b..a858728c4ceb8 100644 --- a/examples/server/json.hpp +++ b/examples/server/json.hpp @@ -1,9 +1,9 @@ // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT /****************************************************************************\ @@ -27,7 +27,6 @@ #endif // JSON_NO_IO #include // random_access_iterator_tag #include // unique_ptr -#include // accumulate #include // string, stoi, to_string #include // declval, forward, move, pair, swap #include // vector @@ -35,10 +34,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -48,10 +47,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -60,7 +59,7 @@ #ifndef JSON_SKIP_LIBRARY_VERSION_CHECK #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH) - #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2 + #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3 #warning "Already included a different version of the library!" #endif #endif @@ -68,7 +67,7 @@ #define NLOHMANN_JSON_VERSION_MAJOR 3 // NOLINT(modernize-macro-to-enum) #define NLOHMANN_JSON_VERSION_MINOR 11 // NOLINT(modernize-macro-to-enum) -#define NLOHMANN_JSON_VERSION_PATCH 2 // NOLINT(modernize-macro-to-enum) +#define NLOHMANN_JSON_VERSION_PATCH 3 // NOLINT(modernize-macro-to-enum) #ifndef JSON_DIAGNOSTICS #define JSON_DIAGNOSTICS 0 @@ -150,10 +149,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -173,16 +172,19 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT #include // nullptr_t #include // exception +#if JSON_DIAGNOSTICS + #include // accumulate +#endif #include // runtime_error #include // to_string #include // vector @@ -190,10 +192,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -206,10 +208,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -218,10 +220,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -231,10 +233,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -318,10 +320,10 @@ NLOHMANN_JSON_NAMESPACE_END // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-FileCopyrightText: 2016-2021 Evan Nemerson // SPDX-License-Identifier: MIT @@ -2483,6 +2485,14 @@ JSON_HEDLEY_DIAGNOSTIC_POP #endif #endif +#ifndef JSON_HAS_STATIC_RTTI + #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0 + #define JSON_HAS_STATIC_RTTI 1 + #else + #define JSON_HAS_STATIC_RTTI 0 + #endif +#endif + #ifdef JSON_HAS_CPP_17 #define JSON_INLINE_VARIABLE inline #else @@ -2590,12 +2600,13 @@ JSON_HEDLEY_DIAGNOSTIC_POP class NumberUnsignedType, class NumberFloatType, \ template class AllocatorType, \ template class JSONSerializer, \ - class BinaryType> + class BinaryType, \ + class CustomBaseClass> #define NLOHMANN_BASIC_JSON_TPL \ basic_json + AllocatorType, JSONSerializer, BinaryType, CustomBaseClass> // Macros to simplify conversion from/to types @@ -2745,7 +2756,10 @@ JSON_HEDLEY_DIAGNOSTIC_POP #define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...) \ friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ - friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } + friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } + +#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...) \ + friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } /*! @brief macro @@ -2756,10 +2770,12 @@ JSON_HEDLEY_DIAGNOSTIC_POP inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) } +#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...) \ + inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } + #define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...) \ inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ - inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } - + inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } // inspired from https://stackoverflow.com/a/26745591 // allows to call any std function as if (e.g. with begin): @@ -2923,10 +2939,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -2998,10 +3014,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3040,10 +3056,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-FileCopyrightText: 2018 The Abseil Authors // SPDX-License-Identifier: MIT @@ -3214,10 +3230,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3226,14 +3242,15 @@ NLOHMANN_JSON_NAMESPACE_END #include // false_type, is_constructible, is_integral, is_same, true_type #include // declval #include // tuple +#include // char_traits // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3298,10 +3315,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3318,10 +3335,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3342,10 +3359,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT #ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_ @@ -3389,7 +3406,8 @@ NLOHMANN_JSON_NAMESPACE_END template class AllocatorType = std::allocator, template class JSONSerializer = adl_serializer, - class BinaryType = std::vector> + class BinaryType = std::vector, // cppcheck-suppress syntaxError + class CustomBaseClass = void> class basic_json; /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document @@ -3577,6 +3595,63 @@ struct actual_object_comparator template using actual_object_comparator_t = typename actual_object_comparator::type; +///////////////// +// char_traits // +///////////////// + +// Primary template of char_traits calls std char_traits +template +struct char_traits : std::char_traits +{}; + +// Explicitly define char traits for unsigned char since it is not standard +template<> +struct char_traits : std::char_traits +{ + using char_type = unsigned char; + using int_type = uint64_t; + + // Redefine to_int_type function + static int_type to_int_type(char_type c) noexcept + { + return static_cast(c); + } + + static char_type to_char_type(int_type i) noexcept + { + return static_cast(i); + } + + static constexpr int_type eof() noexcept + { + return static_cast(EOF); + } +}; + +// Explicitly define char traits for signed char since it is not standard +template<> +struct char_traits : std::char_traits +{ + using char_type = signed char; + using int_type = uint64_t; + + // Redefine to_int_type function + static int_type to_int_type(char_type c) noexcept + { + return static_cast(c); + } + + static char_type to_char_type(int_type i) noexcept + { + return static_cast(i); + } + + static constexpr int_type eof() noexcept + { + return static_cast(EOF); + } +}; + /////////////////// // is_ functions // /////////////////// @@ -3613,7 +3688,6 @@ template struct is_default_constructible> : conjunction...> {}; - template struct is_constructible : std::is_constructible {}; @@ -3629,7 +3703,6 @@ struct is_constructible> : is_default_constructible struct is_constructible> : is_default_constructible> {}; - template struct is_iterator_traits : std::false_type {}; @@ -4039,7 +4112,6 @@ struct value_in_range_of_impl2 } }; - template struct value_in_range_of_impl2 { @@ -4138,10 +4210,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -4165,28 +4237,28 @@ inline std::size_t concat_length() } template -inline std::size_t concat_length(const char* cstr, Args&& ... rest); +inline std::size_t concat_length(const char* cstr, const Args& ... rest); template -inline std::size_t concat_length(const StringType& str, Args&& ... rest); +inline std::size_t concat_length(const StringType& str, const Args& ... rest); template -inline std::size_t concat_length(const char /*c*/, Args&& ... rest) +inline std::size_t concat_length(const char /*c*/, const Args& ... rest) { - return 1 + concat_length(std::forward(rest)...); + return 1 + concat_length(rest...); } template -inline std::size_t concat_length(const char* cstr, Args&& ... rest) +inline std::size_t concat_length(const char* cstr, const Args& ... rest) { // cppcheck-suppress ignoredReturnValue - return ::strlen(cstr) + concat_length(std::forward(rest)...); + return ::strlen(cstr) + concat_length(rest...); } template -inline std::size_t concat_length(const StringType& str, Args&& ... rest) +inline std::size_t concat_length(const StringType& str, const Args& ... rest) { - return str.size() + concat_length(std::forward(rest)...); + return str.size() + concat_length(rest...); } template @@ -4277,7 +4349,7 @@ template inline OutStringType concat(Args && ... args) { OutStringType str; - str.reserve(concat_length(std::forward(args)...)); + str.reserve(concat_length(args...)); concat_into(str, std::forward(args)...); return str; } @@ -4286,7 +4358,6 @@ inline OutStringType concat(Args && ... args) NLOHMANN_JSON_NAMESPACE_END - NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail { @@ -4334,9 +4405,9 @@ class exception : public std::exception { case value_t::array: { - for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i) + for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i) { - if (¤t->m_parent->m_value.array->operator[](i) == current) + if (¤t->m_parent->m_data.m_value.array->operator[](i) == current) { tokens.emplace_back(std::to_string(i)); break; @@ -4347,7 +4418,7 @@ class exception : public std::exception case value_t::object: { - for (const auto& element : *current->m_parent->m_value.object) + for (const auto& element : *current->m_parent->m_data.m_value.object) { if (&element.second == current) { @@ -4410,17 +4481,17 @@ class parse_error : public exception template::value, int> = 0> static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("parse_error", id_), "parse error", - position_string(pos), ": ", exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("parse_error", id_), "parse error", + position_string(pos), ": ", exception::diagnostics(context), what_arg); return {id_, pos.chars_read_total, w.c_str()}; } template::value, int> = 0> static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("parse_error", id_), "parse error", - (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""), - ": ", exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("parse_error", id_), "parse error", + (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""), + ": ", exception::diagnostics(context), what_arg); return {id_, byte_, w.c_str()}; } @@ -4454,7 +4525,7 @@ class invalid_iterator : public exception template::value, int> = 0> static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4472,7 +4543,7 @@ class type_error : public exception template::value, int> = 0> static type_error create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4489,7 +4560,7 @@ class out_of_range : public exception template::value, int> = 0> static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4506,7 +4577,7 @@ class other_error : public exception template::value, int> = 0> static other_error create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4525,10 +4596,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -4549,10 +4620,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5055,10 +5126,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5075,10 +5146,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5147,10 +5218,10 @@ template class iteration_proxy_value // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions iteration_proxy_value(iteration_proxy_value&&) noexcept(std::is_nothrow_move_constructible::value - && std::is_nothrow_move_constructible::value) = default; + && std::is_nothrow_move_constructible::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations) iteration_proxy_value& operator=(iteration_proxy_value&&) noexcept(std::is_nothrow_move_assignable::value - && std::is_nothrow_move_assignable::value) = default; + && std::is_nothrow_move_assignable::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations) ~iteration_proxy_value() = default; /// dereference operator (needed for range-based for) @@ -5297,11 +5368,11 @@ namespace std #pragma clang diagnostic ignored "-Wmismatched-tags" #endif template -class tuple_size<::nlohmann::detail::iteration_proxy_value> +class tuple_size<::nlohmann::detail::iteration_proxy_value> // NOLINT(cert-dcl58-cpp) : public std::integral_constant {}; template -class tuple_element> +class tuple_element> // NOLINT(cert-dcl58-cpp) { public: using type = decltype( @@ -5340,7 +5411,7 @@ namespace detail /* * Note all external_constructor<>::construct functions need to call - * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an + * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an * allocated value (e.g., a string). See bug issue * https://github.com/nlohmann/json/issues/2865 for more information. */ @@ -5353,9 +5424,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::boolean; - j.m_value = b; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::boolean; + j.m_data.m_value = b; j.assert_invariant(); } }; @@ -5366,18 +5437,18 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value = s; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value = s; j.assert_invariant(); } template static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value = std::move(s); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value = std::move(s); j.assert_invariant(); } @@ -5386,9 +5457,9 @@ struct external_constructor int > = 0 > static void construct(BasicJsonType& j, const CompatibleStringType& str) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value.string = j.template create(str); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value.string = j.template create(str); j.assert_invariant(); } }; @@ -5399,18 +5470,18 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::binary; - j.m_value = typename BasicJsonType::binary_t(b); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::binary; + j.m_data.m_value = typename BasicJsonType::binary_t(b); j.assert_invariant(); } template static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::binary; - j.m_value = typename BasicJsonType::binary_t(std::move(b)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::binary; + j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b)); j.assert_invariant(); } }; @@ -5421,9 +5492,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_float; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_float; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5434,9 +5505,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_unsigned; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_unsigned; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5447,9 +5518,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_integer; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_integer; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5460,9 +5531,9 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = arr; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = arr; j.set_parents(); j.assert_invariant(); } @@ -5470,9 +5541,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = std::move(arr); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = std::move(arr); j.set_parents(); j.assert_invariant(); } @@ -5485,9 +5556,9 @@ struct external_constructor using std::begin; using std::end; - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value.array = j.template create(begin(arr), end(arr)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value.array = j.template create(begin(arr), end(arr)); j.set_parents(); j.assert_invariant(); } @@ -5495,14 +5566,14 @@ struct external_constructor template static void construct(BasicJsonType& j, const std::vector& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = value_t::array; - j.m_value.array->reserve(arr.size()); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = value_t::array; + j.m_data.m_value.array->reserve(arr.size()); for (const bool x : arr) { - j.m_value.array->push_back(x); - j.set_parent(j.m_value.array->back()); + j.m_data.m_value.array->push_back(x); + j.set_parent(j.m_data.m_value.array->back()); } j.assert_invariant(); } @@ -5511,13 +5582,13 @@ struct external_constructor enable_if_t::value, int> = 0> static void construct(BasicJsonType& j, const std::valarray& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = value_t::array; - j.m_value.array->resize(arr.size()); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = value_t::array; + j.m_data.m_value.array->resize(arr.size()); if (arr.size() > 0) { - std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin()); + std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin()); } j.set_parents(); j.assert_invariant(); @@ -5530,9 +5601,9 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value = obj; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value = obj; j.set_parents(); j.assert_invariant(); } @@ -5540,9 +5611,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value = std::move(obj); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value = std::move(obj); j.set_parents(); j.assert_invariant(); } @@ -5554,9 +5625,9 @@ struct external_constructor using std::begin; using std::end; - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value.object = j.template create(begin(obj), end(obj)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value.object = j.template create(begin(obj), end(obj)); j.set_parents(); j.assert_invariant(); } @@ -5626,7 +5697,8 @@ template::type; - external_constructor::construct(j, static_cast(e)); + static constexpr value_t integral_value_t = std::is_unsigned::value ? value_t::number_unsigned : value_t::number_integer; + external_constructor::construct(j, static_cast(e)); } #endif // JSON_DISABLE_ENUM_SERIALIZATION @@ -5796,10 +5868,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5908,10 +5980,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6041,10 +6113,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6067,10 +6139,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6094,6 +6166,8 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include + NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail @@ -6140,7 +6214,6 @@ class file_input_adapter std::FILE* m_file; }; - /*! Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at beginning of input. Does not support changing the underlying std::streambuf @@ -6214,16 +6287,16 @@ class iterator_input_adapter : current(std::move(first)), end(std::move(last)) {} - typename std::char_traits::int_type get_character() + typename char_traits::int_type get_character() { if (JSON_HEDLEY_LIKELY(current != end)) { - auto result = std::char_traits::to_int_type(*current); + auto result = char_traits::to_int_type(*current); std::advance(current, 1); return result; } - return std::char_traits::eof(); + return char_traits::eof(); } private: @@ -6239,7 +6312,6 @@ class iterator_input_adapter } }; - template struct wide_string_input_helper; @@ -6363,7 +6435,7 @@ struct wide_string_input_helper } }; -// Wraps another input apdater to convert wide character types into individual bytes. +// Wraps another input adapter to convert wide character types into individual bytes. template class wide_string_input_adapter { @@ -6408,7 +6480,6 @@ class wide_string_input_adapter std::size_t utf8_bytes_filled = 0; }; - template struct iterator_input_adapter_factory { @@ -6565,10 +6636,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6710,7 +6781,6 @@ struct json_sax virtual ~json_sax() = default; }; - namespace detail { /*! @@ -6812,7 +6882,7 @@ class json_sax_dom_parser JSON_ASSERT(ref_stack.back()->is_object()); // add null at given key and store the reference for later - object_element = &(ref_stack.back()->m_value.object->operator[](val)); + object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val)); return true; } @@ -6887,8 +6957,8 @@ class json_sax_dom_parser if (ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->emplace_back(std::forward(v)); - return &(ref_stack.back()->m_value.array->back()); + ref_stack.back()->m_data.m_value.array->emplace_back(std::forward(v)); + return &(ref_stack.back()->m_data.m_value.array->back()); } JSON_ASSERT(ref_stack.back()->is_object()); @@ -7007,7 +7077,7 @@ class json_sax_dom_callback_parser // add discarded value at given key and store the reference for later if (keep && ref_stack.back()) { - object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded); + object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded); } return true; @@ -7092,7 +7162,7 @@ class json_sax_dom_callback_parser // remove discarded value if (!keep && !ref_stack.empty() && ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->pop_back(); + ref_stack.back()->m_data.m_value.array->pop_back(); } return true; @@ -7159,7 +7229,7 @@ class json_sax_dom_callback_parser if (ref_stack.empty()) { root = std::move(value); - return {true, &root}; + return {true, & root}; } // skip this value if we already decided to skip the parent @@ -7175,8 +7245,8 @@ class json_sax_dom_callback_parser // array if (ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->emplace_back(std::move(value)); - return {true, &(ref_stack.back()->m_value.array->back())}; + ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value)); + return {true, & (ref_stack.back()->m_data.m_value.array->back())}; } // object @@ -7201,9 +7271,9 @@ class json_sax_dom_callback_parser /// stack to model hierarchy of values std::vector ref_stack {}; /// stack to manage which values to keep - std::vector keep_stack {}; + std::vector keep_stack {}; // NOLINT(readability-redundant-member-init) /// stack to manage which object keys to keep - std::vector key_keep_stack {}; + std::vector key_keep_stack {}; // NOLINT(readability-redundant-member-init) /// helper to hold the reference for the next object element BasicJsonType* object_element = nullptr; /// whether a syntax error occurred @@ -7298,10 +7368,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -7322,6 +7392,8 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include + NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail @@ -7416,7 +7488,7 @@ class lexer : public lexer_base using number_float_t = typename BasicJsonType::number_float_t; using string_t = typename BasicJsonType::string_t; using char_type = typename InputAdapterType::char_type; - using char_int_type = typename std::char_traits::int_type; + using char_int_type = typename char_traits::int_type; public: using token_type = typename lexer_base::token_type; @@ -7523,7 +7595,7 @@ class lexer : public lexer_base for (auto range = ranges.begin(); range != ranges.end(); ++range) { get(); - if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) + if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) // NOLINT(bugprone-inc-dec-in-conditions) { add(current); } @@ -7566,7 +7638,7 @@ class lexer : public lexer_base switch (get()) { // end of file while parsing string - case std::char_traits::eof(): + case char_traits::eof(): { error_message = "invalid string: missing closing quote"; return token_type::parse_error; @@ -8155,7 +8227,7 @@ class lexer : public lexer_base { case '\n': case '\r': - case std::char_traits::eof(): + case char_traits::eof(): case '\0': return true; @@ -8172,7 +8244,7 @@ class lexer : public lexer_base { switch (get()) { - case std::char_traits::eof(): + case char_traits::eof(): case '\0': { error_message = "invalid comment; missing closing '*/'"; @@ -8601,10 +8673,10 @@ class lexer : public lexer_base token_type scan_literal(const char_type* literal_text, const std::size_t length, token_type return_type) { - JSON_ASSERT(std::char_traits::to_char_type(current) == literal_text[0]); + JSON_ASSERT(char_traits::to_char_type(current) == literal_text[0]); for (std::size_t i = 1; i < length; ++i) { - if (JSON_HEDLEY_UNLIKELY(std::char_traits::to_char_type(get()) != literal_text[i])) + if (JSON_HEDLEY_UNLIKELY(char_traits::to_char_type(get()) != literal_text[i])) { error_message = "invalid literal"; return token_type::parse_error; @@ -8622,7 +8694,7 @@ class lexer : public lexer_base { token_buffer.clear(); token_string.clear(); - token_string.push_back(std::char_traits::to_char_type(current)); + token_string.push_back(char_traits::to_char_type(current)); } /* @@ -8630,7 +8702,7 @@ class lexer : public lexer_base This function provides the interface to the used input adapter. It does not throw in case the input reached EOF, but returns a - `std::char_traits::eof()` in that case. Stores the scanned characters + `char_traits::eof()` in that case. Stores the scanned characters for use in error messages. @return character read from the input @@ -8650,9 +8722,9 @@ class lexer : public lexer_base current = ia.get_character(); } - if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_LIKELY(current != char_traits::eof())) { - token_string.push_back(std::char_traits::to_char_type(current)); + token_string.push_back(char_traits::to_char_type(current)); } if (current == '\n') @@ -8691,7 +8763,7 @@ class lexer : public lexer_base --position.chars_read_current_line; } - if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_LIKELY(current != char_traits::eof())) { JSON_ASSERT(!token_string.empty()); token_string.pop_back(); @@ -8885,7 +8957,7 @@ class lexer : public lexer_base // end of input (the null byte is needed when parsing from // string literals) case '\0': - case std::char_traits::eof(): + case char_traits::eof(): return token_type::end_of_input; // error @@ -8903,7 +8975,7 @@ class lexer : public lexer_base const bool ignore_comments = false; /// the current character - char_int_type current = std::char_traits::eof(); + char_int_type current = char_traits::eof(); /// whether the next get() call should just return current bool next_unget = false; @@ -8937,10 +9009,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -9129,7 +9201,6 @@ static inline bool little_endianness(int num = 1) noexcept return *reinterpret_cast(&num) == 1; } - /////////////////// // binary reader // /////////////////// @@ -9147,7 +9218,7 @@ class binary_reader using binary_t = typename BasicJsonType::binary_t; using json_sax_t = SAX; using char_type = typename InputAdapterType::char_type; - using char_int_type = typename std::char_traits::int_type; + using char_int_type = typename char_traits::int_type; public: /*! @@ -9220,7 +9291,7 @@ class binary_reader get(); } - if (JSON_HEDLEY_UNLIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_UNLIKELY(current != char_traits::eof())) { return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read, exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr)); @@ -9303,7 +9374,7 @@ class binary_reader exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr)); } - return get_string(input_format_t::bson, len - static_cast(1), result) && get() != std::char_traits::eof(); + return get_string(input_format_t::bson, len - static_cast(1), result) && get() != char_traits::eof(); } /*! @@ -9404,7 +9475,7 @@ class binary_reader { std::array cr{{}}; static_cast((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) - std::string cr_str{cr.data()}; + const std::string cr_str{cr.data()}; return sax->parse_error(element_type_parse_position, cr_str, parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr)); } @@ -9497,7 +9568,7 @@ class binary_reader switch (get_char ? get() : current) { // EOF - case std::char_traits::eof(): + case char_traits::eof(): return unexpect_eof(input_format_t::cbor, "value"); // Integer 0x00..0x17 (0..23) @@ -10272,7 +10343,7 @@ class binary_reader switch (get()) { // EOF - case std::char_traits::eof(): + case char_traits::eof(): return unexpect_eof(input_format_t::msgpack, "value"); // positive fixint @@ -11339,7 +11410,7 @@ class binary_reader exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr)); } - bool is_error = get_ubjson_size_value(result.first, is_ndarray); + const bool is_error = get_ubjson_size_value(result.first, is_ndarray); if (input_format == input_format_t::bjdata && is_ndarray) { if (inside_ndarray) @@ -11354,7 +11425,7 @@ class binary_reader if (current == '#') { - bool is_error = get_ubjson_size_value(result.first, is_ndarray); + const bool is_error = get_ubjson_size_value(result.first, is_ndarray); if (input_format == input_format_t::bjdata && is_ndarray) { return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read, @@ -11374,7 +11445,7 @@ class binary_reader { switch (prefix) { - case std::char_traits::eof(): // EOF + case char_traits::eof(): // EOF return unexpect_eof(input_format, "value"); case 'T': // true @@ -11819,7 +11890,7 @@ class binary_reader This function provides the interface to the used input adapter. It does not throw in case the input reached EOF, but returns a -'ve valued - `std::char_traits::eof()` in that case. + `char_traits::eof()` in that case. @return character read from the input */ @@ -11961,7 +12032,7 @@ class binary_reader JSON_HEDLEY_NON_NULL(3) bool unexpect_eof(const input_format_t format, const char* context) const { - if (JSON_HEDLEY_UNLIKELY(current == std::char_traits::eof())) + if (JSON_HEDLEY_UNLIKELY(current == char_traits::eof())) { return sax->parse_error(chars_read, "", parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr)); @@ -12028,7 +12099,7 @@ class binary_reader InputAdapterType ia; /// the current character - char_int_type current = std::char_traits::eof(); + char_int_type current = char_traits::eof(); /// the number of characters read std::size_t chars_read = 0; @@ -12090,10 +12161,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12439,13 +12510,25 @@ class parser m_lexer.get_token_string(), parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr)); } + case token_type::end_of_input: + { + if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), + "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr)); + } + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr)); + } case token_type::uninitialized: case token_type::end_array: case token_type::end_object: case token_type::name_separator: case token_type::value_separator: - case token_type::end_of_input: case token_type::literal_or_value: default: // the last token was unexpected { @@ -12607,10 +12690,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12620,10 +12703,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12779,10 +12862,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12887,7 +12970,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -12984,17 +13067,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - m_it.object_iterator = m_object->m_value.object->begin(); + m_it.object_iterator = m_object->m_data.m_value.object->begin(); break; } case value_t::array: { - m_it.array_iterator = m_object->m_value.array->begin(); + m_it.array_iterator = m_object->m_data.m_value.array->begin(); break; } @@ -13028,17 +13111,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - m_it.object_iterator = m_object->m_value.object->end(); + m_it.object_iterator = m_object->m_data.m_value.object->end(); break; } case value_t::array: { - m_it.array_iterator = m_object->m_value.array->end(); + m_it.array_iterator = m_object->m_data.m_value.array->end(); break; } @@ -13067,17 +13150,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end()); return m_it.object_iterator->second; } case value_t::array: { - JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end()); return *m_it.array_iterator; } @@ -13111,17 +13194,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end()); return &(m_it.object_iterator->second); } case value_t::array: { - JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end()); return &*m_it.array_iterator; } @@ -13164,7 +13247,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -13215,7 +13298,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -13262,7 +13345,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: return (m_it.object_iterator == other.m_it.object_iterator); @@ -13307,7 +13390,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object)); @@ -13363,7 +13446,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object)); @@ -13442,7 +13525,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object)); @@ -13471,7 +13554,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object)); @@ -13541,10 +13624,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -13673,13 +13756,55 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include +// __ _____ _____ _____ +// __| | __| | | | JSON for Modern C++ +// | | |__ | | | | | | version 3.11.3 +// |_____|_____|_____|_|___| https://github.com/nlohmann/json +// +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann +// SPDX-License-Identifier: MIT + + + +#include // conditional, is_same + +// #include + + +NLOHMANN_JSON_NAMESPACE_BEGIN +namespace detail +{ + +/*! +@brief Default base class of the @ref basic_json class. + +So that the correct implementations of the copy / move ctors / assign operators +of @ref basic_json do not require complex case distinctions +(no base class / custom base class used as customization point), +@ref basic_json always has a base class. +By default, this class is used because it is empty and thus has no effect +on the behavior of @ref basic_json. +*/ +struct json_default_base {}; + +template +using json_base_class = typename std::conditional < + std::is_same::value, + json_default_base, + T + >::type; + +} // namespace detail +NLOHMANN_JSON_NAMESPACE_END + // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -13911,7 +14036,7 @@ class json_pointer const char* p = s.c_str(); char* p_end = nullptr; errno = 0; // strtoull doesn't reset errno - unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int) + const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int) if (p == p_end // invalid input or empty string || errno == ERANGE // out of range || JSON_HEDLEY_UNLIKELY(static_cast(p_end - p) != s.size())) // incomplete read @@ -14067,7 +14192,7 @@ class json_pointer if (reference_token == "-") { // explicitly treat "-" as index beyond the end - ptr = &ptr->operator[](ptr->m_value.array->size()); + ptr = &ptr->operator[](ptr->m_data.m_value.array->size()); } else { @@ -14119,7 +14244,7 @@ class json_pointer { // "-" always fails the range check JSON_THROW(detail::out_of_range::create(402, detail::concat( - "array index '-' (", std::to_string(ptr->m_value.array->size()), + "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } @@ -14176,7 +14301,7 @@ class json_pointer if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) { // "-" cannot be used for const access - JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_value.array->size()), ") is out of range"), ptr)); + JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } // use unchecked array access @@ -14226,7 +14351,7 @@ class json_pointer { // "-" always fails the range check JSON_THROW(detail::out_of_range::create(402, detail::concat( - "array index '-' (", std::to_string(ptr->m_value.array->size()), + "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } @@ -14421,7 +14546,7 @@ class json_pointer { case detail::value_t::array: { - if (value.m_value.array->empty()) + if (value.m_data.m_value.array->empty()) { // flatten empty array as null result[reference_string] = nullptr; @@ -14429,10 +14554,10 @@ class json_pointer else { // iterate array and use index as reference string - for (std::size_t i = 0; i < value.m_value.array->size(); ++i) + for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i) { flatten(detail::concat(reference_string, '/', std::to_string(i)), - value.m_value.array->operator[](i), result); + value.m_data.m_value.array->operator[](i), result); } } break; @@ -14440,7 +14565,7 @@ class json_pointer case detail::value_t::object: { - if (value.m_value.object->empty()) + if (value.m_data.m_value.object->empty()) { // flatten empty object as null result[reference_string] = nullptr; @@ -14448,7 +14573,7 @@ class json_pointer else { // iterate object and use keys as reference string - for (const auto& element : *value.m_value.object) + for (const auto& element : *value.m_data.m_value.object) { flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result); } @@ -14495,7 +14620,7 @@ class json_pointer BasicJsonType result; // iterate the JSON object values - for (const auto& element : *value.m_value.object) + for (const auto& element : *value.m_data.m_value.object) { if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive())) { @@ -14671,10 +14796,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14763,10 +14888,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14789,10 +14914,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14978,7 +15103,7 @@ class binary_writer { case value_t::object: { - write_bson_object(*j.m_value.object); + write_bson_object(*j.m_data.m_value.object); break; } @@ -15013,7 +15138,7 @@ class binary_writer case value_t::boolean: { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type(0xF5) : to_char_type(0xF4)); break; @@ -15021,42 +15146,42 @@ class binary_writer case value_t::number_integer: { - if (j.m_value.number_integer >= 0) + if (j.m_data.m_value.number_integer >= 0) { // CBOR does not differentiate between positive signed // integers and unsigned integers. Therefore, we used the // code from the value_t::number_unsigned case here. - if (j.m_value.number_integer <= 0x17) + if (j.m_data.m_value.number_integer <= 0x17) { - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x18)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x19)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x1A)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } else { oa->write_character(to_char_type(0x1B)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } else { // The conversions below encode the sign in the first // byte, and the value is converted to a positive number. - const auto positive_number = -1 - j.m_value.number_integer; - if (j.m_value.number_integer >= -24) + const auto positive_number = -1 - j.m_data.m_value.number_integer; + if (j.m_data.m_value.number_integer >= -24) { write_number(static_cast(0x20 + positive_number)); } @@ -15086,52 +15211,52 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned <= 0x17) + if (j.m_data.m_value.number_unsigned <= 0x17) { - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x18)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x19)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x1A)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } else { oa->write_character(to_char_type(0x1B)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } break; } case value_t::number_float: { - if (std::isnan(j.m_value.number_float)) + if (std::isnan(j.m_data.m_value.number_float)) { // NaN is 0xf97e00 in CBOR oa->write_character(to_char_type(0xF9)); oa->write_character(to_char_type(0x7E)); oa->write_character(to_char_type(0x00)); } - else if (std::isinf(j.m_value.number_float)) + else if (std::isinf(j.m_data.m_value.number_float)) { // Infinity is 0xf97c00, -Infinity is 0xf9fc00 oa->write_character(to_char_type(0xf9)); - oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC)); + oa->write_character(j.m_data.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC)); oa->write_character(to_char_type(0x00)); } else { - write_compact_float(j.m_value.number_float, detail::input_format_t::cbor); + write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::cbor); } break; } @@ -15139,7 +15264,7 @@ class binary_writer case value_t::string: { // step 1: write control byte and the string length - const auto N = j.m_value.string->size(); + const auto N = j.m_data.m_value.string->size(); if (N <= 0x17) { write_number(static_cast(0x60 + N)); @@ -15169,15 +15294,15 @@ class binary_writer // step 2: write the string oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } case value_t::array: { // step 1: write control byte and the array size - const auto N = j.m_value.array->size(); + const auto N = j.m_data.m_value.array->size(); if (N <= 0x17) { write_number(static_cast(0x80 + N)); @@ -15206,7 +15331,7 @@ class binary_writer // LCOV_EXCL_STOP // step 2: write each element - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_cbor(el); } @@ -15215,32 +15340,32 @@ class binary_writer case value_t::binary: { - if (j.m_value.binary->has_subtype()) + if (j.m_data.m_value.binary->has_subtype()) { - if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xd8)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xd9)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xda)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xdb)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } } // step 1: write control byte and the binary array size - const auto N = j.m_value.binary->size(); + const auto N = j.m_data.m_value.binary->size(); if (N <= 0x17) { write_number(static_cast(0x40 + N)); @@ -15270,7 +15395,7 @@ class binary_writer // step 2: write each element oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), + reinterpret_cast(j.m_data.m_value.binary->data()), N); break; @@ -15279,7 +15404,7 @@ class binary_writer case value_t::object: { // step 1: write control byte and the object size - const auto N = j.m_value.object->size(); + const auto N = j.m_data.m_value.object->size(); if (N <= 0x17) { write_number(static_cast(0xA0 + N)); @@ -15308,7 +15433,7 @@ class binary_writer // LCOV_EXCL_STOP // step 2: write each element - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_cbor(el.first); write_cbor(el.second); @@ -15337,7 +15462,7 @@ class binary_writer case value_t::boolean: // true and false { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type(0xC3) : to_char_type(0xC2)); break; @@ -15345,75 +15470,75 @@ class binary_writer case value_t::number_integer: { - if (j.m_value.number_integer >= 0) + if (j.m_data.m_value.number_integer >= 0) { // MessagePack does not differentiate between positive // signed integers and unsigned integers. Therefore, we used // the code from the value_t::number_unsigned case here. - if (j.m_value.number_unsigned < 128) + if (j.m_data.m_value.number_unsigned < 128) { // positive fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 8 oa->write_character(to_char_type(0xCC)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 16 oa->write_character(to_char_type(0xCD)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 32 oa->write_character(to_char_type(0xCE)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 64 oa->write_character(to_char_type(0xCF)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } else { - if (j.m_value.number_integer >= -32) + if (j.m_data.m_value.number_integer >= -32) { // negative fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 8 oa->write_character(to_char_type(0xD0)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 16 oa->write_character(to_char_type(0xD1)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 32 oa->write_character(to_char_type(0xD2)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 64 oa->write_character(to_char_type(0xD3)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } break; @@ -15421,48 +15546,48 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned < 128) + if (j.m_data.m_value.number_unsigned < 128) { // positive fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 8 oa->write_character(to_char_type(0xCC)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 16 oa->write_character(to_char_type(0xCD)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 32 oa->write_character(to_char_type(0xCE)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 64 oa->write_character(to_char_type(0xCF)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } break; } case value_t::number_float: { - write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack); + write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::msgpack); break; } case value_t::string: { // step 1: write control byte and the string length - const auto N = j.m_value.string->size(); + const auto N = j.m_data.m_value.string->size(); if (N <= 31) { // fixstr @@ -15489,15 +15614,15 @@ class binary_writer // step 2: write the string oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } case value_t::array: { // step 1: write control byte and the array size - const auto N = j.m_value.array->size(); + const auto N = j.m_data.m_value.array->size(); if (N <= 15) { // fixarray @@ -15517,7 +15642,7 @@ class binary_writer } // step 2: write each element - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_msgpack(el); } @@ -15528,10 +15653,10 @@ class binary_writer { // step 0: determine if the binary type has a set subtype to // determine whether or not to use the ext or fixext types - const bool use_ext = j.m_value.binary->has_subtype(); + const bool use_ext = j.m_data.m_value.binary->has_subtype(); // step 1: write control byte and the byte string length - const auto N = j.m_value.binary->size(); + const auto N = j.m_data.m_value.binary->size(); if (N <= (std::numeric_limits::max)()) { std::uint8_t output_type{}; @@ -15576,18 +15701,18 @@ class binary_writer } else if (N <= (std::numeric_limits::max)()) { - std::uint8_t output_type = use_ext - ? 0xC8 // ext 16 - : 0xC5; // bin 16 + const std::uint8_t output_type = use_ext + ? 0xC8 // ext 16 + : 0xC5; // bin 16 oa->write_character(to_char_type(output_type)); write_number(static_cast(N)); } else if (N <= (std::numeric_limits::max)()) { - std::uint8_t output_type = use_ext - ? 0xC9 // ext 32 - : 0xC6; // bin 32 + const std::uint8_t output_type = use_ext + ? 0xC9 // ext 32 + : 0xC6; // bin 32 oa->write_character(to_char_type(output_type)); write_number(static_cast(N)); @@ -15596,12 +15721,12 @@ class binary_writer // step 1.5: if this is an ext type, write the subtype if (use_ext) { - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } // step 2: write the byte string oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), + reinterpret_cast(j.m_data.m_value.binary->data()), N); break; @@ -15610,7 +15735,7 @@ class binary_writer case value_t::object: { // step 1: write control byte and the object size - const auto N = j.m_value.object->size(); + const auto N = j.m_data.m_value.object->size(); if (N <= 15) { // fixmap @@ -15630,7 +15755,7 @@ class binary_writer } // step 2: write each element - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_msgpack(el.first); write_msgpack(el.second); @@ -15670,7 +15795,7 @@ class binary_writer { if (add_prefix) { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type('T') : to_char_type('F')); } @@ -15679,19 +15804,19 @@ class binary_writer case value_t::number_integer: { - write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_integer, add_prefix, use_bjdata); break; } case value_t::number_unsigned: { - write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_unsigned, add_prefix, use_bjdata); break; } case value_t::number_float: { - write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_float, add_prefix, use_bjdata); break; } @@ -15701,10 +15826,10 @@ class binary_writer { oa->write_character(to_char_type('S')); } - write_number_with_ubjson_prefix(j.m_value.string->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.string->size(), true, use_bjdata); oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } @@ -15716,7 +15841,7 @@ class binary_writer } bool prefix_required = true; - if (use_type && !j.m_value.array->empty()) + if (use_type && !j.m_data.m_value.array->empty()) { JSON_ASSERT(use_count); const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata); @@ -15739,10 +15864,10 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.array->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.array->size(), true, use_bjdata); } - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_ubjson(el, use_count, use_type, prefix_required, use_bjdata); } @@ -15762,7 +15887,7 @@ class binary_writer oa->write_character(to_char_type('[')); } - if (use_type && !j.m_value.binary->empty()) + if (use_type && !j.m_data.m_value.binary->empty()) { JSON_ASSERT(use_count); oa->write_character(to_char_type('$')); @@ -15772,21 +15897,21 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.binary->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.binary->size(), true, use_bjdata); } if (use_type) { oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), - j.m_value.binary->size()); + reinterpret_cast(j.m_data.m_value.binary->data()), + j.m_data.m_value.binary->size()); } else { - for (size_t i = 0; i < j.m_value.binary->size(); ++i) + for (size_t i = 0; i < j.m_data.m_value.binary->size(); ++i) { oa->write_character(to_char_type('U')); - oa->write_character(j.m_value.binary->data()[i]); + oa->write_character(j.m_data.m_value.binary->data()[i]); } } @@ -15800,9 +15925,9 @@ class binary_writer case value_t::object: { - if (use_bjdata && j.m_value.object->size() == 3 && j.m_value.object->find("_ArrayType_") != j.m_value.object->end() && j.m_value.object->find("_ArraySize_") != j.m_value.object->end() && j.m_value.object->find("_ArrayData_") != j.m_value.object->end()) + if (use_bjdata && j.m_data.m_value.object->size() == 3 && j.m_data.m_value.object->find("_ArrayType_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArraySize_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArrayData_") != j.m_data.m_value.object->end()) { - if (!write_bjdata_ndarray(*j.m_value.object, use_count, use_type)) // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata) + if (!write_bjdata_ndarray(*j.m_data.m_value.object, use_count, use_type)) // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata) { break; } @@ -15814,7 +15939,7 @@ class binary_writer } bool prefix_required = true; - if (use_type && !j.m_value.object->empty()) + if (use_type && !j.m_data.m_value.object->empty()) { JSON_ASSERT(use_count); const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata); @@ -15837,10 +15962,10 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.object->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.object->size(), true, use_bjdata); } - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata); oa->write_characters( @@ -15990,19 +16115,19 @@ class binary_writer void write_bson_unsigned(const string_t& name, const BasicJsonType& j) { - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { write_bson_entry_header(name, 0x10 /* int32 */); - write_number(static_cast(j.m_value.number_unsigned), true); + write_number(static_cast(j.m_data.m_value.number_unsigned), true); } - else if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + else if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { write_bson_entry_header(name, 0x12 /* int64 */); - write_number(static_cast(j.m_value.number_unsigned), true); + write_number(static_cast(j.m_data.m_value.number_unsigned), true); } else { - JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j)); + JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_data.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j)); } } @@ -16083,13 +16208,13 @@ class binary_writer switch (j.type()) { case value_t::object: - return header_size + calc_bson_object_size(*j.m_value.object); + return header_size + calc_bson_object_size(*j.m_data.m_value.object); case value_t::array: - return header_size + calc_bson_array_size(*j.m_value.array); + return header_size + calc_bson_array_size(*j.m_data.m_value.array); case value_t::binary: - return header_size + calc_bson_binary_size(*j.m_value.binary); + return header_size + calc_bson_binary_size(*j.m_data.m_value.binary); case value_t::boolean: return header_size + 1ul; @@ -16098,13 +16223,13 @@ class binary_writer return header_size + 8ul; case value_t::number_integer: - return header_size + calc_bson_integer_size(j.m_value.number_integer); + return header_size + calc_bson_integer_size(j.m_data.m_value.number_integer); case value_t::number_unsigned: - return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned); + return header_size + calc_bson_unsigned_size(j.m_data.m_value.number_unsigned); case value_t::string: - return header_size + calc_bson_string_size(*j.m_value.string); + return header_size + calc_bson_string_size(*j.m_data.m_value.string); case value_t::null: return header_size + 0ul; @@ -16130,28 +16255,28 @@ class binary_writer switch (j.type()) { case value_t::object: - return write_bson_object_entry(name, *j.m_value.object); + return write_bson_object_entry(name, *j.m_data.m_value.object); case value_t::array: - return write_bson_array(name, *j.m_value.array); + return write_bson_array(name, *j.m_data.m_value.array); case value_t::binary: - return write_bson_binary(name, *j.m_value.binary); + return write_bson_binary(name, *j.m_data.m_value.binary); case value_t::boolean: - return write_bson_boolean(name, j.m_value.boolean); + return write_bson_boolean(name, j.m_data.m_value.boolean); case value_t::number_float: - return write_bson_double(name, j.m_value.number_float); + return write_bson_double(name, j.m_data.m_value.number_float); case value_t::number_integer: - return write_bson_integer(name, j.m_value.number_integer); + return write_bson_integer(name, j.m_data.m_value.number_integer); case value_t::number_unsigned: return write_bson_unsigned(name, j); case value_t::string: - return write_bson_string(name, *j.m_value.string); + return write_bson_string(name, *j.m_data.m_value.string); case value_t::null: return write_bson_null(name); @@ -16173,8 +16298,8 @@ class binary_writer */ static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value) { - std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast(0), - [](size_t result, const typename BasicJsonType::object_t::value_type & el) + const std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast(0), + [](size_t result, const typename BasicJsonType::object_t::value_type & el) { return result += calc_bson_element_size(el.first, el.second); }); @@ -16424,35 +16549,35 @@ class binary_writer return 'Z'; case value_t::boolean: - return j.m_value.boolean ? 'T' : 'F'; + return j.m_data.m_value.boolean ? 'T' : 'F'; case value_t::number_integer: { - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'i'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'U'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'I'; } - if (use_bjdata && ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)())) + if (use_bjdata && ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)())) { return 'u'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'l'; } - if (use_bjdata && ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)())) + if (use_bjdata && ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)())) { return 'm'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'L'; } @@ -16462,35 +16587,35 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'i'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'U'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'I'; } - if (use_bjdata && j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'u'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'l'; } - if (use_bjdata && j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'm'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'L'; } - if (use_bjdata && j.m_value.number_unsigned <= (std::numeric_limits::max)()) + if (use_bjdata && j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { return 'M'; } @@ -16499,7 +16624,7 @@ class binary_writer } case value_t::number_float: - return get_ubjson_float_prefix(j.m_value.number_float); + return get_ubjson_float_prefix(j.m_data.m_value.number_float); case value_t::string: return 'S'; @@ -16548,7 +16673,7 @@ class binary_writer std::size_t len = (value.at(key).empty() ? 0 : 1); for (const auto& el : value.at(key)) { - len *= static_cast(el.m_value.number_unsigned); + len *= static_cast(el.m_data.m_value.number_unsigned); } key = "_ArrayData_"; @@ -16570,70 +16695,70 @@ class binary_writer { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'i') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'u') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'I') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'm') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'l') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'M') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'L') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'd') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_float), true); + write_number(static_cast(el.m_data.m_value.number_float), true); } } else if (dtype == 'D') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_float), true); + write_number(static_cast(el.m_data.m_value.number_float), true); } } return false; @@ -16757,11 +16882,11 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // // SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -16782,11 +16907,11 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // // SPDX-FileCopyrightText: 2009 Florian Loitsch -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -17692,7 +17817,7 @@ void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value) // NB: If the neighbors are computed for single-precision numbers, there is a single float // (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision // value is off by 1 ulp. -#if 0 +#if 0 // NOLINT(readability-avoid-unconditional-preprocessor-if) const boundaries w = compute_boundaries(static_cast(value)); #else const boundaries w = compute_boundaries(value); @@ -17994,11 +18119,11 @@ class serializer const unsigned int indent_step, const unsigned int current_indent = 0) { - switch (val.m_type) + switch (val.m_data.m_type) { case value_t::object: { - if (val.m_value.object->empty()) + if (val.m_data.m_value.object->empty()) { o->write_characters("{}", 2); return; @@ -18016,8 +18141,8 @@ class serializer } // first n-1 elements - auto i = val.m_value.object->cbegin(); - for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + auto i = val.m_data.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i) { o->write_characters(indent_string.c_str(), new_indent); o->write_character('\"'); @@ -18028,8 +18153,8 @@ class serializer } // last element - JSON_ASSERT(i != val.m_value.object->cend()); - JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + JSON_ASSERT(i != val.m_data.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend()); o->write_characters(indent_string.c_str(), new_indent); o->write_character('\"'); dump_escaped(i->first, ensure_ascii); @@ -18045,8 +18170,8 @@ class serializer o->write_character('{'); // first n-1 elements - auto i = val.m_value.object->cbegin(); - for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + auto i = val.m_data.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i) { o->write_character('\"'); dump_escaped(i->first, ensure_ascii); @@ -18056,8 +18181,8 @@ class serializer } // last element - JSON_ASSERT(i != val.m_value.object->cend()); - JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + JSON_ASSERT(i != val.m_data.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend()); o->write_character('\"'); dump_escaped(i->first, ensure_ascii); o->write_characters("\":", 2); @@ -18071,7 +18196,7 @@ class serializer case value_t::array: { - if (val.m_value.array->empty()) + if (val.m_data.m_value.array->empty()) { o->write_characters("[]", 2); return; @@ -18089,8 +18214,8 @@ class serializer } // first n-1 elements - for (auto i = val.m_value.array->cbegin(); - i != val.m_value.array->cend() - 1; ++i) + for (auto i = val.m_data.m_value.array->cbegin(); + i != val.m_data.m_value.array->cend() - 1; ++i) { o->write_characters(indent_string.c_str(), new_indent); dump(*i, true, ensure_ascii, indent_step, new_indent); @@ -18098,9 +18223,9 @@ class serializer } // last element - JSON_ASSERT(!val.m_value.array->empty()); + JSON_ASSERT(!val.m_data.m_value.array->empty()); o->write_characters(indent_string.c_str(), new_indent); - dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent); + dump(val.m_data.m_value.array->back(), true, ensure_ascii, indent_step, new_indent); o->write_character('\n'); o->write_characters(indent_string.c_str(), current_indent); @@ -18111,16 +18236,16 @@ class serializer o->write_character('['); // first n-1 elements - for (auto i = val.m_value.array->cbegin(); - i != val.m_value.array->cend() - 1; ++i) + for (auto i = val.m_data.m_value.array->cbegin(); + i != val.m_data.m_value.array->cend() - 1; ++i) { dump(*i, false, ensure_ascii, indent_step, current_indent); o->write_character(','); } // last element - JSON_ASSERT(!val.m_value.array->empty()); - dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent); + JSON_ASSERT(!val.m_data.m_value.array->empty()); + dump(val.m_data.m_value.array->back(), false, ensure_ascii, indent_step, current_indent); o->write_character(']'); } @@ -18131,7 +18256,7 @@ class serializer case value_t::string: { o->write_character('\"'); - dump_escaped(*val.m_value.string, ensure_ascii); + dump_escaped(*val.m_data.m_value.string, ensure_ascii); o->write_character('\"'); return; } @@ -18153,24 +18278,24 @@ class serializer o->write_characters("\"bytes\": [", 10); - if (!val.m_value.binary->empty()) + if (!val.m_data.m_value.binary->empty()) { - for (auto i = val.m_value.binary->cbegin(); - i != val.m_value.binary->cend() - 1; ++i) + for (auto i = val.m_data.m_value.binary->cbegin(); + i != val.m_data.m_value.binary->cend() - 1; ++i) { dump_integer(*i); o->write_characters(", ", 2); } - dump_integer(val.m_value.binary->back()); + dump_integer(val.m_data.m_value.binary->back()); } o->write_characters("],\n", 3); o->write_characters(indent_string.c_str(), new_indent); o->write_characters("\"subtype\": ", 11); - if (val.m_value.binary->has_subtype()) + if (val.m_data.m_value.binary->has_subtype()) { - dump_integer(val.m_value.binary->subtype()); + dump_integer(val.m_data.m_value.binary->subtype()); } else { @@ -18184,21 +18309,21 @@ class serializer { o->write_characters("{\"bytes\":[", 10); - if (!val.m_value.binary->empty()) + if (!val.m_data.m_value.binary->empty()) { - for (auto i = val.m_value.binary->cbegin(); - i != val.m_value.binary->cend() - 1; ++i) + for (auto i = val.m_data.m_value.binary->cbegin(); + i != val.m_data.m_value.binary->cend() - 1; ++i) { dump_integer(*i); o->write_character(','); } - dump_integer(val.m_value.binary->back()); + dump_integer(val.m_data.m_value.binary->back()); } o->write_characters("],\"subtype\":", 12); - if (val.m_value.binary->has_subtype()) + if (val.m_data.m_value.binary->has_subtype()) { - dump_integer(val.m_value.binary->subtype()); + dump_integer(val.m_data.m_value.binary->subtype()); o->write_character('}'); } else @@ -18211,7 +18336,7 @@ class serializer case value_t::boolean: { - if (val.m_value.boolean) + if (val.m_data.m_value.boolean) { o->write_characters("true", 4); } @@ -18224,19 +18349,19 @@ class serializer case value_t::number_integer: { - dump_integer(val.m_value.number_integer); + dump_integer(val.m_data.m_value.number_integer); return; } case value_t::number_unsigned: { - dump_integer(val.m_value.number_unsigned); + dump_integer(val.m_data.m_value.number_unsigned); return; } case value_t::number_float: { - dump_float(val.m_value.number_float); + dump_float(val.m_data.m_value.number_float); return; } @@ -18810,8 +18935,8 @@ class serializer ? (byte & 0x3fu) | (codep << 6u) : (0xFFu >> type) & (byte); - std::size_t index = 256u + static_cast(state) * 16u + static_cast(type); - JSON_ASSERT(index < 400); + const std::size_t index = 256u + static_cast(state) * 16u + static_cast(type); + JSON_ASSERT(index < utf8d.size()); state = utf8d[index]; return state; } @@ -18878,10 +19003,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -18998,7 +19123,7 @@ template , template::value, int> = 0> - T & at(KeyType && key) + T & at(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19026,7 +19151,7 @@ template , template::value, int> = 0> - const T & at(KeyType && key) const + const T & at(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19060,7 +19185,7 @@ template , template::value, int> = 0> - size_type erase(KeyType && key) + size_type erase(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19151,7 +19276,7 @@ template , template::value, int> = 0> - size_type count(KeyType && key) const + size_type count(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19177,7 +19302,7 @@ template , template::value, int> = 0> - iterator find(KeyType && key) + iterator find(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19240,7 +19365,9 @@ NLOHMANN_JSON_NAMESPACE_END #if defined(JSON_HAS_CPP_17) - #include + #if JSON_HAS_STATIC_RTTI + #include + #endif #include #endif @@ -19271,6 +19398,7 @@ The invariants are checked by member function assert_invariant(). */ NLOHMANN_BASIC_JSON_TPL_DECLARATION class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions) + : public ::nlohmann::detail::json_base_class { private: template friend struct detail::external_constructor; @@ -19297,6 +19425,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// workaround type for MSVC using basic_json_t = NLOHMANN_BASIC_JSON_TPL; + using json_base_class_t = ::nlohmann::detail::json_base_class; JSON_PRIVATE_UNLESS_TESTED: // convenience aliases for types residing in namespace detail; @@ -19368,7 +19497,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - ///////////////////// // container types // ///////////////////// @@ -19410,7 +19538,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /// @brief returns the allocator associated with the container /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/ static allocator_type get_allocator() @@ -19425,7 +19552,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { basic_json result; - result["copyright"] = "(C) 2013-2022 Niels Lohmann"; + result["copyright"] = "(C) 2013-2023 Niels Lohmann"; result["name"] = "JSON for Modern C++"; result["url"] = "https://github.com/nlohmann/json"; result["version"]["string"] = @@ -19473,7 +19600,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}}; #endif - #if defined(_MSVC_LANG) result["compiler"]["c++"] = std::to_string(_MSVC_LANG); #elif defined(__cplusplus) @@ -19484,7 +19610,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return result; } - /////////////////////////// // JSON value data types // /////////////////////////// @@ -19692,7 +19817,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec object = nullptr; // silence warning, see #821 if (JSON_HEDLEY_UNLIKELY(t == value_t::null)) { - JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.2", nullptr)); // LCOV_EXCL_LINE + JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3", nullptr)); // LCOV_EXCL_LINE } break; } @@ -19731,6 +19856,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void destroy(value_t t) { + if ( + (t == value_t::object && object == nullptr) || + (t == value_t::array && array == nullptr) || + (t == value_t::string && string == nullptr) || + (t == value_t::binary && binary == nullptr) + ) + { + //not initialized (e.g. due to exception in the ctor) + return; + } if (t == value_t::array || t == value_t::object) { // flatten the current json_value to a heap-allocated stack @@ -19761,18 +19896,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // its children to the stack to be processed later if (current_item.is_array()) { - std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack)); + std::move(current_item.m_data.m_value.array->begin(), current_item.m_data.m_value.array->end(), std::back_inserter(stack)); - current_item.m_value.array->clear(); + current_item.m_data.m_value.array->clear(); } else if (current_item.is_object()) { - for (auto&& it : *current_item.m_value.object) + for (auto&& it : *current_item.m_data.m_value.object) { stack.push_back(std::move(it.second)); } - current_item.m_value.object->clear(); + current_item.m_data.m_value.object->clear(); } // it's now safe that current_item get destructed @@ -19849,10 +19984,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec */ void assert_invariant(bool check_parents = true) const noexcept { - JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr); - JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr); - JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr); - JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr); + JSON_ASSERT(m_data.m_type != value_t::object || m_data.m_value.object != nullptr); + JSON_ASSERT(m_data.m_type != value_t::array || m_data.m_value.array != nullptr); + JSON_ASSERT(m_data.m_type != value_t::string || m_data.m_value.string != nullptr); + JSON_ASSERT(m_data.m_type != value_t::binary || m_data.m_value.binary != nullptr); #if JSON_DIAGNOSTICS JSON_TRY @@ -19871,11 +20006,11 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void set_parents() { #if JSON_DIAGNOSTICS - switch (m_type) + switch (m_data.m_type) { case value_t::array: { - for (auto& element : *m_value.array) + for (auto& element : *m_data.m_value.array) { element.m_parent = this; } @@ -19884,7 +20019,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::object: { - for (auto& element : *m_value.object) + for (auto& element : *m_data.m_value.object) { element.second.m_parent = this; } @@ -19925,7 +20060,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { // see https://github.com/nlohmann/json/issues/2838 JSON_ASSERT(type() == value_t::array); - if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity)) { // capacity has changed: update all parents set_parents(); @@ -19981,7 +20116,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief create an empty value with a given type /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(const value_t v) - : m_type(v), m_value(v) + : m_data(v) { assert_invariant(); } @@ -20055,12 +20190,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec *this = nullptr; break; case value_t::discarded: - m_type = value_t::discarded; + m_data.m_type = value_t::discarded; break; default: // LCOV_EXCL_LINE JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE } - JSON_ASSERT(m_type == val.type()); + JSON_ASSERT(m_data.m_type == val.type()); set_parents(); assert_invariant(); } @@ -20076,7 +20211,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec bool is_an_object = std::all_of(init.begin(), init.end(), [](const detail::json_ref& element_ref) { - return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string(); + // The cast is to ensure op[size_type] is called, bearing in mind size_type may not be int; + // (many string types can be constructed from 0 via its null-pointer guise, so we get a + // broken call to op[key_type], the wrong semantics and a 4804 warning on Windows) + return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[static_cast(0)].is_string(); }); // adjust type if type deduction is not wanted @@ -20098,22 +20236,22 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_an_object) { // the initializer list is a list of pairs -> create object - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; for (auto& element_ref : init) { auto element = element_ref.moved_or_copied(); - m_value.object->emplace( - std::move(*((*element.m_value.array)[0].m_value.string)), - std::move((*element.m_value.array)[1])); + m_data.m_value.object->emplace( + std::move(*((*element.m_data.m_value.array)[0].m_data.m_value.string)), + std::move((*element.m_data.m_value.array)[1])); } } else { // the initializer list describes an array -> create array - m_type = value_t::array; - m_value.array = create(init.begin(), init.end()); + m_data.m_type = value_t::array; + m_data.m_value.array = create(init.begin(), init.end()); } set_parents(); @@ -20126,8 +20264,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(const typename binary_t::container_type& init) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = init; + res.m_data.m_type = value_t::binary; + res.m_data.m_value = init; return res; } @@ -20137,8 +20275,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = binary_t(init, subtype); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = binary_t(init, subtype); return res; } @@ -20148,8 +20286,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(typename binary_t::container_type&& init) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = std::move(init); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = std::move(init); return res; } @@ -20159,8 +20297,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = binary_t(std::move(init), subtype); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = binary_t(std::move(init), subtype); return res; } @@ -20182,10 +20320,9 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief construct an array with count copies of given value /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ - basic_json(size_type cnt, const basic_json& val) - : m_type(value_t::array) + basic_json(size_type cnt, const basic_json& val): + m_data{cnt, val} { - m_value.array = create(cnt, val); set_parents(); assert_invariant(); } @@ -20207,10 +20344,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } // copy type from first iterator - m_type = first.m_object->m_type; + m_data.m_type = first.m_object->m_data.m_type; // check if iterator range is complete for primitive values - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -20235,55 +20372,55 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec break; } - switch (m_type) + switch (m_data.m_type) { case value_t::number_integer: { - m_value.number_integer = first.m_object->m_value.number_integer; + m_data.m_value.number_integer = first.m_object->m_data.m_value.number_integer; break; } case value_t::number_unsigned: { - m_value.number_unsigned = first.m_object->m_value.number_unsigned; + m_data.m_value.number_unsigned = first.m_object->m_data.m_value.number_unsigned; break; } case value_t::number_float: { - m_value.number_float = first.m_object->m_value.number_float; + m_data.m_value.number_float = first.m_object->m_data.m_value.number_float; break; } case value_t::boolean: { - m_value.boolean = first.m_object->m_value.boolean; + m_data.m_value.boolean = first.m_object->m_data.m_value.boolean; break; } case value_t::string: { - m_value = *first.m_object->m_value.string; + m_data.m_value = *first.m_object->m_data.m_value.string; break; } case value_t::object: { - m_value.object = create(first.m_it.object_iterator, - last.m_it.object_iterator); + m_data.m_value.object = create(first.m_it.object_iterator, + last.m_it.object_iterator); break; } case value_t::array: { - m_value.array = create(first.m_it.array_iterator, - last.m_it.array_iterator); + m_data.m_value.array = create(first.m_it.array_iterator, + last.m_it.array_iterator); break; } case value_t::binary: { - m_value = *first.m_object->m_value.binary; + m_data.m_value = *first.m_object->m_data.m_value.binary; break; } @@ -20297,7 +20434,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec assert_invariant(); } - /////////////////////////////////////// // other constructors and destructor // /////////////////////////////////////// @@ -20310,58 +20446,59 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief copy constructor /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(const basic_json& other) - : m_type(other.m_type) + : json_base_class_t(other) { + m_data.m_type = other.m_data.m_type; // check of passed value is valid other.assert_invariant(); - switch (m_type) + switch (m_data.m_type) { case value_t::object: { - m_value = *other.m_value.object; + m_data.m_value = *other.m_data.m_value.object; break; } case value_t::array: { - m_value = *other.m_value.array; + m_data.m_value = *other.m_data.m_value.array; break; } case value_t::string: { - m_value = *other.m_value.string; + m_data.m_value = *other.m_data.m_value.string; break; } case value_t::boolean: { - m_value = other.m_value.boolean; + m_data.m_value = other.m_data.m_value.boolean; break; } case value_t::number_integer: { - m_value = other.m_value.number_integer; + m_data.m_value = other.m_data.m_value.number_integer; break; } case value_t::number_unsigned: { - m_value = other.m_value.number_unsigned; + m_data.m_value = other.m_data.m_value.number_unsigned; break; } case value_t::number_float: { - m_value = other.m_value.number_float; + m_data.m_value = other.m_data.m_value.number_float; break; } case value_t::binary: { - m_value = *other.m_value.binary; + m_data.m_value = *other.m_data.m_value.binary; break; } @@ -20378,15 +20515,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief move constructor /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(basic_json&& other) noexcept - : m_type(std::move(other.m_type)), - m_value(std::move(other.m_value)) + : json_base_class_t(std::forward(other)), + m_data(std::move(other.m_data)) { // check that passed value is valid other.assert_invariant(false); // invalidate payload - other.m_type = value_t::null; - other.m_value = {}; + other.m_data.m_type = value_t::null; + other.m_data.m_value = {}; set_parents(); assert_invariant(); @@ -20398,15 +20535,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& std::is_nothrow_move_constructible::value&& - std::is_nothrow_move_assignable::value + std::is_nothrow_move_assignable::value&& + std::is_nothrow_move_assignable::value ) { // check that passed value is valid other.assert_invariant(); using std::swap; - swap(m_type, other.m_type); - swap(m_value, other.m_value); + swap(m_data.m_type, other.m_data.m_type); + swap(m_data.m_value, other.m_data.m_value); + json_base_class_t::operator=(std::move(other)); set_parents(); assert_invariant(); @@ -20418,7 +20557,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec ~basic_json() noexcept { assert_invariant(false); - m_value.destroy(m_type); } /// @} @@ -20458,7 +20596,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/type/ constexpr value_t type() const noexcept { - return m_type; + return m_data.m_type; } /// @brief return whether type is primitive @@ -20479,14 +20617,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/is_null/ constexpr bool is_null() const noexcept { - return m_type == value_t::null; + return m_data.m_type == value_t::null; } /// @brief return whether value is a boolean /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/ constexpr bool is_boolean() const noexcept { - return m_type == value_t::boolean; + return m_data.m_type == value_t::boolean; } /// @brief return whether value is a number @@ -20500,63 +20638,63 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/ constexpr bool is_number_integer() const noexcept { - return m_type == value_t::number_integer || m_type == value_t::number_unsigned; + return m_data.m_type == value_t::number_integer || m_data.m_type == value_t::number_unsigned; } /// @brief return whether value is an unsigned integer number /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/ constexpr bool is_number_unsigned() const noexcept { - return m_type == value_t::number_unsigned; + return m_data.m_type == value_t::number_unsigned; } /// @brief return whether value is a floating-point number /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/ constexpr bool is_number_float() const noexcept { - return m_type == value_t::number_float; + return m_data.m_type == value_t::number_float; } /// @brief return whether value is an object /// @sa https://json.nlohmann.me/api/basic_json/is_object/ constexpr bool is_object() const noexcept { - return m_type == value_t::object; + return m_data.m_type == value_t::object; } /// @brief return whether value is an array /// @sa https://json.nlohmann.me/api/basic_json/is_array/ constexpr bool is_array() const noexcept { - return m_type == value_t::array; + return m_data.m_type == value_t::array; } /// @brief return whether value is a string /// @sa https://json.nlohmann.me/api/basic_json/is_string/ constexpr bool is_string() const noexcept { - return m_type == value_t::string; + return m_data.m_type == value_t::string; } /// @brief return whether value is a binary array /// @sa https://json.nlohmann.me/api/basic_json/is_binary/ constexpr bool is_binary() const noexcept { - return m_type == value_t::binary; + return m_data.m_type == value_t::binary; } /// @brief return whether value is discarded /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/ constexpr bool is_discarded() const noexcept { - return m_type == value_t::discarded; + return m_data.m_type == value_t::discarded; } /// @brief return the type of the JSON value (implicit) /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/ constexpr operator value_t() const noexcept { - return m_type; + return m_data.m_type; } /// @} @@ -20571,7 +20709,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { if (JSON_HEDLEY_LIKELY(is_boolean())) { - return m_value.boolean; + return m_data.m_value.boolean; } JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this)); @@ -20580,97 +20718,97 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// get a pointer to the value (object) object_t* get_impl_ptr(object_t* /*unused*/) noexcept { - return is_object() ? m_value.object : nullptr; + return is_object() ? m_data.m_value.object : nullptr; } /// get a pointer to the value (object) constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept { - return is_object() ? m_value.object : nullptr; + return is_object() ? m_data.m_value.object : nullptr; } /// get a pointer to the value (array) array_t* get_impl_ptr(array_t* /*unused*/) noexcept { - return is_array() ? m_value.array : nullptr; + return is_array() ? m_data.m_value.array : nullptr; } /// get a pointer to the value (array) constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept { - return is_array() ? m_value.array : nullptr; + return is_array() ? m_data.m_value.array : nullptr; } /// get a pointer to the value (string) string_t* get_impl_ptr(string_t* /*unused*/) noexcept { - return is_string() ? m_value.string : nullptr; + return is_string() ? m_data.m_value.string : nullptr; } /// get a pointer to the value (string) constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept { - return is_string() ? m_value.string : nullptr; + return is_string() ? m_data.m_value.string : nullptr; } /// get a pointer to the value (boolean) boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept { - return is_boolean() ? &m_value.boolean : nullptr; + return is_boolean() ? &m_data.m_value.boolean : nullptr; } /// get a pointer to the value (boolean) constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept { - return is_boolean() ? &m_value.boolean : nullptr; + return is_boolean() ? &m_data.m_value.boolean : nullptr; } /// get a pointer to the value (integer number) number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept { - return is_number_integer() ? &m_value.number_integer : nullptr; + return is_number_integer() ? &m_data.m_value.number_integer : nullptr; } /// get a pointer to the value (integer number) constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept { - return is_number_integer() ? &m_value.number_integer : nullptr; + return is_number_integer() ? &m_data.m_value.number_integer : nullptr; } /// get a pointer to the value (unsigned number) number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept { - return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr; } /// get a pointer to the value (unsigned number) constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept { - return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr; } /// get a pointer to the value (floating-point number) number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept { - return is_number_float() ? &m_value.number_float : nullptr; + return is_number_float() ? &m_data.m_value.number_float : nullptr; } /// get a pointer to the value (floating-point number) constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept { - return is_number_float() ? &m_value.number_float : nullptr; + return is_number_float() ? &m_data.m_value.number_float : nullptr; } /// get a pointer to the value (binary) binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept { - return is_binary() ? m_value.binary : nullptr; + return is_binary() ? m_data.m_value.binary : nullptr; } /// get a pointer to the value (binary) constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept { - return is_binary() ? m_value.binary : nullptr; + return is_binary() ? m_data.m_value.binary : nullptr; } /*! @@ -21053,7 +21191,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec #if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914)) detail::negation>, #endif -#if defined(JSON_HAS_CPP_17) +#if defined(JSON_HAS_CPP_17) && JSON_HAS_STATIC_RTTI detail::negation>, #endif detail::is_detected_lazy @@ -21090,7 +21228,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - //////////////////// // element access // //////////////////// @@ -21108,7 +21245,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { JSON_TRY { - return set_parent(m_value.array->at(idx)); + return set_parent(m_data.m_value.array->at(idx)); } JSON_CATCH (std::out_of_range&) { @@ -21131,7 +21268,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { JSON_TRY { - return m_value.array->at(idx); + return m_data.m_value.array->at(idx); } JSON_CATCH (std::out_of_range&) { @@ -21155,8 +21292,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(key); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(key); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this)); } @@ -21175,8 +21312,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(std::forward(key)); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(std::forward(key)); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward(key)), "' not found"), this)); } @@ -21193,8 +21330,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(key); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(key); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this)); } @@ -21213,8 +21350,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(std::forward(key)); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(std::forward(key)); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward(key)), "' not found"), this)); } @@ -21228,8 +21365,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty array if (is_null()) { - m_type = value_t::array; - m_value.array = create(); + m_data.m_type = value_t::array; + m_data.m_value.array = create(); assert_invariant(); } @@ -21237,17 +21374,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (JSON_HEDLEY_LIKELY(is_array())) { // fill up array with null values if given idx is outside range - if (idx >= m_value.array->size()) + if (idx >= m_data.m_value.array->size()) { #if JSON_DIAGNOSTICS // remember array size & capacity before resizing - const auto old_size = m_value.array->size(); - const auto old_capacity = m_value.array->capacity(); + const auto old_size = m_data.m_value.array->size(); + const auto old_capacity = m_data.m_value.array->capacity(); #endif - m_value.array->resize(idx + 1); + m_data.m_value.array->resize(idx + 1); #if JSON_DIAGNOSTICS - if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity)) { // capacity has changed: update all parents set_parents(); @@ -21261,7 +21398,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec assert_invariant(); } - return m_value.array->operator[](idx); + return m_data.m_value.array->operator[](idx); } JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this)); @@ -21274,7 +21411,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for arrays if (JSON_HEDLEY_LIKELY(is_array())) { - return m_value.array->operator[](idx); + return m_data.m_value.array->operator[](idx); } JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this)); @@ -21287,15 +21424,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } // operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto result = m_value.object->emplace(std::move(key), nullptr); + auto result = m_data.m_value.object->emplace(std::move(key), nullptr); return set_parent(result.first->second); } @@ -21309,8 +21446,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto it = m_value.object->find(key); - JSON_ASSERT(it != m_value.object->end()); + auto it = m_data.m_value.object->find(key); + JSON_ASSERT(it != m_data.m_value.object->end()); return it->second; } @@ -21340,15 +21477,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } // operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto result = m_value.object->emplace(std::forward(key), nullptr); + auto result = m_data.m_value.object->emplace(std::forward(key), nullptr); return set_parent(result.first->second); } @@ -21364,8 +21501,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto it = m_value.object->find(std::forward(key)); - JSON_ASSERT(it != m_value.object->end()); + auto it = m_data.m_value.object->find(std::forward(key)); + JSON_ASSERT(it != m_data.m_value.object->end()); return it->second; } @@ -21602,7 +21739,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec IteratorType result = end(); - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -21619,32 +21756,32 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_string()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.string); - std::allocator_traits::deallocate(alloc, m_value.string, 1); - m_value.string = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.string); + std::allocator_traits::deallocate(alloc, m_data.m_value.string, 1); + m_data.m_value.string = nullptr; } else if (is_binary()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.binary); - std::allocator_traits::deallocate(alloc, m_value.binary, 1); - m_value.binary = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.binary); + std::allocator_traits::deallocate(alloc, m_data.m_value.binary, 1); + m_data.m_value.binary = nullptr; } - m_type = value_t::null; + m_data.m_type = value_t::null; assert_invariant(); break; } case value_t::object: { - result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator); + result.m_it.object_iterator = m_data.m_value.object->erase(pos.m_it.object_iterator); break; } case value_t::array: { - result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator); + result.m_it.array_iterator = m_data.m_value.array->erase(pos.m_it.array_iterator); break; } @@ -21672,7 +21809,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec IteratorType result = end(); - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -21690,33 +21827,33 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_string()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.string); - std::allocator_traits::deallocate(alloc, m_value.string, 1); - m_value.string = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.string); + std::allocator_traits::deallocate(alloc, m_data.m_value.string, 1); + m_data.m_value.string = nullptr; } else if (is_binary()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.binary); - std::allocator_traits::deallocate(alloc, m_value.binary, 1); - m_value.binary = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.binary); + std::allocator_traits::deallocate(alloc, m_data.m_value.binary, 1); + m_data.m_value.binary = nullptr; } - m_type = value_t::null; + m_data.m_type = value_t::null; assert_invariant(); break; } case value_t::object: { - result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator, + result.m_it.object_iterator = m_data.m_value.object->erase(first.m_it.object_iterator, last.m_it.object_iterator); break; } case value_t::array: { - result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator, + result.m_it.array_iterator = m_data.m_value.array->erase(first.m_it.array_iterator, last.m_it.array_iterator); break; } @@ -21741,7 +21878,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this)); } - return m_value.object->erase(std::forward(key)); + return m_data.m_value.object->erase(std::forward(key)); } template < typename KeyType, detail::enable_if_t < @@ -21754,10 +21891,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this)); } - const auto it = m_value.object->find(std::forward(key)); - if (it != m_value.object->end()) + const auto it = m_data.m_value.object->find(std::forward(key)); + if (it != m_data.m_value.object->end()) { - m_value.object->erase(it); + m_data.m_value.object->erase(it); return 1; } return 0; @@ -21795,7 +21932,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this)); } - m_value.array->erase(m_value.array->begin() + static_cast(idx)); + m_data.m_value.array->erase(m_data.m_value.array->begin() + static_cast(idx)); } else { @@ -21805,7 +21942,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - //////////// // lookup // //////////// @@ -21821,7 +21957,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(key); + result.m_it.object_iterator = m_data.m_value.object->find(key); } return result; @@ -21835,7 +21971,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(key); + result.m_it.object_iterator = m_data.m_value.object->find(key); } return result; @@ -21851,7 +21987,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(std::forward(key)); + result.m_it.object_iterator = m_data.m_value.object->find(std::forward(key)); } return result; @@ -21867,7 +22003,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(std::forward(key)); + result.m_it.object_iterator = m_data.m_value.object->find(std::forward(key)); } return result; @@ -21878,7 +22014,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec size_type count(const typename object_t::key_type& key) const { // return 0 for all nonobject types - return is_object() ? m_value.object->count(key) : 0; + return is_object() ? m_data.m_value.object->count(key) : 0; } /// @brief returns the number of occurrences of a key in a JSON object @@ -21888,14 +22024,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec size_type count(KeyType && key) const { // return 0 for all nonobject types - return is_object() ? m_value.object->count(std::forward(key)) : 0; + return is_object() ? m_data.m_value.object->count(std::forward(key)) : 0; } /// @brief check the existence of an element in a JSON object /// @sa https://json.nlohmann.me/api/basic_json/contains/ bool contains(const typename object_t::key_type& key) const { - return is_object() && m_value.object->find(key) != m_value.object->end(); + return is_object() && m_data.m_value.object->find(key) != m_data.m_value.object->end(); } /// @brief check the existence of an element in a JSON object @@ -21904,7 +22040,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec detail::is_usable_as_basic_json_key_type::value, int> = 0> bool contains(KeyType && key) const { - return is_object() && m_value.object->find(std::forward(key)) != m_value.object->end(); + return is_object() && m_data.m_value.object->find(std::forward(key)) != m_data.m_value.object->end(); } /// @brief check the existence of an element in a JSON object given a JSON pointer @@ -21923,7 +22059,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /////////////// // iterators // /////////////// @@ -22062,7 +22197,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - ////////////// // capacity // ////////////// @@ -22074,7 +22208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/empty/ bool empty() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: { @@ -22085,13 +22219,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::array: { // delegate call to array_t::empty() - return m_value.array->empty(); + return m_data.m_value.array->empty(); } case value_t::object: { // delegate call to object_t::empty() - return m_value.object->empty(); + return m_data.m_value.object->empty(); } case value_t::string: @@ -22113,7 +22247,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/size/ size_type size() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: { @@ -22124,13 +22258,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::array: { // delegate call to array_t::size() - return m_value.array->size(); + return m_data.m_value.array->size(); } case value_t::object: { // delegate call to object_t::size() - return m_value.object->size(); + return m_data.m_value.object->size(); } case value_t::string: @@ -22152,18 +22286,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/max_size/ size_type max_size() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::array: { // delegate call to array_t::max_size() - return m_value.array->max_size(); + return m_data.m_value.array->max_size(); } case value_t::object: { // delegate call to object_t::max_size() - return m_value.object->max_size(); + return m_data.m_value.object->max_size(); } case value_t::null: @@ -22184,7 +22318,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /////////////// // modifiers // /////////////// @@ -22196,53 +22329,53 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/clear/ void clear() noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::number_integer: { - m_value.number_integer = 0; + m_data.m_value.number_integer = 0; break; } case value_t::number_unsigned: { - m_value.number_unsigned = 0; + m_data.m_value.number_unsigned = 0; break; } case value_t::number_float: { - m_value.number_float = 0.0; + m_data.m_value.number_float = 0.0; break; } case value_t::boolean: { - m_value.boolean = false; + m_data.m_value.boolean = false; break; } case value_t::string: { - m_value.string->clear(); + m_data.m_value.string->clear(); break; } case value_t::binary: { - m_value.binary->clear(); + m_data.m_value.binary->clear(); break; } case value_t::array: { - m_value.array->clear(); + m_data.m_value.array->clear(); break; } case value_t::object: { - m_value.object->clear(); + m_data.m_value.object->clear(); break; } @@ -22266,15 +22399,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array (move semantics) - const auto old_capacity = m_value.array->capacity(); - m_value.array->push_back(std::move(val)); - set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->push_back(std::move(val)); + set_parent(m_data.m_value.array->back(), old_capacity); // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor } @@ -22299,15 +22432,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array - const auto old_capacity = m_value.array->capacity(); - m_value.array->push_back(val); - set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->push_back(val); + set_parent(m_data.m_value.array->back(), old_capacity); } /// @brief add an object to an array @@ -22331,13 +22464,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an object if (is_null()) { - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; assert_invariant(); } // add element to object - auto res = m_value.object->insert(val); + auto res = m_data.m_value.object->insert(val); set_parent(res.first->second); } @@ -22387,15 +22520,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array (perfect forwarding) - const auto old_capacity = m_value.array->capacity(); - m_value.array->emplace_back(std::forward(args)...); - return set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->emplace_back(std::forward(args)...); + return set_parent(m_data.m_value.array->back(), old_capacity); } /// @brief add an object to an object if key does not exist @@ -22412,13 +22545,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an object if (is_null()) { - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; assert_invariant(); } // add element to array (perfect forwarding) - auto res = m_value.object->emplace(std::forward(args)...); + auto res = m_data.m_value.object->emplace(std::forward(args)...); set_parent(res.first->second); // create result iterator and set iterator to the result of emplace @@ -22436,14 +22569,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec iterator insert_iterator(const_iterator pos, Args&& ... args) { iterator result(this); - JSON_ASSERT(m_value.array != nullptr); + JSON_ASSERT(m_data.m_value.array != nullptr); - auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator); - m_value.array->insert(pos.m_it.array_iterator, std::forward(args)...); - result.m_it.array_iterator = m_value.array->begin() + insert_pos; + auto insert_pos = std::distance(m_data.m_value.array->begin(), pos.m_it.array_iterator); + m_data.m_value.array->insert(pos.m_it.array_iterator, std::forward(args)...); + result.m_it.array_iterator = m_data.m_value.array->begin() + insert_pos; // This could have been written as: - // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val); + // result.m_it.array_iterator = m_data.m_value.array->insert(pos.m_it.array_iterator, cnt, val); // but the return value of insert is missing in GCC 4.8, so it is written this way instead. set_parents(); @@ -22570,7 +22703,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this)); } - m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator); + m_data.m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator); } /// @brief updates a JSON object from another object, overwriting existing keys @@ -22587,8 +22720,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } @@ -22613,16 +22746,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { if (merge_objects && it.value().is_object()) { - auto it2 = m_value.object->find(it.key()); - if (it2 != m_value.object->end()) + auto it2 = m_data.m_value.object->find(it.key()); + if (it2 != m_data.m_value.object->end()) { it2->second.update(it.value(), true); continue; } } - m_value.object->operator[](it.key()) = it.value(); + m_data.m_value.object->operator[](it.key()) = it.value(); #if JSON_DIAGNOSTICS - m_value.object->operator[](it.key()).m_parent = this; + m_data.m_value.object->operator[](it.key()).m_parent = this; #endif } } @@ -22632,12 +22765,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void swap(reference other) noexcept ( std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& - std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_constructible::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap) std::is_nothrow_move_assignable::value ) { - std::swap(m_type, other.m_type); - std::swap(m_value, other.m_value); + std::swap(m_data.m_type, other.m_data.m_type); + std::swap(m_data.m_value, other.m_data.m_value); set_parents(); other.set_parents(); @@ -22649,7 +22782,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec friend void swap(reference left, reference right) noexcept ( std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& - std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_constructible::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap) std::is_nothrow_move_assignable::value ) { @@ -22658,13 +22791,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(array_t& other) // NOLINT(bugprone-exception-escape) + void swap(array_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for arrays if (JSON_HEDLEY_LIKELY(is_array())) { using std::swap; - swap(*(m_value.array), other); + swap(*(m_data.m_value.array), other); } else { @@ -22674,13 +22807,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(object_t& other) // NOLINT(bugprone-exception-escape) + void swap(object_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { using std::swap; - swap(*(m_value.object), other); + swap(*(m_data.m_value.object), other); } else { @@ -22690,13 +22823,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(string_t& other) // NOLINT(bugprone-exception-escape) + void swap(string_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for strings if (JSON_HEDLEY_LIKELY(is_string())) { using std::swap; - swap(*(m_value.string), other); + swap(*(m_data.m_value.string), other); } else { @@ -22706,13 +22839,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(binary_t& other) // NOLINT(bugprone-exception-escape) + void swap(binary_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for strings if (JSON_HEDLEY_LIKELY(is_binary())) { using std::swap; - swap(*(m_value.binary), other); + swap(*(m_data.m_value.binary), other); } else { @@ -22728,7 +22861,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (JSON_HEDLEY_LIKELY(is_binary())) { using std::swap; - swap(*(m_value.binary), other); + swap(*(m_data.m_value.binary), other); } else { @@ -22756,31 +22889,31 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec switch (lhs_type) \ { \ case value_t::array: \ - return (*lhs.m_value.array) op (*rhs.m_value.array); \ + return (*lhs.m_data.m_value.array) op (*rhs.m_data.m_value.array); \ \ case value_t::object: \ - return (*lhs.m_value.object) op (*rhs.m_value.object); \ + return (*lhs.m_data.m_value.object) op (*rhs.m_data.m_value.object); \ \ case value_t::null: \ return (null_result); \ \ case value_t::string: \ - return (*lhs.m_value.string) op (*rhs.m_value.string); \ + return (*lhs.m_data.m_value.string) op (*rhs.m_data.m_value.string); \ \ case value_t::boolean: \ - return (lhs.m_value.boolean) op (rhs.m_value.boolean); \ + return (lhs.m_data.m_value.boolean) op (rhs.m_data.m_value.boolean); \ \ case value_t::number_integer: \ - return (lhs.m_value.number_integer) op (rhs.m_value.number_integer); \ + return (lhs.m_data.m_value.number_integer) op (rhs.m_data.m_value.number_integer); \ \ case value_t::number_unsigned: \ - return (lhs.m_value.number_unsigned) op (rhs.m_value.number_unsigned); \ + return (lhs.m_data.m_value.number_unsigned) op (rhs.m_data.m_value.number_unsigned); \ \ case value_t::number_float: \ - return (lhs.m_value.number_float) op (rhs.m_value.number_float); \ + return (lhs.m_data.m_value.number_float) op (rhs.m_data.m_value.number_float); \ \ case value_t::binary: \ - return (*lhs.m_value.binary) op (*rhs.m_value.binary); \ + return (*lhs.m_data.m_value.binary) op (*rhs.m_data.m_value.binary); \ \ case value_t::discarded: \ default: \ @@ -22789,27 +22922,27 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } \ else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float) \ { \ - return static_cast(lhs.m_value.number_integer) op rhs.m_value.number_float; \ + return static_cast(lhs.m_data.m_value.number_integer) op rhs.m_data.m_value.number_float; \ } \ else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer) \ { \ - return lhs.m_value.number_float op static_cast(rhs.m_value.number_integer); \ + return lhs.m_data.m_value.number_float op static_cast(rhs.m_data.m_value.number_integer); \ } \ else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float) \ { \ - return static_cast(lhs.m_value.number_unsigned) op rhs.m_value.number_float; \ + return static_cast(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_float; \ } \ else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned) \ { \ - return lhs.m_value.number_float op static_cast(rhs.m_value.number_unsigned); \ + return lhs.m_data.m_value.number_float op static_cast(rhs.m_data.m_value.number_unsigned); \ } \ else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer) \ { \ - return static_cast(lhs.m_value.number_unsigned) op rhs.m_value.number_integer; \ + return static_cast(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_integer; \ } \ else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned) \ { \ - return lhs.m_value.number_integer op static_cast(rhs.m_value.number_unsigned); \ + return lhs.m_data.m_value.number_integer op static_cast(rhs.m_data.m_value.number_unsigned); \ } \ else if(compares_unordered(lhs, rhs))\ {\ @@ -22826,8 +22959,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // an operation is computed as an odd number of inverses of others static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept { - if ((lhs.is_number_float() && std::isnan(lhs.m_value.number_float) && rhs.is_number()) - || (rhs.is_number_float() && std::isnan(rhs.m_value.number_float) && lhs.is_number())) + if ((lhs.is_number_float() && std::isnan(lhs.m_data.m_value.number_float) && rhs.is_number()) + || (rhs.is_number_float() && std::isnan(rhs.m_data.m_value.number_float) && lhs.is_number())) { return true; } @@ -23171,7 +23304,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec #endif // JSON_NO_IO /// @} - ///////////////////// // deserialization // ///////////////////// @@ -23328,7 +23460,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_HEDLEY_RETURNS_NON_NULL const char* type_name() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: return "null"; @@ -23352,17 +23484,43 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } } - JSON_PRIVATE_UNLESS_TESTED: ////////////////////// // member variables // ////////////////////// - /// the type of the current element - value_t m_type = value_t::null; + struct data + { + /// the type of the current element + value_t m_type = value_t::null; + + /// the value of the current element + json_value m_value = {}; + + data(const value_t v) + : m_type(v), m_value(v) + { + } + + data(size_type cnt, const basic_json& val) + : m_type(value_t::array) + { + m_value.array = create(cnt, val); + } + + data() noexcept = default; + data(data&&) noexcept = default; + data(const data&) noexcept = delete; + data& operator=(data&&) noexcept = delete; + data& operator=(const data&) noexcept = delete; + + ~data() noexcept + { + m_value.destroy(m_type); + } + }; - /// the value of the current element - json_value m_value = {}; + data m_data = {}; #if JSON_DIAGNOSTICS /// a pointer to a parent value (for debugging purposes) @@ -23543,7 +23701,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler); } - JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len)) static basic_json from_cbor(detail::span_input_adapter&& i, @@ -23667,7 +23824,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return res ? result : basic_json(value_t::discarded); } - /// @brief create a JSON value from an input in BJData format /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/ template @@ -23890,7 +24046,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } // make sure the top element of the pointer exists - json_pointer top_pointer = ptr.top(); + json_pointer const top_pointer = ptr.top(); if (top_pointer != ptr) { result.at(top_pointer); @@ -23902,7 +24058,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // parent must exist when performing patch add per RFC6902 specs basic_json& parent = result.at(ptr); - switch (parent.m_type) + switch (parent.m_data.m_type) { case value_t::null: case value_t::object: @@ -23948,7 +24104,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec }; // wrapper for "remove" operation; remove value at ptr - const auto operation_remove = [this, &result](json_pointer & ptr) + const auto operation_remove = [this, & result](json_pointer & ptr) { // get reference to parent of JSON pointer ptr const auto last_path = ptr.back(); @@ -23991,13 +24147,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec bool string_type) -> basic_json & { // find value - auto it = val.m_value.object->find(member); + auto it = val.m_data.m_value.object->find(member); // context-sensitive error message - const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); + const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); // NOLINT(bugprone-unused-local-non-trivial-variable) // check if desired value is present - if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end())) + if (JSON_HEDLEY_UNLIKELY(it == val.m_data.m_value.object->end())) { // NOLINTNEXTLINE(performance-inefficient-string-concatenation) JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val)); @@ -24052,7 +24208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec json_pointer from_ptr(from_path); // the "from" location must exist - use at() - basic_json v = result.at(from_ptr); + basic_json const v = result.at(from_ptr); // The move operation is functionally identical to a // "remove" operation on the "from" location, followed @@ -24069,7 +24225,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec const json_pointer from_ptr(from_path); // the "from" location must exist - use at() - basic_json v = result.at(from_ptr); + basic_json const v = result.at(from_ptr); // The copy is functionally identical to an "add" // operation at the target location using the value @@ -24311,7 +24467,11 @@ inline namespace json_literals /// @brief user-defined string literal for JSON values /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/ JSON_HEDLEY_NON_NULL(1) -inline nlohmann::json operator "" _json(const char* s, std::size_t n) +#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + inline nlohmann::json operator ""_json(const char* s, std::size_t n) +#else + inline nlohmann::json operator "" _json(const char* s, std::size_t n) +#endif { return nlohmann::json::parse(s, s + n); } @@ -24319,7 +24479,11 @@ inline nlohmann::json operator "" _json(const char* s, std::size_t n) /// @brief user-defined string literal for JSON pointer /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/ JSON_HEDLEY_NON_NULL(1) -inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n) +#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + inline nlohmann::json::json_pointer operator ""_json_pointer(const char* s, std::size_t n) +#else + inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n) +#endif { return nlohmann::json::json_pointer(std::string(s, n)); } @@ -24338,7 +24502,7 @@ namespace std // NOLINT(cert-dcl58-cpp) /// @brief hash value for JSON objects /// @sa https://json.nlohmann.me/api/basic_json/std_hash/ NLOHMANN_BASIC_JSON_TPL_DECLARATION -struct hash +struct hash // NOLINT(cert-dcl58-cpp) { std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const { @@ -24371,8 +24535,8 @@ struct less< ::nlohmann::detail::value_t> // do not remove the space after '<', /// @brief exchanges the values of two JSON objects /// @sa https://json.nlohmann.me/api/basic_json/std_swap/ NLOHMANN_BASIC_JSON_TPL_DECLARATION -inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept( // NOLINT(readability-inconsistent-declaration-parameter-name) - is_nothrow_move_constructible::value&& // NOLINT(misc-redundant-expression) +inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept( // NOLINT(readability-inconsistent-declaration-parameter-name, cert-dcl58-cpp) + is_nothrow_move_constructible::value&& // NOLINT(misc-redundant-expression,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) is_nothrow_move_assignable::value) { j1.swap(j2); @@ -24383,17 +24547,22 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC } // namespace std #if JSON_USE_GLOBAL_UDLS - using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) - using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + using nlohmann::literals::json_literals::operator ""_json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) + using nlohmann::literals::json_literals::operator ""_json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #else + using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) + using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #endif #endif // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -24428,16 +24597,17 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM #undef JSON_HAS_THREE_WAY_COMPARISON #undef JSON_HAS_RANGES + #undef JSON_HAS_STATIC_RTTI #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON #endif // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT From ccf58aa3ec4d20b10162ba40898dc038ad4c3fad Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 20 Mar 2024 14:42:59 +0100 Subject: [PATCH 309/401] cuda : refactor to remove global resources (#6170) * cuda : refactor to remove global resources --- ggml-cuda.cu | 17234 ++++++++++++++++++++++++------------------------- 1 file changed, 8609 insertions(+), 8625 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 139025588ed61..e70b79c300a26 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -12,6 +12,7 @@ #include "ggml-common.h" #include +#include #include #include #include @@ -19,12 +20,13 @@ #include #include #include +#include +#include +#include #include #include #include #include -#include -#include // stringize macro for converting __CUDA_ARCH_LIST__ (list of integers) to string #define STRINGIZE_IMPL(...) #__VA_ARGS__ @@ -52,6 +54,7 @@ #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width) #define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6 #define cublasCreate hipblasCreate +#define cublasDestroy hipblasDestroy #define cublasGemmEx hipblasGemmEx #define cublasGemmBatchedEx hipblasGemmBatchedEx #define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx @@ -108,6 +111,7 @@ #define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize #define cudaSetDevice hipSetDevice #define cudaStreamCreateWithFlags hipStreamCreateWithFlags +#define cudaStreamDestroy hipStreamDestroy #define cudaStreamFireAndForget hipStreamFireAndForget #define cudaStreamNonBlocking hipStreamNonBlocking #define cudaStreamPerThread hipStreamPerThread @@ -151,8 +155,6 @@ #define CC_RDNA2 (CC_OFFSET_AMD + 1030) #define CC_RDNA3 (CC_OFFSET_AMD + 1100) -#define GGML_CUDA_MAX_NODES 8192 - // define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication // on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant // for large computational tasks. the drawback is that this requires some extra amount of VRAM: @@ -170,88 +172,8 @@ #define MMVQ_MAX_BATCH_SIZE 8 // max batch size to use MMVQ kernels #define MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available -#if defined(GGML_USE_HIPBLAS) -#define __CUDA_ARCH__ 1300 - -#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \ - defined(__gfx1150__) || defined(__gfx1151__) -#define RDNA3 -#endif - -#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \ - defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__) -#define RDNA2 -#endif - -#ifndef __has_builtin - #define __has_builtin(x) 0 -#endif - -typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); -typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4))); -static __device__ __forceinline__ int __vsubss4(const int a, const int b) { - const int8x4_t va = reinterpret_cast(a); - const int8x4_t vb = reinterpret_cast(b); -#if __has_builtin(__builtin_elementwise_sub_sat) - const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); - return reinterpret_cast(c); -#else - int8x4_t c; - int16_t tmp; -#pragma unroll - for (int i = 0; i < 4; i++) { - tmp = va[i] - vb[i]; - if(tmp > std::numeric_limits::max()) tmp = std::numeric_limits::max(); - if(tmp < std::numeric_limits::min()) tmp = std::numeric_limits::min(); - c[i] = tmp; - } - return reinterpret_cast(c); -#endif // __has_builtin(__builtin_elementwise_sub_sat) -} - -static __device__ __forceinline__ int __vsub4(const int a, const int b) { - return __vsubss4(a, b); -} - -static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) { - const uint8x4_t& va = reinterpret_cast(a); - const uint8x4_t& vb = reinterpret_cast(b); - unsigned int c; - uint8x4_t& vc = reinterpret_cast(c); -#pragma unroll - for (int i = 0; i < 4; ++i) { - vc[i] = va[i] == vb[i] ? 0xff : 0x00; - } - return c; -} +#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses -static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { -#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) - c = __builtin_amdgcn_sdot4(a, b, c, false); -#elif defined(RDNA3) - c = __builtin_amdgcn_sudot4( true, a, true, b, c, false); -#elif defined(__gfx1010__) || defined(__gfx900__) - int tmp1; - int tmp2; - asm("\n \ - v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \ - v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \ - v_add3_u32 %0, %1, %2, %0 \n \ - v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \ - v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \ - v_add3_u32 %0, %1, %2, %0 \n \ - " - : "+v"(c), "=&v"(tmp1), "=&v"(tmp2) - : "v"(a), "v"(b) - ); -#else - const int8x4_t va = reinterpret_cast(a); - const int8x4_t vb = reinterpret_cast(b); - c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3]; -#endif - return c; -} -#endif // defined(GGML_USE_HIPBLAS) #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -319,3598 +241,3759 @@ static const char * cu_get_error_str(CUresult err) { #define GGML_CUDA_ASSUME(x) #endif // CUDART_VERSION >= 11100 -#ifdef GGML_CUDA_F16 -typedef half dfloat; // dequantize float -typedef half2 dfloat2; -#else -typedef float dfloat; // dequantize float -typedef float2 dfloat2; -#endif //GGML_CUDA_F16 - -static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) { - const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment - int x32 = 0; - x32 |= x16[0] << 0; - x32 |= x16[1] << 16; +#define GGML_CUDA_MAX_STREAMS 8 - return x32; -} +struct ggml_tensor_extra_gpu { + void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors + cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs +}; -static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) { - const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment +// this is faster on Windows +// probably because the Windows CUDA libraries forget to make this check before invoking the drivers +static void ggml_cuda_set_device(const int device) { + int current_device; + CUDA_CHECK(cudaGetDevice(¤t_device)); - int x32 = 0; - x32 |= x16[0] << 0; - x32 |= x16[1] << 16; + if (device == current_device) { + return; + } - return x32; + CUDA_CHECK(cudaSetDevice(device)); } -static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) { - return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment +static int ggml_cuda_get_device() { + int id; + CUDA_CHECK(cudaGetDevice(&id)); + return id; } -static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) { - return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment -} +struct ggml_cuda_device_info { + int device_count; -template -using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream); -typedef to_t_cuda_t to_fp32_cuda_t; -typedef to_t_cuda_t to_fp16_cuda_t; + struct cuda_device_info { + int cc; // compute capability + size_t smpb; // max. shared memory per block + bool vmm; // virtual memory support + size_t vmm_granularity; // granularity of virtual memory + size_t total_vram; + }; -typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); -typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); -typedef void (*cpy_kernel_t)(const char * cx, char * cdst); -typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); -typedef void (*ggml_cuda_op_mul_mat_t)( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream); -typedef void (*ggml_cuda_op_flatten_t)( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream); + cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {}; -typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); -typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc); -typedef void (*load_tiles_cuda_t)( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row); -typedef float (*vec_dot_q_mul_mat_cuda_t)( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); + std::array default_tensor_split = {}; +}; -#define WARP_SIZE 32 -#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses +static ggml_cuda_device_info ggml_cuda_init() { +#ifdef __HIP_PLATFORM_AMD__ + // Workaround for a rocBLAS bug when using multiple graphics cards: + // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346 + rocblas_initialize(); + CUDA_CHECK(cudaDeviceSynchronize()); +#endif -#define CUDA_GELU_BLOCK_SIZE 256 -#define CUDA_SILU_BLOCK_SIZE 256 -#define CUDA_TANH_BLOCK_SIZE 256 -#define CUDA_RELU_BLOCK_SIZE 256 -#define CUDA_HARDSIGMOID_BLOCK_SIZE 256 -#define CUDA_HARDSWISH_BLOCK_SIZE 256 -#define CUDA_SQR_BLOCK_SIZE 256 -#define CUDA_CPY_BLOCK_SIZE 32 -#define CUDA_SCALE_BLOCK_SIZE 256 -#define CUDA_CLAMP_BLOCK_SIZE 256 -#define CUDA_ROPE_BLOCK_SIZE 256 -#define CUDA_SOFT_MAX_BLOCK_SIZE 1024 -#define CUDA_ALIBI_BLOCK_SIZE 32 -#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32 -#define CUDA_QUANTIZE_BLOCK_SIZE 256 -#define CUDA_DEQUANTIZE_BLOCK_SIZE 256 -#define CUDA_GET_ROWS_BLOCK_SIZE 256 -#define CUDA_UPSCALE_BLOCK_SIZE 256 -#define CUDA_CONCAT_BLOCK_SIZE 256 -#define CUDA_PAD_BLOCK_SIZE 256 -#define CUDA_ARANGE_BLOCK_SIZE 256 -#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 -#define CUDA_ACC_BLOCK_SIZE 256 -#define CUDA_IM2COL_BLOCK_SIZE 256 -#define CUDA_POOL2D_BLOCK_SIZE 256 + ggml_cuda_device_info info = {}; -#define CUDA_Q8_0_NE_ALIGN 2048 + if (cudaGetDeviceCount(&info.device_count) != cudaSuccess) { + fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__); + return info; + } -// dmmv = dequantize_mul_mat_vec -#ifndef GGML_CUDA_DMMV_X -#define GGML_CUDA_DMMV_X 32 -#endif -#ifndef GGML_CUDA_MMV_Y -#define GGML_CUDA_MMV_Y 1 -#endif + GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 2 + int64_t total_vram = 0; +#if defined(GGML_CUDA_FORCE_MMQ) + fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); #else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); + fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); +#endif +#if defined(CUDA_USE_TENSOR_CORES) + fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); +#else + fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); #endif + fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + for (int id = 0; id < info.device_count; ++id) { + int device_vmm = 0; -#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE -#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128 -#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE +#if !defined(GGML_USE_HIPBLAS) + CUdevice device; + CU_CHECK(cuDeviceGet(&device, id)); + CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); -#define MUL_MAT_SRC1_COL_STRIDE 128 + if (device_vmm) { + CUmemAllocationProp alloc_prop = {}; + alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + alloc_prop.location.id = id; + CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); + } +#endif // !defined(GGML_USE_HIPBLAS) + info.devices[id].vmm = !!device_vmm; -#define MAX_STREAMS 8 -static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { { nullptr } }; + cudaDeviceProp prop; + CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); + fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); -struct ggml_tensor_extra_gpu { - void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors - cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs -}; + info.default_tensor_split[id] = total_vram; + total_vram += prop.totalGlobalMem; -// this is faster on Windows -// probably because the Windows CUDA libraries forget to make this check before invoking the drivers -static void ggml_cuda_set_device(const int device) { - int current_device; - CUDA_CHECK(cudaGetDevice(¤t_device)); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; +#else + info.devices[id].cc = 100*prop.major + 10*prop.minor; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + info.devices[id].smpb = prop.sharedMemPerBlock; + } - if (device == current_device) { - return; + for (int id = 0; id < info.device_count; ++id) { + info.default_tensor_split[id] /= total_vram; } - CUDA_CHECK(cudaSetDevice(device)); + // configure logging to stdout + // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); + + return info; } -static int g_device_count = -1; -static int g_main_device = 0; -static std::array g_default_tensor_split = {}; +static const ggml_cuda_device_info & get_cuda_global_info() { + static ggml_cuda_device_info info = ggml_cuda_init(); + return info; +} -struct cuda_device_capabilities { - int cc; // compute capability - size_t smpb; // max. shared memory per block - bool vmm; // virtual memory support - size_t vmm_granularity; // granularity of virtual memory -}; +// #define DEBUG_CUDA_MALLOC -static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} }; +// buffer pool for cuda (legacy) +struct ggml_cuda_pool { + virtual ~ggml_cuda_pool() = default; -static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; + virtual void * alloc(size_t size, size_t * actual_size) = 0; + virtual void free(void * ptr, size_t size) = 0; -[[noreturn]] -static __device__ void no_device_code( - const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { + ggml_cuda_pool() = default; + ggml_cuda_pool(const ggml_cuda_pool &) = delete; + ggml_cuda_pool(ggml_cuda_pool &&) = delete; + ggml_cuda_pool& operator=(const ggml_cuda_pool &) = delete; + ggml_cuda_pool& operator=(ggml_cuda_pool &&) = delete; +}; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", - file_name, line, function_name, arch); - (void) arch_list; -#else - printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", - file_name, line, function_name, arch, arch_list); -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - __trap(); +struct ggml_cuda_pool_leg : public ggml_cuda_pool { + static const int MAX_BUFFERS = 256; - (void) no_device_code; // suppress unused function warning -} + int device; + struct ggml_cuda_buffer { + void * ptr = nullptr; + size_t size = 0; + }; -#ifdef __CUDA_ARCH__ -#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__)) -#else -#define NO_DEVICE_CODE GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.") -#endif // __CUDA_ARCH__ + ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {}; + size_t pool_size = 0; -static __device__ __forceinline__ float warp_reduce_sum(float x) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); + explicit ggml_cuda_pool_leg(int device) : + device(device) { } - return x; -} -static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); - a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + ~ggml_cuda_pool_leg() { + ggml_cuda_set_device(device); + for (int i = 0; i < MAX_BUFFERS; ++i) { + ggml_cuda_buffer & b = buffer_pool[i]; + if (b.ptr != nullptr) { + CUDA_CHECK(cudaFree(b.ptr)); + pool_size -= b.size; + } + } + GGML_ASSERT(pool_size == 0); } - return a; -} - -#ifdef GGML_CUDA_F16 -static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); - } - return a; -#else - (void) a; - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -} -#endif // GGML_CUDA_F16 -static __device__ __forceinline__ float warp_reduce_max(float x) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + void * alloc(size_t size, size_t * actual_size) override { +#ifdef DEBUG_CUDA_MALLOC + int nnz = 0; + size_t max_size = 0; +#endif + size_t best_diff = 1ull << 36; + int ibest = -1; + for (int i = 0; i < MAX_BUFFERS; ++i) { + ggml_cuda_buffer& b = buffer_pool[i]; + if (b.ptr != nullptr) { +#ifdef DEBUG_CUDA_MALLOC + ++nnz; + if (b.size > max_size) max_size = b.size; +#endif + if (b.size >= size) { + size_t diff = b.size - size; + if (diff < best_diff) { + best_diff = diff; + ibest = i; + if (!best_diff) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + } + } + } + } + if (ibest >= 0) { + ggml_cuda_buffer& b = buffer_pool[ibest]; + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + void * ptr; + size_t look_ahead_size = (size_t) (1.05 * size); + look_ahead_size = 256 * ((look_ahead_size + 255)/256); + ggml_cuda_set_device(device); + CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); + *actual_size = look_ahead_size; + pool_size += look_ahead_size; +#ifdef DEBUG_CUDA_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024)); +#endif + return ptr; } - return x; -} -//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -//#pragma unroll -// for (int mask = 16; mask > 0; mask >>= 1) { -// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); -// } -// return x; -//#else -// (void) x; -// NO_DEVICE_CODE; -//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -//} + void free(void * ptr, size_t size) override { + for (int i = 0; i < MAX_BUFFERS; ++i) { + ggml_cuda_buffer& b = buffer_pool[i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + ggml_cuda_set_device(device); + CUDA_CHECK(cudaFree(ptr)); + pool_size -= size; + } +}; -static __device__ __forceinline__ float op_repeat(const float a, const float b) { - return b; - GGML_UNUSED(a); -} +// pool with virtual memory +#if !defined(GGML_USE_HIPBLAS) +struct ggml_cuda_pool_vmm : public ggml_cuda_pool { + static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB -static __device__ __forceinline__ float op_add(const float a, const float b) { - return a + b; -} + int device; + CUdeviceptr pool_addr = 0; + size_t pool_used = 0; + size_t pool_size = 0; + size_t granularity; -static __device__ __forceinline__ float op_mul(const float a, const float b) { - return a * b; -} + explicit ggml_cuda_pool_vmm(int device) : + device(device), + granularity(get_cuda_global_info().devices[device].vmm_granularity) { + } -static __device__ __forceinline__ float op_div(const float a, const float b) { - return a / b; -} + ~ggml_cuda_pool_vmm() { + if (pool_addr != 0) { + CU_CHECK(cuMemUnmap(pool_addr, pool_size)); + CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE)); + } + } -template -static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13) { - const int i0s = blockDim.x*blockIdx.x + threadIdx.x; - const int i1 = (blockDim.y*blockIdx.y + threadIdx.y); - const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3; - const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3; + void * alloc(size_t size, size_t * actual_size) override { + // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types + const size_t alignment = 128; + size = alignment * ((size + alignment - 1) / alignment); - if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } + size_t avail = pool_size - pool_used; - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; + if (size > avail) { + // round up to the next multiple of the granularity + size_t reserve_size = size - avail; + reserve_size = granularity * ((reserve_size + granularity - 1) / granularity); - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; + GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; + // allocate more physical memory + CUmemAllocationProp prop = {}; + prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = device; + CUmemGenericAllocationHandle handle; + CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); - for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) { - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); - } -} + // reserve virtual address space (if not already reserved) + if (pool_addr == 0) { + CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); + } -template -static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13) { + // map at the end of the pool + CU_CHECK(cuMemMap(pool_addr + pool_size, reserve_size, 0, handle, 0)); - const int i = blockDim.x*blockIdx.x + threadIdx.x; + // the memory allocation handle is no longer needed after mapping + CU_CHECK(cuMemRelease(handle)); - const int i3 = i/(ne2*ne1*ne0); - const int i2 = (i/(ne1*ne0)) % ne2; - const int i1 = (i/ne0) % ne1; - const int i0 = i % ne0; + // set access + CUmemAccessDesc access = {}; + access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; + access.location.id = device; + access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; + CU_CHECK(cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1)); - if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } + // add to the pool + pool_size += reserve_size; - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; + //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n", + // id, (unsigned long long) (pool_size[id]/1024/1024), + // (unsigned long long) (reserve_size/1024/1024)); + } - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; + GGML_ASSERT(pool_addr != 0); - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; + void * ptr = (void *) (pool_addr + pool_used); + *actual_size = size; + pool_used += size; - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); -} +#ifdef DEBUG_CUDA_MALLOC + printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr); +#endif -static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, int offset) { - const int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i >= ne) { - return; - } - int src1_idx = i - offset; - int oz = src1_idx / nb2; - int oy = (src1_idx - (oz * nb2)) / nb1; - int ox = src1_idx % nb1; - if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { - dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; - } else { - dst[i] = x[i]; + return ptr; } -} -static __global__ void gelu_f32(const float * x, float * dst, const int k) { - const float GELU_COEF_A = 0.044715f; - const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - const int i = blockDim.x*blockIdx.x + threadIdx.x; + void free(void * ptr, size_t size) override { +#ifdef DEBUG_CUDA_MALLOC + printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr); +#endif - if (i >= k) { - return; + pool_used -= size; + + // all deallocations must be in reverse order of the allocations + GGML_ASSERT(ptr == (void *) (pool_addr + pool_used)); } +}; +#endif // !defined(GGML_USE_HIPBLAS) - float xi = x[i]; - dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi))); -} +template +struct ggml_cuda_pool_alloc { + ggml_cuda_pool * pool = nullptr; + T * ptr = nullptr; + size_t actual_size = 0; -static __global__ void silu_f32(const float * x, float * dst, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + ggml_cuda_pool_alloc() = default; - if (i >= k) { - return; + explicit ggml_cuda_pool_alloc(ggml_cuda_pool & pool) : pool(&pool) { } - dst[i] = x[i] / (1.0f + expf(-x[i])); -} -static __global__ void gelu_quick_f32(const float * x, float * dst, int k) { - const float GELU_QUICK_COEF = -1.702f; - const int i = blockDim.x*blockIdx.x + threadIdx.x; - if (i >= k) { - return; + ggml_cuda_pool_alloc(ggml_cuda_pool & pool, size_t size) : pool(&pool) { + alloc(size); } - dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i]))); -} -static __global__ void tanh_f32(const float * x, float * dst, int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; - if (i >= k) { - return; + ~ggml_cuda_pool_alloc() { + if (ptr != nullptr) { + pool->free(ptr, actual_size); + } } - dst[i] = tanhf(x[i]); -} - -static __global__ void relu_f32(const float * x, float * dst, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; - if (i >= k) { - return; + // size is in number of elements + T * alloc(size_t size) { + GGML_ASSERT(pool != nullptr); + GGML_ASSERT(ptr == nullptr); + ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size); + return ptr; } - dst[i] = fmaxf(x[i], 0); -} -static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + T * alloc(ggml_cuda_pool & pool, size_t size) { + this->pool = &pool; + return alloc(size); + } - if (i >= k) { - return; + T * get() { + return ptr; } - dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); -} -static __global__ void hardswish_f32(const float * x, float * dst, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + ggml_cuda_pool_alloc(const ggml_cuda_pool_alloc &) = delete; + ggml_cuda_pool_alloc(ggml_cuda_pool_alloc &&) = delete; + ggml_cuda_pool_alloc& operator=(const ggml_cuda_pool_alloc &) = delete; + ggml_cuda_pool_alloc& operator=(ggml_cuda_pool_alloc &&) = delete; +}; - if (i >= k) { - return; - } - dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); -} -static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; - if (i >= k) { - return; +// backend interface + +struct ggml_backend_cuda_context { + int device; + std::string name; + cudaEvent_t copy_event = nullptr; + + cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } }; + cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; + + explicit ggml_backend_cuda_context(int device) : + device(device), + name(GGML_CUDA_NAME + std::to_string(device)) { } - dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope; -} -static __global__ void sqr_f32(const float * x, float * dst, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + ~ggml_backend_cuda_context() { + if (copy_event != nullptr) { + CUDA_CHECK(cudaEventDestroy(copy_event)); + } + for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) { + for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) { + if (streams[i][j] != nullptr) { + CUDA_CHECK(cudaStreamDestroy(streams[i][j])); + } + } + if (cublas_handles[i] != nullptr) { + CUBLAS_CHECK(cublasDestroy(cublas_handles[i])); + } + } + } - if (i >= k) { - return; + cudaStream_t stream(int device, int stream) { + if (streams[device][stream] == nullptr) { + ggml_cuda_set_device(device); + CUDA_CHECK(cudaStreamCreateWithFlags(&streams[device][stream], cudaStreamNonBlocking)); + } + return streams[device][stream]; } - dst[i] = x[i] * x[i]; -} -template -static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) { - const int row = blockIdx.x*blockDim.y + threadIdx.y; - const int tid = threadIdx.x; + cudaStream_t stream() { + return stream(device, 0); + } - float2 mean_var = make_float2(0.f, 0.f); + cublasHandle_t cublas_handle(int device) { + if (cublas_handles[device] == nullptr) { + ggml_cuda_set_device(device); + CUBLAS_CHECK(cublasCreate(&cublas_handles[device])); + CUBLAS_CHECK(cublasSetMathMode(cublas_handles[device], CUBLAS_TF32_TENSOR_OP_MATH)); + } + return cublas_handles[device]; + } - for (int col = tid; col < ncols; col += block_size) { - const float xi = x[row*ncols + col]; - mean_var.x += xi; - mean_var.y += xi * xi; + cublasHandle_t cublas_handle() { + return cublas_handle(device); } - // sum up partial sums - mean_var = warp_reduce_sum(mean_var); - if (block_size > WARP_SIZE) { - __shared__ float2 s_sum[32]; - int warp_id = threadIdx.x / WARP_SIZE; - int lane_id = threadIdx.x % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = mean_var; + // pool + std::unique_ptr pools[GGML_CUDA_MAX_DEVICES]; + + static std::unique_ptr new_pool_for_device(int device) { +#if !defined(GGML_USE_HIPBLAS) + if (get_cuda_global_info().devices[device].vmm) { + return std::unique_ptr(new ggml_cuda_pool_vmm(device)); } - __syncthreads(); - mean_var = s_sum[lane_id]; - mean_var = warp_reduce_sum(mean_var); +#endif + return std::unique_ptr(new ggml_cuda_pool_leg(device)); } - const float mean = mean_var.x / ncols; - const float var = mean_var.y / ncols - mean * mean; - const float inv_std = rsqrtf(var + eps); + ggml_cuda_pool & pool(int device) { + if (pools[device] == nullptr) { + pools[device] = new_pool_for_device(device); + } + return *pools[device]; + } - for (int col = tid; col < ncols; col += block_size) { - dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; + ggml_cuda_pool & pool() { + return pool(device); } -} +}; -static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { - return; +// cuda buffer + +struct ggml_backend_cuda_buffer_context { + int device; + void * dev_ptr = nullptr; + std::string name; + + ggml_backend_cuda_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr), + name(GGML_CUDA_NAME + std::to_string(device)) { } - // operation - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - if (blockIdx.z < ne02) { // src0 - int offset_src = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; - } else { - int offset_src = - nidx + - blockIdx.y * ne0 + - (blockIdx.z - ne02) * ne0 * gridDim.y; - dst[offset_dst] = y[offset_src]; + + ~ggml_backend_cuda_buffer_context() { + CUDA_CHECK(cudaFree(dev_ptr)); } +}; + +GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + return ctx->name.c_str(); } -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { - // blockIdx.z: idx of ne02*ne03 - // blockIdx.y: idx of ne01*scale_factor， aka ne1 - // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE - // ne00xne01: ne00 * ne01 - int ne0 = ne00 * scale_factor; - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { - return; - } - // operation - int i00 = nidx / scale_factor; - int i01 = blockIdx.y / scale_factor; - int offset_src = - i00 + - i01 * ne00 + - blockIdx.z * ne00xne01; - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; +GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; } -static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) { - // blockIdx.z: idx of ne2*ne3, aka ne02*ne03 - // blockIdx.y: idx of ne1 - // blockIDx.x: idx of ne0 / BLOCK_SIZE - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { - return; - } +GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + delete ctx; +} - // operation - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) { - int offset_src = - nidx + - blockIdx.y * ne00 + - blockIdx.z * ne00 * ne01; - dst[offset_dst] = x[offset_src]; - } else { - dst[offset_dst] = 0.0f; - } +GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + return ctx->dev_ptr; } -static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) { - // blockIDx.x: idx of ne0 / BLOCK_SIZE - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { +GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + + if (tensor->view_src != NULL && tensor->view_offs == 0) { + assert(tensor->view_src->buffer->buft == buffer->buft); + tensor->backend = tensor->view_src->backend; + tensor->extra = tensor->view_src->extra; return; } - dst[nidx] = start + step * nidx; -} -static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) { - // blockIDx.y: idx of timesteps->ne[0] - // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE - int i = blockIdx.y; - int j = threadIdx.x + blockIdx.x * blockDim.x; - float * embed_data = (float *)((char *)dst + i*nb1); + if (ggml_is_quantized(tensor->type)) { + // initialize padding to 0 to avoid possible NaN values + size_t original_size = ggml_nbytes(tensor); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - if (dim % 2 != 0 && j == ((dim + 1) / 2)) { - embed_data[dim] = 0.f; + if (padded_size > original_size && tensor->view_src == nullptr) { + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size)); + } } +} - int half = dim / 2; - if (j >= half) { - return; - } +GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - float timestep = timesteps[i]; - float freq = (float)expf(-logf(max_period) * j / half); - float arg = timestep * freq; - embed_data[j] = cosf(arg); - embed_data[j + half] = sinf(arg); + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread)); + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } -template -static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) { - // blockIdx.x: num_groups idx - // threadIdx.x: block_size idx - int start = blockIdx.x * group_size; - int end = start + group_size; +GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - start += threadIdx.x; + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); +} - if (end >= ne_elements) { - end = ne_elements; +GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_backend_buffer_is_cuda(src->buffer)) { + ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; + ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; + if (src_ctx->device == dst_ctx->device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); + } else { + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); + } + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); + return true; } + return false; - float tmp = 0.0f; // partial sum for thread in warp + GGML_UNUSED(buffer); +} - for (int j = start; j < end; j += block_size) { - tmp += x[j]; - } +GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - tmp = warp_reduce_sum(tmp); - if (block_size > WARP_SIZE) { - __shared__ float s_sum[32]; - int warp_id = threadIdx.x / WARP_SIZE; - int lane_id = threadIdx.x % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - __syncthreads(); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp); - } + ggml_cuda_set_device(ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); + CUDA_CHECK(cudaDeviceSynchronize()); +} - float mean = tmp / group_size; - tmp = 0.0f; +static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_get_name, + /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, + /* .get_base = */ ggml_backend_cuda_buffer_get_base, + /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cuda_buffer_clear, + /* .reset = */ NULL, +}; - for (int j = start; j < end; j += block_size) { - float xi = x[j] - mean; - dst[j] = xi; - tmp += xi * xi; - } +// cuda buffer type +struct ggml_backend_cuda_buffer_type_context { + int device; + std::string name; +}; - tmp = warp_reduce_sum(tmp); - if (block_size > WARP_SIZE) { - __shared__ float s_sum[32]; - int warp_id = threadIdx.x / WARP_SIZE; - int lane_id = threadIdx.x % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - __syncthreads(); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp); - } +GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - float variance = tmp / group_size; - float scale = rsqrtf(variance + eps); - for (int j = start; j < end; j += block_size) { - dst[j] *= scale; - } + return ctx->name.c_str(); } -template -static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) { - const int row = blockIdx.x*blockDim.y + threadIdx.y; - const int tid = threadIdx.x; - - float tmp = 0.0f; // partial sum for thread in warp - - for (int col = tid; col < ncols; col += block_size) { - const float xi = x[row*ncols + col]; - tmp += xi * xi; - } +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - // sum up partial sums - tmp = warp_reduce_sum(tmp); - if (block_size > WARP_SIZE) { - __shared__ float s_sum[32]; - int warp_id = threadIdx.x / WARP_SIZE; - int lane_id = threadIdx.x % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - __syncthreads(); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp); - } + ggml_cuda_set_device(buft_ctx->device); - const float mean = tmp / ncols; - const float scale = rsqrtf(mean + eps); + size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 - for (int col = tid; col < ncols; col += block_size) { - dst[row*ncols + col] = scale * x[row*ncols + col]; + void * dev_ptr; + cudaError_t err = cudaMalloc(&dev_ptr, size); + if (err != cudaSuccess) { + fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + return nullptr; } -} - -static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const block_q4_0 * x = (const block_q4_0 *) vx; - const dfloat d = x[ib].d; + ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr); - const int vui = x[ib].qs[iqs]; + return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); +} - v.x = vui & 0xF; - v.y = vui >> 4; +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; -#ifdef GGML_CUDA_F16 - v = __hsub2(v, {8.0f, 8.0f}); - v = __hmul2(v, {d, d}); -#else - v.x = (v.x - 8.0f) * d; - v.y = (v.y - 8.0f) * d; -#endif // GGML_CUDA_F16 + GGML_UNUSED(buft); } -static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const block_q4_1 * x = (const block_q4_1 *) vx; - - const dfloat d = __low2half(x[ib].dm); - const dfloat m = __high2half(x[ib].dm); +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); + int64_t ne0 = tensor->ne[0]; - const int vui = x[ib].qs[iqs]; + if (ggml_is_quantized(tensor->type)) { + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } - v.x = vui & 0xF; - v.y = vui >> 4; + return size; -#ifdef GGML_CUDA_F16 - v = __hmul2(v, {d, d}); - v = __hadd2(v, {m, m}); -#else - v.x = (v.x * d) + m; - v.y = (v.y * d) + m; -#endif // GGML_CUDA_F16 + GGML_UNUSED(buft); } -static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const block_q5_0 * x = (const block_q5_0 *) vx; - - const dfloat d = x[ib].d; - - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); - - const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; +GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_cuda(backend)) { + return false; + } - v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); - v.y = ((x[ib].qs[iqs] >> 4) | xh_1); + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; -#ifdef GGML_CUDA_F16 - v = __hsub2(v, {16.0f, 16.0f}); - v = __hmul2(v, {d, d}); -#else - v.x = (v.x - 16.0f) * d; - v.y = (v.y - 16.0f) * d; -#endif // GGML_CUDA_F16 + return buft_ctx->device == cuda_ctx->device; } -static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const block_q5_1 * x = (const block_q5_1 *) vx; +static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; - const dfloat d = __low2half(x[ib].dm); - const dfloat m = __high2half(x[ib].dm); +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); + if (device >= ggml_backend_cuda_get_device_count()) { + return nullptr; + } - const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; + static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; - v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); - v.y = ((x[ib].qs[iqs] >> 4) | xh_1); + static bool ggml_backend_cuda_buffer_type_initialized = false; -#ifdef GGML_CUDA_F16 - v = __hmul2(v, {d, d}); - v = __hadd2(v, {m, m}); -#else - v.x = (v.x * d) + m; - v.y = (v.y * d) + m; -#endif // GGML_CUDA_F16 -} + if (!ggml_backend_cuda_buffer_type_initialized) { + for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) { + ggml_backend_cuda_buffer_types[i] = { + /* .iface = */ ggml_backend_cuda_buffer_type_interface, + /* .context = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)}, + }; + } + ggml_backend_cuda_buffer_type_initialized = true; + } -static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const block_q8_0 * x = (const block_q8_0 *) vx; + return &ggml_backend_cuda_buffer_types[device]; +} - const dfloat d = x[ib].d; +// cuda split buffer - v.x = x[ib].qs[iqs + 0]; - v.y = x[ib].qs[iqs + 1]; +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { + int64_t min_compute_capability = INT_MAX; + int64_t max_compute_capability = INT_MIN; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + if (tensor_split[id] < (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { + if (min_compute_capability > get_cuda_global_info().devices[id].cc) { + min_compute_capability = get_cuda_global_info().devices[id].cc; + } + if (max_compute_capability < get_cuda_global_info().devices[id].cc) { + max_compute_capability = get_cuda_global_info().devices[id].cc; + } + } + } -#ifdef GGML_CUDA_F16 - v = __hmul2(v, {d, d}); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + switch(type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return max_compute_capability >= CC_RDNA2 ? 128 : 64; + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return 1; + case GGML_TYPE_Q2_K: + return max_compute_capability >= CC_RDNA2 ? 128 : 32; + case GGML_TYPE_Q3_K: + return min_compute_capability < CC_RDNA2 ? 128 : 64; + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + return max_compute_capability >= CC_RDNA2 ? 128 : 64; + default: + GGML_ASSERT(false); + } #else - v.x *= d; - v.y *= d; -#endif // GGML_CUDA_F16 + switch(type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return max_compute_capability >= CC_VOLTA ? 128 : 64; + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return 64; + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return 1; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + return max_compute_capability >= CC_VOLTA ? 128 : 64; + case GGML_TYPE_Q6_K: + return 64; + default: + GGML_ASSERT(false); + } +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } -template -static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); - const int i = blockIdx.x; + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; - // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; - const int ib = 8*i + ir; - if (ib >= nb32) { - return; + if (id == ggml_backend_cuda_get_device_count() - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; } +} - dst_t * y = yy + 256*i + 32*ir + 4*il; +static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - const block_q4_0 * x = (const block_q4_0 *)vx + ib; - const float d = __half2float(x->d); - const float dm = -8*d; + return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); +} - const uint8_t * q = x->qs + 4*il; +struct ggml_backend_cuda_split_buffer_type_context { + std::array tensor_split; +}; - for (int l = 0; l < 4; ++l) { - y[l+ 0] = d * (q[l] & 0xF) + dm; - y[l+16] = d * (q[l] >> 4) + dm; +struct ggml_backend_cuda_split_buffer_context { + ~ggml_backend_cuda_split_buffer_context() { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int id = 0; id < GGML_CUDA_MAX_DEVICES; ++id) { + for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) { + if (extra->events[id][is] != nullptr) { + CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); + } + } + if (extra->data_device[id] != nullptr) { + CUDA_CHECK(cudaFree(extra->data_device[id])); + } + } + delete extra; + } } -} -template -static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { + std::vector tensor_extras; +}; - const int i = blockIdx.x; +GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Split"; - // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; - const int ib = 8*i + ir; - if (ib >= nb32) { - return; - } + GGML_UNUSED(buffer); +} - dst_t * y = yy + 256*i + 32*ir + 4*il; +static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; + GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds +} - const block_q4_1 * x = (const block_q4_1 *)vx + ib; - const float2 d = __half22float2(x->dm); +GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + delete ctx; +} - const uint8_t * q = x->qs + 4*il; +GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; - for (int l = 0; l < 4; ++l) { - y[l+ 0] = d.x * (q[l] & 0xF) + d.y; - y[l+16] = d.x * (q[l] >> 4) + d.y; - } + GGML_UNUSED(buffer); } -//================================== k-quants +GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported -template -static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; - const int i = blockIdx.x; - const block_q2_K * x = (const block_q2_K *) vx; + const int64_t ne0 = tensor->ne[0]; - const int tid = threadIdx.x; -#if QK_K == 256 - const int n = tid/32; - const int l = tid - 32*n; - const int is = 8*n + l/16; + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + ctx->tensor_extras.push_back(extra); - const uint8_t q = x[i].qs[32*n + l]; - dst_t * y = yy + i*QK_K + 128*n; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); - y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); - y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); -#else - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const uint8_t q = x[i].qs[il] >> (2*is); - dst_t * y = yy + i*QK_K + 16*is + il; - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); -#endif + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } -} + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; -template -static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } - const int i = blockIdx.x; - const block_q3_K * x = (const block_q3_K *) vx; + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_cuda_set_device(id); + char * buf; + CUDA_CHECK(cudaMalloc(&buf, size)); -#if QK_K == 256 - const int r = threadIdx.x/4; - const int tid = r/2; - const int is0 = r%2; - const int l0 = 16*is0 + 4*(threadIdx.x%4); - const int n = tid / 4; - const int j = tid - 4*n; + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); + } - uint8_t m = 1 << (4*n + j); - int is = 8*n + 2*j + is0; - int shift = 2*j; + extra->data_device[id] = buf; - int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) : - (x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4); - float d_all = x[i].d; - float dl = d_all * (us - 32); + for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) { + CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); + } + } + tensor->extra = extra; +} - dst_t * y = yy + i*QK_K + 128*n + 32*j; - const uint8_t * q = x[i].qs + 32*n; - const uint8_t * hm = x[i].hmask; +GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); - for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -#else - const int tid = threadIdx.x; - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const int im = il/8; // 0...1 - const int in = il%8; // 0...7 + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; - dst_t * y = yy + i*QK_K + 16*is + il; + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - const uint8_t q = x[i].qs[il] >> (2*is); - const uint8_t h = x[i].hmask[in] >> (2*is + im); - const float d = (float)x[i].d; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - if (is == 0) { - y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } else { - y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } -#endif + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } -} + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; -#if QK_K == 256 -static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { - if (j < 4) { - d = q[j] & 63; m = q[j + 4] & 63; - } else { - d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); - m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread)); + } + + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } } -#endif -template -static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q4_K * x = (const block_q4_K *) vx; +GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); - const int i = blockIdx.x; + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; -#if QK_K == 256 - // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; - const int is = 2*il; - const int n = 4; + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - dst_t * y = yy + i*QK_K + 64*il + n*ir; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } - const uint8_t * q = x[i].qs + 32*il + n*ir; + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; - for (int l = 0; l < n; ++l) { - y[l + 0] = d1 * (q[l] & 0xF) - m1; - y[l +32] = d2 * (q[l] >> 4) - m2; + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); + } + + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); } -#else - const int tid = threadIdx.x; - const uint8_t * q = x[i].qs; - dst_t * y = yy + i*QK_K; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); - y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); -#endif } -template -static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q5_K * x = (const block_q5_K *) vx; +GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + GGML_UNUSED(buffer); + GGML_UNUSED(value); +} - const int i = blockIdx.x; - -#if QK_K == 256 - // assume 64 threads - this is very slightly better than the one below - const int tid = threadIdx.x; - const int il = tid/16; // il is in 0...3 - const int ir = tid%16; // ir is in 0...15 - const int is = 2*il; // is is in 0...6 +static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_cuda_split_buffer_clear, + /* .reset = */ NULL, +}; - dst_t * y = yy + i*QK_K + 64*il + 2*ir; +// cuda split buffer type - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); +GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Split"; - const uint8_t * ql = x[i].qs + 32*il + 2*ir; - const uint8_t * qh = x[i].qh + 2*ir; + GGML_UNUSED(buft); +} - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context(); - uint8_t hm = 1 << (2*il); - y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1; - y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1; - hm <<= 1; - y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; - y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; -#else - const int tid = threadIdx.x; - const uint8_t q = x[i].qs[tid]; - const int im = tid/8; // 0...3 - const int in = tid%8; // 0...7 - const int is = tid/16; // 0 or 1 - const uint8_t h = x[i].qh[in] >> im; - const float d = x[i].d; - dst_t * y = yy + i*QK_K + tid; - y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); - y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); -#endif + return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); } -template -static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q6_K * x = (const block_q6_K *) vx; +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; - const int i = blockIdx.x; -#if QK_K == 256 + GGML_UNUSED(buft); +} - // assume 64 threads - this is very slightly better than the one below - const int tid = threadIdx.x; - const int ip = tid/32; // ip is 0 or 1 - const int il = tid - 32*ip; // 0...32 - const int is = 8*ip + il/16; +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; - dst_t * y = yy + i*QK_K + 128*ip + il; + size_t total_size = 0; - const float d = x[i].d; + const int64_t ne0 = tensor->ne[0]; - const uint8_t * ql = x[i].ql + 64*ip + il; - const uint8_t qh = x[i].qh[32*ip + il]; - const int8_t * sc = x[i].scales + is; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id); - y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); - y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); - y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -#else + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } - // assume 32 threads - const int tid = threadIdx.x; - const int ip = tid/16; // 0 or 1 - const int il = tid - 16*ip; // 0...15 + total_size += ggml_nbytes_split(tensor, nrows_split); - dst_t * y = yy + i*QK_K + 16*ip + il; + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } - const float d = x[i].d; + return total_size; +} - const uint8_t ql = x[i].ql[16*ip + il]; - const uint8_t qh = x[i].qh[il] >> (2*ip); - const int8_t * sc = x[i].scales; +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_cuda(backend); - y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); -#endif + GGML_UNUSED(buft); } -inline bool ggml_cuda_supports_mmq(enum ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - return true; - default: - return false; - } +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); } -template -static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { +static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, +}; - const int i = blockIdx.x; - const block_iq2_xxs * x = (const block_iq2_xxs *) vx; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { + static std::mutex mutex; + std::lock_guard lock(mutex); - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif + static std::map, struct ggml_backend_buffer_type> buft_map; -} + std::array tensor_split_arr = {}; -template -static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = get_cuda_global_info().default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) { + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) { + tensor_split_arr[i] /= split_sum; + } + } - const int i = blockIdx.x; - const block_iq2_xs * x = (const block_iq2_xs *) vx; + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; - for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, + /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, + }; + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; } -template -static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - - const int i = blockIdx.x; - const block_iq2_s * x = (const block_iq2_s *) vx; +// host buffer type - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; - for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif +GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Host"; + GGML_UNUSED(buft); } -template -static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - - const int i = blockIdx.x; - const block_iq3_xxs * x = (const block_iq3_xxs *) vx; +GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Host"; - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * q3 = x[i].qs + 8*ib; - const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 4; ++j) { - y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); - y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); - } -#else - assert(false); -#endif + GGML_UNUSED(buffer); +} +GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + CUDA_CHECK(cudaFreeHost(buffer->context)); } -template -static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { +static void * ggml_cuda_host_malloc(size_t size) { + if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { + return nullptr; + } - const int i = blockIdx.x; - const block_iq3_s * x = (const block_iq3_s *) vx; - - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); - const uint8_t signs = x[i].signs[4*ib + il]; - for (int j = 0; j < 4; ++j) { - y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); - y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + void * ptr = nullptr; + cudaError_t err = cudaMallocHost((void **) &ptr, size); + if (err != cudaSuccess) { + // clear the error + cudaGetLastError(); + fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, + size/1024.0/1024.0, cudaGetErrorString(err)); + return nullptr; } -#else - assert(false); -#endif + return ptr; } -template -static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - - const int i = blockIdx.x; - const block_iq1_s * x = (const block_iq1_s *) vx; +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_cuda_host_malloc(size); - const int tid = threadIdx.x; -#if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; - const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); - uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - for (int j = 0; j < 8; ++j) { - y[j] = d * (q[j] + delta); + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); } -#else - assert(false); -#endif + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; + buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; + + return buffer; } -static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .context = */ nullptr, + }; -template -static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { + return &ggml_backend_cuda_buffer_type_host; +} - const int i = blockIdx.x; - const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); +//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) { +// return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name; +//} - const int tid = threadIdx.x; - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[ib].qs + 4*il; - const float d = (float)x[ib].d; - for (int j = 0; j < 4; ++j) { - y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; - y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; - } + +/// kernels + + +#if defined(GGML_USE_HIPBLAS) +#define __CUDA_ARCH__ 1300 + +#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \ + defined(__gfx1150__) || defined(__gfx1151__) +#define RDNA3 +#endif + +#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \ + defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__) +#define RDNA2 +#endif + +#ifndef __has_builtin + #define __has_builtin(x) 0 +#endif + +typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); +typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4))); +static __device__ __forceinline__ int __vsubss4(const int a, const int b) { + const int8x4_t va = reinterpret_cast(a); + const int8x4_t vb = reinterpret_cast(b); +#if __has_builtin(__builtin_elementwise_sub_sat) + const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); + return reinterpret_cast(c); +#else + int8x4_t c; + int16_t tmp; +#pragma unroll + for (int i = 0; i < 4; i++) { + tmp = va[i] - vb[i]; + if(tmp > std::numeric_limits::max()) tmp = std::numeric_limits::max(); + if(tmp < std::numeric_limits::min()) tmp = std::numeric_limits::min(); + c[i] = tmp; + } + return reinterpret_cast(c); +#endif // __has_builtin(__builtin_elementwise_sub_sat) } -#if QK_K != 64 -template -static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; - const block_iq4_xs * x = (const block_iq4_xs *)vx; +static __device__ __forceinline__ int __vsub4(const int a, const int b) { + return __vsubss4(a, b); +} - const int tid = threadIdx.x; - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[i].qs + 16*ib + 4*il; - const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); - for (int j = 0; j < 4; ++j) { - y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; - y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; +static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) { + const uint8x4_t& va = reinterpret_cast(a); + const uint8x4_t& vb = reinterpret_cast(b); + unsigned int c; + uint8x4_t& vc = reinterpret_cast(c); +#pragma unroll + for (int i = 0; i < 4; ++i) { + vc[i] = va[i] == vb[i] ? 0xff : 0x00; } + return c; } -#endif -static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { +static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { +#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) + c = __builtin_amdgcn_sdot4(a, b, c, false); +#elif defined(RDNA3) + c = __builtin_amdgcn_sudot4( true, a, true, b, c, false); +#elif defined(__gfx1010__) || defined(__gfx900__) + int tmp1; + int tmp2; + asm("\n \ + v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \ + v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \ + v_add3_u32 %0, %1, %2, %0 \n \ + v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \ + v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \ + v_add3_u32 %0, %1, %2, %0 \n \ + " + : "+v"(c), "=&v"(tmp1), "=&v"(tmp2) + : "v"(a), "v"(b) + ); +#else + const int8x4_t va = reinterpret_cast(a); + const int8x4_t vb = reinterpret_cast(b); + c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3]; +#endif + return c; +} +#endif // defined(GGML_USE_HIPBLAS) - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; +#ifdef GGML_CUDA_F16 +typedef half dfloat; // dequantize float +typedef half2 dfloat2; +#else +typedef float dfloat; // dequantize float +typedef float2 dfloat2; +#endif //GGML_CUDA_F16 - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; +static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) { + const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment - const block_q2_K * x = (const block_q2_K *)vx + ib0; + int x32 = 0; + x32 |= x16[0] << 0; + x32 |= x16[1] << 16; - float tmp = 0; // partial sum for thread in warp + return x32; +} -#if QK_K == 256 - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 +static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) { + const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment - const int step = 16/K_QUANTS_PER_ITERATION; + int x32 = 0; + x32 |= x16[0] << 0; + x32 |= x16[1] << 16; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 + return x32; +} - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int s_offset = 8*im; - const int y_offset = 128*im + l0; +static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) { + return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment +} - uint32_t aux[4]; - const uint8_t * d = (const uint8_t *)aux; - const uint8_t * m = (const uint8_t *)(aux + 2); +static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) { + return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment +} - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { +template +using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream); +typedef to_t_cuda_t to_fp32_cuda_t; +typedef to_t_cuda_t to_fp16_cuda_t; - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; +typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); +typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); +typedef void (*cpy_kernel_t)(const char * cx, char * cdst); - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); +typedef void (*ggml_cuda_func_t)(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); - const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset); - aux[0] = a[0] & 0x0f0f0f0f; - aux[1] = a[1] & 0x0f0f0f0f; - aux[2] = (a[0] >> 4) & 0x0f0f0f0f; - aux[3] = (a[1] >> 4) & 0x0f0f0f0f; +typedef void (*ggml_cuda_op_mul_mat_t)( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream); - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) - + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) - + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) - + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) - + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) - + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) - + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) - +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); - sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] - + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; +typedef void (*ggml_cuda_op_flatten_t)( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream); - } - tmp += dall * sum1 - dmin * sum2; +typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); +typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc); +typedef void (*load_tiles_cuda_t)( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row); +typedef float (*vec_dot_q_mul_mat_cuda_t)( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); - } +#define WARP_SIZE 32 + +#define CUDA_GELU_BLOCK_SIZE 256 +#define CUDA_SILU_BLOCK_SIZE 256 +#define CUDA_TANH_BLOCK_SIZE 256 +#define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_HARDSIGMOID_BLOCK_SIZE 256 +#define CUDA_HARDSWISH_BLOCK_SIZE 256 +#define CUDA_SQR_BLOCK_SIZE 256 +#define CUDA_CPY_BLOCK_SIZE 32 +#define CUDA_SCALE_BLOCK_SIZE 256 +#define CUDA_CLAMP_BLOCK_SIZE 256 +#define CUDA_ROPE_BLOCK_SIZE 256 +#define CUDA_SOFT_MAX_BLOCK_SIZE 1024 +#define CUDA_ALIBI_BLOCK_SIZE 32 +#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32 +#define CUDA_QUANTIZE_BLOCK_SIZE 256 +#define CUDA_DEQUANTIZE_BLOCK_SIZE 256 +#define CUDA_GET_ROWS_BLOCK_SIZE 256 +#define CUDA_UPSCALE_BLOCK_SIZE 256 +#define CUDA_CONCAT_BLOCK_SIZE 256 +#define CUDA_PAD_BLOCK_SIZE 256 +#define CUDA_ARANGE_BLOCK_SIZE 256 +#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 +#define CUDA_ACC_BLOCK_SIZE 256 +#define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_POOL2D_BLOCK_SIZE 256 + +#define CUDA_Q8_0_NE_ALIGN 2048 + +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_CUDA_DMMV_X +#define GGML_CUDA_DMMV_X 32 +#endif +#ifndef GGML_CUDA_MMV_Y +#define GGML_CUDA_MMV_Y 1 +#endif + +#ifndef K_QUANTS_PER_ITERATION +#define K_QUANTS_PER_ITERATION 2 #else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; +static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); +#endif - uint32_t uaux[2]; - const uint8_t * d = (const uint8_t *)uaux; +#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE +#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128 +#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { +#define MUL_MAT_SRC1_COL_STRIDE 128 - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint32_t * s = (const uint32_t *)x[i].scales; +[[noreturn]] +static __device__ void no_device_code( + const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { - uaux[0] = s[0] & 0x0f0f0f0f; - uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", + file_name, line, function_name, arch); + GGML_UNUSED(arch_list); +#else + printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", + file_name, line, function_name, arch, arch_list); +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + __trap(); - const float2 dall = __half22float2(x[i].dm); + GGML_UNUSED(no_device_code); // suppress unused function warning +} - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t ql = q[l]; - sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) - + y[l+16] * d[1] * ((ql >> 2) & 3) - + y[l+32] * d[2] * ((ql >> 4) & 3) - + y[l+48] * d[3] * ((ql >> 6) & 3); - sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; - } - tmp += dall.x * sum1 - dall.y * sum2; +#ifdef __CUDA_ARCH__ +#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__)) +#else +//#define NO_DEVICE_CODE GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.") +#define NO_DEVICE_CODE +#endif // __CUDA_ARCH__ + +static __device__ __forceinline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, mask, 32); } -#endif + return x; +} - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); +static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + } + return a; +} - if (threadIdx.x == 0) { - dst[row] = tmp; +#ifdef GGML_CUDA_F16 +static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + } + return a; +#else + GGML_UNUSED(a); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +} +#endif // GGML_CUDA_F16 + +static __device__ __forceinline__ float warp_reduce_max(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); } + return x; } -static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { +//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { +//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +//#pragma unroll +// for (int mask = 16; mask > 0; mask >>= 1) { +// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); +// } +// return x; +//#else +// GGML_UNUSED(x); +// NO_DEVICE_CODE; +//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +//} - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; +static __device__ __forceinline__ float op_repeat(const float a, const float b) { + return b; + GGML_UNUSED(a); +} - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; +static __device__ __forceinline__ float op_add(const float a, const float b) { + return a + b; +} - const block_q3_K * x = (const block_q3_K *)vx + ib0; +static __device__ __forceinline__ float op_mul(const float a, const float b) { + return a * b; +} - float tmp = 0; // partial sum for thread in warp +static __device__ __forceinline__ float op_div(const float a, const float b) { + return a / b; +} -#if QK_K == 256 +template +static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13) { + const int i0s = blockDim.x*blockIdx.x + threadIdx.x; + const int i1 = (blockDim.y*blockIdx.y + threadIdx.y); + const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3; + const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3; - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; + if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; - const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop - const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0....15 or 0...7 + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; - const uint8_t m = 1 << (4*im); + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; - const int l0 = n*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int y_offset = 128*im + l0; + for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) { + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + } +} - uint16_t utmp[4]; - const int8_t * s = (const int8_t *)utmp; +template +static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13) { - const uint16_t s_shift = 4*im; + const int i = blockDim.x*blockIdx.x + threadIdx.x; - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const int i3 = i/(ne2*ne1*ne0); + const int i2 = (i/(ne1*ne0)) % ne2; + const int i1 = (i/ne0) % ne1; + const int i0 = i % ne0; - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; - const uint8_t * h = x[i].hmask + l0; + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } - const uint16_t * a = (const uint16_t *)x[i].scales; - utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); - utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); - utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); - utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; - const float d = x[i].d; + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; - float sum = 0; - for (int l = 0; l < n; ++l) { - sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) - + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) - + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) - + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); - sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) - + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) - + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) - + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); - } - tmp += d * sum; - - } -#else - - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 - const int in = offset/8; // 0 or 1 - const int im = offset%8; // 0...7 - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint8_t * s = x[i].scales; + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; - const float dall = (float)x[i].d; + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); +} - float sum = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t hl = x[i].hmask[im+l] >> in; - const uint8_t ql = q[l]; - sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) - + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) - + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) - + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); - } - tmp += sum; +static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne, + const int ne10, const int ne11, const int ne12, + const int nb1, const int nb2, int offset) { + const int i = blockDim.x * blockIdx.x + threadIdx.x; + if (i >= ne) { + return; } -#endif - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; + int src1_idx = i - offset; + int oz = src1_idx / nb2; + int oy = (src1_idx - (oz * nb2)) / nb1; + int ox = src1_idx % nb1; + if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { + dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; + } else { + dst[i] = x[i]; } } -static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; +static __global__ void gelu_f32(const float * x, float * dst, const int k) { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const int i = blockDim.x*blockIdx.x + threadIdx.x; - const block_q4_K * x = (const block_q4_K *)vx + ib0; + if (i >= k) { + return; + } -#if QK_K == 256 - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; + float xi = x[i]; + dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi))); +} - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 +static __global__ void silu_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + if (i >= k) { + return; + } + dst[i] = x[i] / (1.0f + expf(-x[i])); +} - const int il = tid/step; // 0...3 - const int ir = tid - step*il; // 0...7 or 0...3 - const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 +static __global__ void gelu_quick_f32(const float * x, float * dst, int k) { + const float GELU_QUICK_COEF = -1.702f; + const int i = blockDim.x*blockIdx.x + threadIdx.x; + if (i >= k) { + return; + } + dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i]))); +} - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; +static __global__ void tanh_f32(const float * x, float * dst, int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + if (i >= k) { + return; + } + dst[i] = tanhf(x[i]); +} - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; +static __global__ void relu_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; + if (i >= k) { + return; + } + dst[i] = fmaxf(x[i], 0); +} -#if K_QUANTS_PER_ITERATION == 2 - uint32_t q32[4]; - const uint8_t * q4 = (const uint8_t *)q32; -#else - uint16_t q16[4]; - const uint8_t * q4 = (const uint8_t *)q16; -#endif +static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - float tmp = 0; // partial sum for thread in warp + if (i >= k) { + return; + } + dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { +static __global__ void hardswish_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; + if (i >= k) { + return; + } + dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); +static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + if (i >= k) { + return; + } + dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope; +} - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); +static __global__ void sqr_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; -#if K_QUANTS_PER_ITERATION == 2 - const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset); - const uint32_t * q2 = q1 + 16; + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} - q32[0] = q1[0] & 0x0f0f0f0f; - q32[1] = q1[0] & 0xf0f0f0f0; - q32[2] = q2[0] & 0x0f0f0f0f; - q32[3] = q2[0] & 0xf0f0f0f0; +template +static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) { + const int row = blockIdx.x*blockDim.y + threadIdx.y; + const int tid = threadIdx.x; - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 4; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4]; - s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#else - const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset); - const uint16_t * q2 = q1 + 32; + float2 mean_var = make_float2(0.f, 0.f); - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[0] & 0xf0f0; - q16[2] = q2[0] & 0x0f0f; - q16[3] = q2[0] & 0xf0f0; + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row*ncols + col]; + mean_var.x += xi; + mean_var.y += xi * xi; + } - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 2; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; - s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + // sum up partial sums + mean_var = warp_reduce_sum(mean_var); + if (block_size > WARP_SIZE) { + __shared__ float2 s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#endif - + __syncthreads(); + mean_var = s_sum[lane_id]; + mean_var = warp_reduce_sum(mean_var); } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - - const int step = tid * K_QUANTS_PER_ITERATION; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - float tmp = 0; + const float mean = mean_var.x / ncols; + const float var = mean_var.y / ncols - mean * mean; + const float inv_std = rsqrtf(var + eps); - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const float * y = yy + i*QK_K + step; - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) - + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) - + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) - + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); - } - tmp += sum; + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; } +} -#endif - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { - dst[row] = tmp; +static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + // operation + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + if (blockIdx.z < ne02) { // src0 + int offset_src = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + nidx + + blockIdx.y * ne0 + + (blockIdx.z - ne02) * ne0 * gridDim.y; + dst[offset_dst] = y[offset_src]; } } -static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) { - - const int row = blockIdx.x; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q5_K * x = (const block_q5_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp +static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { + // blockIdx.z: idx of ne02*ne03 + // blockIdx.y: idx of ne01*scale_factor， aka ne1 + // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE + // ne00xne01: ne00 * ne01 + int ne0 = ne00 * scale_factor; + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + // operation + int i00 = nidx / scale_factor; + int i01 = blockIdx.y / scale_factor; + int offset_src = + i00 + + i01 * ne00 + + blockIdx.z * ne00xne01; + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + dst[offset_dst] = x[offset_src]; +} -#if QK_K == 256 - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; +static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) { + // blockIdx.z: idx of ne2*ne3, aka ne02*ne03 + // blockIdx.y: idx of ne1 + // blockIDx.x: idx of ne0 / BLOCK_SIZE + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } - const int tid = threadIdx.x/2; // 0...15 - const int ix = threadIdx.x%2; + // operation + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) { + int offset_src = + nidx + + blockIdx.y * ne00 + + blockIdx.z * ne00 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + dst[offset_dst] = 0.0f; + } +} - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 2; +static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) { + // blockIDx.x: idx of ne0 / BLOCK_SIZE + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + dst[nidx] = start + step * nidx; +} - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; +static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) { + // blockIDx.y: idx of timesteps->ne[0] + // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE + int i = blockIdx.y; + int j = threadIdx.x + blockIdx.x * blockDim.x; + float * embed_data = (float *)((char *)dst + i*nb1); - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; + if (dim % 2 != 0 && j == ((dim + 1) / 2)) { + embed_data[dim] = 0.f; + } - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; + int half = dim / 2; + if (j >= half) { + return; + } - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; + float timestep = timesteps[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); +} - uint16_t q16[8]; - const uint8_t * q4 = (const uint8_t *)q16; +template +static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) { + // blockIdx.x: num_groups idx + // threadIdx.x: block_size idx + int start = blockIdx.x * group_size; + int end = start + group_size; - for (int i = ix; i < num_blocks_per_row; i += 2) { + start += threadIdx.x; - const uint8_t * ql1 = x[i].qs + q_offset; - const uint8_t * qh = x[i].qh + l0; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; + if (end >= ne_elements) { + end = ne_elements; + } - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); + float tmp = 0.0f; // partial sum for thread in warp - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); + for (int j = start; j < end; j += block_size) { + tmp += x[j]; + } - float4 sum = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - const uint16_t * q1 = (const uint16_t *)ql1; - const uint16_t * q2 = q1 + 32; - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[8] & 0x0f0f; - q16[2] = (q1[0] >> 4) & 0x0f0f; - q16[3] = (q1[8] >> 4) & 0x0f0f; - q16[4] = q2[0] & 0x0f0f; - q16[5] = q2[8] & 0x0f0f; - q16[6] = (q2[0] >> 4) & 0x0f0f; - q16[7] = (q2[8] >> 4) & 0x0f0f; - for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) - + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) - + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) - + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) - + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; } - tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - const int step = tid * K_QUANTS_PER_ITERATION; - const int im = step/8; - const int in = step%8; + float mean = tmp / group_size; + tmp = 0.0f; - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const int8_t * s = x[i].scales; - const float * y = yy + i*QK_K + step; - const float d = x[i].d; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - const uint8_t h = x[i].qh[in+j] >> im; - sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) - + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) - + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) - + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); - } - tmp += sum; + for (int j = start; j < end; j += block_size) { + float xi = x[j] - mean; + dst[j] = xi; + tmp += xi * xi; } -#endif - // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); + } - if (threadIdx.x == 0) { - dst[row] = tmp; + float variance = tmp / group_size; + float scale = rsqrtf(variance + eps); + for (int j = start; j < end; j += block_size) { + dst[j] *= scale; } } -static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - +template +static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) { const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q6_K * x = (const block_q6_K *)vx + ib0; - -#if QK_K == 256 + const int tid = threadIdx.x; - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + float tmp = 0.0f; // partial sum for thread in warp - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row*ncols + col]; + tmp += xi * xi; + } - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 - const int is = 0; -#else - const int l0 = 4 * in; // 0, 4, 8, ..., 28 - const int is = in / 4; -#endif - const int ql_offset = 64*im + l0; - const int qh_offset = 32*im + l0; - const int s_offset = 8*im + is; - const int y_offset = 128*im + l0; + // sum up partial sums + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); + } - float tmp = 0; // partial sum for thread in warp + const float mean = tmp / ncols; + const float scale = rsqrtf(mean + eps); - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = scale * x[row*ncols + col]; + } +} - const float * y = yy + i * QK_K + y_offset; - const uint8_t * ql = x[i].ql + ql_offset; - const uint8_t * qh = x[i].qh + qh_offset; - const int8_t * s = x[i].scales + s_offset; +static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const block_q4_0 * x = (const block_q4_0 *) vx; - const float d = x[i].d; + const dfloat d = x[ib].d; -#if K_QUANTS_PER_ITERATION == 1 - float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) - + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) - + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) - + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) - + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) - + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) - + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) - +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); - tmp += sum; -#else - float sum = 0; - for (int l = 0; l < 4; ++l) { - sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) - + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) - + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) - + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); - } - tmp += sum; -#endif + const int vui = x[ib].qs[iqs]; - } + v.x = vui & 0xF; + v.y = vui >> 4; +#ifdef GGML_CUDA_F16 + v = __hsub2(v, {8.0f, 8.0f}); + v = __hmul2(v, {d, d}); #else + v.x = (v.x - 8.0f) * d; + v.y = (v.y - 8.0f) * d; +#endif // GGML_CUDA_F16 +} - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 +static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const block_q4_1 * x = (const block_q4_1 *) vx; - const int step = tid * K_QUANTS_PER_ITERATION; + const dfloat d = __low2half(x[ib].dm); + const dfloat m = __high2half(x[ib].dm); - float tmp = 0; // partial sum for thread in warp + const int vui = x[ib].qs[iqs]; - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + v.x = vui & 0xF; + v.y = vui >> 4; - const float * y = yy + i * QK_K + step; - const uint8_t * ql = x[i].ql + step; - const uint8_t * qh = x[i].qh + step; - const int8_t * s = x[i].scales; +#ifdef GGML_CUDA_F16 + v = __hmul2(v, {d, d}); + v = __hadd2(v, {m, m}); +#else + v.x = (v.x * d) + m; + v.y = (v.y * d) + m; +#endif // GGML_CUDA_F16 +} - const float d = x[i+0].d; +static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const block_q5_0 * x = (const block_q5_0 *) vx; - float sum = 0; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) - + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) - + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) - + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); - } - tmp += sum; + const dfloat d = x[ib].d; - } + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); -#endif + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); + v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y = ((x[ib].qs[iqs] >> 4) | xh_1); - if (tid == 0) { - dst[row] = tmp; - } +#ifdef GGML_CUDA_F16 + v = __hsub2(v, {16.0f, 16.0f}); + v = __hmul2(v, {d, d}); +#else + v.x = (v.x - 16.0f) * d; + v.y = (v.y - 16.0f) * d; +#endif // GGML_CUDA_F16 } -static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ - const half * x = (const half *) vx; +static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const block_q5_1 * x = (const block_q5_1 *) vx; - // automatic half -> float type cast if dfloat == float - v.x = x[ib + iqs + 0]; - v.y = x[ib + iqs + 1]; -} + const dfloat d = __low2half(x[ib].dm); + const dfloat m = __high2half(x[ib].dm); -static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) { - const int ix = blockDim.x*blockIdx.x + threadIdx.x; + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); - if (ix >= kx_padded) { - return; - } + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - const int iy = blockDim.y*blockIdx.y + threadIdx.y; + v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y = ((x[ib].qs[iqs] >> 4) | xh_1); - const int i_padded = iy*kx_padded + ix; +#ifdef GGML_CUDA_F16 + v = __hmul2(v, {d, d}); + v = __hadd2(v, {m, m}); +#else + v.x = (v.x * d) + m; + v.y = (v.y * d) + m; +#endif // GGML_CUDA_F16 +} - block_q8_1 * y = (block_q8_1 *) vy; +static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const block_q8_0 * x = (const block_q8_0 *) vx; - const int ib = i_padded / QK8_1; // block index - const int iqs = i_padded % QK8_1; // quant index + const dfloat d = x[ib].d; - const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; - float amax = fabsf(xi); - float sum = xi; + v.x = x[ib].qs[iqs + 0]; + v.y = x[ib].qs[iqs + 1]; - amax = warp_reduce_max(amax); - sum = warp_reduce_sum(sum); +#ifdef GGML_CUDA_F16 + v = __hmul2(v, {d, d}); +#else + v.x *= d; + v.y *= d; +#endif // GGML_CUDA_F16 +} - const float d = amax / 127; - const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); +template +static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { - y[ib].qs[iqs] = q; + const int i = blockIdx.x; - if (iqs > 0) { + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { return; } - reinterpret_cast(y[ib].ds.x) = d; - reinterpret_cast(y[ib].ds.y) = sum; + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_0 * x = (const block_q4_0 *)vx + ib; + const float d = __half2float(x->d); + const float dm = -8*d; + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d * (q[l] & 0xF) + dm; + y[l+16] = d * (q[l] >> 4) + dm; + } } -template -static __global__ void k_get_rows( - const void * src0, const int32_t * src1, dst_t * dst, - int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ - /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ - /*size_t s0,*/ size_t s1, size_t s2, size_t s3, - /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, - size_t s10, size_t s11, size_t s12/*, size_t s13*/) { +template +static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { - const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2; - const int i10 = blockDim.y*blockIdx.y + threadIdx.y; - const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12; - const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12; + const int i = blockIdx.x; - if (i00 >= ne00) { + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { return; } - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; - - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; + dst_t * y = yy + 256*i + 32*ir + 4*il; - const int ib = i00/qk; // block index - const int iqs = (i00%qk)/qr; // quant index - const int iybs = i00 - i00%qk; // dst block start index - const int y_offset = qr == 1 ? 1 : qk/2; + const block_q4_1 * x = (const block_q4_1 *)vx + ib; + const float2 d = __half22float2(x->dm); - // dequantize - dfloat2 v; - dequantize_kernel(src0_row, ib, iqs, v); + const uint8_t * q = x->qs + 4*il; - dst_row[iybs + iqs + 0] = v.x; - dst_row[iybs + iqs + y_offset] = v.y; + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d.x * (q[l] & 0xF) + d.y; + y[l+16] = d.x * (q[l] >> 4) + d.y; + } } -template -static __global__ void k_get_rows_float( - const src0_t * src0, const int32_t * src1, dst_t * dst, - int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ - /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ - /*size_t s0,*/ size_t s1, size_t s2, size_t s3, - /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, - size_t s10, size_t s11, size_t s12/*, size_t s13*/) { +//================================== k-quants - const int i00 = blockIdx.x*blockDim.x + threadIdx.x; - const int i10 = blockDim.y*blockIdx.y + threadIdx.y; - const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12; - const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12; +template +static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - if (i00 >= ne00) { - return; - } + const int i = blockIdx.x; + const block_q2_K * x = (const block_q2_K *) vx; - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + const int tid = threadIdx.x; +#if QK_K == 256 + const int n = tid/32; + const int l = tid - 32*n; + const int is = 8*n + l/16; - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03); + const uint8_t q = x[i].qs[32*n + l]; + dst_t * y = yy + i*QK_K + 128*n; + + float dall = __low2half(x[i].dm); + float dmin = __high2half(x[i].dm); + y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); + y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); + y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); + y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); +#else + const int is = tid/16; // 0 or 1 + const int il = tid%16; // 0...15 + const uint8_t q = x[i].qs[il] >> (2*is); + dst_t * y = yy + i*QK_K + 16*is + il; + float dall = __low2half(x[i].dm); + float dmin = __high2half(x[i].dm); + y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); + y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); +#endif - dst_row[i00] = src0_row[i00]; } -template -static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { - const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x); +template +static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - if (i >= k) { - return; - } + const int i = blockIdx.x; + const block_q3_K * x = (const block_q3_K *) vx; - const int ib = i/qk; // block index - const int iqs = (i%qk)/qr; // quant index - const int iybs = i - i%qk; // y block start index - const int y_offset = qr == 1 ? 1 : qk/2; +#if QK_K == 256 + const int r = threadIdx.x/4; + const int tid = r/2; + const int is0 = r%2; + const int l0 = 16*is0 + 4*(threadIdx.x%4); + const int n = tid / 4; + const int j = tid - 4*n; - // dequantize - dfloat2 v; - dequantize_kernel(vx, ib, iqs, v); + uint8_t m = 1 << (4*n + j); + int is = 8*n + 2*j + is0; + int shift = 2*j; - y[iybs + iqs + 0] = v.x; - y[iybs + iqs + y_offset] = v.y; -} + int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) : + (x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4); + float d_all = x[i].d; + float dl = d_all * (us - 32); -template -static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + dst_t * y = yy + i*QK_K + 128*n + 32*j; + const uint8_t * q = x[i].qs + 32*n; + const uint8_t * hm = x[i].hmask; - if (i >= k) { - return; - } + for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); +#else + const int tid = threadIdx.x; + const int is = tid/16; // 0 or 1 + const int il = tid%16; // 0...15 + const int im = il/8; // 0...1 + const int in = il%8; // 0...7 - const src_t * x = (src_t *) vx; + dst_t * y = yy + i*QK_K + 16*is + il; + + const uint8_t q = x[i].qs[il] >> (2*is); + const uint8_t h = x[i].hmask[in] >> (2*is + im); + const float d = (float)x[i].d; + + if (is == 0) { + y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); + y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); + } else { + y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); + y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); + } +#endif - y[i] = x[i]; } -template -static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) { -#if __CUDA_ARCH__ >= CC_PASCAL - constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; +#if QK_K == 256 +static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { + if (j < 4) { + d = q[j] & 63; m = q[j + 4] & 63; + } else { + d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} +#endif - const int i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; - const int * x0 = ((int *) vx) + blockIdx.x * nint; - half2 * y2 = (half2 *) (y + i0); +template +static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const block_q4_K * x = (const block_q4_K *) vx; - __shared__ int vals[nint]; + const int i = blockIdx.x; -#pragma unroll - for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) { - if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) { - break; - } +#if QK_K == 256 + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int is = 2*il; + const int n = 4; - const int ix = ix0 + threadIdx.x; - vals[ix] = x0[ix]; - } + dst_t * y = yy + i*QK_K + 64*il + n*ir; -#pragma unroll - for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) { - if (need_check && i0 + iy + 2*threadIdx.x >= k) { - return; - } + const float dall = __low2half(x[i].dm); + const float dmin = __high2half(x[i].dm); - const half * b0 = ((const half *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0); - const half d = *b0; - const char2 qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)]; + const uint8_t * q = x[i].qs + 32*il + n*ir; - y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d)); + uint8_t sc, m; + get_scale_min_k4(is + 0, x[i].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[i].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + for (int l = 0; l < n; ++l) { + y[l + 0] = d1 * (q[l] & 0xF) - m1; + y[l +32] = d2 * (q[l] >> 4) - m2; } #else - (void) vx; (void) y; (void) k; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_PASCAL + const int tid = threadIdx.x; + const uint8_t * q = x[i].qs; + dst_t * y = yy + i*QK_K; + const float d = (float)x[i].dm[0]; + const float m = (float)x[i].dm[1]; + y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); + y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); +#endif } -// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called -// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q +template +static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const block_q5_K * x = (const block_q5_K *) vx; -#define VDR_Q4_0_Q8_1_MMVQ 2 -#define VDR_Q4_0_Q8_1_MMQ 4 + const int i = blockIdx.x; -template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl( - const int * v, const int * u, const float & d4, const half2 & ds8) { +#if QK_K == 256 + // assume 64 threads - this is very slightly better than the one below + const int tid = threadIdx.x; + const int il = tid/16; // il is in 0...3 + const int ir = tid%16; // ir is in 0...15 + const int is = 2*il; // is is in 0...6 -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + dst_t * y = yy + i*QK_K + 64*il + 2*ir; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; - const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; + const float dall = __low2half(x[i].dm); + const float dmin = __high2half(x[i].dm); - // SIMD dot product of quantized values - sumi = __dp4a(vi0, u[2*i+0], sumi); - sumi = __dp4a(vi1, u[2*i+1], sumi); - } + const uint8_t * ql = x[i].qs + 32*il + 2*ir; + const uint8_t * qh = x[i].qh + 2*ir; - const float2 ds8f = __half22float2(ds8); + uint8_t sc, m; + get_scale_min_k4(is + 0, x[i].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[i].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; - // second part effectively subtracts 8 from each quant value - return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y); + uint8_t hm = 1 << (2*il); + y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1; + y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1; + hm <<= 1; + y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; + y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A + const int tid = threadIdx.x; + const uint8_t q = x[i].qs[tid]; + const int im = tid/8; // 0...3 + const int in = tid%8; // 0...7 + const int is = tid/16; // 0 or 1 + const uint8_t h = x[i].qh[in] >> im; + const float d = x[i].d; + dst_t * y = yy + i*QK_K + tid; + y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); + y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); +#endif } -#define VDR_Q4_1_Q8_1_MMVQ 2 -#define VDR_Q4_1_Q8_1_MMQ 4 +template +static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const block_q6_K * x = (const block_q6_K *) vx; -template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl( - const int * v, const int * u, const half2 & dm4, const half2 & ds8) { + const int i = blockIdx.x; +#if QK_K == 256 -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + // assume 64 threads - this is very slightly better than the one below + const int tid = threadIdx.x; + const int ip = tid/32; // ip is 0 or 1 + const int il = tid - 32*ip; // 0...32 + const int is = 8*ip + il/16; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; - const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; + dst_t * y = yy + i*QK_K + 128*ip + il; - // SIMD dot product of quantized values - sumi = __dp4a(vi0, u[2*i+0], sumi); - sumi = __dp4a(vi1, u[2*i+1], sumi); - } + const float d = x[i].d; -#ifdef GGML_CUDA_F16 - const float2 tmp = __half22float2(__hmul2(dm4, ds8)); - const float d4d8 = tmp.x; - const float m4s8 = tmp.y; -#else - const float2 dm4f = __half22float2(dm4); - const float2 ds8f = __half22float2(ds8); - const float d4d8 = dm4f.x * ds8f.x; - const float m4s8 = dm4f.y * ds8f.y; -#endif // GGML_CUDA_F16 + const uint8_t * ql = x[i].ql + 64*ip + il; + const uint8_t qh = x[i].qh[32*ip + il]; + const int8_t * sc = x[i].scales + is; - // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it - return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); + y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32); + y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); + y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); + y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} -#define VDR_Q5_0_Q8_1_MMVQ 2 -#define VDR_Q5_0_Q8_1_MMQ 4 + // assume 32 threads + const int tid = threadIdx.x; + const int ip = tid/16; // 0 or 1 + const int il = tid - 16*ip; // 0...15 -template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl( - const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) { + dst_t * y = yy + i*QK_K + 16*ip + il; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + const float d = x[i].d; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits - vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 - vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 - vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 - vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 - sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values + const uint8_t ql = x[i].ql[16*ip + il]; + const uint8_t qh = x[i].qh[il] >> (2*ip); + const int8_t * sc = x[i].scales; - int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits - vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 - vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 - vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 - vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 - sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values + y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); + y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); +#endif +} + +inline bool ggml_cuda_supports_mmq(enum ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + return true; + default: + return false; } +} - const float2 ds8f = __half22float2(ds8); +template +static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - // second part effectively subtracts 16 from each quant value - return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y); + const int i = blockIdx.x; + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + assert(false); +#endif -#define VDR_Q5_1_Q8_1_MMVQ 2 -#define VDR_Q5_1_Q8_1_MMQ 4 +} -template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl( - const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) { +template +static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + const int i = blockIdx.x; + const block_iq2_xs * x = (const block_iq2_xs *) vx; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits - vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 - vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 - vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 - vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 - sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint16_t * q2 = x[i].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif - int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits - vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 - vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 - vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 - vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 - sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values - } +} -#ifdef GGML_CUDA_F16 - const float2 tmp = __half22float2(__hmul2(dm5, ds8)); - const float d5d8 = tmp.x; - const float m5s8 = tmp.y; -#else - const float2 dm5f = __half22float2(dm5); - const float2 ds8f = __half22float2(ds8); - const float d5d8 = dm5f.x * ds8f.x; - const float m5s8 = dm5f.y * ds8f.y; -#endif // GGML_CUDA_F16 +template +static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it - return sumi*d5d8 + m5s8 / (QI5_1 / vdr); + const int i = blockIdx.x; + const block_iq2_s * x = (const block_iq2_s *) vx; + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + assert(false); +#endif -#define VDR_Q8_0_Q8_1_MMVQ 2 -#define VDR_Q8_0_Q8_1_MMQ 8 +} -template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl( - const int * v, const int * u, const float & d8_0, const float & d8_1) { +template +static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + const int i = blockIdx.x; + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - // SIMD dot product of quantized values - sumi = __dp4a(v[i], u[i], sumi); + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * q3 = x[i].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } - - return d8_0*d8_1 * sumi; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A + assert(false); +#endif + } -template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl( - const int * v, const int * u, const half2 & dm8, const half2 & ds8) { +template +static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + const int i = blockIdx.x; + const block_iq3_s * x = (const block_iq3_s *) vx; -#pragma unroll - for (int i = 0; i < vdr; ++i) { - // SIMD dot product of quantized values - sumi = __dp4a(v[i], u[i], sumi); + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * qs = x[i].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); + const uint8_t signs = x[i].signs[4*ib + il]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } - -#ifdef GGML_CUDA_F16 - const float2 tmp = __half22float2(__hmul2(dm8, ds8)); - const float d8d8 = tmp.x; - const float m8s8 = tmp.y; #else - const float2 dm8f = __half22float2(dm8); - const float2 ds8f = __half22float2(ds8); - const float d8d8 = dm8f.x * ds8f.x; - const float m8s8 = dm8f.y * ds8f.y; -#endif // GGML_CUDA_F16 + assert(false); +#endif - // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it - return sumi*d8d8 + m8s8 / (QI8_1 / vdr); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define VDR_Q2_K_Q8_1_MMVQ 1 -#define VDR_Q2_K_Q8_1_MMQ 2 - -// contiguous v/x values -static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( - const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales, - const half2 & dm2, const float * __restrict__ d8) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; - float sumf_m = 0.0f; - -#pragma unroll - for (int i = 0; i < QR2_K; ++i) { - const int sc = scales[2*i]; - - const int vi = (v >> (2*i)) & 0x03030303; +template +static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product + const int i = blockIdx.x; + const block_iq1_s * x = (const block_iq1_s *) vx; - // fill int with 4x m - int m = sc >> 4; - m |= m << 8; - m |= m << 16; - sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; + const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + for (int j = 0; j < 8; ++j) { + y[j] = d * (q[j] + delta); } - - const float2 dm2f = __half22float2(dm2); - - return dm2f.x*sumf_d - dm2f.y*sumf_m; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A + assert(false); +#endif + } -// contiguous u/y values -static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales, - const half2 & dm2, const float & d8) { +static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi_d = 0; - int sumi_m = 0; +template +static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { -#pragma unroll - for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) { - int sumi_d_sc = 0; + const int i = blockIdx.x; + const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); - const int sc = scales[i0 / (QI8_1/2)]; + const int tid = threadIdx.x; + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = (float)x[ib].d; + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; + y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; + } - // fill int with 4x m - int m = sc >> 4; - m |= m << 8; - m |= m << 16; +} -#pragma unroll - for (int i = i0; i < i0 + QI8_1/2; ++i) { - sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product - sumi_m = __dp4a(m, u[i], sumi_m); // multiply sum of q8_1 values with m - } +#if QK_K != 64 +template +static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int i = blockIdx.x; + const block_iq4_xs * x = (const block_iq4_xs *)vx; - sumi_d += sumi_d_sc * (sc & 0xF); + const int tid = threadIdx.x; + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[i].qs + 16*ib + 4*il; + const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; + y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } - - const float2 dm2f = __half22float2(dm2); - - return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } +#endif -#define VDR_Q3_K_Q8_1_MMVQ 1 -#define VDR_Q3_K_Q8_1_MMQ 2 +static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { -// contiguous v/x values -static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( - const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales, - const int & scale_offset, const float & d3, const float * __restrict__ d8) { + static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf = 0.0f; + const int row = blockIdx.x*blockDim.y + threadIdx.y; + if (row > nrows) return; -#pragma unroll - for (int i = 0; i < QR3_K; ++i) { - const int isc = scale_offset + 2*i; + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - const int isc_low = isc % (QK_K/32); - const int sc_shift_low = 4 * (isc / (QK_K/32)); - const int sc_low = (scales[isc_low] >> sc_shift_low) & 0xF; + const block_q2_K * x = (const block_q2_K *)vx + ib0; - const int isc_high = isc % (QK_K/64); - const int sc_shift_high = 2 * (isc / (QK_K/64)); - const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4; + float tmp = 0; // partial sum for thread in warp - const int sc = (sc_low | sc_high) - 32; +#if QK_K == 256 + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - const int vil = (vl >> (2*i)) & 0x03030303; + const int step = 16/K_QUANTS_PER_ITERATION; - const int vih = ((vh >> i) << 2) & 0x04040404; + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0...15 or 0...7 - const int vi = __vsubss4(vil, vih); + const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 + const int q_offset = 32*im + l0; + const int s_offset = 8*im; + const int y_offset = 128*im + l0; - sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product - } + uint32_t aux[4]; + const uint8_t * d = (const uint8_t *)aux; + const uint8_t * m = (const uint8_t *)(aux + 2); - return d3 * sumf; -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { -// contiguous u/y values -static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales, - const float & d3, const float & d8) { + const float * y = yy + i * QK_K + y_offset; + const uint8_t * q = x[i].qs + q_offset; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi = 0; + const float dall = __low2half(x[i].dm); + const float dmin = __high2half(x[i].dm); -#pragma unroll - for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) { - int sumi_sc = 0; + const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset); + aux[0] = a[0] & 0x0f0f0f0f; + aux[1] = a[1] & 0x0f0f0f0f; + aux[2] = (a[0] >> 4) & 0x0f0f0f0f; + aux[3] = (a[1] >> 4) & 0x0f0f0f0f; + + float sum1 = 0, sum2 = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) + + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) + + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) + + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) + + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) + + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) + + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) + +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); + sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] + + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - for (int i = i0; i < i0 + QI8_1/2; ++i) { - sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product } + tmp += dall * sum1 - dmin * sum2; - sumi += sumi_sc * scales[i0 / (QI8_1/2)]; } - - return d3*d8 * sumi; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int offset = tid * K_QUANTS_PER_ITERATION; -#define VDR_Q4_K_Q8_1_MMVQ 2 -#define VDR_Q4_K_Q8_1_MMQ 8 + uint32_t uaux[2]; + const uint8_t * d = (const uint8_t *)uaux; -// contiguous v/x values -static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( - const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, - const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) { + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; - float sumf_m = 0.0f; + const float * y = yy + i * QK_K + offset; + const uint8_t * q = x[i].qs + offset; + const uint32_t * s = (const uint32_t *)x[i].scales; -#pragma unroll - for (int i = 0; i < QR4_K; ++i) { - const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F; - const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F; + uaux[0] = s[0] & 0x0f0f0f0f; + uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product - const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u + const float2 dall = __half22float2(x[i].dm); - sumf_d += d8[i] * (dot1 * sc[i]); - sumf_m += d8[i] * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values + float sum1 = 0, sum2 = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + const uint8_t ql = q[l]; + sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) + + y[l+16] * d[1] * ((ql >> 2) & 3) + + y[l+32] * d[2] * ((ql >> 4) & 3) + + y[l+48] * d[3] * ((ql >> 6) & 3); + sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; + } + tmp += dall.x * sum1 - dall.y * sum2; } +#endif - const float2 dm4f = __half22float2(dm4); - - return dm4f.x*sumf_d - dm4f.y*sumf_m; + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A + if (threadIdx.x == 0) { + dst[row] = tmp; + } } -// contiguous u/y values -static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, - const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { +static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; - float sumf_m = 0.0f; + const int row = blockIdx.x*blockDim.y + threadIdx.y; + if (row > nrows) return; -#pragma unroll - for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) { - int sumi_d = 0; - -#pragma unroll - for (int j = 0; j < QI8_1; ++j) { - sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product - } - - const float2 ds8f = __half22float2(ds8[i]); + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - sumf_d += ds8f.x * (sc[i] * sumi_d); - sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val - } + const block_q3_K * x = (const block_q3_K *)vx + ib0; - const float2 dm4f = __half22float2(dm4); + float tmp = 0; // partial sum for thread in warp - return dm4f.x*sumf_d - dm4f.y*sumf_m; +#if QK_K == 256 -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + const uint16_t kmask1 = 0x0303; + const uint16_t kmask2 = 0x0f0f; -#define VDR_Q5_K_Q8_1_MMVQ 2 -#define VDR_Q5_K_Q8_1_MMQ 8 + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 -// contiguous v/x values -static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( - const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc, - const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) { + const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop + const int step = 16/K_QUANTS_PER_ITERATION; + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0....15 or 0...7 -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; - float sumf_m = 0.0f; + const uint8_t m = 1 << (4*im); -#pragma unroll - for (int i = 0; i < QR5_K; ++i) { - const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F; - const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F; + const int l0 = n*in; // 0...15 or 0...14 in steps of 2 + const int q_offset = 32*im + l0; + const int y_offset = 128*im + l0; - const int vh0i = ((vh[0] >> i) << 4) & 0x10101010; - const int vh1i = ((vh[1] >> i) << 4) & 0x10101010; + uint16_t utmp[4]; + const int8_t * s = (const int8_t *)utmp; - const int v0i = vl0i | vh0i; - const int v1i = vl1i | vh1i; + const uint16_t s_shift = 4*im; - const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product - const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - sumf_d += d8[i] * (dot1 * sc[i]); - sumf_m += d8[i] * (dot2 * m[i]); + const float * y = yy + i * QK_K + y_offset; + const uint8_t * q = x[i].qs + q_offset; + const uint8_t * h = x[i].hmask + l0; - } + const uint16_t * a = (const uint16_t *)x[i].scales; + utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); + utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); + utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); + utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); - const float2 dm5f = __half22float2(dm5); + const float d = x[i].d; - return dm5f.x*sumf_d - dm5f.y*sumf_m; + float sum = 0; + for (int l = 0; l < n; ++l) { + sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) + + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) + + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) + + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); + sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) + + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) + + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) + + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); + } + tmp += d * sum; + } #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} -// contiguous u/y values -static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, - const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 + const int in = offset/8; // 0 or 1 + const int im = offset%8; // 0...7 -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; - float sumf_m = 0.0f; + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { -#pragma unroll - for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) { - int sumi_d = 0; + const float * y = yy + i * QK_K + offset; + const uint8_t * q = x[i].qs + offset; + const uint8_t * s = x[i].scales; -#pragma unroll - for (int j = 0; j < QI8_1; ++j) { - sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product + const float dall = (float)x[i].d; + + float sum = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + const uint8_t hl = x[i].hmask[im+l] >> in; + const uint8_t ql = q[l]; + sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) + + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) + + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) + + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); } + tmp += sum; + } +#endif - const float2 ds8f = __half22float2(ds8[i]); + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); - sumf_d += ds8f.x * (sc[i] * sumi_d); - sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val + if (threadIdx.x == 0) { + dst[row] = tmp; } +} - const float2 dm4f = __half22float2(dm4); - - return dm4f.x*sumf_d - dm4f.y*sumf_m; +static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + const int row = blockIdx.x*blockDim.y + threadIdx.y; + if (row > nrows) return; + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; -#define VDR_Q6_K_Q8_1_MMVQ 1 -#define VDR_Q6_K_Q8_1_MMQ 8 + const block_q4_K * x = (const block_q4_K *)vx + ib0; -// contiguous v/x values -static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( - const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales, - const float & d, const float * __restrict__ d8) { +#if QK_K == 256 + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf = 0.0f; + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 -#pragma unroll - for (int i = 0; i < QR6_K; ++i) { - const int sc = scales[4*i]; + const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - const int vil = (vl >> (4*i)) & 0x0F0F0F0F; + const int il = tid/step; // 0...3 + const int ir = tid - step*il; // 0...7 or 0...3 + const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - const int vih = ((vh >> (4*i)) << 4) & 0x30303030; + const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int in = il%2; - const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32 + const int l0 = n*(2*ir + in); + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; - sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product - } + uint16_t aux[4]; + const uint8_t * sc = (const uint8_t *)aux; - return d*sumf; +#if K_QUANTS_PER_ITERATION == 2 + uint32_t q32[4]; + const uint8_t * q4 = (const uint8_t *)q32; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + uint16_t q16[4]; + const uint8_t * q4 = (const uint8_t *)q16; +#endif -// contiguous u/y values -static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc, - const float & d6, const float * __restrict__ d8) { + float tmp = 0; // partial sum for thread in warp -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - float sumf_d = 0.0f; + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { -#pragma unroll - for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) { - int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale + const float * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; -#pragma unroll - for (int i = i0; i < i0 + 2; ++i) { - sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product - sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product + const float dall = __low2half(x[i].dm); + const float dmin = __high2half(x[i].dm); - sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product - sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product - } + const uint16_t * a = (const uint16_t *)x[i].scales; + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y); - } +#if K_QUANTS_PER_ITERATION == 2 + const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset); + const uint32_t * q2 = q1 + 16; - return d6 * sumf_d; + q32[0] = q1[0] & 0x0f0f0f0f; + q32[1] = q1[0] & 0xf0f0f0f0; + q32[2] = q2[0] & 0x0f0f0f0f; + q32[3] = q2[0] & 0xf0f0f0f0; + float4 s = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + for (int l = 0; l < 4; ++l) { + s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4]; + s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12]; + smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + } + tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; #else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} + const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset); + const uint16_t * q2 = q1 + 32; -static __device__ __forceinline__ float vec_dot_q4_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[0] & 0xf0f0; + q16[2] = q2[0] & 0x0f0f; + q16[3] = q2[0] & 0xf0f0; - const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + float4 s = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + for (int l = 0; l < 2; ++l) { + s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; + s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6]; + smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + } + tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; +#endif - int v[VDR_Q4_0_Q8_1_MMVQ]; - int u[2*VDR_Q4_0_Q8_1_MMVQ]; - -#pragma unroll - for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_uint8(bq4_0->qs, iqs + i); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0); } +#else + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - return vec_dot_q4_0_q8_1_impl(v, u, bq4_0->d, bq8_1->ds); -} - -template static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; (void)x_sc; - - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0]; - - *x_ql = tile_x_qs; - *x_dm = (half2 *) tile_x_d; -} - -template static __device__ __forceinline__ void load_tiles_q4_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; (void)x_sc; - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI4_0; - const int kqsx = k % QI4_0; - - const block_q4_0 * bx0 = (const block_q4_0 *) vx; + const int step = tid * K_QUANTS_PER_ITERATION; - float * x_dmf = (float *) x_dm; + uint16_t aux16[2]; + const uint8_t * s = (const uint8_t *)aux16; -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + float tmp = 0; - if (need_check) { - i = min(i, i_max); + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + const uint8_t * q = x[i].qs + step; + const float * y = yy + i*QK_K + step; + const uint16_t * a = (const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + const float d = (float)x[i].dm[0]; + const float m = (float)x[i].dm[1]; + float sum = 0.f; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) + + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) + + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) + + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); } - - const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); - // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d; + tmp += sum; } - const int blocks_per_tile_x_row = WARP_SIZE / QI4_0; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) { - int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } +#endif - const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd; + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); - x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d; + if (tid == 0) { + dst[row] = tmp; } } -static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; (void)x_sc; - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const float * x_dmf = (const float *) x_dm; - - int u[2*VDR_Q4_0_Q8_1_MMQ]; +static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) { -#pragma unroll - for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE]; - } + const int row = blockIdx.x; + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - return vec_dot_q4_0_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0], - y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} + const block_q5_K * x = (const block_q5_K *)vx + ib0; -static __device__ __forceinline__ float vec_dot_q4_1_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + float tmp = 0; // partial sum for thread in warp - const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; +#if QK_K == 256 + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; - int v[VDR_Q4_1_Q8_1_MMVQ]; - int u[2*VDR_Q4_1_Q8_1_MMVQ]; + const int tid = threadIdx.x/2; // 0...15 + const int ix = threadIdx.x%2; -#pragma unroll - for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1); - } + const int il = tid/4; // 0...3 + const int ir = tid - 4*il;// 0...3 + const int n = 2; - return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); -} + const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int in = il%2; -template static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; (void)x_sc; + const int l0 = n*(2*ir + in); + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1]; + const uint8_t hm1 = 1 << (2*im); + const uint8_t hm2 = hm1 << 4; - *x_ql = tile_x_qs; - *x_dm = tile_x_dm; -} + uint16_t aux[4]; + const uint8_t * sc = (const uint8_t *)aux; -template static __device__ __forceinline__ void load_tiles_q4_1( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; (void)x_sc; + uint16_t q16[8]; + const uint8_t * q4 = (const uint8_t *)q16; - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); + for (int i = ix; i < num_blocks_per_row; i += 2) { - const int kbx = k / QI4_1; - const int kqsx = k % QI4_1; + const uint8_t * ql1 = x[i].qs + q_offset; + const uint8_t * qh = x[i].qh + l0; + const float * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; - const block_q4_1 * bx0 = (const block_q4_1 *) vx; + const float dall = __low2half(x[i].dm); + const float dmin = __high2half(x[i].dm); -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + const uint16_t * a = (const uint16_t *)x[i].scales; + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - if (need_check) { - i = min(i, i_max); + float4 sum = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + const uint16_t * q1 = (const uint16_t *)ql1; + const uint16_t * q2 = q1 + 32; + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[8] & 0x0f0f; + q16[2] = (q1[0] >> 4) & 0x0f0f; + q16[3] = (q1[8] >> 4) & 0x0f0f; + q16[4] = q2[0] & 0x0f0f; + q16[5] = q2[8] & 0x0f0f; + q16[6] = (q2[0] >> 4) & 0x0f0f; + q16[7] = (q2[8] >> 4) & 0x0f0f; + for (int l = 0; l < n; ++l) { + sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) + + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0)); + sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) + + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0)); + sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) + + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0)); + sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) + + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0)); + smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] + + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; } - - const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; } - const int blocks_per_tile_x_row = WARP_SIZE / QI4_1; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) { - int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row; +#else + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int step = tid * K_QUANTS_PER_ITERATION; + const int im = step/8; + const int in = step%8; - if (need_check) { - i = min(i, i_max); + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + const uint8_t * q = x[i].qs + step; + const int8_t * s = x[i].scales; + const float * y = yy + i*QK_K + step; + const float d = x[i].d; + float sum = 0.f; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + const uint8_t h = x[i].qh[in+j] >> im; + sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) + + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) + + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) + + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); } + tmp += sum; + } +#endif - const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd; + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); - x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm; + if (threadIdx.x == 0) { + dst[row] = tmp; } } -static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; (void)x_sc; +static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - int u[2*VDR_Q4_1_Q8_1_MMQ]; + const int row = blockIdx.x*blockDim.y + threadIdx.y; + if (row > nrows) return; -#pragma unroll - for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE]; - } + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - return vec_dot_q4_1_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1], - y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} + const block_q6_K * x = (const block_q6_K *)vx + ib0; -static __device__ __forceinline__ float vec_dot_q5_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 - const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; - - int vl[VDR_Q5_0_Q8_1_MMVQ]; - int vh[VDR_Q5_0_Q8_1_MMVQ]; - int u[2*VDR_Q5_0_Q8_1_MMVQ]; - -#pragma unroll - for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) { - vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i); - vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i)); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0); - } - - return vec_dot_q5_0_q8_1_impl(vl, vh, u, bq5_0->d, bq8_1->ds); -} - -template static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; (void)x_sc; + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0]; + const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - *x_ql = tile_x_ql; - *x_dm = (half2 *) tile_x_d; -} + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0...15 or 0...7 -template static __device__ __forceinline__ void load_tiles_q5_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; (void)x_sc; +#if K_QUANTS_PER_ITERATION == 1 + const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 + const int is = 0; +#else + const int l0 = 4 * in; // 0, 4, 8, ..., 28 + const int is = in / 4; +#endif + const int ql_offset = 64*im + l0; + const int qh_offset = 32*im + l0; + const int s_offset = 8*im + is; + const int y_offset = 128*im + l0; - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); + float tmp = 0; // partial sum for thread in warp - const int kbx = k / QI5_0; - const int kqsx = k % QI5_0; + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const block_q5_0 * bx0 = (const block_q5_0 *) vx; + const float * y = yy + i * QK_K + y_offset; + const uint8_t * ql = x[i].ql + ql_offset; + const uint8_t * qh = x[i].qh + qh_offset; + const int8_t * s = x[i].scales + s_offset; -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + const float d = x[i].d; - if (need_check) { - i = min(i, i_max); +#if K_QUANTS_PER_ITERATION == 1 + float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) + + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) + + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) + + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) + + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) + + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) + + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) + +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); + tmp += sum; +#else + float sum = 0; + for (int l = 0; l < 4; ++l) { + sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) + + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) + + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) + + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); } + tmp += sum; +#endif - const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx; + } - const int ql = get_int_from_uint8(bxi->qs, kqsx); - const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0)); +#else - int qs0 = (ql >> 0) & 0x0F0F0F0F; - qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 - qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 - qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 - qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 - qs0 = __vsubss4(qs0, 0x10101010); // subtract 16 + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 - x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; + const int step = tid * K_QUANTS_PER_ITERATION; - int qs1 = (ql >> 4) & 0x0F0F0F0F; - qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 - qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 - qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 - qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 - qs1 = __vsubss4(qs1, 0x10101010); // subtract 16 + float tmp = 0; // partial sum for thread in warp - x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; - } + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const int blocks_per_tile_x_row = WARP_SIZE / QI5_0; - const int kbxd = k % blocks_per_tile_x_row; - float * x_dmf = (float *) x_dm; + const float * y = yy + i * QK_K + step; + const uint8_t * ql = x[i].ql + step; + const uint8_t * qh = x[i].qh + step; + const int8_t * s = x[i].scales; -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) { - int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row; + const float d = x[i+0].d; - if (need_check) { - i = min(i, i_max); + float sum = 0; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) + + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) + + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) + + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); } + tmp += sum; - const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d; } -} - -static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; (void)x_sc; - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0; - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; +#endif - int u[2*VDR_Q5_0_Q8_1_MMQ]; + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); -#pragma unroll - for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; + if (tid == 0) { + dst[row] = tmp; } - - return vec_dot_q8_0_q8_1_impl - (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); } -static __device__ __forceinline__ float vec_dot_q5_1_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - - const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; - - int vl[VDR_Q5_1_Q8_1_MMVQ]; - int vh[VDR_Q5_1_Q8_1_MMVQ]; - int u[2*VDR_Q5_1_Q8_1_MMVQ]; - -#pragma unroll - for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) { - vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i); - vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i)); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1); - } +static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const half * x = (const half *) vx; - return vec_dot_q5_1_q8_1_impl(vl, vh, u, bq5_1->dm, bq8_1->ds); + // automatic half -> float type cast if dfloat == float + v.x = x[ib + iqs + 0]; + v.y = x[ib + iqs + 1]; } -template static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; (void)x_sc; +static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) { + const int ix = blockDim.x*blockIdx.x + threadIdx.x; - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1]; + if (ix >= kx_padded) { + return; + } - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; -} + const int iy = blockDim.y*blockIdx.y + threadIdx.y; -template static __device__ __forceinline__ void load_tiles_q5_1( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; (void)x_sc; + const int i_padded = iy*kx_padded + ix; - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); + block_q8_1 * y = (block_q8_1 *) vy; - const int kbx = k / QI5_1; - const int kqsx = k % QI5_1; + const int ib = i_padded / QK8_1; // block index + const int iqs = i_padded % QK8_1; // quant index - const block_q5_1 * bx0 = (const block_q5_1 *) vx; + const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; + float amax = fabsf(xi); + float sum = xi; -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + amax = warp_reduce_max(amax); + sum = warp_reduce_sum(sum); - if (need_check) { - i = min(i, i_max); - } + const float d = amax / 127; + const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); - const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx; + y[ib].qs[iqs] = q; - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); - const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1)); + if (iqs > 0) { + return; + } - int qs0 = (ql >> 0) & 0x0F0F0F0F; - qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 - qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 - qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 - qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + reinterpret_cast(y[ib].ds.x) = d; + reinterpret_cast(y[ib].ds.y) = sum; +} - x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; +template +static __global__ void k_get_rows( + const void * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ + /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ + /*size_t s0,*/ size_t s1, size_t s2, size_t s3, + /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12/*, size_t s13*/) { - int qs1 = (ql >> 4) & 0x0F0F0F0F; - qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 - qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 - qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 - qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2; + const int i10 = blockDim.y*blockIdx.y + threadIdx.y; + const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12; + const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12; - x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; + if (i00 >= ne00) { + return; } - const int blocks_per_tile_x_row = WARP_SIZE / QI5_1; - const int kbxd = k % blocks_per_tile_x_row; + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) { - int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row; + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; - if (need_check) { - i = min(i, i_max); - } + const int ib = i00/qk; // block index + const int iqs = (i00%qk)/qr; // quant index + const int iybs = i00 - i00%qk; // dst block start index + const int y_offset = qr == 1 ? 1 : qk/2; - const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd; + // dequantize + dfloat2 v; + dequantize_kernel(src0_row, ib, iqs, v); - x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm; - } + dst_row[iybs + iqs + 0] = v.x; + dst_row[iybs + iqs + y_offset] = v.y; } -static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; (void)x_sc; - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1; +template +static __global__ void k_get_rows_float( + const src0_t * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ + /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ + /*size_t s0,*/ size_t s1, size_t s2, size_t s3, + /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12/*, size_t s13*/) { - int u[2*VDR_Q5_1_Q8_1_MMQ]; + const int i00 = blockIdx.x*blockDim.x + threadIdx.x; + const int i10 = blockDim.y*blockIdx.y + threadIdx.y; + const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12; + const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12; -#pragma unroll - for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE]; + if (i00 >= ne00) { + return; } - return vec_dot_q8_1_q8_1_impl - (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; -static __device__ __forceinline__ float vec_dot_q8_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03); - const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; + dst_row[i00] = src0_row[i00]; +} - int v[VDR_Q8_0_Q8_1_MMVQ]; - int u[VDR_Q8_0_Q8_1_MMVQ]; +template +static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { + const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x); -#pragma unroll - for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_int8(bq8_0->qs, iqs + i); - u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + if (i >= k) { + return; } - return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); + const int ib = i/qk; // block index + const int iqs = (i%qk)/qr; // quant index + const int iybs = i - i%qk; // y block start index + const int y_offset = qr == 1 ? 1 : qk/2; + + // dequantize + dfloat2 v; + dequantize_kernel(vx, ib, iqs, v); + + y[iybs + iqs + 0] = v.x; + y[iybs + iqs + y_offset] = v.y; } -template static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; (void)x_sc; +template +static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0]; + if (i >= k) { + return; + } - *x_ql = tile_x_qs; - *x_dm = (half2 *) tile_x_d; -} + const src_t * x = (src_t *) vx; -template static __device__ __forceinline__ void load_tiles_q8_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; (void)x_sc; + y[i] = x[i]; +} - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); +template +static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) { +#if __CUDA_ARCH__ >= CC_PASCAL + constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; - const int kbx = k / QI8_0; - const int kqsx = k % QI8_0; - float * x_dmf = (float *) x_dm; + const int i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; + const int * x0 = ((int *) vx) + blockIdx.x * nint; + half2 * y2 = (half2 *) (y + i0); - const block_q8_0 * bx0 = (const block_q8_0 *) vx; + __shared__ int vals[nint]; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); + for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) { + if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) { + break; } - const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx); + const int ix = ix0 + threadIdx.x; + vals[ix] = x0[ix]; } - const int blocks_per_tile_x_row = WARP_SIZE / QI8_0; - const int kbxd = k % blocks_per_tile_x_row; - #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) { - int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); + for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) { + if (need_check && i0 + iy + 2*threadIdx.x >= k) { + return; } - const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd; + const half * b0 = ((const half *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0); + const half d = *b0; + const char2 qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)]; - x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d; + y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d)); } +#else + GGML_UNUSED(vx); + GGML_UNUSED(y); + GGML_UNUSED(k); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_PASCAL } -static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; (void)x_sc; - - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; - - return vec_dot_q8_0_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0], - y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]); -} - -static __device__ __forceinline__ float vec_dot_q2_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - - const block_q2_K * bq2_K = (const block_q2_K *) vbq; +// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called +// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q - const int bq8_offset = QR2_K * (iqs / QI8_1); - const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); +#define VDR_Q4_0_Q8_1_MMVQ 2 +#define VDR_Q4_0_Q8_1_MMQ 4 - const uint8_t * scales = bq2_K->scales + scale_offset; +template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl( + const int * v, const int * u, const float & d4, const half2 & ds8) { - const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs); - int u[QR2_K]; - float d8[QR2_K]; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; #pragma unroll - for (int i = 0; i < QR2_K; ++ i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); - d8[i] = __low2float(bq8_1[bq8_offset + i].ds); - } - - return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8); -} + for (int i = 0; i < vdr; ++i) { + const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; + const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; -template static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; + // SIMD dot product of quantized values + sumi = __dp4a(vi0, u[2*i+0], sumi); + sumi = __dp4a(vi1, u[2*i+1], sumi); + } - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; + const float2 ds8f = __half22float2(ds8); - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; + // second part effectively subtracts 8 from each quant value + return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -template static __device__ __forceinline__ void load_tiles_q2_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); +#define VDR_Q4_1_Q8_1_MMVQ 2 +#define VDR_Q4_1_Q8_1_MMQ 4 - const int kbx = k / QI2_K; - const int kqsx = k % QI2_K; +template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl( + const int * v, const int * u, const half2 & dm4, const half2 & ds8) { - const block_q2_K * bx0 = (const block_q2_K *) vx; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx; + for (int i = 0; i < vdr; ++i) { + const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; + const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + // SIMD dot product of quantized values + sumi = __dp4a(vi0, u[2*i+0], sumi); + sumi = __dp4a(vi1, u[2*i+1], sumi); } - const int blocks_per_tile_x_row = WARP_SIZE / QI2_K; - const int kbxd = k % blocks_per_tile_x_row; +#ifdef GGML_CUDA_F16 + const float2 tmp = __half22float2(__hmul2(dm4, ds8)); + const float d4d8 = tmp.x; + const float m4s8 = tmp.y; +#else + const float2 dm4f = __half22float2(dm4); + const float2 ds8f = __half22float2(ds8); + const float d4d8 = dm4f.x * ds8f.x; + const float m4s8 = dm4f.y * ds8f.y; +#endif // GGML_CUDA_F16 -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) { - int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y; + // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it + return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - if (need_check) { - i = min(i, i_max); - } +#define VDR_Q5_0_Q8_1_MMVQ 2 +#define VDR_Q5_0_Q8_1_MMQ 4 - const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd; +template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl( + const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) { - x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm; - } +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { - int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); + for (int i = 0; i < vdr; ++i) { + int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits + vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 + vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 + vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 + vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 + sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values - if (need_check) { - i = min(i, i_max); - } + int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits + vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 + vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 + vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 + vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 + sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values + } - const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4); + const float2 ds8f = __half22float2(ds8); - x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4)); - } + // second part effectively subtracts 16 from each quant value + return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; - - const int kbx = k / QI2_K; - const int ky = (k % QI2_K) * QR2_K; - const float * y_df = (const float *) y_ds; +#define VDR_Q5_1_Q8_1_MMVQ 2 +#define VDR_Q5_1_Q8_1_MMQ 4 - int v[QR2_K*VDR_Q2_K_Q8_1_MMQ]; +template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl( + const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) { - const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2); - const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2)); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; #pragma unroll - for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) { - v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303; + for (int i = 0; i < vdr; ++i) { + int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits + vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 + vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 + vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 + vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 + sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values + + int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits + vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 + vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 + vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 + vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 + sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values } - const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4; +#ifdef GGML_CUDA_F16 + const float2 tmp = __half22float2(__hmul2(dm5, ds8)); + const float d5d8 = tmp.x; + const float m5s8 = tmp.y; +#else + const float2 dm5f = __half22float2(dm5); + const float2 ds8f = __half22float2(ds8); + const float d5d8 = dm5f.x * ds8f.x; + const float m5s8 = dm5f.y * ds8f.y; +#endif // GGML_CUDA_F16 - const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE; - return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]); + // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it + return sumi*d5d8 + m5s8 / (QI5_1 / vdr); + +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -static __device__ __forceinline__ float vec_dot_q3_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#define VDR_Q8_0_Q8_1_MMVQ 2 +#define VDR_Q8_0_Q8_1_MMQ 8 - const block_q3_K * bq3_K = (const block_q3_K *) vbq; +template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl( + const int * v, const int * u, const float & d8_0, const float & d8_1) { - const int bq8_offset = QR3_K * (iqs / (QI3_K/2)); - const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; - const float d = bq3_K->d; +#pragma unroll + for (int i = 0; i < vdr; ++i) { + // SIMD dot product of quantized values + sumi = __dp4a(v[i], u[i], sumi); + } - const int vl = get_int_from_uint8(bq3_K->qs, iqs); + return d8_0*d8_1 * sumi; +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted - const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset; +template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl( + const int * v, const int * u, const half2 & dm8, const half2 & ds8) { - int u[QR3_K]; - float d8[QR3_K]; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; #pragma unroll - for (int i = 0; i < QR3_K; ++i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); - d8[i] = __low2float(bq8_1[bq8_offset + i].ds); + for (int i = 0; i < vdr; ++i) { + // SIMD dot product of quantized values + sumi = __dp4a(v[i], u[i], sumi); } - return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8); -} - -template static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K]; - __shared__ int tile_x_qh[mmq_y * (WARP_SIZE/2) + mmq_y/2]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; +#ifdef GGML_CUDA_F16 + const float2 tmp = __half22float2(__hmul2(dm8, ds8)); + const float d8d8 = tmp.x; + const float m8s8 = tmp.y; +#else + const float2 dm8f = __half22float2(dm8); + const float2 ds8f = __half22float2(ds8); + const float d8d8 = dm8f.x * ds8f.x; + const float m8s8 = dm8f.y * ds8f.y; +#endif // GGML_CUDA_F16 - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_qh = tile_x_qh; - *x_sc = tile_x_sc; + // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it + return sumi*d8d8 + m8s8 / (QI8_1 / vdr); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -template static __device__ __forceinline__ void load_tiles_q3_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); +#define VDR_Q2_K_Q8_1_MMVQ 1 +#define VDR_Q2_K_Q8_1_MMQ 2 - const int kbx = k / QI3_K; - const int kqsx = k % QI3_K; +// contiguous v/x values +static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( + const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales, + const half2 & dm2, const float * __restrict__ d8) { - const block_q3_K * bx0 = (const block_q3_K *) vx; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf_d = 0.0f; + float sumf_m = 0.0f; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + for (int i = 0; i < QR2_K; ++i) { + const int sc = scales[2*i]; - if (need_check) { - i = min(i, i_max); - } + const int vi = (v >> (2*i)) & 0x03030303; - const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx; + sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); + // fill int with 4x m + int m = sc >> 4; + m |= m << 8; + m |= m << 16; + sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values } - const int blocks_per_tile_x_row = WARP_SIZE / QI3_K; - const int kbxd = k % blocks_per_tile_x_row; - float * x_dmf = (float *) x_dm; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) { - int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y; + const float2 dm2f = __half22float2(dm2); - if (need_check) { - i = min(i, i_max); - } + return dm2f.x*sumf_d - dm2f.y*sumf_m; +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd; +// contiguous u/y values +static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( + const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales, + const half2 & dm2, const float & d8) { - x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d; - } +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi_d = 0; + int sumi_m = 0; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) { - int i = i0 + i_offset * 2 + k / (WARP_SIZE/2); - - if (need_check) { - i = min(i, i_max); - } + for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) { + int sumi_d_sc = 0; - const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2); + const int sc = scales[i0 / (QI8_1/2)]; - // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted - x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2)); - } + // fill int with 4x m + int m = sc >> 4; + m |= m << 8; + m |= m << 16; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { - int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); - - if (need_check) { - i = min(i, i_max); + for (int i = i0; i < i0 + QI8_1/2; ++i) { + sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product + sumi_m = __dp4a(m, u[i], sumi_m); // multiply sum of q8_1 values with m } - const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4); + sumi_d += sumi_d_sc * (sc & 0xF); + } - const int ksc = k % (QI3_K/4); + const float2 dm2f = __half22float2(dm2); - const int ksc_low = ksc % (QI3_K/8); - const int shift_low = 4 * (ksc / (QI3_K/8)); - const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; + return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - const int ksc_high = QI3_K/8; - const int shift_high = 2 * ksc; - const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; +#define VDR_Q3_K_Q8_1_MMVQ 1 +#define VDR_Q3_K_Q8_1_MMQ 2 - const int sc = __vsubss4(sc_low | sc_high, 0x20202020); +// contiguous v/x values +static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( + const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales, + const int & scale_offset, const float & d3, const float * __restrict__ d8) { - x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc; - } -} +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf = 0.0f; -static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { +#pragma unroll + for (int i = 0; i < QR3_K; ++i) { + const int isc = scale_offset + 2*i; - const int kbx = k / QI3_K; - const int ky = (k % QI3_K) * QR3_K; - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; + const int isc_low = isc % (QK_K/32); + const int sc_shift_low = 4 * (isc / (QK_K/32)); + const int sc_low = (scales[isc_low] >> sc_shift_low) & 0xF; - const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; + const int isc_high = isc % (QK_K/64); + const int sc_shift_high = 2 * (isc / (QK_K/64)); + const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4; - int v[QR3_K*VDR_Q3_K_Q8_1_MMQ]; + const int sc = (sc_low | sc_high) - 32; -#pragma unroll - for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) { - const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2); - const int shift = 2 * ((ky % 32) / 8); - const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303; + const int vil = (vl >> (2*i)) & 0x03030303; - const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8); - const int vlh = (vh << 2) & 0x04040404; + const int vih = ((vh >> i) << 2) & 0x04040404; - v[l] = __vsubss4(vll, vlh); + const int vi = __vsubss4(vil, vih); + + sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product } - const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE; - return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]); + return d3 * sumf; +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -static __device__ __forceinline__ float vec_dot_q4_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - -#ifndef GGML_QKK_64 - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - - int v[2]; - int u[2*QR4_K]; - float d8[QR4_K]; +// contiguous u/y values +static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( + const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales, + const float & d3, const float & d8) { - // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6 - const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2)); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + int sumi = 0; - // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12 - // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44 - // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76 - // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108 +#pragma unroll + for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) { + int sumi_sc = 0; - const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); - v[0] = q4[0]; - v[1] = q4[4]; + for (int i = i0; i < i0 + QI8_1/2; ++i) { + sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product + } - const uint16_t * scales = (const uint16_t *)bq4_K->scales; - uint16_t aux[2]; - const int j = bq8_offset/2; - if (j < 2) { - aux[0] = scales[j+0] & 0x3f3f; - aux[1] = scales[j+2] & 0x3f3f; - } else { - aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); - aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); + sumi += sumi_sc * scales[i0 / (QI8_1/2)]; } - const uint8_t * sc = (const uint8_t *)aux; - const uint8_t * m = sc + 2; - - for (int i = 0; i < QR4_K; ++i) { - const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; - d8[i] = __low2float(bq8i->ds); - const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); - u[2*i+0] = q8[0]; - u[2*i+1] = q8[4]; - } + return d3*d8 * sumi; +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); +#define VDR_Q4_K_Q8_1_MMVQ 2 +#define VDR_Q4_K_Q8_1_MMQ 8 -#else +// contiguous v/x values +static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( + const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, + const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - float sumf_d = 0.0f; float sumf_m = 0.0f; - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - const uint16_t * a = (const uint16_t *)bq4_K->scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const float dall = bq4_K->dm[0]; - const float dmin = bq4_K->dm[1]; - - const float d8_1 = __low2float(bq8_1[0].ds); - const float d8_2 = __low2float(bq8_1[1].ds); - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); +#pragma unroll + for (int i = 0; i < QR4_K; ++i) { + const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F; + const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F; - const int * q4 = (const int *)bq4_K->qs + (iqs/2); - const int v1 = q4[0]; - const int v2 = q4[4]; + const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product + const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u - const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); - const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); + sumf_d += d8[i] * (dot1 * sc[i]); + sumf_m += d8[i] * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values + } - sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); - sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); + const float2 dm4f = __half22float2(dm4); - return dall * sumf_d - dmin * sumf_m; + return dm4f.x*sumf_d - dm4f.y*sumf_m; #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif -} - -template static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; - - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; } -template static __device__ __forceinline__ void load_tiles_q4_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI4_K; // == 0 if QK_K == 256 - const int kqsx = k % QI4_K; // == k if QK_K == 256 +// contiguous u/y values +static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( + const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, + const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { - const block_q4_K * bx0 = (const block_q4_K *) vx; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf_d = 0.0f; + float sumf_m = 0.0f; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; + for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) { + int sumi_d = 0; - if (need_check) { - i = min(i, i_max); +#pragma unroll + for (int j = 0; j < QI8_1; ++j) { + sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product } - const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx; + const float2 ds8f = __half22float2(ds8[i]); - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + sumf_d += ds8f.x * (sc[i] * sumi_d); + sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val } - const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 + const float2 dm4f = __half22float2(dm4); -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) { - int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y; + return dm4f.x*sumf_d - dm4f.y*sumf_m; - if (need_check) { - i = min(i, i_max); - } +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; +#define VDR_Q5_K_Q8_1_MMVQ 2 +#define VDR_Q5_K_Q8_1_MMQ 8 -#if QK_K == 256 - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif - } +// contiguous v/x values +static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( + const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc, + const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) { -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf_d = 0.0f; + float sumf_m = 0.0f; - if (need_check) { - i = min(i, i_max); - } +#pragma unroll + for (int i = 0; i < QR5_K; ++i) { + const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F; + const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F; - const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8); + const int vh0i = ((vh[0] >> i) << 4) & 0x10101010; + const int vh1i = ((vh[1] >> i) << 4) & 0x10101010; - const int * scales = (const int *) bxi->scales; + const int v0i = vl0i | vh0i; + const int v1i = vl1i | vh1i; - const int ksc = k % (WARP_SIZE/8); + const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product + const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u - // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 - int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits - scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + sumf_d += d8[i] * (dot1 * sc[i]); + sumf_m += d8[i] * (dot2 * m[i]); - x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; } -} -static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; + const float2 dm5f = __half22float2(dm5); - const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8); + return dm5f.x*sumf_d - dm5f.y*sumf_m; - const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE; - return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8, - x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -static __device__ __forceinline__ float vec_dot_q5_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - -#ifndef GGML_QKK_64 - const block_q5_K * bq5_K = (const block_q5_K *) vbq; +// contiguous u/y values +static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( + const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, + const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { - int vl[2]; - int vh[2]; - int u[2*QR5_K]; - float d8[QR5_K]; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf_d = 0.0f; + float sumf_m = 0.0f; - const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2)); - const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); - const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4)); +#pragma unroll + for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) { + int sumi_d = 0; - vl[0] = ql[0]; - vl[1] = ql[4]; +#pragma unroll + for (int j = 0; j < QI8_1; ++j) { + sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product + } - vh[0] = qh[0] >> bq8_offset; - vh[1] = qh[4] >> bq8_offset; + const float2 ds8f = __half22float2(ds8[i]); - const uint16_t * scales = (const uint16_t *)bq5_K->scales; - uint16_t aux[2]; - const int j = bq8_offset/2; - if (j < 2) { - aux[0] = scales[j+0] & 0x3f3f; - aux[1] = scales[j+2] & 0x3f3f; - } else { - aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); - aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); + sumf_d += ds8f.x * (sc[i] * sumi_d); + sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val } - const uint8_t * sc = (const uint8_t *)aux; - const uint8_t * m = sc + 2; - -#pragma unroll - for (int i = 0; i < QR5_K; ++i) { - const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; - d8[i] = __low2float(bq8i->ds); - const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); - u[2*i+0] = q8[0]; - u[2*i+1] = q8[4]; - } + const float2 dm4f = __half22float2(dm4); - return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); + return dm4f.x*sumf_d - dm4f.y*sumf_m; #else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +#define VDR_Q6_K_Q8_1_MMVQ 1 +#define VDR_Q6_K_Q8_1_MMQ 8 + +// contiguous v/x values +static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( + const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales, + const float & d, const float * __restrict__ d8) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q5_K * bq5_K = (const block_q5_K *) vbq; + float sumf = 0.0f; - const int8_t * s = bq5_K->scales; +#pragma unroll + for (int i = 0; i < QR6_K; ++i) { + const int sc = scales[4*i]; - const float d = bq5_K->d; + const int vil = (vl >> (4*i)) & 0x0F0F0F0F; - const float d8_1 = __low2half(bq8_1[0].ds); - const float d8_2 = __low2half(bq8_1[1].ds); + const int vih = ((vh >> (4*i)) << 4) & 0x30303030; - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); + const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32 - const int * ql = (const int *)bq5_K->qs + (iqs/2); - const int vl1 = ql[0]; - const int vl2 = ql[4]; + sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product + } - const int step = 4 * (iqs/2); // 0, 4, 8, 12 - const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 - const int in = step%8; // 0, 4, 0, 4 - const int vh = (*((const int *)(bq5_K->qh + in))) >> im; + return d*sumf; +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} - const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); - const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); - const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); - const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); +// contiguous u/y values +static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( + const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc, + const float & d6, const float * __restrict__ d8) { - const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + float sumf_d = 0.0f; - return d * sumf_d; +#pragma unroll + for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) { + int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale + +#pragma unroll + for (int i = i0; i < i0 + 2; ++i) { + sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product + sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product + + sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product + sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product + } + + sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y); + } + + return d6 * sumf_d; #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} -#endif +static __device__ __forceinline__ float vec_dot_q4_0_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + + const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + + int v[VDR_Q4_0_Q8_1_MMVQ]; + int u[2*VDR_Q4_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_uint8(bq4_0->qs, iqs + i); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0); + } + + return vec_dot_q4_0_q8_1_impl(v, u, bq4_0->d, bq8_1->ds); } -template static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; +template static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); + GGML_UNUSED(x_sc); - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; + __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; + __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0]; - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; + *x_ql = tile_x_qs; + *x_dm = (half2 *) tile_x_d; } -template static __device__ __forceinline__ void load_tiles_q5_K( +template static __device__ __forceinline__ void load_tiles_q4_0( const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; - + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); GGML_CUDA_ASSUME(i_offset >= 0); GGML_CUDA_ASSUME(i_offset < nwarps); GGML_CUDA_ASSUME(k >= 0); GGML_CUDA_ASSUME(k < WARP_SIZE); - const int kbx = k / QI5_K; // == 0 if QK_K == 256 - const int kqsx = k % QI5_K; // == k if QK_K == 256 + const int kbx = k / QI4_0; + const int kqsx = k % QI4_0; - const block_q5_K * bx0 = (const block_q5_K *) vx; + const block_q4_0 * bx0 = (const block_q4_0 *) vx; + + float * x_dmf = (float *) x_dm; #pragma unroll for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { @@ -3920,129 +4003,92 @@ template static __device__ __forceinlin i = min(i, i_max); } - const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx; - const int ky = QR5_K*kqsx; - - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); - const int ql0 = (ql >> 0) & 0x0F0F0F0F; - const int ql1 = (ql >> 4) & 0x0F0F0F0F; - - const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); - const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; - const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; - - const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0; - const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4); + const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx; - x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0; - x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1; + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); + // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d; } - const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) { - int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; - -#if QK_K == 256 - x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif - } + const int blocks_per_tile_x_row = WARP_SIZE / QI4_0; + const int kbxd = k % blocks_per_tile_x_row; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) { + int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row; if (need_check) { i = min(i, i_max); } - const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8); - - const int * scales = (const int *) bxi->scales; - - const int ksc = k % (WARP_SIZE/8); - - // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 - int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits - scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd; - x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; + x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d; } } -static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat( +static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8); + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const float * x_dmf = (const float *) x_dm; - const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k; - const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE; - return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8, - x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]); + int u[2*VDR_Q4_0_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE]; + } + + return vec_dot_q4_0_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0], + y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); } -static __device__ __forceinline__ float vec_dot_q6_K_q8_1( +static __device__ __forceinline__ float vec_dot_q4_1_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const block_q6_K * bq6_K = (const block_q6_K *) vbq; + const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; - const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4); - const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8); - const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4)); - - const int vl = get_int_from_uint8(bq6_K->ql, iqs); - const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift; - - const int8_t * scales = bq6_K->scales + scale_offset; - - int u[QR6_K]; - float d8[QR6_K]; + int v[VDR_Q4_1_Q8_1_MMVQ]; + int u[2*VDR_Q4_1_Q8_1_MMVQ]; #pragma unroll - for (int i = 0; i < QR6_K; ++i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); - d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds); + for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1); } - return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8); + return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); } -template static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - (void)x_qh; +template static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; + __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1]; - *x_ql = tile_x_ql; + *x_ql = tile_x_qs; *x_dm = tile_x_dm; - *x_sc = tile_x_sc; } -template static __device__ __forceinline__ void load_tiles_q6_K( +template static __device__ __forceinline__ void load_tiles_q4_1( const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - (void)x_qh; + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); GGML_CUDA_ASSUME(i_offset >= 0); GGML_CUDA_ASSUME(i_offset < nwarps); GGML_CUDA_ASSUME(k >= 0); GGML_CUDA_ASSUME(k < WARP_SIZE); - const int kbx = k / QI6_K; // == 0 if QK_K == 256 - const int kqsx = k % QI6_K; // == k if QK_K == 256 + const int kbx = k / QI4_1; + const int kqsx = k % QI4_1; - const block_q6_K * bx0 = (const block_q6_K *) vx; + const block_q4_1 * bx0 = (const block_q4_1 *) vx; #pragma unroll for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { @@ -4052,7066 +4098,7014 @@ template static __device__ __forceinlin i = min(i, i_max); } - const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx; - const int ky = QR6_K*kqsx; + const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx; - const int ql = get_int_from_uint8(bxi->ql, kqsx); - const int ql0 = (ql >> 0) & 0x0F0F0F0F; - const int ql1 = (ql >> 4) & 0x0F0F0F0F; + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } - const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); - const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; - const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; + const int blocks_per_tile_x_row = WARP_SIZE / QI4_1; + const int kbxd = k % blocks_per_tile_x_row; - const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0; - const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2); +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) { + int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row; - x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020); - x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020); + if (need_check) { + i = min(i, i_max); + } + + const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm; } +} - const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - float * x_dmf = (float *) x_dm; +static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + + int u[2*VDR_Q4_1_Q8_1_MMQ]; #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) { - int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y; + for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE]; + } + + return vec_dot_q4_1_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1], + y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); +} + +static __device__ __forceinline__ float vec_dot_q5_0_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + + const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; + + int vl[VDR_Q5_0_Q8_1_MMVQ]; + int vh[VDR_Q5_0_Q8_1_MMVQ]; + int u[2*VDR_Q5_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) { + vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i); + vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0); + } + + return vec_dot_q5_0_q8_1_impl(vl, vh, u, bq5_0->d, bq8_1->ds); +} + +template static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; + __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0]; + + *x_ql = tile_x_ql; + *x_dm = (half2 *) tile_x_d; +} + +template static __device__ __forceinline__ void load_tiles_q5_0( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI5_0; + const int kqsx = k % QI5_0; + + const block_q5_0 * bx0 = (const block_q5_0 *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; if (need_check) { i = min(i, i_max); } - const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd; + const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx; - x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d; + const int ql = get_int_from_uint8(bxi->qs, kqsx); + const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + qs0 = __vsubss4(qs0, 0x10101010); // subtract 16 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + qs1 = __vsubss4(qs1, 0x10101010); // subtract 16 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; } + const int blocks_per_tile_x_row = WARP_SIZE / QI5_0; + const int kbxd = k % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; + #pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) { + int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row; if (need_check) { i = min(i, i_max); } - const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4; + const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd; - x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8)); + x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d; } } -static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( +static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - (void)x_qh; + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0; const float * x_dmf = (const float *) x_dm; const float * y_df = (const float *) y_ds; - const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]); + int u[2*VDR_Q5_0_Q8_1_MMQ]; - const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k; - const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE; - return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); +#pragma unroll + for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; + } + + return vec_dot_q8_0_q8_1_impl + (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); } -static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( +static __device__ __forceinline__ float vec_dot_q5_1_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 - const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; -#if QR2_XXS == 8 - const int ib32 = iqs; - const uint16_t * q2 = bq2->qs + 4*ib32; - const uint8_t * aux8 = (const uint8_t *)q2; - const int8_t * q8 = bq8_1[ib32].qs; - uint32_t aux32 = q2[2] | (q2[3] << 16); - int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); - const uint8_t signs = ksigns_iq2xs[aux32 & 127]; - for (int j = 0; j < 8; ++j) { - sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - aux32 >>= 7; - } - const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f; - return d * sumi; -#else - // iqs is 0...15 - const int ib32 = iqs/2; - const int il = iqs%2; - const uint16_t * q2 = bq2->qs + 4*ib32; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f; - const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; - const int8_t * q8 = bq8_1[ib32].qs + 16*il; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); - sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); + const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; + + int vl[VDR_Q5_1_Q8_1_MMVQ]; + int vh[VDR_Q5_1_Q8_1_MMVQ]; + int u[2*VDR_Q5_1_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) { + vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i); + vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1); } - return d * (sumi1 + sumi2); -#endif -#else - assert(false); - return 0.f; -#endif + + return vec_dot_q5_1_q8_1_impl(vl, vh, u, bq5_1->dm, bq8_1->ds); } -static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 - const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; +template static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - const int ib32 = iqs; - const uint16_t * q2 = bq2->qs + 4*ib32; - const int8_t * q8 = bq8_1[ib32].qs; - const uint8_t ls1 = bq2->scales[ib32] & 0xf; - const uint8_t ls2 = bq2->scales[ib32] >> 4; - int sumi1 = 0; - for (int l = 0; l < 2; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); - const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); - const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); - sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); - sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); - q8 += 8; - } - int sumi2 = 0; - for (int l = 2; l < 4; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); - const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); - const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); - sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); - sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); - q8 += 8; - } - const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; - return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - (void) ksigns64; - assert(false); - return 0.f; -#endif -#else - (void) ksigns64; - assert(false); - return 0.f; -#endif + __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1]; + + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; } -// TODO -static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 - const block_iq2_s * bq2 = (const block_iq2_s *) vbq; +template static __device__ __forceinline__ void load_tiles_q5_1( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - const int ib32 = iqs; - const int8_t * q8 = bq8_1[ib32].qs; - const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32; - const uint8_t ls1 = bq2->scales[ib32] & 0xf; - const uint8_t ls2 = bq2->scales[ib32] >> 4; - int sumi1 = 0; - for (int l = 0; l < 2; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); - const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid[1] ^ signs1, signs1); - sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); - sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); - q8 += 8; - } - int sumi2 = 0; - for (int l = 2; l < 4; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); - const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid[1] ^ signs1, signs1); - sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); - sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); - q8 += 8; - } - const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; - return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - (void) ksigns64; - assert(false); - return 0.f; -#endif -#else - (void) ksigns64; - assert(false); - return 0.f; -#endif -} + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); -static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 - const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + const int kbx = k / QI5_1; + const int kqsx = k % QI5_1; - const int ib32 = iqs; - const uint8_t * q3 = bq2->qs + 8*ib32; - const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; - const int8_t * q8 = bq8_1[ib32].qs; - uint32_t aux32 = gas[0] | (gas[1] << 16); - int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; - const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; - const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); - const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]); - sumi = __dp4a(grid_l, *((int *)q8+0), sumi); - sumi = __dp4a(grid_h, *((int *)q8+1), sumi); - q8 += 8; - aux32 >>= 7; - } - const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f; - return d * sumi; -#else - assert(false); - return 0.f; -#endif -#else - assert(false); - return 0.f; -#endif -} + const block_q5_1 * bx0 = (const block_q5_1 *) vx; -// TODO: don't use lookup table for signs -static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 - const block_iq3_s * bq2 = (const block_iq3_s *) vbq; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; - const int ib32 = iqs; - const uint8_t * qs = bq2->qs + 8*ib32; - const int8_t * q8 = bq8_1[ib32].qs; - int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); - const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); - uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid2[0] ^ signs1, signs1); - sumi = __dp4a(grid_l, *((int *)q8+0), sumi); - sumi = __dp4a(grid_h, *((int *)q8+1), sumi); - q8 += 8; - } - const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); - return d * sumi; -#else - assert(false); - return 0.f; -#endif -#else - assert(false); - return 0.f; -#endif -} + if (need_check) { + i = min(i, i_max); + } -static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 - const block_iq1_s * bq1 = (const block_iq1_s *) vbq; + const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx; - const int ib32 = iqs; - int sumi = 0; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const int * q8 = (const int *)bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); - int grid0 = grid[0] & 0x0f0f0f0f; - int grid1 = (grid[0] >> 4) & 0x0f0f0f0f; - sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi)); + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; } -#else - const int8_t * q8 = bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); - for (int j = 0; j < 4; ++j) { - sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4); + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_1; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) { + int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); } - q8 += 8; + + const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm; } -#endif - const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA; - const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1); - const float d = d1q * __low2float (bq8_1[ib32].ds); - const float m = d1q * __high2float(bq8_1[ib32].ds); - return d * sumi + m * delta; -#else - assert(false); - return 0.f; -#endif } -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values, - int & val1, int & val2) { +static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32; - aux32 = q4 & 0x0f0f0f0f; - uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8); - uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8); - val1 = v1 | (v2 << 16); - aux32 = (q4 >> 4) & 0x0f0f0f0f; - v1 = values[q8[0]] | (values[q8[1]] << 8); - v2 = values[q8[2]] | (values[q8[3]] << 8); - val2 = v1 | (v2 << 16); + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1; + + int u[2*VDR_Q5_1_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE]; + } + + return vec_dot_q8_1_q8_1_impl + (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); } -#endif -static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( +static __device__ __forceinline__ float vec_dot_q8_0_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const block_iq4_nl * bq = (const block_iq4_nl *) vbq; + const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs; - const int32_t * q8 = (const int32_t *)bq8_1->qs + iqs; - - const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + int v[VDR_Q8_0_Q8_1_MMVQ]; + int u[VDR_Q8_0_Q8_1_MMVQ]; - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) { - const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16); - get_int_from_table_16(aux, values, v1, v2); - sumi1 = __dp4a(v1, q8[l+0], sumi1); - sumi2 = __dp4a(v2, q8[l+4], sumi2); +#pragma unroll + for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_int8(bq8_0->qs, iqs + i); + u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); } -#else - const uint8_t * q4 = bq->qs + 4*iqs; - const int8_t * q8 = bq8_1->qs + 4*iqs; + return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); +} - int sumi1 = 0, sumi2 = 0; - for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) { - sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf]; - sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >> 4]; - } -#endif - const float d = (float)bq->d * __low2float(bq8_1->ds); - return d * (sumi1 + sumi2); +template static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; + __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0]; + + *x_ql = tile_x_qs; + *x_dm = (half2 *) tile_x_d; } -static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +template static __device__ __forceinline__ void load_tiles_q8_0( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); -#if QK_K == 256 -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); - const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; - const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + const int kbx = k / QI8_0; + const int kqsx = k % QI8_0; + float * x_dmf = (float *) x_dm; - //// iqs is 0...7 - //const int ib64 = iqs/2; - //const int il = iqs%2; - //const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il; - //const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il; - //const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il; - //const uint32_t * q4_2 = q4_1 + 4; - //const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4); - //const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4); - //const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds); - //const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds); - //int v1, v2; - //int sumi1 = 0, sumi2 = 0; - //for (int j = 0; j < 2; ++j) { - // get_int_from_table_16(q4_1[j], values, v1, v2); - // sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1)); - // get_int_from_table_16(q4_2[j], values, v1, v2); - // sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2)); - //} - //return d1 * sumi1 + d2 * sumi2; + const block_q8_0 * bx0 = (const block_q8_0 *) vx; - // iqs is 0...7 - const int ib32 = iqs; - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; - const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); - const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 4; ++j) { - get_int_from_table_16(q4[j], values, v1, v2); - sumi1 = __dp4a(v1, q8[j+0], sumi1); - sumi2 = __dp4a(v2, q8[j+4], sumi2); +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = min(i, i_max); + } + + const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx); } - return d * (sumi1 + sumi2); - //// iqs is 0...15 - //const int ib32 = iqs/2; - //const int il = iqs%2; - //const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il; - //const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il; - //const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); - //const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); - //int v1, v2; - //int sumi1 = 0, sumi2 = 0; - //for (int j = 0; j < 2; ++j) { - // get_int_from_table_16(q4[j], values, v1, v2); - // sumi1 = __dp4a(v1, q8[j+0], sumi1); - // sumi2 = __dp4a(v2, q8[j+4], sumi2); - //} - //return d * (sumi1 + sumi2); -#else - assert(false); - return 0.f; -#endif -#else - return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); -#endif -} + const int blocks_per_tile_x_row = WARP_SIZE / QI8_0; + const int kbxd = k % blocks_per_tile_x_row; -template -static __device__ __forceinline__ void mul_mat_q( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) { + int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row; - const block_q_t * x = (const block_q_t *) vx; - const block_q8_1 * y = (const block_q8_1 *) vy; + if (need_check) { + i = min(i, i_max); + } - const int blocks_per_row_x = ncols_x / qk; - const int blocks_per_col_y = nrows_y / QK8_1; - const int blocks_per_warp = WARP_SIZE / qi; + const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd; - const int & ncols_dst = ncols_y; + x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d; + } +} - const int row_dst_0 = blockIdx.x*mmq_y; - const int & row_x_0 = row_dst_0; +static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - const int col_dst_0 = blockIdx.y*mmq_x; - const int & col_y_0 = col_dst_0; + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; - int * tile_x_ql = nullptr; - half2 * tile_x_dm = nullptr; - int * tile_x_qh = nullptr; - int * tile_x_sc = nullptr; + return vec_dot_q8_0_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0], + y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]); +} - allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc); +static __device__ __forceinline__ float vec_dot_q2_K_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - __shared__ int tile_y_qs[mmq_x * WARP_SIZE]; - __shared__ half2 tile_y_ds[mmq_x * WARP_SIZE/QI8_1]; + const block_q2_K * bq2_K = (const block_q2_K *) vbq; - float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}}; + const int bq8_offset = QR2_K * (iqs / QI8_1); + const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); - for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) { + const uint8_t * scales = bq2_K->scales + scale_offset; - load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, - threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x); + const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs); + int u[QR2_K]; + float d8[QR2_K]; #pragma unroll - for (int ir = 0; ir < qr; ++ir) { - const int kqs = ir*WARP_SIZE + threadIdx.x; - const int kbxd = kqs / QI8_1; + for (int i = 0; i < QR2_K; ++ i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + d8[i] = __low2float(bq8_1[bq8_offset + i].ds); + } -#pragma unroll - for (int i = 0; i < mmq_x; i += nwarps) { - const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses + return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8); +} - const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd]; +template static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); - const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE; - tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1); - } + __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K]; + __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; -#pragma unroll - for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) { - const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x; - const int kby = threadIdx.x % (WARP_SIZE/QI8_1); - const int col_y_eff = min(col_y_0 + ids, ncols_y-1); + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_sc = tile_x_sc; +} - // if the sum is not needed it's faster to transform the scale to f32 ahead of time - const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds; - half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby]; - if (need_sum) { - *dsi_dst = *dsi_src; - } else { - float * dfi_dst = (float *) dsi_dst; - *dfi_dst = __low2float(*dsi_src); - } - } +template static __device__ __forceinline__ void load_tiles_q2_K( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); - __syncthreads(); + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); -// #pragma unroll // unrolling this loop causes too much register pressure - for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) { -#pragma unroll - for (int j = 0; j < mmq_x; j += nwarps) { -#pragma unroll - for (int i = 0; i < mmq_y; i += WARP_SIZE) { - sum[i/WARP_SIZE][j/nwarps] += vec_dot( - tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds, - threadIdx.x + i, threadIdx.y + j, k); - } - } - } + const int kbx = k / QI2_K; + const int kqsx = k % QI2_K; - __syncthreads(); - } - } + const block_q2_K * bx0 = (const block_q2_K *) vx; #pragma unroll - for (int j = 0; j < mmq_x; j += nwarps) { - const int col_dst = col_dst_0 + j + threadIdx.y; + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; - if (col_dst >= ncols_dst) { - return; + if (need_check) { + i = min(i, i_max); } -#pragma unroll - for (int i = 0; i < mmq_y; i += WARP_SIZE) { - const int row_dst = row_dst_0 + threadIdx.x + i; + const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx; - if (row_dst >= nrows_dst) { - continue; - } - - dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps]; - } + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); } -} -#define MMQ_X_Q4_0_RDNA2 64 -#define MMQ_Y_Q4_0_RDNA2 128 -#define NWARPS_Q4_0_RDNA2 8 -#define MMQ_X_Q4_0_RDNA1 64 -#define MMQ_Y_Q4_0_RDNA1 64 -#define NWARPS_Q4_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_0_AMPERE 4 -#define MMQ_Y_Q4_0_AMPERE 32 -#define NWARPS_Q4_0_AMPERE 4 -#else -#define MMQ_X_Q4_0_AMPERE 64 -#define MMQ_Y_Q4_0_AMPERE 128 -#define NWARPS_Q4_0_AMPERE 4 -#endif -#define MMQ_X_Q4_0_PASCAL 64 -#define MMQ_Y_Q4_0_PASCAL 64 -#define NWARPS_Q4_0_PASCAL 8 + const int blocks_per_tile_x_row = WARP_SIZE / QI2_K; + const int kbxd = k % blocks_per_tile_x_row; -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q4_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) { + int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_0_RDNA2; - const int mmq_y = MMQ_Y_Q4_0_RDNA2; - const int nwarps = NWARPS_Q4_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_0_RDNA1; - const int mmq_y = MMQ_Y_Q4_0_RDNA1; - const int nwarps = NWARPS_Q4_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + if (need_check) { + i = min(i, i_max); + } - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd; -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_0_AMPERE; - const int mmq_y = MMQ_Y_Q4_0_AMPERE; - const int nwarps = NWARPS_Q4_0_AMPERE; + x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm; + } - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_0_PASCAL; - const int mmq_y = MMQ_Y_Q4_0_PASCAL; - const int nwarps = NWARPS_Q4_0_PASCAL; + if (need_check) { + i = min(i, i_max); + } - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q4_0_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} + const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4); -#define MMQ_X_Q4_1_RDNA2 64 -#define MMQ_Y_Q4_1_RDNA2 128 -#define NWARPS_Q4_1_RDNA2 8 -#define MMQ_X_Q4_1_RDNA1 64 -#define MMQ_Y_Q4_1_RDNA1 64 -#define NWARPS_Q4_1_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_1_AMPERE 4 -#define MMQ_Y_Q4_1_AMPERE 32 -#define NWARPS_Q4_1_AMPERE 4 -#else -#define MMQ_X_Q4_1_AMPERE 64 -#define MMQ_Y_Q4_1_AMPERE 128 -#define NWARPS_Q4_1_AMPERE 4 -#endif -#define MMQ_X_Q4_1_PASCAL 64 -#define MMQ_Y_Q4_1_PASCAL 64 -#define NWARPS_Q4_1_PASCAL 8 + x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4)); + } +} -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q4_1( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { +static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_1_RDNA2; - const int mmq_y = MMQ_Y_Q4_1_RDNA2; - const int nwarps = NWARPS_Q4_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_1_RDNA1; - const int mmq_y = MMQ_Y_Q4_1_RDNA1; - const int nwarps = NWARPS_Q4_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + const int kbx = k / QI2_K; + const int ky = (k % QI2_K) * QR2_K; + const float * y_df = (const float *) y_ds; - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + int v[QR2_K*VDR_Q2_K_Q8_1_MMQ]; -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_1_AMPERE; - const int mmq_y = MMQ_Y_Q4_1_AMPERE; - const int nwarps = NWARPS_Q4_1_AMPERE; + const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2); + const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2)); - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#pragma unroll + for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) { + v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303; + } -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_1_PASCAL; - const int mmq_y = MMQ_Y_Q4_1_PASCAL; - const int nwarps = NWARPS_Q4_1_PASCAL; + const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4; - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q4_1_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA + const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE; + return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]); } -#define MMQ_X_Q5_0_RDNA2 64 -#define MMQ_Y_Q5_0_RDNA2 128 -#define NWARPS_Q5_0_RDNA2 8 -#define MMQ_X_Q5_0_RDNA1 64 -#define MMQ_Y_Q5_0_RDNA1 64 -#define NWARPS_Q5_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_0_AMPERE 4 -#define MMQ_Y_Q5_0_AMPERE 32 -#define NWARPS_Q5_0_AMPERE 4 -#else -#define MMQ_X_Q5_0_AMPERE 128 -#define MMQ_Y_Q5_0_AMPERE 64 -#define NWARPS_Q5_0_AMPERE 4 -#endif -#define MMQ_X_Q5_0_PASCAL 64 -#define MMQ_Y_Q5_0_PASCAL 64 -#define NWARPS_Q5_0_PASCAL 8 +static __device__ __forceinline__ float vec_dot_q3_K_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q5_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + const block_q3_K * bq3_K = (const block_q3_K *) vbq; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_0_RDNA2; - const int mmq_y = MMQ_Y_Q5_0_RDNA2; - const int nwarps = NWARPS_Q5_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_0_RDNA1; - const int mmq_y = MMQ_Y_Q5_0_RDNA1; - const int nwarps = NWARPS_Q5_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + const int bq8_offset = QR3_K * (iqs / (QI3_K/2)); + const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const float d = bq3_K->d; -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_0_AMPERE; - const int mmq_y = MMQ_Y_Q5_0_AMPERE; - const int nwarps = NWARPS_Q5_0_AMPERE; + const int vl = get_int_from_uint8(bq3_K->qs, iqs); - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted + const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset; -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_0_PASCAL; - const int mmq_y = MMQ_Y_Q5_0_PASCAL; - const int nwarps = NWARPS_Q5_0_PASCAL; + int u[QR3_K]; + float d8[QR3_K]; - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q5_0_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#pragma unroll + for (int i = 0; i < QR3_K; ++i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + d8[i] = __low2float(bq8_1[bq8_offset + i].ds); + } + + return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8); } -#define MMQ_X_Q5_1_RDNA2 64 -#define MMQ_Y_Q5_1_RDNA2 128 -#define NWARPS_Q5_1_RDNA2 8 -#define MMQ_X_Q5_1_RDNA1 64 -#define MMQ_Y_Q5_1_RDNA1 64 -#define NWARPS_Q5_1_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_1_AMPERE 4 -#define MMQ_Y_Q5_1_AMPERE 32 -#define NWARPS_Q5_1_AMPERE 4 -#else -#define MMQ_X_Q5_1_AMPERE 128 -#define MMQ_Y_Q5_1_AMPERE 64 -#define NWARPS_Q5_1_AMPERE 4 -#endif -#define MMQ_X_Q5_1_PASCAL 64 -#define MMQ_Y_Q5_1_PASCAL 64 -#define NWARPS_Q5_1_PASCAL 8 +template static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q5_1( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K]; + __shared__ int tile_x_qh[mmq_y * (WARP_SIZE/2) + mmq_y/2]; + __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_1_RDNA2; - const int mmq_y = MMQ_Y_Q5_1_RDNA2; - const int nwarps = NWARPS_Q5_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_1_RDNA1; - const int mmq_y = MMQ_Y_Q5_1_RDNA1; - const int nwarps = NWARPS_Q5_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_qh = tile_x_qh; + *x_sc = tile_x_sc; +} - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +template static __device__ __forceinline__ void load_tiles_q3_K( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_1_AMPERE; - const int mmq_y = MMQ_Y_Q5_1_AMPERE; - const int nwarps = NWARPS_Q5_1_AMPERE; + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const int kbx = k / QI3_K; + const int kqsx = k % QI3_K; -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_1_PASCAL; - const int mmq_y = MMQ_Y_Q5_1_PASCAL; - const int nwarps = NWARPS_Q5_1_PASCAL; + const block_q3_K * bx0 = (const block_q3_K *) vx; - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q5_1_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; -#define MMQ_X_Q8_0_RDNA2 64 -#define MMQ_Y_Q8_0_RDNA2 128 -#define NWARPS_Q8_0_RDNA2 8 -#define MMQ_X_Q8_0_RDNA1 64 -#define MMQ_Y_Q8_0_RDNA1 64 -#define NWARPS_Q8_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q8_0_AMPERE 4 -#define MMQ_Y_Q8_0_AMPERE 32 -#define NWARPS_Q8_0_AMPERE 4 -#else -#define MMQ_X_Q8_0_AMPERE 128 -#define MMQ_Y_Q8_0_AMPERE 64 -#define NWARPS_Q8_0_AMPERE 4 -#endif -#define MMQ_X_Q8_0_PASCAL 64 -#define MMQ_Y_Q8_0_PASCAL 64 -#define NWARPS_Q8_0_PASCAL 8 + if (need_check) { + i = min(i, i_max); + } -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q8_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q8_0_RDNA2; - const int mmq_y = MMQ_Y_Q8_0_RDNA2; - const int nwarps = NWARPS_Q8_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q8_0_RDNA1; - const int mmq_y = MMQ_Y_Q8_0_RDNA1; - const int nwarps = NWARPS_Q8_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); + } - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const int blocks_per_tile_x_row = WARP_SIZE / QI3_K; + const int kbxd = k % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q8_0_AMPERE; - const int mmq_y = MMQ_Y_Q8_0_AMPERE; - const int nwarps = NWARPS_Q8_0_AMPERE; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) { + int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y; - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + if (need_check) { + i = min(i, i_max); + } -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q8_0_PASCAL; - const int mmq_y = MMQ_Y_Q8_0_PASCAL; - const int nwarps = NWARPS_Q8_0_PASCAL; + const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd; - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q8_0_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} + x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d; + } -#define MMQ_X_Q2_K_RDNA2 64 -#define MMQ_Y_Q2_K_RDNA2 128 -#define NWARPS_Q2_K_RDNA2 8 -#define MMQ_X_Q2_K_RDNA1 128 -#define MMQ_Y_Q2_K_RDNA1 32 -#define NWARPS_Q2_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q2_K_AMPERE 4 -#define MMQ_Y_Q2_K_AMPERE 32 -#define NWARPS_Q2_K_AMPERE 4 -#else -#define MMQ_X_Q2_K_AMPERE 64 -#define MMQ_Y_Q2_K_AMPERE 128 -#define NWARPS_Q2_K_AMPERE 4 -#endif -#define MMQ_X_Q2_K_PASCAL 64 -#define MMQ_Y_Q2_K_PASCAL 64 -#define NWARPS_Q2_K_PASCAL 8 +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) { + int i = i0 + i_offset * 2 + k / (WARP_SIZE/2); -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q2_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + if (need_check) { + i = min(i, i_max); + } -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q2_K_RDNA2; - const int mmq_y = MMQ_Y_Q2_K_RDNA2; - const int nwarps = NWARPS_Q2_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q2_K_RDNA1; - const int mmq_y = MMQ_Y_Q2_K_RDNA1; - const int nwarps = NWARPS_Q2_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2); - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted + x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2)); + } -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q2_K_AMPERE; - const int mmq_y = MMQ_Y_Q2_K_AMPERE; - const int nwarps = NWARPS_Q2_K_AMPERE; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + if (need_check) { + i = min(i, i_max); + } -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q2_K_PASCAL; - const int mmq_y = MMQ_Y_Q2_K_PASCAL; - const int nwarps = NWARPS_Q2_K_PASCAL; + const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4); - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q2_K_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} + const int ksc = k % (QI3_K/4); -#define MMQ_X_Q3_K_RDNA2 128 -#define MMQ_Y_Q3_K_RDNA2 64 -#define NWARPS_Q3_K_RDNA2 8 -#define MMQ_X_Q3_K_RDNA1 32 -#define MMQ_Y_Q3_K_RDNA1 128 -#define NWARPS_Q3_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q3_K_AMPERE 4 -#define MMQ_Y_Q3_K_AMPERE 32 -#define NWARPS_Q3_K_AMPERE 4 -#else -#define MMQ_X_Q3_K_AMPERE 128 -#define MMQ_Y_Q3_K_AMPERE 128 -#define NWARPS_Q3_K_AMPERE 4 -#endif -#define MMQ_X_Q3_K_PASCAL 64 -#define MMQ_Y_Q3_K_PASCAL 64 -#define NWARPS_Q3_K_PASCAL 8 + const int ksc_low = ksc % (QI3_K/8); + const int shift_low = 4 * (ksc / (QI3_K/8)); + const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q3_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + const int ksc_high = QI3_K/8; + const int shift_high = 2 * ksc; + const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q3_K_RDNA2; - const int mmq_y = MMQ_Y_Q3_K_RDNA2; - const int nwarps = NWARPS_Q3_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q3_K_RDNA1; - const int mmq_y = MMQ_Y_Q3_K_RDNA1; - const int nwarps = NWARPS_Q3_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + const int sc = __vsubss4(sc_low | sc_high, 0x20202020); - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc; + } +} -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q3_K_AMPERE; - const int mmq_y = MMQ_Y_Q3_K_AMPERE; - const int nwarps = NWARPS_Q3_K_AMPERE; +static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const int kbx = k / QI3_K; + const int ky = (k % QI3_K) * QR3_K; + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q3_K_PASCAL; - const int mmq_y = MMQ_Y_Q3_K_PASCAL; - const int nwarps = NWARPS_Q3_K_PASCAL; + const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q3_K_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA + int v[QR3_K*VDR_Q3_K_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) { + const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2); + const int shift = 2 * ((ky % 32) / 8); + const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303; + + const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8); + const int vlh = (vh << 2) & 0x04040404; + + v[l] = __vsubss4(vll, vlh); + } + + const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE; + return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]); } -#define MMQ_X_Q4_K_RDNA2 64 -#define MMQ_Y_Q4_K_RDNA2 128 -#define NWARPS_Q4_K_RDNA2 8 -#define MMQ_X_Q4_K_RDNA1 32 -#define MMQ_Y_Q4_K_RDNA1 64 -#define NWARPS_Q4_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_K_AMPERE 4 -#define MMQ_Y_Q4_K_AMPERE 32 -#define NWARPS_Q4_K_AMPERE 4 -#else -#define MMQ_X_Q4_K_AMPERE 64 -#define MMQ_Y_Q4_K_AMPERE 128 -#define NWARPS_Q4_K_AMPERE 4 -#endif -#define MMQ_X_Q4_K_PASCAL 64 -#define MMQ_Y_Q4_K_PASCAL 64 -#define NWARPS_Q4_K_PASCAL 8 +static __device__ __forceinline__ float vec_dot_q4_K_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q4_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { +#ifndef GGML_QKK_64 + const block_q4_K * bq4_K = (const block_q4_K *) vbq; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_K_RDNA2; - const int mmq_y = MMQ_Y_Q4_K_RDNA2; - const int nwarps = NWARPS_Q4_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_K_RDNA1; - const int mmq_y = MMQ_Y_Q4_K_RDNA1; - const int nwarps = NWARPS_Q4_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) + int v[2]; + int u[2*QR4_K]; + float d8[QR4_K]; - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6 + const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2)); -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_K_AMPERE; - const int mmq_y = MMQ_Y_Q4_K_AMPERE; - const int nwarps = NWARPS_Q4_K_AMPERE; + // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12 + // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44 + // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76 + // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108 - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); + v[0] = q4[0]; + v[1] = q4[4]; -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_K_PASCAL; - const int mmq_y = MMQ_Y_Q4_K_PASCAL; - const int nwarps = NWARPS_Q4_K_PASCAL; + const uint16_t * scales = (const uint16_t *)bq4_K->scales; + uint16_t aux[2]; + const int j = bq8_offset/2; + if (j < 2) { + aux[0] = scales[j+0] & 0x3f3f; + aux[1] = scales[j+2] & 0x3f3f; + } else { + aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); + aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); + } + const uint8_t * sc = (const uint8_t *)aux; + const uint8_t * m = sc + 2; - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q4_K_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} + for (int i = 0; i < QR4_K; ++i) { + const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; + d8[i] = __low2float(bq8i->ds); -#define MMQ_X_Q5_K_RDNA2 64 -#define MMQ_Y_Q5_K_RDNA2 128 -#define NWARPS_Q5_K_RDNA2 8 -#define MMQ_X_Q5_K_RDNA1 32 -#define MMQ_Y_Q5_K_RDNA1 64 -#define NWARPS_Q5_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_K_AMPERE 4 -#define MMQ_Y_Q5_K_AMPERE 32 -#define NWARPS_Q5_K_AMPERE 4 -#else -#define MMQ_X_Q5_K_AMPERE 64 -#define MMQ_Y_Q5_K_AMPERE 128 -#define NWARPS_Q5_K_AMPERE 4 -#endif -#define MMQ_X_Q5_K_PASCAL 64 -#define MMQ_Y_Q5_K_PASCAL 64 -#define NWARPS_Q5_K_PASCAL 8 + const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); + u[2*i+0] = q8[0]; + u[2*i+1] = q8[4]; + } -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q5_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_K_RDNA2; - const int mmq_y = MMQ_Y_Q5_K_RDNA2; - const int nwarps = NWARPS_Q5_K_RDNA2; #else - const int mmq_x = MMQ_X_Q5_K_RDNA1; - const int mmq_y = MMQ_Y_Q5_K_RDNA1; - const int nwarps = NWARPS_Q5_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const block_q4_K * bq4_K = (const block_q4_K *) vbq; -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_K_AMPERE; - const int mmq_y = MMQ_Y_Q5_K_AMPERE; - const int nwarps = NWARPS_Q5_K_AMPERE; + float sumf_d = 0.0f; + float sumf_m = 0.0f; - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + uint16_t aux16[2]; + const uint8_t * s = (const uint8_t *)aux16; -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_K_PASCAL; - const int mmq_y = MMQ_Y_Q5_K_PASCAL; - const int nwarps = NWARPS_Q5_K_PASCAL; + const uint16_t * a = (const uint16_t *)bq4_K->scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + const float dall = bq4_K->dm[0]; + const float dmin = bq4_K->dm[1]; + + const float d8_1 = __low2float(bq8_1[0].ds); + const float d8_2 = __low2float(bq8_1[1].ds); + + const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); + const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); + const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); + const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); + + const int * q4 = (const int *)bq4_K->qs + (iqs/2); + const int v1 = q4[0]; + const int v2 = q4[4]; + + const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); + const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); + const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); + const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); + + sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); + sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); + + return dall * sumf_d - dmin * sumf_m; - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else - (void) vec_dot_q5_K_q8_1_mul_mat; NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -#define MMQ_X_Q6_K_RDNA2 64 -#define MMQ_Y_Q6_K_RDNA2 128 -#define NWARPS_Q6_K_RDNA2 8 -#define MMQ_X_Q6_K_RDNA1 32 -#define MMQ_Y_Q6_K_RDNA1 64 -#define NWARPS_Q6_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q6_K_AMPERE 4 -#define MMQ_Y_Q6_K_AMPERE 32 -#define NWARPS_Q6_K_AMPERE 4 -#else -#define MMQ_X_Q6_K_AMPERE 64 -#define MMQ_Y_Q6_K_AMPERE 64 -#define NWARPS_Q6_K_AMPERE 4 #endif -#define MMQ_X_Q6_K_PASCAL 64 -#define MMQ_Y_Q6_K_PASCAL 64 -#define NWARPS_Q6_K_PASCAL 8 +} -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q6_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q6_K_RDNA2; - const int mmq_y = MMQ_Y_Q6_K_RDNA2; - const int nwarps = NWARPS_Q6_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q6_K_RDNA1; - const int mmq_y = MMQ_Y_Q6_K_RDNA1; - const int nwarps = NWARPS_Q6_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q6_K_AMPERE; - const int mmq_y = MMQ_Y_Q6_K_AMPERE; - const int nwarps = NWARPS_Q6_K_AMPERE; - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +template static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q6_K_PASCAL; - const int mmq_y = MMQ_Y_Q6_K_PASCAL; - const int nwarps = NWARPS_Q6_K_PASCAL; + __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K]; + __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - (void) vec_dot_q6_K_q8_1_mul_mat; - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_sc = tile_x_sc; } -template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -// tell the compiler to use as many registers as it wants, see nwarps definition below -__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void mul_mat_vec_q( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { +template static __device__ __forceinline__ void load_tiles_q4_K( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) - constexpr int nwarps = 1; - constexpr int rows_per_cuda_block = 1; -#else - constexpr int nwarps = ncols_y <= 4 ? 4 : 2; - constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - const int row0 = rows_per_cuda_block*blockIdx.x; - const int blocks_per_row_x = ncols_x / qk; - const int blocks_per_col_y = nrows_y / QK8_1; - constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; + const int kbx = k / QI4_K; // == 0 if QK_K == 256 + const int kqsx = k % QI4_K; // == k if QK_K == 256 -// partial sum for each thread - float tmp[ncols_y][rows_per_cuda_block] = {0.0f}; + const block_q4_K * bx0 = (const block_q4_K *) vx; - const block_q_t * x = (const block_q_t *) vx; - const block_q8_1 * y = (const block_q8_1 *) vy; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; - for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) { - const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx + if (need_check) { + i = min(i, i_max); + } - // x block quant index when casting the quants to int - const int kqs = vdr * (tid % (qi/vdr)); + const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx; -#pragma unroll - for (int j = 0; j < ncols_y; ++j) { -#pragma unroll - for (int i = 0; i < rows_per_cuda_block; ++i) { - tmp[j][i] += vec_dot_q_cuda( - &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs); - } - } + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); } - __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE]; - if (threadIdx.y > 0) { -#pragma unroll - for (int j = 0; j < ncols_y; ++j) { -#pragma unroll - for (int i = 0; i < rows_per_cuda_block; ++i) { - tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i]; - } - } - } - __syncthreads(); - if (threadIdx.y > 0) { - return; - } + const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - // sum up partial sums and write back result #pragma unroll - for (int j = 0; j < ncols_y; ++j) { -#pragma unroll - for (int i = 0; i < rows_per_cuda_block; ++i) { -#pragma unroll - for (int l = 0; l < nwarps-1; ++l) { - tmp[j][i] += tmp_shared[l][j][i][threadIdx.x]; - } - tmp[j][i] = warp_reduce_sum(tmp[j][i]); - } + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) { + int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y; - if (threadIdx.x < rows_per_cuda_block) { - dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x]; + if (need_check) { + i = min(i, i_max); } - } -} -template -static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { - // qk = quantized weights per x block - // qr = number of quantized weights per data value in x block - const int row = blockIdx.x*blockDim.y + threadIdx.y; + const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; - if (row >= nrows) { - return; +#if QK_K == 256 + x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; +#else + x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; +#endif } - const int tid = threadIdx.x; - - const int iter_stride = 2*GGML_CUDA_DMMV_X; - const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter - const int y_offset = qr == 1 ? 1 : qk/2; - -// partial sum for each thread -#ifdef GGML_CUDA_F16 - half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics -#else - float tmp = 0.0f; -#endif // GGML_CUDA_F16 +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - for (int i = 0; i < ncols; i += iter_stride) { - const int col = i + vals_per_iter*tid; - const int ib = (row*ncols + col)/qk; // x block index - const int iqs = (col%qk)/qr; // x quant index - const int iybs = col - col%qk; // y block start index + if (need_check) { + i = min(i, i_max); + } -// processing >2 values per i iter is faster for fast GPUs -#pragma unroll - for (int j = 0; j < vals_per_iter; j += 2) { - // process 2 vals per j iter + const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8); - // dequantize - // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val - dfloat2 v; - dequantize_kernel(vx, ib, iqs + j/qr, v); + const int * scales = (const int *) bxi->scales; - // matrix multiplication - // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 -#ifdef GGML_CUDA_F16 - tmp += __hmul2(v, { - y[iybs + iqs + j/qr + 0], - y[iybs + iqs + j/qr + y_offset] - }); -#else - tmp += v.x * y[iybs + iqs + j/qr + 0]; - tmp += v.y * y[iybs + iqs + j/qr + y_offset]; -#endif // GGML_CUDA_F16 - } - } + const int ksc = k % (WARP_SIZE/8); - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits - if (tid == 0) { -#ifdef GGML_CUDA_F16 - dst[row] = tmp.x + tmp.y; -#else - dst[row] = tmp; -#endif // GGML_CUDA_F16 + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; } } -static __global__ void mul_mat_p021_f16_f32( - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) { - - const half * x = (const half *) vx; +static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / (nchannels_y / nchannels_x); + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8); - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; + const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE; + return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8, + x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]); +} - float tmp = 0.0f; +static __device__ __forceinline__ float vec_dot_q5_K_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; +#ifndef GGML_QKK_64 + const block_q5_K * bq5_K = (const block_q5_K *) vbq; - if (col_x >= ncols_x) { - break; - } + int vl[2]; + int vh[2]; + int u[2*QR5_K]; + float d8[QR5_K]; - // x is transposed and permuted - const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; - const float xi = __half2float(x[ix]); + const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2)); + const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); + const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4)); - const int row_y = col_x; + vl[0] = ql[0]; + vl[1] = ql[4]; - // y is not transposed but permuted - const int iy = channel*nrows_y + row_y; + vh[0] = qh[0] >> bq8_offset; + vh[1] = qh[4] >> bq8_offset; - tmp += xi * y[iy]; + const uint16_t * scales = (const uint16_t *)bq5_K->scales; + uint16_t aux[2]; + const int j = bq8_offset/2; + if (j < 2) { + aux[0] = scales[j+0] & 0x3f3f; + aux[1] = scales[j+2] & 0x3f3f; + } else { + aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); + aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); } + const uint8_t * sc = (const uint8_t *)aux; + const uint8_t * m = sc + 2; - // dst is not transposed and not permuted - const int idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); +#pragma unroll + for (int i = 0; i < QR5_K; ++i) { + const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; + d8[i] = __low2float(bq8i->ds); - if (threadIdx.x == 0) { - dst[idst] = tmp; + const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); + u[2*i+0] = q8[0]; + u[2*i+1] = q8[4]; } -} -static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) { + return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - const half * x = (const half *) vx; +#else - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / channel_x_divisor; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const block_q5_K * bq5_K = (const block_q5_K *) vbq; - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; + const int8_t * s = bq5_K->scales; - const int idst = channel*nrows_dst + row_dst; + const float d = bq5_K->d; - float tmp = 0.0f; + const float d8_1 = __low2half(bq8_1[0].ds); + const float d8_2 = __low2half(bq8_1[1].ds); - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; + const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); + const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); + const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); + const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - if (col_x >= ncols_x) { - break; - } + const int * ql = (const int *)bq5_K->qs + (iqs/2); + const int vl1 = ql[0]; + const int vl2 = ql[4]; - const int row_y = col_x; + const int step = 4 * (iqs/2); // 0, 4, 8, 12 + const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 + const int in = step%8; // 0, 4, 0, 4 + const int vh = (*((const int *)(bq5_K->qh + in))) >> im; - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const int iy = channel*nrows_y + row_y; + const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); + const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); + const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); + const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - const float xi = __half2float(x[ix]); + const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) + + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); - tmp += xi * y[iy]; - } + return d * sumf_d; - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); +#else + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - if (threadIdx.x == 0) { - dst[idst] = tmp; - } +#endif } -static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - float * dsti = (float *) cdsti; - - *dsti = *xi; -} +template static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); -static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - half * dsti = (half *) cdsti; + __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K]; + __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - *dsti = __float2half(*xi); + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_sc = tile_x_sc; } -static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { - const half * xi = (const half *) cxi; - half * dsti = (half *) cdsti; - - *dsti = *xi; -} +template static __device__ __forceinline__ void load_tiles_q5_K( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); -static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) { - const half * xi = (const half *) cxi; - float * dsti = (float *) cdsti; + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); - *dsti = *xi; -} + const int kbx = k / QI5_K; // == 0 if QK_K == 256 + const int kqsx = k % QI5_K; // == k if QK_K == 256 -template -static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13) { - const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; + const block_q5_K * bx0 = (const block_q5_K *) vx; - if (i >= ne) { - return; - } +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; - // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor - // then combine those indices with the corresponding byte offsets to get the total offsets - const int64_t i03 = i/(ne00 * ne01 * ne02); - const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + if (need_check) { + i = min(i, i_max); + } - const int64_t i13 = i/(ne10 * ne11 * ne12); - const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; + const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx; + const int ky = QR5_K*kqsx; - cpy_1(cx + x_offset, cdst + dst_offset); -} + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; -static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q8_0 * dsti = (block_q8_0 *) cdsti; + const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); + const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; + const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; - float amax = 0.0f; // absolute max + const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0; + const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4); - for (int j = 0; j < QK8_0; j++) { - const float v = xi[j]; - amax = fmaxf(amax, fabsf(v)); + x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0; + x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1; } - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; + const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - dsti->d = d; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) { + int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y; - for (int j = 0; j < QK8_0; ++j) { - const float x0 = xi[j]*id; + if (need_check) { + i = min(i, i_max); + } - dsti->qs[j] = roundf(x0); - } -} + const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_0 * dsti = (block_q4_0 *) cdsti; +#if QK_K == 256 + x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; +#endif + } - float amax = 0.0f; - float vmax = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - for (int j = 0; j < QK4_0; ++j) { - const float v = xi[j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - vmax = v; + if (need_check) { + i = min(i, i_max); } - } - const float d = vmax / -8; - const float id = d ? 1.0f/d : 0.0f; + const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8); - dsti->d = d; + const int * scales = (const int *) bxi->scales; - for (int j = 0; j < QK4_0/2; ++j) { - const float x0 = xi[0 + j]*id; - const float x1 = xi[QK4_0/2 + j]*id; + const int ksc = k % (WARP_SIZE/8); - const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f)); - const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f)); + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; } } -static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_1 * dsti = (block_q4_1 *) cdsti; +static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); - float vmin = FLT_MAX; - float vmax = -FLT_MAX; + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8); - for (int j = 0; j < QK4_1; ++j) { - const float v = xi[j]; + const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k; + const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE; + return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8, + x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]); +} - if (v < vmin) vmin = v; - if (v > vmax) vmax = v; - } +static __device__ __forceinline__ float vec_dot_q6_K_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const float d = (vmax - vmin) / ((1 << 4) - 1); - const float id = d ? 1.0f/d : 0.0f; + const block_q6_K * bq6_K = (const block_q6_K *) vbq; - dsti->dm.x = d; - dsti->dm.y = vmin; + const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4); + const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8); + const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4)); - for (int j = 0; j < QK4_1/2; ++j) { - const float x0 = (xi[0 + j] - vmin)*id; - const float x1 = (xi[QK4_1/2 + j] - vmin)*id; - - const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f)); - const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f)); + const int vl = get_int_from_uint8(bq6_K->ql, iqs); + const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift; - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; - } -} + const int8_t * scales = bq6_K->scales + scale_offset; -template -static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13) { - const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk; + int u[QR6_K]; + float d8[QR6_K]; - if (i >= ne) { - return; +#pragma unroll + for (int i = 0; i < QR6_K; ++i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); + d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds); } - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; - - cpy_blck(cx + x_offset, cdst + dst_offset); -} - -static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); + return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8); } -struct rope_corr_dims { - float v[4]; -}; +template static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { + GGML_UNUSED(x_qh); -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static __device__ void rope_yarn( - float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta -) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; + __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K]; + __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); - } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_sc = tile_x_sc; } -// rope == RoPE == rotary positional embedding -template -static __global__ void rope( - const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - float ext_factor, float attn_factor, rope_corr_dims corr_dims -) { - const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); - - if (col >= ncols) { - return; - } - - const int row = blockDim.x*blockIdx.x + threadIdx.x; - const int i = row*ncols + col; - const int i2 = row/p_delta_rows; +template static __device__ __forceinline__ void load_tiles_q6_K( + const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, + int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { + GGML_UNUSED(x_qh); - const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*powf(freq_base, -float(col)/ncols); + GGML_CUDA_ASSUME(i_offset >= 0); + GGML_CUDA_ASSUME(i_offset < nwarps); + GGML_CUDA_ASSUME(k >= 0); + GGML_CUDA_ASSUME(k < WARP_SIZE); - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); + const int kbx = k / QI6_K; // == 0 if QK_K == 256 + const int kqsx = k % QI6_K; // == k if QK_K == 256 - const float x0 = x[i + 0]; - const float x1 = x[i + 1]; + const block_q6_K * bx0 = (const block_q6_K *) vx; - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + 1] = x0*sin_theta + x1*cos_theta; -} +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; -template -static __global__ void rope_neox( - const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims -) { - const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); + if (need_check) { + i = min(i, i_max); + } - if (col >= ncols) { - return; - } + const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx; + const int ky = QR6_K*kqsx; - const int row = blockDim.x*blockIdx.x + threadIdx.x; - const int ib = col / n_dims; - const int ic = col % n_dims; + const int ql = get_int_from_uint8(bxi->ql, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; - if (ib > 0) { - const int i = row*ncols + ib*n_dims + ic; + const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); + const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; + const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; - dst[i + 0] = x[i + 0]; - dst[i + 1] = x[i + 1]; + const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0; + const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2); - return; + x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020); + x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020); } - const int i = row*ncols + ib*n_dims + ic/2; - const int i2 = row/p_delta_rows; - - float cur_rot = inv_ndims * ic - ib; - - const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f); - - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 + float * x_dmf = (float *) x_dm; - const float x0 = x[i + 0]; - const float x1 = x[i + n_dims/2]; +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) { + int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y; - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; -} + if (need_check) { + i = min(i, i_max); + } -static __global__ void rope_glm_f32( - const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - int n_ctx -) { - const int col = blockDim.x*blockIdx.x + threadIdx.x; - const int half_n_dims = ncols/4; + const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd; - if (col >= half_n_dims) { - return; + x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d; } - const int row = blockDim.y*blockIdx.y + threadIdx.y; - const int i = row*ncols + col; - const int i2 = row/p_delta_rows; - - const float col_theta_scale = powf(freq_base, -2.0f*col/ncols); - // FIXME: this is likely wrong - const int p = pos != nullptr ? pos[i2] : 0; - - const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale; - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); - - const float x0 = x[i + 0]; - const float x1 = x[i + half_n_dims]; - - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta; - - const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale; - const float sin_block_theta = sinf(block_theta); - const float cos_block_theta = cosf(block_theta); - - const float x2 = x[i + half_n_dims * 2]; - const float x3 = x[i + half_n_dims * 3]; - - dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta; - dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; -} - -static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, - const int n_heads_log2_floor, const float m0, const float m1) { - const int col = blockDim.x*blockIdx.x + threadIdx.x; - - if (col >= ncols) { - return; - } +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - const int row = blockDim.y*blockIdx.y + threadIdx.y; - const int i = row*ncols + col; + if (need_check) { + i = min(i, i_max); + } - const int k = row/k_rows; + const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4; - float m_k; - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); + x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8)); } - - dst[i] = col * m_k + x[i]; } -static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { - const int row = blockIdx.x; - const int col = threadIdx.x; - - float sum = 0.0f; - for (int i = col; i < ncols; i += blockDim.x) { - sum += x[row * ncols + i]; - } +static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { + GGML_UNUSED(x_qh); - sum = warp_reduce_sum(sum); + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; - if (col == 0) { - dst[row] = sum; - } -} + const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]); -template -static inline __device__ void swap(T & a, T & b) { - T tmp = a; - a = b; - b = tmp; + const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k; + const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE; + return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); } -template -static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols) { - // bitonic sort - int col = threadIdx.x; - int row = blockIdx.y; +static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; + +#if QR2_XXS == 8 + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = q2[2] | (q2[3] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[aux32 & 127]; + for (int j = 0; j < 8; ++j) { + sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f; + return d * sumi; +#else + // iqs is 0...15 + const int ib32 = iqs/2; + const int il = iqs%2; + const uint16_t * q2 = bq2->qs + 4*ib32; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f; + const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; + const int8_t * q8 = bq8_1[ib32].qs + 16*il; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); + sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); + } + return d * (sumi1 + sumi2); +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; + + const int ib32 = iqs; + const uint16_t * q2 = bq2->qs + 4*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); + sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); + sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + GGML_UNUSED(ksigns64); + assert(false); + return 0.f; +#endif +#else + GGML_UNUSED(ksigns64); + assert(false); + return 0.f; +#endif +} + +// TODO +static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_s * bq2 = (const block_iq2_s *) vbq; + + const int ib32 = iqs; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + GGML_UNUSED(ksigns64); + assert(false); + return 0.f; +#endif +#else + GGML_UNUSED(ksigns64); + assert(false); + return 0.f; +#endif +} + +static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + + const int ib32 = iqs; + const uint8_t * q3 = bq2->qs + 8*ib32; + const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = gas[0] | (gas[1] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; + const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; + const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); + const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]); + sumi = __dp4a(grid_l, *((int *)q8+0), sumi); + sumi = __dp4a(grid_h, *((int *)q8+1), sumi); + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + +// TODO: don't use lookup table for signs +static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_s * bq2 = (const block_iq3_s *) vbq; + + const int ib32 = iqs; + const uint8_t * qs = bq2->qs + 8*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid2[0] ^ signs1, signs1); + sumi = __dp4a(grid_l, *((int *)q8+0), sumi); + sumi = __dp4a(grid_h, *((int *)q8+1), sumi); + q8 += 8; + } + const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif +} + +static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if QK_K == 256 + const block_iq1_s * bq1 = (const block_iq1_s *) vbq; + + const int ib32 = iqs; + int sumi = 0; +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const int * q8 = (const int *)bq8_1[ib32].qs; + for (int l = 0; l < 4; ++l) { + const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); + int grid0 = grid[0] & 0x0f0f0f0f; + int grid1 = (grid[0] >> 4) & 0x0f0f0f0f; + sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi)); + } +#else + const int8_t * q8 = bq8_1[ib32].qs; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); + for (int j = 0; j < 4; ++j) { + sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4); + } + q8 += 8; + } +#endif + const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA; + const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1); + const float d = d1q * __low2float (bq8_1[ib32].ds); + const float m = d1q * __high2float(bq8_1[ib32].ds); + return d * sumi + m * delta; +#else + assert(false); + return 0.f; +#endif +} + +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values, + int & val1, int & val2) { + + uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32; + aux32 = q4 & 0x0f0f0f0f; + uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8); + uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8); + val1 = v1 | (v2 << 16); + aux32 = (q4 >> 4) & 0x0f0f0f0f; + v1 = values[q8[0]] | (values[q8[1]] << 8); + v2 = values[q8[2]] | (values[q8[3]] << 8); + val2 = v1 | (v2 << 16); +} +#endif + +static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + + const block_iq4_nl * bq = (const block_iq4_nl *) vbq; + +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs; + const int32_t * q8 = (const int32_t *)bq8_1->qs + iqs; + + const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) { + const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16); + get_int_from_table_16(aux, values, v1, v2); + sumi1 = __dp4a(v1, q8[l+0], sumi1); + sumi2 = __dp4a(v2, q8[l+4], sumi2); + } + +#else + const uint8_t * q4 = bq->qs + 4*iqs; + const int8_t * q8 = bq8_1->qs + 4*iqs; + + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) { + sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf]; + sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >> 4]; + } +#endif + const float d = (float)bq->d * __low2float(bq8_1->ds); + return d * (sumi1 + sumi2); +} + +static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + +#if QK_K == 256 +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + + const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; + const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + + //// iqs is 0...7 + //const int ib64 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il; + //const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il; + //const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il; + //const uint32_t * q4_2 = q4_1 + 4; + //const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4); + //const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4); + //const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds); + //const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4_1[j], values, v1, v2); + // sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1)); + // get_int_from_table_16(q4_2[j], values, v1, v2); + // sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2)); + //} + //return d1 * sumi1 + d2 * sumi2; - if (col >= ncols) return; + // iqs is 0...7 + const int ib32 = iqs; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; + const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + get_int_from_table_16(q4[j], values, v1, v2); + sumi1 = __dp4a(v1, q8[j+0], sumi1); + sumi2 = __dp4a(v2, q8[j+4], sumi2); + } + return d * (sumi1 + sumi2); - const float * x_row = x + row * ncols; - int * dst_row = dst + row * ncols; + //// iqs is 0...15 + //const int ib32 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il; + //const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il; + //const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + //const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4[j], values, v1, v2); + // sumi1 = __dp4a(v1, q8[j+0], sumi1); + // sumi2 = __dp4a(v2, q8[j+4], sumi2); + //} + //return d * (sumi1 + sumi2); +#else + assert(false); + return 0.f; +#endif +#else + return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); +#endif +} - // initialize indices - if (col < ncols) { - dst_row[col] = col; - } - __syncthreads(); +template +static __device__ __forceinline__ void mul_mat_q( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - for (int k = 2; k <= ncols; k *= 2) { - for (int j = k / 2; j > 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { - swap(dst_row[col], dst_row[ixj]); - } + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; + const int blocks_per_warp = WARP_SIZE / qi; + + const int & ncols_dst = ncols_y; + + const int row_dst_0 = blockIdx.x*mmq_y; + const int & row_x_0 = row_dst_0; + + const int col_dst_0 = blockIdx.y*mmq_x; + const int & col_y_0 = col_dst_0; + + int * tile_x_ql = nullptr; + half2 * tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + + allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc); + + __shared__ int tile_y_qs[mmq_x * WARP_SIZE]; + __shared__ half2 tile_y_ds[mmq_x * WARP_SIZE/QI8_1]; + + float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}}; + + for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) { + + load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, + threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x); + +#pragma unroll + for (int ir = 0; ir < qr; ++ir) { + const int kqs = ir*WARP_SIZE + threadIdx.x; + const int kbxd = kqs / QI8_1; + +#pragma unroll + for (int i = 0; i < mmq_x; i += nwarps) { + const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses + + const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd]; + + const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE; + tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1); + } + +#pragma unroll + for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) { + const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x; + const int kby = threadIdx.x % (WARP_SIZE/QI8_1); + const int col_y_eff = min(col_y_0 + ids, ncols_y-1); + + // if the sum is not needed it's faster to transform the scale to f32 ahead of time + const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds; + half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby]; + if (need_sum) { + *dsi_dst = *dsi_src; } else { - if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { - swap(dst_row[col], dst_row[ixj]); + float * dfi_dst = (float *) dsi_dst; + *dfi_dst = __low2float(*dsi_src); + } + } + + __syncthreads(); + +// #pragma unroll // unrolling this loop causes too much register pressure + for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) { +#pragma unroll + for (int j = 0; j < mmq_x; j += nwarps) { +#pragma unroll + for (int i = 0; i < mmq_y; i += WARP_SIZE) { + sum[i/WARP_SIZE][j/nwarps] += vec_dot( + tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds, + threadIdx.x + i, threadIdx.y + j, k); } } } + __syncthreads(); } } + +#pragma unroll + for (int j = 0; j < mmq_x; j += nwarps) { + const int col_dst = col_dst_0 + j + threadIdx.y; + + if (col_dst >= ncols_dst) { + return; + } + +#pragma unroll + for (int i = 0; i < mmq_y; i += WARP_SIZE) { + const int row_dst = row_dst_0 + threadIdx.x + i; + + if (row_dst >= nrows_dst) { + continue; + } + + dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps]; + } + } } -static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) { - const int col = blockDim.y*blockIdx.y + threadIdx.y; - const int row = blockDim.x*blockIdx.x + threadIdx.x; +#define MMQ_X_Q4_0_RDNA2 64 +#define MMQ_Y_Q4_0_RDNA2 128 +#define NWARPS_Q4_0_RDNA2 8 +#define MMQ_X_Q4_0_RDNA1 64 +#define MMQ_Y_Q4_0_RDNA1 64 +#define NWARPS_Q4_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_0_AMPERE 4 +#define MMQ_Y_Q4_0_AMPERE 32 +#define NWARPS_Q4_0_AMPERE 4 +#else +#define MMQ_X_Q4_0_AMPERE 64 +#define MMQ_Y_Q4_0_AMPERE 128 +#define NWARPS_Q4_0_AMPERE 4 +#endif +#define MMQ_X_Q4_0_PASCAL 64 +#define MMQ_Y_Q4_0_PASCAL 64 +#define NWARPS_Q4_0_PASCAL 8 + +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + mul_mat_q4_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q4_0_RDNA2; + const int mmq_y = MMQ_Y_Q4_0_RDNA2; + const int nwarps = NWARPS_Q4_0_RDNA2; +#else + const int mmq_x = MMQ_X_Q4_0_RDNA1; + const int mmq_y = MMQ_Y_Q4_0_RDNA1; + const int nwarps = NWARPS_Q4_0_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) + + mul_mat_q, + load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q4_0_AMPERE; + const int mmq_y = MMQ_Y_Q4_0_AMPERE; + const int nwarps = NWARPS_Q4_0_AMPERE; + + mul_mat_q, + load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q4_0_PASCAL; + const int mmq_y = MMQ_Y_Q4_0_PASCAL; + const int nwarps = NWARPS_Q4_0_PASCAL; + + mul_mat_q, + load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} + +#define MMQ_X_Q4_1_RDNA2 64 +#define MMQ_Y_Q4_1_RDNA2 128 +#define NWARPS_Q4_1_RDNA2 8 +#define MMQ_X_Q4_1_RDNA1 64 +#define MMQ_Y_Q4_1_RDNA1 64 +#define NWARPS_Q4_1_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_1_AMPERE 4 +#define MMQ_Y_Q4_1_AMPERE 32 +#define NWARPS_Q4_1_AMPERE 4 +#else +#define MMQ_X_Q4_1_AMPERE 64 +#define MMQ_Y_Q4_1_AMPERE 128 +#define NWARPS_Q4_1_AMPERE 4 +#endif +#define MMQ_X_Q4_1_PASCAL 64 +#define MMQ_Y_Q4_1_PASCAL 64 +#define NWARPS_Q4_1_PASCAL 8 - if (col >= ncols) { - return; - } +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#elif __CUDA_ARCH__ < CC_VOLTA + __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2) +#endif // __CUDA_ARCH__ < CC_VOLTA + mul_mat_q4_1( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - const int i = row*ncols + col; - //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i]; - //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU - dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; -} +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q4_1_RDNA2; + const int mmq_y = MMQ_Y_Q4_1_RDNA2; + const int nwarps = NWARPS_Q4_1_RDNA2; +#else + const int mmq_x = MMQ_X_Q4_1_RDNA1; + const int mmq_y = MMQ_Y_Q4_1_RDNA1; + const int nwarps = NWARPS_Q4_1_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) -template -static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { - const int ncols = ncols_template == 0 ? ncols_par : ncols_template; + mul_mat_q, + load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const int tid = threadIdx.x; - const int rowx = blockIdx.x; - const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q4_1_AMPERE; + const int mmq_y = MMQ_Y_Q4_1_AMPERE; + const int nwarps = NWARPS_Q4_1_AMPERE; - const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; + mul_mat_q, + load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const int warp_id = threadIdx.x / WARP_SIZE; - const int lane_id = threadIdx.x % WARP_SIZE; +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q4_1_PASCAL; + const int mmq_y = MMQ_Y_Q4_1_PASCAL; + const int nwarps = NWARPS_Q4_1_PASCAL; - float slope = 0.0f; + mul_mat_q, + load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} - // ALiBi - if (max_bias > 0.0f) { - const int h = rowx/nrows_y; // head index +#define MMQ_X_Q5_0_RDNA2 64 +#define MMQ_Y_Q5_0_RDNA2 128 +#define NWARPS_Q5_0_RDNA2 8 +#define MMQ_X_Q5_0_RDNA1 64 +#define MMQ_Y_Q5_0_RDNA1 64 +#define NWARPS_Q5_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_0_AMPERE 4 +#define MMQ_Y_Q5_0_AMPERE 32 +#define NWARPS_Q5_0_AMPERE 4 +#else +#define MMQ_X_Q5_0_AMPERE 128 +#define MMQ_Y_Q5_0_AMPERE 64 +#define NWARPS_Q5_0_AMPERE 4 +#endif +#define MMQ_X_Q5_0_PASCAL 64 +#define MMQ_Y_Q5_0_PASCAL 64 +#define NWARPS_Q5_0_PASCAL 8 - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + mul_mat_q5_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - slope = powf(base, exp); - } +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q5_0_RDNA2; + const int mmq_y = MMQ_Y_Q5_0_RDNA2; + const int nwarps = NWARPS_Q5_0_RDNA2; +#else + const int mmq_x = MMQ_X_Q5_0_RDNA1; + const int mmq_y = MMQ_Y_Q5_0_RDNA1; + const int nwarps = NWARPS_Q5_0_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - extern __shared__ float data_soft_max_f32[]; - float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication - // shared memory buffer to cache values between iterations: - float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols; + mul_mat_q, + load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - float max_val = -INFINITY; +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q5_0_AMPERE; + const int mmq_y = MMQ_Y_Q5_0_AMPERE; + const int nwarps = NWARPS_Q5_0_AMPERE; -#pragma unroll - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; + mul_mat_q, + load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - if (ncols_template == 0 && col >= ncols) { - break; - } +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q5_0_PASCAL; + const int mmq_y = MMQ_Y_Q5_0_PASCAL; + const int nwarps = NWARPS_Q5_0_PASCAL; - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; + mul_mat_q, + load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} - const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); +#define MMQ_X_Q5_1_RDNA2 64 +#define MMQ_Y_Q5_1_RDNA2 128 +#define NWARPS_Q5_1_RDNA2 8 +#define MMQ_X_Q5_1_RDNA1 64 +#define MMQ_Y_Q5_1_RDNA1 64 +#define NWARPS_Q5_1_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_1_AMPERE 4 +#define MMQ_Y_Q5_1_AMPERE 32 +#define NWARPS_Q5_1_AMPERE 4 +#else +#define MMQ_X_Q5_1_AMPERE 128 +#define MMQ_Y_Q5_1_AMPERE 64 +#define NWARPS_Q5_1_AMPERE 4 +#endif +#define MMQ_X_Q5_1_PASCAL 64 +#define MMQ_Y_Q5_1_PASCAL 64 +#define NWARPS_Q5_1_PASCAL 8 - vals[col] = val; - max_val = max(max_val, val); - } +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +mul_mat_q5_1( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - // find the max value in the block - max_val = warp_reduce_max(max_val); - if (block_size > WARP_SIZE) { - if (warp_id == 0) { - buf_iw[lane_id] = -INFINITY; - } - __syncthreads(); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q5_1_RDNA2; + const int mmq_y = MMQ_Y_Q5_1_RDNA2; + const int nwarps = NWARPS_Q5_1_RDNA2; +#else + const int mmq_x = MMQ_X_Q5_1_RDNA1; + const int mmq_y = MMQ_Y_Q5_1_RDNA1; + const int nwarps = NWARPS_Q5_1_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - if (lane_id == 0) { - buf_iw[warp_id] = max_val; - } - __syncthreads(); + mul_mat_q, + load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - max_val = buf_iw[lane_id]; - max_val = warp_reduce_max(max_val); - } +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q5_1_AMPERE; + const int mmq_y = MMQ_Y_Q5_1_AMPERE; + const int nwarps = NWARPS_Q5_1_AMPERE; - float tmp = 0.0f; // partial sum + mul_mat_q, + load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#pragma unroll - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q5_1_PASCAL; + const int mmq_y = MMQ_Y_Q5_1_PASCAL; + const int nwarps = NWARPS_Q5_1_PASCAL; - if (ncols_template == 0 && col >= ncols) { - break; - } + mul_mat_q, + load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} - const float val = expf(vals[col] - max_val); - tmp += val; - vals[col] = val; - } +#define MMQ_X_Q8_0_RDNA2 64 +#define MMQ_Y_Q8_0_RDNA2 128 +#define NWARPS_Q8_0_RDNA2 8 +#define MMQ_X_Q8_0_RDNA1 64 +#define MMQ_Y_Q8_0_RDNA1 64 +#define NWARPS_Q8_0_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q8_0_AMPERE 4 +#define MMQ_Y_Q8_0_AMPERE 32 +#define NWARPS_Q8_0_AMPERE 4 +#else +#define MMQ_X_Q8_0_AMPERE 128 +#define MMQ_Y_Q8_0_AMPERE 64 +#define NWARPS_Q8_0_AMPERE 4 +#endif +#define MMQ_X_Q8_0_PASCAL 64 +#define MMQ_Y_Q8_0_PASCAL 64 +#define NWARPS_Q8_0_PASCAL 8 - // find the sum of exps in the block - tmp = warp_reduce_sum(tmp); - if (block_size > WARP_SIZE) { - __syncthreads(); - if (warp_id == 0) { - buf_iw[lane_id] = 0.0f; - } - __syncthreads(); +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + mul_mat_q8_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - if (lane_id == 0) { - buf_iw[warp_id] = tmp; - } - __syncthreads(); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q8_0_RDNA2; + const int mmq_y = MMQ_Y_Q8_0_RDNA2; + const int nwarps = NWARPS_Q8_0_RDNA2; +#else + const int mmq_x = MMQ_X_Q8_0_RDNA1; + const int mmq_y = MMQ_Y_Q8_0_RDNA1; + const int nwarps = NWARPS_Q8_0_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - tmp = buf_iw[lane_id]; - tmp = warp_reduce_sum(tmp); - } + mul_mat_q, + load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const float inv_sum = 1.0f / tmp; +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q8_0_AMPERE; + const int mmq_y = MMQ_Y_Q8_0_AMPERE; + const int nwarps = NWARPS_Q8_0_AMPERE; -#pragma unroll - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; + mul_mat_q, + load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - if (ncols_template == 0 && col >= ncols) { - return; - } +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q8_0_PASCAL; + const int mmq_y = MMQ_Y_Q8_0_PASCAL; + const int nwarps = NWARPS_Q8_0_PASCAL; - const int idst = rowx*ncols + col; - dst[idst] = vals[col] * inv_sum; - } + mul_mat_q, + load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA } -static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; +#define MMQ_X_Q2_K_RDNA2 64 +#define MMQ_Y_Q2_K_RDNA2 128 +#define NWARPS_Q2_K_RDNA2 8 +#define MMQ_X_Q2_K_RDNA1 128 +#define MMQ_Y_Q2_K_RDNA1 32 +#define NWARPS_Q2_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q2_K_AMPERE 4 +#define MMQ_Y_Q2_K_AMPERE 32 +#define NWARPS_Q2_K_AMPERE 4 +#else +#define MMQ_X_Q2_K_AMPERE 64 +#define MMQ_Y_Q2_K_AMPERE 128 +#define NWARPS_Q2_K_AMPERE 4 +#endif +#define MMQ_X_Q2_K_PASCAL 64 +#define MMQ_Y_Q2_K_PASCAL 64 +#define NWARPS_Q2_K_PASCAL 8 - if (i >= k) { - return; - } +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +mul_mat_q2_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - dst[i] = scale * x[i]; -} +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q2_K_RDNA2; + const int mmq_y = MMQ_Y_Q2_K_RDNA2; + const int nwarps = NWARPS_Q2_K_RDNA2; +#else + const int mmq_x = MMQ_X_Q2_K_RDNA1; + const int mmq_y = MMQ_Y_Q2_K_RDNA1; + const int nwarps = NWARPS_Q2_K_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) -static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + mul_mat_q, + load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - if (i >= k) { - return; - } +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q2_K_AMPERE; + const int mmq_y = MMQ_Y_Q2_K_AMPERE; + const int nwarps = NWARPS_Q2_K_AMPERE; - dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); + mul_mat_q, + load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q2_K_PASCAL; + const int mmq_y = MMQ_Y_Q2_K_PASCAL; + const int nwarps = NWARPS_Q2_K_PASCAL; + + mul_mat_q, + load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static __global__ void im2col_kernel( - const float * x, T * dst, int64_t batch_offset, - int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW, - int s0, int s1, int p0, int p1, int d0, int d1) { - const int64_t i = threadIdx.x + blockIdx.x * blockDim.x; - if (i >= pelements) { - return; - } +#define MMQ_X_Q3_K_RDNA2 128 +#define MMQ_Y_Q3_K_RDNA2 64 +#define NWARPS_Q3_K_RDNA2 8 +#define MMQ_X_Q3_K_RDNA1 32 +#define MMQ_Y_Q3_K_RDNA1 128 +#define NWARPS_Q3_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q3_K_AMPERE 4 +#define MMQ_Y_Q3_K_AMPERE 32 +#define NWARPS_Q3_K_AMPERE 4 +#else +#define MMQ_X_Q3_K_AMPERE 128 +#define MMQ_Y_Q3_K_AMPERE 128 +#define NWARPS_Q3_K_AMPERE 4 +#endif +#define MMQ_X_Q3_K_PASCAL 64 +#define MMQ_Y_Q3_K_PASCAL 64 +#define NWARPS_Q3_K_PASCAL 8 - const int64_t ksize = OW * (KH > 1 ? KW : 1); - const int64_t kx = i / ksize; - const int64_t kd = kx * ksize; - const int64_t ky = (i - kd) / OW; - const int64_t ix = i % OW; +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#elif __CUDA_ARCH__ < CC_VOLTA + __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2) +#endif // __CUDA_ARCH__ < CC_VOLTA + mul_mat_q3_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - const int64_t oh = blockIdx.y; - const int64_t batch = blockIdx.z / IC; - const int64_t ic = blockIdx.z % IC; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q3_K_RDNA2; + const int mmq_y = MMQ_Y_Q3_K_RDNA2; + const int nwarps = NWARPS_Q3_K_RDNA2; +#else + const int mmq_x = MMQ_X_Q3_K_RDNA1; + const int mmq_y = MMQ_Y_Q3_K_RDNA1; + const int nwarps = NWARPS_Q3_K_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - const int64_t iiw = ix * s0 + kx * d0 - p0; - const int64_t iih = oh * s1 + ky * d1 - p1; + mul_mat_q, + load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const int64_t offset_dst = - ((batch * OH + oh) * OW + ix) * CHW + - (ic * (KW * KH) + ky * KW + kx); +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q3_K_AMPERE; + const int mmq_y = MMQ_Y_Q3_K_AMPERE; + const int nwarps = NWARPS_Q3_K_AMPERE; - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = 0.0f; - } else { - const int64_t offset_src = ic * offset_delta + batch * batch_offset; - dst[offset_dst] = x[offset_src + iih * IW + iiw]; - } -} + mul_mat_q, + load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -template -static __global__ void pool2d_nchw_kernel( - const int ih, const int iw, const int oh, const int ow, - const int kh, const int kw, const int sh, const int sw, - const int ph, const int pw, const int parallel_elements, - const Ti* src, To* dst, const enum ggml_op_pool op) { - int idx = threadIdx.x + blockIdx.x * blockDim.x; - if (idx >= parallel_elements) { - return; - } +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q3_K_PASCAL; + const int mmq_y = MMQ_Y_Q3_K_PASCAL; + const int nwarps = NWARPS_Q3_K_PASCAL; - const int I_HW = ih * iw; - const int O_HW = oh * ow; - const int nc = idx / O_HW; - const int cur_oh = idx % O_HW / ow; - const int cur_ow = idx % O_HW % ow; - const Ti* i_ptr = src + nc * I_HW; - To* o_ptr = dst + nc * O_HW; - const int start_h = cur_oh * sh - ph; - const int bh = max(0, start_h); - const int eh = min(ih, start_h + kh); - const int start_w = cur_ow * sw - pw; - const int bw = max(0, start_w); - const int ew = min(iw, start_w + kw); - const To scale = 1. / (kh * kw); - To res = 0; - - switch (op) { - case GGML_OP_POOL_AVG: res = 0; break; - case GGML_OP_POOL_MAX: res = -FLT_MAX; break; - } - - for (int i = bh; i < eh; i += 1) { - for (int j = bw; j < ew; j += 1) { - #if __CUDA_ARCH__ >= 350 - Ti cur = __ldg(i_ptr + i * iw + j); - #else - Ti cur = i_ptr[i * iw + j]; - #endif - switch (op) { - case GGML_OP_POOL_AVG: res += cur * scale; break; - case GGML_OP_POOL_MAX: res = max(res, (To)cur); break; - } - } - } - o_ptr[cur_oh * ow + cur_ow] = res; + mul_mat_q, + load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { +#define MMQ_X_Q4_K_RDNA2 64 +#define MMQ_Y_Q4_K_RDNA2 128 +#define NWARPS_Q4_K_RDNA2 8 +#define MMQ_X_Q4_K_RDNA1 32 +#define MMQ_Y_Q4_K_RDNA1 64 +#define NWARPS_Q4_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q4_K_AMPERE 4 +#define MMQ_Y_Q4_K_AMPERE 32 +#define NWARPS_Q4_K_AMPERE 4 +#else +#define MMQ_X_Q4_K_AMPERE 64 +#define MMQ_Y_Q4_K_AMPERE 128 +#define NWARPS_Q4_K_AMPERE 4 +#endif +#define MMQ_X_Q4_K_PASCAL 64 +#define MMQ_Y_Q4_K_PASCAL 64 +#define NWARPS_Q4_K_PASCAL 8 - GGML_TENSOR_BINARY_OP_LOCALS +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#elif __CUDA_ARCH__ < CC_VOLTA + __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2) +#endif // __CUDA_ARCH__ < CC_VOLTA + mul_mat_q4_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); - const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE); - const dim3 block_nums(block_num_x, ne10, ne11*ne12); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q4_K_RDNA2; + const int mmq_y = MMQ_Y_Q4_K_RDNA2; + const int nwarps = NWARPS_Q4_K_RDNA2; +#else + const int mmq_x = MMQ_X_Q4_K_RDNA1; + const int mmq_y = MMQ_Y_Q4_K_RDNA1; + const int nwarps = NWARPS_Q4_K_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); + mul_mat_q, + load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q4_K_AMPERE; + const int mmq_y = MMQ_Y_Q4_K_AMPERE; + const int nwarps = NWARPS_Q4_K_AMPERE; - GGML_ASSERT(ne00 % 2 == 0); + mul_mat_q, + load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - k_get_rows<<>>( - src0_dd, src1_dd, dst_dd, - ne00, /*ne01, ne02, ne03,*/ - /*ne10, ne11,*/ ne12, /*ne13,*/ - /* s0,*/ s1, s2, s3, - /* nb00,*/ nb01, nb02, nb03, - s10, s11, s12/*, s13*/); +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q4_K_PASCAL; + const int mmq_y = MMQ_Y_Q4_K_PASCAL; + const int nwarps = NWARPS_Q4_K_PASCAL; - (void) dst; + mul_mat_q, + load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { +#define MMQ_X_Q5_K_RDNA2 64 +#define MMQ_Y_Q5_K_RDNA2 128 +#define NWARPS_Q5_K_RDNA2 8 +#define MMQ_X_Q5_K_RDNA1 32 +#define MMQ_Y_Q5_K_RDNA1 64 +#define NWARPS_Q5_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q5_K_AMPERE 4 +#define MMQ_Y_Q5_K_AMPERE 32 +#define NWARPS_Q5_K_AMPERE 4 +#else +#define MMQ_X_Q5_K_AMPERE 64 +#define MMQ_Y_Q5_K_AMPERE 128 +#define NWARPS_Q5_K_AMPERE 4 +#endif +#define MMQ_X_Q5_K_PASCAL 64 +#define MMQ_Y_Q5_K_PASCAL 64 +#define NWARPS_Q5_K_PASCAL 8 - GGML_TENSOR_BINARY_OP_LOCALS +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +mul_mat_q5_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); - const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; - const dim3 block_nums(block_num_x, ne10, ne11*ne12); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q5_K_RDNA2; + const int mmq_y = MMQ_Y_Q5_K_RDNA2; + const int nwarps = NWARPS_Q5_K_RDNA2; +#else + const int mmq_x = MMQ_X_Q5_K_RDNA1; + const int mmq_y = MMQ_Y_Q5_K_RDNA1; + const int nwarps = NWARPS_Q5_K_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); + mul_mat_q, + load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q5_K_AMPERE; + const int mmq_y = MMQ_Y_Q5_K_AMPERE; + const int nwarps = NWARPS_Q5_K_AMPERE; - k_get_rows_float<<>>( - src0_dd, src1_dd, dst_dd, - ne00, /*ne01, ne02, ne03,*/ - /*ne10, ne11,*/ ne12, /*ne13,*/ - /* s0,*/ s1, s2, s3, - /* nb00,*/ nb01, nb02, nb03, - s10, s11, s12/*, s13*/); + mul_mat_q, + load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - (void) dst; -} +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q5_K_PASCAL; + const int mmq_y = MMQ_Y_Q5_K_PASCAL; + const int nwarps = NWARPS_Q5_K_PASCAL; -template -struct bin_bcast_cuda { - template - void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, - const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd, - cudaStream_t stream) { + mul_mat_q, + load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} - GGML_TENSOR_BINARY_OP_LOCALS +#define MMQ_X_Q6_K_RDNA2 64 +#define MMQ_Y_Q6_K_RDNA2 128 +#define NWARPS_Q6_K_RDNA2 8 +#define MMQ_X_Q6_K_RDNA1 32 +#define MMQ_Y_Q6_K_RDNA1 64 +#define NWARPS_Q6_K_RDNA1 8 +#if defined(CUDA_USE_TENSOR_CORES) +#define MMQ_X_Q6_K_AMPERE 4 +#define MMQ_Y_Q6_K_AMPERE 32 +#define NWARPS_Q6_K_AMPERE 4 +#else +#define MMQ_X_Q6_K_AMPERE 64 +#define MMQ_Y_Q6_K_AMPERE 64 +#define NWARPS_Q6_K_AMPERE 4 +#endif +#define MMQ_X_Q6_K_PASCAL 64 +#define MMQ_Y_Q6_K_PASCAL 64 +#define NWARPS_Q6_K_PASCAL 8 - int nr0 = ne10/ne0; - int nr1 = ne11/ne1; - int nr2 = ne12/ne2; - int nr3 = ne13/ne3; +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#elif __CUDA_ARCH__ < CC_VOLTA + __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2) +#endif // __CUDA_ARCH__ < CC_VOLTA + mul_mat_q6_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - int nr[4] = { nr0, nr1, nr2, nr3 }; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + const int mmq_x = MMQ_X_Q6_K_RDNA2; + const int mmq_y = MMQ_Y_Q6_K_RDNA2; + const int nwarps = NWARPS_Q6_K_RDNA2; +#else + const int mmq_x = MMQ_X_Q6_K_RDNA1; + const int mmq_y = MMQ_Y_Q6_K_RDNA1; + const int nwarps = NWARPS_Q6_K_RDNA1; +#endif // defined(RDNA3) || defined(RDNA2) - // collapse dimensions until first broadcast dimension - int64_t cne0[] = {ne0, ne1, ne2, ne3}; - int64_t cne1[] = {ne10, ne11, ne12, ne13}; - size_t cnb0[] = {nb0, nb1, nb2, nb3}; - size_t cnb1[] = {nb10, nb11, nb12, nb13}; - auto collapse = [](int64_t cne[]) { - cne[0] *= cne[1]; - cne[1] = cne[2]; - cne[2] = cne[3]; - cne[3] = 1; - }; + mul_mat_q, + load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - auto collapse_nb = [](size_t cnb[], const int64_t cne[]) { - cnb[1] *= cne[1]; - cnb[2] *= cne[2]; - cnb[3] *= cne[3]; - }; +#elif __CUDA_ARCH__ >= CC_VOLTA + const int mmq_x = MMQ_X_Q6_K_AMPERE; + const int mmq_y = MMQ_Y_Q6_K_AMPERE; + const int nwarps = NWARPS_Q6_K_AMPERE; - for (int i = 0; i < 4; i++) { - if (nr[i] != 1) { - break; - } - if (i > 0) { - collapse_nb(cnb0, cne0); - collapse_nb(cnb1, cne1); - collapse(cne0); - collapse(cne1); - } - } - { - int64_t ne0 = cne0[0]; - int64_t ne1 = cne0[1]; - int64_t ne2 = cne0[2]; - int64_t ne3 = cne0[3]; + mul_mat_q, + load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - int64_t ne10 = cne1[0]; - int64_t ne11 = cne1[1]; - int64_t ne12 = cne1[2]; - int64_t ne13 = cne1[3]; +#elif __CUDA_ARCH__ >= MIN_CC_DP4A + const int mmq_x = MMQ_X_Q6_K_PASCAL; + const int mmq_y = MMQ_Y_Q6_K_PASCAL; + const int nwarps = NWARPS_Q6_K_PASCAL; - size_t nb0 = cnb0[0]; - size_t nb1 = cnb0[1]; - size_t nb2 = cnb0[2]; - size_t nb3 = cnb0[3]; + mul_mat_q, + load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else + GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= CC_VOLTA +} - size_t nb10 = cnb1[0]; - size_t nb11 = cnb1[1]; - size_t nb12 = cnb1[2]; - size_t nb13 = cnb1[3]; +template +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +// tell the compiler to use as many registers as it wants, see nwarps definition below +__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void mul_mat_vec_q( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { - size_t s0 = nb0 / sizeof(dst_t); - size_t s1 = nb1 / sizeof(dst_t); - size_t s2 = nb2 / sizeof(dst_t); - size_t s3 = nb3 / sizeof(dst_t); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) + constexpr int nwarps = 1; + constexpr int rows_per_cuda_block = 1; +#else + constexpr int nwarps = ncols_y <= 4 ? 4 : 2; + constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) - size_t s10 = nb10 / sizeof(src1_t); - size_t s11 = nb11 / sizeof(src1_t); - size_t s12 = nb12 / sizeof(src1_t); - size_t s13 = nb13 / sizeof(src1_t); + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + const int row0 = rows_per_cuda_block*blockIdx.x; + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; + constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; - GGML_ASSERT(s0 == 1); - GGML_ASSERT(s10 == 1); +// partial sum for each thread + float tmp[ncols_y][rows_per_cuda_block] = {0.0f}; - const int block_size = 128; + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; - int64_t hne0 = std::max(ne0/2LL, 1LL); + for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) { + const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx - dim3 block_dims; - block_dims.x = std::min(hne0, block_size); - block_dims.y = std::min(ne1, block_size / block_dims.x); - block_dims.z = std::min(std::min(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U); + // x block quant index when casting the quants to int + const int kqs = vdr * (tid % (qi/vdr)); - dim3 block_nums( - (hne0 + block_dims.x - 1) / block_dims.x, - (ne1 + block_dims.y - 1) / block_dims.y, - (ne2*ne3 + block_dims.z - 1) / block_dims.z - ); +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { +#pragma unroll + for (int i = 0; i < rows_per_cuda_block; ++i) { + tmp[j][i] += vec_dot_q_cuda( + &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs); + } + } + } - if (block_nums.z > 65535) { - // this is the maximum number of blocks in z direction, fallback to 1D grid kernel - int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; - k_bin_bcast_unravel<<>>( - src0_dd, src1_dd, dst_dd, - ne0, ne1, ne2, ne3, - ne10, ne11, ne12, ne13, - /* s0, */ s1, s2, s3, - /* s10, */ s11, s12, s13); - } else { - k_bin_bcast<<>>( - src0_dd, src1_dd, dst_dd, - ne0, ne1, ne2, ne3, - ne10, ne11, ne12, ne13, - /* s0, */ s1, s2, s3, - /* s10, */ s11, s12, s13); + __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE]; + if (threadIdx.y > 0) { +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { +#pragma unroll + for (int i = 0; i < rows_per_cuda_block; ++i) { + tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i]; } } } -}; + __syncthreads(); + if (threadIdx.y > 0) { + return; + } -static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, const int offset, cudaStream_t stream) { - int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE; - acc_f32<<>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset); -} + // sum up partial sums and write back result +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { +#pragma unroll + for (int i = 0; i < rows_per_cuda_block; ++i) { +#pragma unroll + for (int l = 0; l < nwarps-1; ++l) { + tmp[j][i] += tmp_shared[l][j][i][threadIdx.x]; + } + tmp[j][i] = warp_reduce_sum(tmp[j][i]); + } -static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE; - gelu_f32<<>>(x, dst, k); + if (threadIdx.x < rows_per_cuda_block) { + dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x]; + } + } } -static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE; - silu_f32<<>>(x, dst, k); -} +template +static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { + // qk = quantized weights per x block + // qr = number of quantized weights per data value in x block + const int row = blockIdx.x*blockDim.y + threadIdx.y; -static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE; - gelu_quick_f32<<>>(x, dst, k); -} + if (row >= nrows) { + return; + } -static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE; - tanh_f32<<>>(x, dst, k); -} + const int tid = threadIdx.x; -static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; - relu_f32<<>>(x, dst, k); -} + const int iter_stride = 2*GGML_CUDA_DMMV_X; + const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter + const int y_offset = qr == 1 ? 1 : qk/2; -static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; - hardsigmoid_f32<<>>(x, dst, k); -} +// partial sum for each thread +#ifdef GGML_CUDA_F16 + half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics +#else + float tmp = 0.0f; +#endif // GGML_CUDA_F16 -static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE; - hardswish_f32<<>>(x, dst, k); -} + for (int i = 0; i < ncols; i += iter_stride) { + const int col = i + vals_per_iter*tid; + const int ib = (row*ncols + col)/qk; // x block index + const int iqs = (col%qk)/qr; // x quant index + const int iybs = col - col%qk; // y block start index -static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { - const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; - leaky_relu_f32<<>>(x, dst, k, negative_slope); -} +// processing >2 values per i iter is faster for fast GPUs +#pragma unroll + for (int j = 0; j < vals_per_iter; j += 2) { + // process 2 vals per j iter -static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE; - sqr_f32<<>>(x, dst, k); -} + // dequantize + // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val + dfloat2 v; + dequantize_kernel(vx, ib, iqs + j/qr, v); -static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { - GGML_ASSERT(ncols % WARP_SIZE == 0); - if (ncols < 1024) { - const dim3 block_dims(WARP_SIZE, 1, 1); - norm_f32<<>>(x, dst, ncols, eps); - } else { - const dim3 block_dims(1024, 1, 1); - norm_f32<1024><<>>(x, dst, ncols, eps); + // matrix multiplication + // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 +#ifdef GGML_CUDA_F16 + tmp += __hmul2(v, { + y[iybs + iqs + j/qr + 0], + y[iybs + iqs + j/qr + y_offset] + }); +#else + tmp += v.x * y[iybs + iqs + j/qr + 0]; + tmp += v.y * y[iybs + iqs + j/qr + y_offset]; +#endif // GGML_CUDA_F16 + } } -} -static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) { - static const float eps = 1e-6f; - if (group_size < 1024) { - const dim3 block_dims(WARP_SIZE, 1, 1); - group_norm_f32<<>>(x, dst, group_size, ne_elements, eps); - } else { - const dim3 block_dims(1024, 1, 1); - group_norm_f32<1024><<>>(x, dst, group_size, ne_elements, eps); - } -} + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); -static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { - int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2); - concat_f32<<>>(x, y, dst, ne0, ne02); + if (tid == 0) { +#ifdef GGML_CUDA_F16 + dst[row] = tmp.x + tmp.y; +#else + dst[row] = tmp; +#endif // GGML_CUDA_F16 + } } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, - const int scale_factor, cudaStream_t stream) { - int ne0 = (ne00 * scale_factor); - int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); - upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); -} +static __global__ void mul_mat_p021_f16_f32( + const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) { -static void pad_f32_cuda(const float * x, float * dst, - const int ne00, const int ne01, const int ne02, const int ne03, - const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { - int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2*ne3); - pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02, ne03); -} + const half * x = (const half *) vx; -static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) { - int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE; - arange_f32<<>>(dst, ne0, start, step); -} + const int row_x = blockDim.y*blockIdx.y + threadIdx.y; + const int channel = blockDim.z*blockIdx.z + threadIdx.z; + const int channel_x = channel / (nchannels_y / nchannels_x); -static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1, - const int dim, const int max_period, cudaStream_t stream) { - int half_ceil = (dim + 1) / 2; - int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne00, 1); - timestep_embedding_f32<<>>(x, dst, nb1, dim, max_period); -} + const int nrows_y = ncols_x; + const int nrows_dst = nrows_x; + const int row_dst = row_x; -static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { - GGML_ASSERT(ncols % WARP_SIZE == 0); - if (ncols < 1024) { - const dim3 block_dims(WARP_SIZE, 1, 1); - rms_norm_f32<<>>(x, dst, ncols, eps); - } else { - const dim3 block_dims(1024, 1, 1); - rms_norm_f32<1024><<>>(x, dst, ncols, eps); - } -} + float tmp = 0.0f; -static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { - const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; - const dim3 num_blocks(block_num_x, ky, 1); - const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1); - quantize_q8_1<<>>(x, vy, kx, kx_padded); -} + for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { + const int col_x = col_x0 + threadIdx.x; -template -static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { - const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE); - dequantize_block<<>>(vx, y, k); -} + if (col_x >= ncols_x) { + break; + } -static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN; - if (k % CUDA_Q8_0_NE_ALIGN == 0) { - const bool need_check = false; - dequantize_block_q8_0_f16<<>>(vx, y, k); - } else { - const bool need_check = true; - dequantize_block_q8_0_f16<<>>(vx, y, k); - } -} + // x is transposed and permuted + const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; + const float xi = __half2float(x[ix]); -template -static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; -#if QK_K == 256 - dequantize_block_q2_K<<>>(vx, y); -#else - dequantize_block_q2_K<<>>(vx, y); -#endif -} + const int row_y = col_x; -template -static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; -#if QK_K == 256 - dequantize_block_q3_K<<>>(vx, y); -#else - dequantize_block_q3_K<<>>(vx, y); -#endif -} + // y is not transposed but permuted + const int iy = channel*nrows_y + row_y; -template -static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb32 = k / 32; - const int nb = (k + 255) / 256; - dequantize_block_q4_0<<>>(vx, y, nb32); -} + tmp += xi * y[iy]; + } -template -static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb32 = k / 32; - const int nb = (k + 255) / 256; - dequantize_block_q4_1<<>>(vx, y, nb32); -} + // dst is not transposed and not permuted + const int idst = channel*nrows_dst + row_dst; -template -static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_q4_K<<>>(vx, y); -} + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); -template -static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; -#if QK_K == 256 - dequantize_block_q5_K<<>>(vx, y); -#else - dequantize_block_q5_K<<>>(vx, y); -#endif + if (threadIdx.x == 0) { + dst[idst] = tmp; + } } -template -static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; -#if QK_K == 256 - dequantize_block_q6_K<<>>(vx, y); -#else - dequantize_block_q6_K<<>>(vx, y); -#endif -} +static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous + const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, + const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) { -template -static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq2_xxs<<>>(vx, y); -} + const half * x = (const half *) vx; -template -static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq2_xs<<>>(vx, y); -} + const int row_x = blockDim.y*blockIdx.y + threadIdx.y; + const int channel = blockDim.z*blockIdx.z + threadIdx.z; + const int channel_x = channel / channel_x_divisor; -template -static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq2_s<<>>(vx, y); -} + const int nrows_y = ncols_x; + const int nrows_dst = nrows_x; + const int row_dst = row_x; -template -static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq3_xxs<<>>(vx, y); -} + const int idst = channel*nrows_dst + row_dst; -template -static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq3_s<<>>(vx, y); -} + float tmp = 0.0f; -template -static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = k / QK_K; - dequantize_block_iq1_s<<>>(vx, y); -} + for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { + const int col_x = col_x0 + threadIdx.x; -template -static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = (k + QK_K - 1) / QK_K; - dequantize_block_iq4_nl<<>>(vx, y); -} + if (col_x >= ncols_x) { + break; + } -template -static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { - const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_block_iq4_nl<<>>(vx, y); -#else - dequantize_block_iq4_xs<<>>(vx, y); -#endif -} + const int row_y = col_x; -template -static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; - convert_unary<<>>(vx, y, k); -} + const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; + const int iy = channel*nrows_y + row_y; -static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { - int id; - switch (type) { - case GGML_TYPE_Q4_0: - return dequantize_row_q4_0_cuda; - case GGML_TYPE_Q4_1: - return dequantize_row_q4_1_cuda; - case GGML_TYPE_Q5_0: - return dequantize_block_cuda; - case GGML_TYPE_Q5_1: - return dequantize_block_cuda; - case GGML_TYPE_Q8_0: - CUDA_CHECK(cudaGetDevice(&id)); - if (g_device_caps[id].cc >= CC_PASCAL) { - return dequantize_block_q8_0_f16_cuda; - } - return dequantize_block_cuda; - case GGML_TYPE_Q2_K: - return dequantize_row_q2_K_cuda; - case GGML_TYPE_Q3_K: - return dequantize_row_q3_K_cuda; - case GGML_TYPE_Q4_K: - return dequantize_row_q4_K_cuda; - case GGML_TYPE_Q5_K: - return dequantize_row_q5_K_cuda; - case GGML_TYPE_Q6_K: - return dequantize_row_q6_K_cuda; - case GGML_TYPE_IQ2_XXS: - return dequantize_row_iq2_xxs_cuda; - case GGML_TYPE_IQ2_XS: - return dequantize_row_iq2_xs_cuda; - case GGML_TYPE_IQ2_S: - return dequantize_row_iq2_s_cuda; - case GGML_TYPE_IQ3_XXS: - return dequantize_row_iq3_xxs_cuda; - case GGML_TYPE_IQ1_S: - return dequantize_row_iq1_s_cuda; - case GGML_TYPE_IQ4_NL: - return dequantize_row_iq4_nl_cuda; - case GGML_TYPE_IQ4_XS: - return dequantize_row_iq4_xs_cuda; - case GGML_TYPE_IQ3_S: - return dequantize_row_iq3_s_cuda; - case GGML_TYPE_F32: - return convert_unary_cuda; - default: - return nullptr; - } -} + const float xi = __half2float(x[ix]); -static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - return dequantize_row_q4_0_cuda; - case GGML_TYPE_Q4_1: - return dequantize_row_q4_1_cuda; - case GGML_TYPE_Q5_0: - return dequantize_block_cuda; - case GGML_TYPE_Q5_1: - return dequantize_block_cuda; - case GGML_TYPE_Q8_0: - return dequantize_block_cuda; - case GGML_TYPE_Q2_K: - return dequantize_row_q2_K_cuda; - case GGML_TYPE_Q3_K: - return dequantize_row_q3_K_cuda; - case GGML_TYPE_Q4_K: - return dequantize_row_q4_K_cuda; - case GGML_TYPE_Q5_K: - return dequantize_row_q5_K_cuda; - case GGML_TYPE_Q6_K: - return dequantize_row_q6_K_cuda; - case GGML_TYPE_IQ2_XXS: - return dequantize_row_iq2_xxs_cuda; - case GGML_TYPE_IQ2_XS: - return dequantize_row_iq2_xs_cuda; - case GGML_TYPE_IQ2_S: - return dequantize_row_iq2_s_cuda; - case GGML_TYPE_IQ3_XXS: - return dequantize_row_iq3_xxs_cuda; - case GGML_TYPE_IQ1_S: - return dequantize_row_iq1_s_cuda; - case GGML_TYPE_IQ4_NL: - return dequantize_row_iq4_nl_cuda; - case GGML_TYPE_IQ4_XS: - return dequantize_row_iq4_xs_cuda; - case GGML_TYPE_IQ3_S: - return dequantize_row_iq3_s_cuda; - case GGML_TYPE_F16: - return convert_unary_cuda; - default: - return nullptr; + tmp += xi * y[iy]; + } + + // sum up partial sums and write back result + tmp = warp_reduce_sum(tmp); + + if (threadIdx.x == 0) { + dst[idst] = tmp; } } -static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} +static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + float * dsti = (float *) cdsti; -static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); + *dsti = *xi; } -static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} +static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + half * dsti = (half *) cdsti; -static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); + *dsti = __float2half(*xi); } -static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} +static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { + const half * xi = (const half *) cxi; + half * dsti = (half *) cdsti; -static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); + *dsti = *xi; } -static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); -} +static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) { + const half * xi = (const half *) cxi; + float * dsti = (float *) cdsti; -static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); + *dsti = *xi; } -static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const dim3 block_dims(32, 1, 1); - dequantize_mul_mat_vec_q5_k<<>>(vx, y, dst, ncols); -} +template +static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13) { + const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; -static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); -} + if (i >= ne) { + return; + } -static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec<1, 1, convert_f16> - <<>>(vx, y, dst, ncols, nrows); + // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor + // then combine those indices with the corresponding byte offsets to get the total offsets + const int64_t i03 = i/(ne00 * ne01 * ne02); + const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + + const int64_t i13 = i/(ne10 * ne11 * ne12); + const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; + + cpy_1(cx + x_offset, cdst + dst_offset); } -template -static void mul_mat_vec_q_cuda( - const void * vx, const void * vy, float * dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { +static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q8_0 * dsti = (block_q8_0 *) cdsti; - GGML_ASSERT(ncols_x % qk == 0); - GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); + float amax = 0.0f; // absolute max - int id; - CUDA_CHECK(cudaGetDevice(&id)); + for (int j = 0; j < QK8_0; j++) { + const float v = xi[j]; + amax = fmaxf(amax, fabsf(v)); + } - int64_t nwarps = 1; - int64_t rows_per_cuda_block = 1; + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; - if (g_device_caps[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2 - switch(ncols_y) { - case 1: - nwarps = 4; - rows_per_cuda_block = 1; - break; - case 2: - case 3: - case 4: - nwarps = 4; - rows_per_cuda_block = 2; - break; - case 5: - case 6: - case 7: - case 8: - nwarps = 2; - rows_per_cuda_block = 2; - break; - default: - GGML_ASSERT(false); - break; + dsti->d = d; + + for (int j = 0; j < QK8_0; ++j) { + const float x0 = xi[j]*id; + + dsti->qs[j] = roundf(x0); + } +} + +static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q4_0 * dsti = (block_q4_0 *) cdsti; + + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK4_0; ++j) { + const float v = xi[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; } } - const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block; - const dim3 block_nums(nblocks, 1, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - switch (ncols_y) { - case 1: - mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 2: - mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 3: - mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 4: - mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 5: - mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 6: - mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 7: - mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - case 8: - mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); - break; - default: - GGML_ASSERT(false); - break; + const float d = vmax / -8; + const float id = d ? 1.0f/d : 0.0f; + + dsti->d = d; + + for (int j = 0; j < QK4_0/2; ++j) { + const float x0 = xi[0 + j]*id; + const float x1 = xi[QK4_0/2 + j]*id; + + const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f)); + const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f)); + + dsti->qs[j] = xi0; + dsti->qs[j] |= xi1 << 4; + } +} + +static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q4_1 * dsti = (block_q4_1 *) cdsti; + + float vmin = FLT_MAX; + float vmax = -FLT_MAX; + + for (int j = 0; j < QK4_1; ++j) { + const float v = xi[j]; + + if (v < vmin) vmin = v; + if (v > vmax) vmax = v; } -} -static void ggml_mul_mat_q4_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { + const float d = (vmax - vmin) / ((1 << 4) - 1); + const float id = d ? 1.0f/d : 0.0f; - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + dsti->dm.x = d; + dsti->dm.y = vmin; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_0_RDNA2; - mmq_y = MMQ_Y_Q4_0_RDNA2; - nwarps = NWARPS_Q4_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_0_RDNA1; - mmq_y = MMQ_Y_Q4_0_RDNA1; - nwarps = NWARPS_Q4_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_0_AMPERE; - mmq_y = MMQ_Y_Q4_0_AMPERE; - nwarps = NWARPS_Q4_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_0_PASCAL; - mmq_y = MMQ_Y_Q4_0_PASCAL; - nwarps = NWARPS_Q4_0_PASCAL; - } else { - GGML_ASSERT(false); - } + for (int j = 0; j < QK4_1/2; ++j) { + const float x0 = (xi[0 + j] - vmin)*id; + const float x1 = (xi[QK4_1/2 + j] - vmin)*id; - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f)); + const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f)); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + dsti->qs[j] = xi0; + dsti->qs[j] |= xi1 << 4; } } -static void ggml_mul_mat_q4_1_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; +template +static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13) { + const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_1_RDNA2; - mmq_y = MMQ_Y_Q4_1_RDNA2; - nwarps = NWARPS_Q4_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_1_RDNA1; - mmq_y = MMQ_Y_Q4_1_RDNA1; - nwarps = NWARPS_Q4_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_1_AMPERE; - mmq_y = MMQ_Y_Q4_1_AMPERE; - nwarps = NWARPS_Q4_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_1_PASCAL; - mmq_y = MMQ_Y_Q4_1_PASCAL; - nwarps = NWARPS_Q4_1_PASCAL; - } else { - GGML_ASSERT(false); + if (i >= ne) { + return; } - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; + + cpy_blck(cx + x_offset, cdst + dst_offset); } -static void ggml_mul_mat_q5_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { +static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; +struct rope_corr_dims { + float v[4]; +}; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_0_RDNA2; - mmq_y = MMQ_Y_Q5_0_RDNA2; - nwarps = NWARPS_Q5_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_0_RDNA1; - mmq_y = MMQ_Y_Q5_0_RDNA1; - nwarps = NWARPS_Q5_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_0_AMPERE; - mmq_y = MMQ_Y_Q5_0_AMPERE; - nwarps = NWARPS_Q5_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_0_PASCAL; - mmq_y = MMQ_Y_Q5_0_PASCAL; - nwarps = NWARPS_Q5_0_PASCAL; - } else { - GGML_ASSERT(false); +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static __device__ void rope_yarn( + float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); +// rope == RoPE == rotary positional embedding +template +static __global__ void rope( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +) { + const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + if (col >= ncols) { + return; } + + const int row = blockDim.x*blockIdx.x + threadIdx.x; + const int i = row*ncols + col; + const int i2 = row/p_delta_rows; + + const int p = has_pos ? pos[i2] : 0; + const float theta_base = p*powf(freq_base, -float(col)/ncols); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + 1]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + 1] = x0*sin_theta + x1*cos_theta; } -static void ggml_mul_mat_q5_1_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { +template +static __global__ void rope_neox( + const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims +) { + const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + if (col >= ncols) { + return; + } - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_1_RDNA2; - mmq_y = MMQ_Y_Q5_1_RDNA2; - nwarps = NWARPS_Q5_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_1_RDNA1; - mmq_y = MMQ_Y_Q5_1_RDNA1; - nwarps = NWARPS_Q5_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_1_AMPERE; - mmq_y = MMQ_Y_Q5_1_AMPERE; - nwarps = NWARPS_Q5_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_1_PASCAL; - mmq_y = MMQ_Y_Q5_1_PASCAL; - nwarps = NWARPS_Q5_1_PASCAL; - } else { - GGML_ASSERT(false); + const int row = blockDim.x*blockIdx.x + threadIdx.x; + const int ib = col / n_dims; + const int ic = col % n_dims; + + if (ib > 0) { + const int i = row*ncols + ib*n_dims + ic; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; } - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const int i = row*ncols + ib*n_dims + ic/2; + const int i2 = row/p_delta_rows; + + float cur_rot = inv_ndims * ic - ib; + + const int p = has_pos ? pos[i2] : 0; + const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + n_dims/2]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; +} + +static __global__ void rope_glm_f32( + const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + int n_ctx +) { + const int col = blockDim.x*blockIdx.x + threadIdx.x; + const int half_n_dims = ncols/4; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + if (col >= half_n_dims) { + return; } -} -static void ggml_mul_mat_q8_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { + const int row = blockDim.y*blockIdx.y + threadIdx.y; + const int i = row*ncols + col; + const int i2 = row/p_delta_rows; - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + const float col_theta_scale = powf(freq_base, -2.0f*col/ncols); + // FIXME: this is likely wrong + const int p = pos != nullptr ? pos[i2] : 0; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q8_0_RDNA2; - mmq_y = MMQ_Y_Q8_0_RDNA2; - nwarps = NWARPS_Q8_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q8_0_RDNA1; - mmq_y = MMQ_Y_Q8_0_RDNA1; - nwarps = NWARPS_Q8_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q8_0_AMPERE; - mmq_y = MMQ_Y_Q8_0_AMPERE; - nwarps = NWARPS_Q8_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q8_0_PASCAL; - mmq_y = MMQ_Y_Q8_0_PASCAL; - nwarps = NWARPS_Q8_0_PASCAL; - } else { - GGML_ASSERT(false); - } + const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale; + const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta); - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const float x0 = x[i + 0]; + const float x1 = x[i + half_n_dims]; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta; -static void ggml_mul_mat_q2_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { + const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale; + const float sin_block_theta = sinf(block_theta); + const float cos_block_theta = cosf(block_theta); - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + const float x2 = x[i + half_n_dims * 2]; + const float x3 = x[i + half_n_dims * 3]; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q2_K_RDNA2; - mmq_y = MMQ_Y_Q2_K_RDNA2; - nwarps = NWARPS_Q2_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q2_K_RDNA1; - mmq_y = MMQ_Y_Q2_K_RDNA1; - nwarps = NWARPS_Q2_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q2_K_AMPERE; - mmq_y = MMQ_Y_Q2_K_AMPERE; - nwarps = NWARPS_Q2_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q2_K_PASCAL; - mmq_y = MMQ_Y_Q2_K_PASCAL; - nwarps = NWARPS_Q2_K_PASCAL; - } else { - GGML_ASSERT(false); + dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta; + dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; +} + +static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, + const int n_heads_log2_floor, const float m0, const float m1) { + const int col = blockDim.x*blockIdx.x + threadIdx.x; + + if (col >= ncols) { + return; } - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const int row = blockDim.y*blockIdx.y + threadIdx.y; + const int i = row*ncols + col; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + const int k = row/k_rows; + + float m_k; + if (k < n_heads_log2_floor) { + m_k = powf(m0, k + 1); } else { - const bool need_check = true; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); } -} - -static void ggml_mul_mat_q3_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { -#if QK_K == 256 + dst[i] = col * m_k + x[i]; +} - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; +static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { + const int row = blockIdx.x; + const int col = threadIdx.x; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q3_K_RDNA2; - mmq_y = MMQ_Y_Q3_K_RDNA2; - nwarps = NWARPS_Q3_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q3_K_RDNA1; - mmq_y = MMQ_Y_Q3_K_RDNA1; - nwarps = NWARPS_Q3_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q3_K_AMPERE; - mmq_y = MMQ_Y_Q3_K_AMPERE; - nwarps = NWARPS_Q3_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q3_K_PASCAL; - mmq_y = MMQ_Y_Q3_K_PASCAL; - nwarps = NWARPS_Q3_K_PASCAL; - } else { - GGML_ASSERT(false); + float sum = 0.0f; + for (int i = col; i < ncols; i += blockDim.x) { + sum += x[row * ncols + i]; } - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + sum = warp_reduce_sum(sum); - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + if (col == 0) { + dst[row] = sum; } -#endif } -static void ggml_mul_mat_q4_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { +template +static inline __device__ void swap(T & a, T & b) { + T tmp = a; + a = b; + b = tmp; +} - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; +template +static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols) { + // bitonic sort + int col = threadIdx.x; + int row = blockIdx.y; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_K_RDNA2; - mmq_y = MMQ_Y_Q4_K_RDNA2; - nwarps = NWARPS_Q4_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_K_RDNA1; - mmq_y = MMQ_Y_Q4_K_RDNA1; - nwarps = NWARPS_Q4_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_K_AMPERE; - mmq_y = MMQ_Y_Q4_K_AMPERE; - nwarps = NWARPS_Q4_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_K_PASCAL; - mmq_y = MMQ_Y_Q4_K_PASCAL; - nwarps = NWARPS_Q4_K_PASCAL; - } else { - GGML_ASSERT(false); + if (col >= ncols) return; + + const float * x_row = x + row * ncols; + int * dst_row = dst + row * ncols; + + // initialize indices + if (col < ncols) { + dst_row[col] = col; } + __syncthreads(); - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + for (int k = 2; k <= ncols; k *= 2) { + for (int j = k / 2; j > 0; j /= 2) { + int ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { + swap(dst_row[col], dst_row[ixj]); + } + } else { + if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { + swap(dst_row[col], dst_row[ixj]); + } + } + } + __syncthreads(); + } + } +} - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) { + const int col = blockDim.y*blockIdx.y + threadIdx.y; + const int row = blockDim.x*blockIdx.x + threadIdx.x; + + if (col >= ncols) { + return; } + + const int i = row*ncols + col; + //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i]; + //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU + dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } -static void ggml_mul_mat_q5_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { +template +static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + const int tid = threadIdx.x; + const int rowx = blockIdx.x; + const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_K_RDNA2; - mmq_y = MMQ_Y_Q5_K_RDNA2; - nwarps = NWARPS_Q5_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_K_RDNA1; - mmq_y = MMQ_Y_Q5_K_RDNA1; - nwarps = NWARPS_Q5_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_K_AMPERE; - mmq_y = MMQ_Y_Q5_K_AMPERE; - nwarps = NWARPS_Q5_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_K_PASCAL; - mmq_y = MMQ_Y_Q5_K_PASCAL; - nwarps = NWARPS_Q5_K_PASCAL; - } else { - GGML_ASSERT(false); - } + const int block_size = block_size_template == 0 ? blockDim.x : block_size_template; - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} + float slope = 0.0f; -static void ggml_mul_mat_q6_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { + // ALiBi + if (max_bias > 0.0f) { + const int h = rowx/nrows_y; // head index - int id; - CUDA_CHECK(cudaGetDevice(&id)); - const int compute_capability = g_device_caps[id].cc; + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q6_K_RDNA2; - mmq_y = MMQ_Y_Q6_K_RDNA2; - nwarps = NWARPS_Q6_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q6_K_RDNA1; - mmq_y = MMQ_Y_Q6_K_RDNA1; - nwarps = NWARPS_Q6_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q6_K_AMPERE; - mmq_y = MMQ_Y_Q6_K_AMPERE; - nwarps = NWARPS_Q6_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q6_K_PASCAL; - mmq_y = MMQ_Y_Q6_K_PASCAL; - nwarps = NWARPS_Q6_K_PASCAL; - } else { - GGML_ASSERT(false); + slope = powf(base, exp); } - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); + extern __shared__ float data_soft_max_f32[]; + float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication + // shared memory buffer to cache values between iterations: + float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols; - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} + float max_val = -INFINITY; -static void ggml_mul_mat_p021_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, - const int nchannels_x, const int nchannels_y, cudaStream_t stream) { +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_p021_f16_f32<<>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y); -} + if (ncols_template == 0 && col >= ncols) { + break; + } -static void ggml_mul_mat_vec_nc_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, - const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) { + const int ix = rowx*ncols + col; + const int iy = rowy*ncols + col; - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_vec_nc_f16_f32<<>> - (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); -} + const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + vals[col] = val; + max_val = max(max_val, val); + } -static void ggml_cpy_f16_f32_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + // find the max value in the block + max_val = warp_reduce_max(max_val); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf_iw[lane_id] = -INFINITY; + } + __syncthreads(); - const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); -} + if (lane_id == 0) { + buf_iw[warp_id] = max_val; + } + __syncthreads(); -static void ggml_cpy_f32_f32_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + max_val = buf_iw[lane_id]; + max_val = warp_reduce_max(max_val); + } - const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); -} + float tmp = 0.0f; // partial sum -static void ggml_cpy_f32_f16_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; - const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); -} + if (ncols_template == 0 && col >= ncols) { + break; + } -static void ggml_cpy_f32_q8_0_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + const float val = expf(vals[col] - max_val); + tmp += val; + vals[col] = val; + } - GGML_ASSERT(ne % QK8_0 == 0); - const int num_blocks = ne / QK8_0; - cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); -} + // find the sum of exps in the block + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __syncthreads(); + if (warp_id == 0) { + buf_iw[lane_id] = 0.0f; + } + __syncthreads(); -static void ggml_cpy_f32_q4_0_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + if (lane_id == 0) { + buf_iw[warp_id] = tmp; + } + __syncthreads(); - GGML_ASSERT(ne % QK4_0 == 0); - const int num_blocks = ne / QK4_0; - cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); + tmp = buf_iw[lane_id]; + tmp = warp_reduce_sum(tmp); + } + + const float inv_sum = 1.0f / tmp; + +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + return; + } + + const int idst = rowx*ncols + col; + dst[idst] = vals[col] * inv_sum; + } } -static void ggml_cpy_f32_q4_1_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { +static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; - GGML_ASSERT(ne % QK4_1 == 0); - const int num_blocks = ne / QK4_1; - cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); + if (i >= k) { + return; + } + + dst[i] = scale * x[i]; } -static void ggml_cpy_f16_f16_cuda( - const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { +static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } - const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); + dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } +template +static __global__ void im2col_kernel( + const float * x, T * dst, int64_t batch_offset, + int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW, + int s0, int s1, int p0, int p1, int d0, int d1) { + const int64_t i = threadIdx.x + blockIdx.x * blockDim.x; + if (i >= pelements) { + return; + } + const int64_t ksize = OW * (KH > 1 ? KW : 1); + const int64_t kx = i / ksize; + const int64_t kd = kx * ksize; + const int64_t ky = (i - kd) / OW; + const int64_t ix = i % OW; -static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE; - scale_f32<<>>(x, dst, scale, k); -} + const int64_t oh = blockIdx.y; + const int64_t batch = blockIdx.z / IC; + const int64_t ic = blockIdx.z % IC; -static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE; - clamp_f32<<>>(x, dst, min, max, k); -} + const int64_t iiw = ix * s0 + kx * d0 - p0; + const int64_t iih = oh * s1 + ky * d1 - p1; -template -static void rope_cuda( - const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream -) { - GGML_ASSERT(ncols % 2 == 0); - const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); - const dim3 block_nums(nrows, num_blocks_x, 1); - if (pos == nullptr) { - rope<<>>( - x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims - ); + const int64_t offset_dst = + ((batch * OH + oh) * OW + ix) * CHW + + (ic * (KW * KH) + ky * KW + kx); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = 0.0f; } else { - rope<<>>( - x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims - ); + const int64_t offset_src = ic * offset_delta + batch * batch_offset; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; } } -template -static void rope_neox_cuda( - const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream -) { - GGML_ASSERT(ncols % 2 == 0); - const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); - const dim3 block_nums(nrows, num_blocks_x, 1); - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; +template +static __global__ void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= parallel_elements) { + return; + } - if (pos == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); - } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = max(0, start_h); + const int eh = min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = max(0, start_w); + const int ew = min(iw, start_w + kw); + const To scale = 1. / (kh * kw); + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + default: assert(false); + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { +#if __CUDA_ARCH__ >= 350 + Ti cur = __ldg(i_ptr + i * iw + j); +#else + Ti cur = i_ptr[i * iw + j]; +#endif + switch (op) { + case GGML_OP_POOL_AVG: res += cur * scale; break; + case GGML_OP_POOL_MAX: res = max(res, (To)cur); break; + default: assert(false); + } + } } + o_ptr[cur_oh * ow + cur_ow] = res; } -static void rope_glm_f32_cuda( - const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, int n_ctx, cudaStream_t stream -) { - GGML_ASSERT(ncols % 4 == 0); - const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1); - const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE; - const dim3 block_nums(num_blocks_x, nrows, 1); - rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx); -} +template +static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { -static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, - const int k_rows, const int n_heads_log2_floor, const float m0, - const float m1, cudaStream_t stream) { - const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1); - const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE); - const dim3 block_nums(num_blocks_x, nrows, 1); - alibi_f32<<>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1); -} + GGML_TENSOR_BINARY_OP_LOCALS -static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - const dim3 block_dims(WARP_SIZE, 1, 1); - const dim3 block_nums(nrows, 1, 1); - k_sum_rows_f32<<>>(x, dst, ncols); -} + const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); + const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE); + const dim3 block_nums(block_num_x, ne10, ne11*ne12); -static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) { - // bitonic sort requires ncols to be power of 2 - GGML_ASSERT((ncols & (ncols - 1)) == 0); + // strides in elements + //const size_t s0 = nb0 / ggml_element_size(dst); + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); - const dim3 block_dims(ncols, 1, 1); - const dim3 block_nums(1, nrows, 1); - if (order == GGML_SORT_ORDER_ASC) { - k_argsort_f32_i32<<>>(x, dst, ncols); - } else if (order == GGML_SORT_ORDER_DESC) { - k_argsort_f32_i32<<>>(x, dst, ncols); - } else { - GGML_ASSERT(false); - } -} + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + //const size_t s13 = nb13 / ggml_element_size(src1); -static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) { - const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1); - const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE; - const dim3 block_nums(nrows_x, block_num_x, 1); - diag_mask_inf_f32<<>>(x, dst, ncols_x, rows_per_channel, n_past); + GGML_ASSERT(ne00 % 2 == 0); + + k_get_rows<<>>( + src0_dd, src1_dd, dst_dd, + ne00, /*ne01, ne02, ne03,*/ + /*ne10, ne11,*/ ne12, /*ne13,*/ + /* s0,*/ s1, s2, s3, + /* nb00,*/ nb01, nb02, nb03, + s10, s11, s12/*, s13*/); + + GGML_UNUSED(dst); } -static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { - int nth = WARP_SIZE; - while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; - const dim3 block_dims(nth, 1, 1); - const dim3 block_nums(nrows_x, 1, 1); - const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); - static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); +template +static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + GGML_TENSOR_BINARY_OP_LOCALS - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); + const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; + const dim3 block_nums(block_num_x, ne10, ne11*ne12); - if (shmem < g_device_caps[g_main_device].smpb) { - switch (ncols_x) { - case 32: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 64: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 128: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 256: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 512: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 1024: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 2048: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - case 4096: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - default: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - break; - } - } else { - const size_t shmem_low = WARP_SIZE*sizeof(float); - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); - } -} + // strides in elements + //const size_t s0 = nb0 / ggml_element_size(dst); + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); -template -static void im2col_cuda(const float* x, T* dst, - int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC, - int64_t batch, int64_t batch_offset, int64_t offset_delta, - int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { - const int parallel_elements = OW * KW * KH; - const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; - dim3 block_nums(num_blocks, OH, batch * IC); - im2col_kernel<<>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + //const size_t s13 = nb13 / ggml_element_size(src1); + + k_get_rows_float<<>>( + src0_dd, src1_dd, dst_dd, + ne00, /*ne01, ne02, ne03,*/ + /*ne10, ne11,*/ ne12, /*ne13,*/ + /* s0,*/ s1, s2, s3, + /* nb00,*/ nb01, nb02, nb03, + s10, s11, s12/*, s13*/); + + GGML_UNUSED(dst); } -// buffer pool for cuda -#define MAX_CUDA_BUFFERS 256 +template +struct bin_bcast_cuda { + template + void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, + const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd, + cudaStream_t stream) { + + GGML_TENSOR_BINARY_OP_LOCALS -struct scoped_spin_lock { - std::atomic_flag& lock; - scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { - while (lock.test_and_set(std::memory_order_acquire)) { - ; // spin - } - } - ~scoped_spin_lock() { - lock.clear(std::memory_order_release); - } - scoped_spin_lock(const scoped_spin_lock&) = delete; - scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; -}; + int nr0 = ne10/ne0; + int nr1 = ne11/ne1; + int nr2 = ne12/ne2; + int nr3 = ne13/ne3; -static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT; + int nr[4] = { nr0, nr1, nr2, nr3 }; -// #define DEBUG_CUDA_MALLOC -struct ggml_cuda_buffer { - void * ptr = nullptr; - size_t size = 0; -}; + // collapse dimensions until first broadcast dimension + int64_t cne0[] = {ne0, ne1, ne2, ne3}; + int64_t cne1[] = {ne10, ne11, ne12, ne13}; + size_t cnb0[] = {nb0, nb1, nb2, nb3}; + size_t cnb1[] = {nb10, nb11, nb12, nb13}; + auto collapse = [](int64_t cne[]) { + cne[0] *= cne[1]; + cne[1] = cne[2]; + cne[2] = cne[3]; + cne[3] = 1; + }; -static ggml_cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS]; -static size_t g_cuda_pool_size[GGML_CUDA_MAX_DEVICES] = {0}; + auto collapse_nb = [](size_t cnb[], const int64_t cne[]) { + cnb[1] *= cne[1]; + cnb[2] *= cne[2]; + cnb[3] *= cne[3]; + }; -static void * ggml_cuda_pool_malloc_leg(int device, size_t size, size_t * actual_size) { - scoped_spin_lock lock(g_cuda_pool_lock); -#ifdef DEBUG_CUDA_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { - ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_CUDA_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } + for (int i = 0; i < 4; i++) { + if (nr[i] != 1) { + break; + } + if (i > 0) { + collapse_nb(cnb0, cne0); + collapse_nb(cnb1, cne1); + collapse(cne0); + collapse(cne1); } } - } - if (ibest >= 0) { - ggml_cuda_buffer& b = g_cuda_buffer_pool[device][ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - look_ahead_size = 256 * ((look_ahead_size + 255)/256); - ggml_cuda_set_device(device); - CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); - *actual_size = look_ahead_size; - g_cuda_pool_size[device] += look_ahead_size; -#ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[device]/1024/1024), (uint32_t)(size/1024/1024)); -#endif - return ptr; -} - -static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) { - scoped_spin_lock lock(g_cuda_pool_lock); - - for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { - ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); - ggml_cuda_set_device(device); - CUDA_CHECK(cudaFree(ptr)); - g_cuda_pool_size[device] -= size; -} + { + int64_t ne0 = cne0[0]; + int64_t ne1 = cne0[1]; + int64_t ne2 = cne0[2]; + int64_t ne3 = cne0[3]; -#if !defined(GGML_USE_HIPBLAS) -// pool with virtual memory -static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0}; -static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0}; -static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB + int64_t ne10 = cne1[0]; + int64_t ne11 = cne1[1]; + int64_t ne12 = cne1[2]; + int64_t ne13 = cne1[3]; -static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) { - scoped_spin_lock lock(g_cuda_pool_lock); + size_t nb0 = cnb0[0]; + size_t nb1 = cnb0[1]; + size_t nb2 = cnb0[2]; + size_t nb3 = cnb0[3]; - // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types - const size_t alignment = 128; - size = alignment * ((size + alignment - 1) / alignment); + size_t nb10 = cnb1[0]; + size_t nb11 = cnb1[1]; + size_t nb12 = cnb1[2]; + size_t nb13 = cnb1[3]; - size_t avail = g_cuda_pool_size[device] - g_cuda_pool_used[device]; + size_t s0 = nb0 / sizeof(dst_t); + size_t s1 = nb1 / sizeof(dst_t); + size_t s2 = nb2 / sizeof(dst_t); + size_t s3 = nb3 / sizeof(dst_t); - if (size > avail) { - // round up to the next multiple of the granularity - size_t reserve_size = size - avail; - const size_t granularity = g_device_caps[device].vmm_granularity; - reserve_size = granularity * ((reserve_size + granularity - 1) / granularity); + size_t s10 = nb10 / sizeof(src1_t); + size_t s11 = nb11 / sizeof(src1_t); + size_t s12 = nb12 / sizeof(src1_t); + size_t s13 = nb13 / sizeof(src1_t); - GGML_ASSERT(g_cuda_pool_size[device] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); + GGML_ASSERT(s0 == 1); + GGML_ASSERT(s10 == 1); - // allocate more physical memory - CUmemAllocationProp prop = {}; - prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; - prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; - CUmemGenericAllocationHandle handle; - CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); + const int block_size = 128; - // reserve virtual address space (if not already reserved) - if (g_cuda_pool_addr[device] == 0) { - CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[device], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); - } + int64_t hne0 = std::max(ne0/2LL, 1LL); - // map at the end of the pool - CU_CHECK(cuMemMap(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, 0, handle, 0)); + dim3 block_dims; + block_dims.x = std::min(hne0, block_size); + block_dims.y = std::min(ne1, block_size / block_dims.x); + block_dims.z = std::min(std::min(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U); - // the memory allocation handle is no longer needed after mapping - CU_CHECK(cuMemRelease(handle)); + dim3 block_nums( + (hne0 + block_dims.x - 1) / block_dims.x, + (ne1 + block_dims.y - 1) / block_dims.y, + (ne2*ne3 + block_dims.z - 1) / block_dims.z + ); - // set access - CUmemAccessDesc access = {}; - access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = device; - access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; - CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1)); + if (block_nums.z > 65535) { + // this is the maximum number of blocks in z direction, fallback to 1D grid kernel + int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; + k_bin_bcast_unravel<<>>( + src0_dd, src1_dd, dst_dd, + ne0, ne1, ne2, ne3, + ne10, ne11, ne12, ne13, + /* s0, */ s1, s2, s3, + /* s10, */ s11, s12, s13); + } else { + k_bin_bcast<<>>( + src0_dd, src1_dd, dst_dd, + ne0, ne1, ne2, ne3, + ne10, ne11, ne12, ne13, + /* s0, */ s1, s2, s3, + /* s10, */ s11, s12, s13); + } + } + } +}; - // add to the pool - g_cuda_pool_size[device] += reserve_size; +static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements, + const int ne10, const int ne11, const int ne12, + const int nb1, const int nb2, const int offset, cudaStream_t stream) { + int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE; + acc_f32<<>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset); +} - //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n", - // id, (unsigned long long) (g_cuda_pool_size[id]/1024/1024), - // (unsigned long long) (reserve_size/1024/1024)); - } +static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE; + gelu_f32<<>>(x, dst, k); +} - GGML_ASSERT(g_cuda_pool_addr[device] != 0); +static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE; + silu_f32<<>>(x, dst, k); +} - void * ptr = (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]); - *actual_size = size; - g_cuda_pool_used[device] += size; +static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE; + gelu_quick_f32<<>>(x, dst, k); +} -#ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr); -#endif +static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE; + tanh_f32<<>>(x, dst, k); +} - return ptr; +static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; + relu_f32<<>>(x, dst, k); } -static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) { - scoped_spin_lock lock(g_cuda_pool_lock); +static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; + hardsigmoid_f32<<>>(x, dst, k); +} -#ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr); -#endif +static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE; + hardswish_f32<<>>(x, dst, k); +} - g_cuda_pool_used[device] -= size; +static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { + const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; + leaky_relu_f32<<>>(x, dst, k, negative_slope); +} - // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device])); +static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE; + sqr_f32<<>>(x, dst, k); } -static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) { - if (g_device_caps[device].vmm) { - return ggml_cuda_pool_malloc_vmm(device, size, actual_size); +static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + norm_f32<<>>(x, dst, ncols, eps); } else { - return ggml_cuda_pool_malloc_leg(device, size, actual_size); + const dim3 block_dims(1024, 1, 1); + norm_f32<1024><<>>(x, dst, ncols, eps); } } -static void ggml_cuda_pool_free(int device, void * ptr, size_t size) { - if (g_device_caps[device].vmm) { - ggml_cuda_pool_free_vmm(device, ptr, size); +static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) { + static const float eps = 1e-6f; + if (group_size < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + group_norm_f32<<>>(x, dst, group_size, ne_elements, eps); } else { - ggml_cuda_pool_free_leg(device, ptr, size); + const dim3 block_dims(1024, 1, 1); + group_norm_f32<1024><<>>(x, dst, group_size, ne_elements, eps); } } -#else -#define ggml_cuda_pool_malloc ggml_cuda_pool_malloc_leg -#define ggml_cuda_pool_free ggml_cuda_pool_free_leg -#endif // !defined(GGML_USE_HIPBLAS) - -template -struct cuda_pool_alloc { - int device = -1; - T * ptr = nullptr; - size_t actual_size = 0; - - // size is in number of elements - T * alloc(size_t size) { - GGML_ASSERT(ptr == nullptr); - CUDA_CHECK(cudaGetDevice(&device)); - ptr = (T *) ggml_cuda_pool_malloc(device, size * sizeof(T), &this->actual_size); - return ptr; - } - cuda_pool_alloc(size_t size) { - alloc(size); - } +static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne1, ne2); + concat_f32<<>>(x, y, dst, ne0, ne02); +} - ~cuda_pool_alloc() { - if (ptr != nullptr) { - ggml_cuda_pool_free(device, ptr, actual_size); - } - } +static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, + const int scale_factor, cudaStream_t stream) { + int ne0 = (ne00 * scale_factor); + int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; + dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); + upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); +} - T * get() { - return ptr; - } +static void pad_f32_cuda(const float * x, float * dst, + const int ne00, const int ne01, const int ne02, const int ne03, + const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne1, ne2*ne3); + pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02, ne03); +} - cuda_pool_alloc() = default; - cuda_pool_alloc(const cuda_pool_alloc &) = delete; - cuda_pool_alloc(cuda_pool_alloc &&) = delete; - cuda_pool_alloc& operator=(const cuda_pool_alloc &) = delete; - cuda_pool_alloc& operator=(cuda_pool_alloc &&) = delete; -}; +static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE; + arange_f32<<>>(dst, ne0, start, step); +} -static bool g_cublas_loaded = false; +static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1, + const int dim, const int max_period, cudaStream_t stream) { + int half_ceil = (dim + 1) / 2; + int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne00, 1); + timestep_embedding_f32<<>>(x, dst, nb1, dim, max_period); +} -static void ggml_init_cublas() { - static bool initialized = false; +static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + rms_norm_f32<<>>(x, dst, ncols, eps); + } else { + const dim3 block_dims(1024, 1, 1); + rms_norm_f32<1024><<>>(x, dst, ncols, eps); + } +} - if (!initialized) { +static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { + const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; + const dim3 num_blocks(block_num_x, ky, 1); + const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1); + quantize_q8_1<<>>(x, vy, kx, kx_padded); +} -#ifdef __HIP_PLATFORM_AMD__ - // Workaround for a rocBLAS bug when using multiple graphics cards: - // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346 - rocblas_initialize(); - CUDA_CHECK(cudaDeviceSynchronize()); -#endif +template +static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE); + dequantize_block<<>>(vx, y, k); +} - if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) { - initialized = true; - g_cublas_loaded = false; - fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__); - return; - } +static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN; + if (k % CUDA_Q8_0_NE_ALIGN == 0) { + const bool need_check = false; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } else { + const bool need_check = true; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } +} - GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES); - int64_t total_vram = 0; -#if defined(GGML_CUDA_FORCE_MMQ) - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); +template +static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; +#if QK_K == 256 + dequantize_block_q2_K<<>>(vx, y); #else - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); + dequantize_block_q2_K<<>>(vx, y); #endif -#if defined(CUDA_USE_TENSOR_CORES) - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); +} + +template +static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; +#if QK_K == 256 + dequantize_block_q3_K<<>>(vx, y); #else - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); + dequantize_block_q3_K<<>>(vx, y); #endif - fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count); - for (int id = 0; id < g_device_count; ++id) { - int device_vmm = 0; +} -#if !defined(GGML_USE_HIPBLAS) - CUdevice device; - CU_CHECK(cuDeviceGet(&device, id)); - CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); - - if (device_vmm) { - CUmemAllocationProp alloc_prop = {}; - alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; - alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - alloc_prop.location.id = id; - CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); - } -#endif // !defined(GGML_USE_HIPBLAS) - g_device_caps[id].vmm = !!device_vmm; +template +static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_0<<>>(vx, y, nb32); +} - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); +template +static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_1<<>>(vx, y, nb32); +} - g_default_tensor_split[id] = total_vram; - total_vram += prop.totalGlobalMem; +template +static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_q4_K<<>>(vx, y); +} -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; +template +static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; +#if QK_K == 256 + dequantize_block_q5_K<<>>(vx, y); #else - g_device_caps[id].cc = 100*prop.major + 10*prop.minor; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - g_device_caps[id].smpb = prop.sharedMemPerBlock; - } - for (int id = 0; id < g_device_count; ++id) { - g_default_tensor_split[id] /= total_vram; - } - - for (int id = 0; id < g_device_count; ++id) { - ggml_cuda_set_device(id); - - // create cuda streams - for (int is = 0; is < MAX_STREAMS; ++is) { - CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking)); - } + dequantize_block_q5_K<<>>(vx, y); +#endif +} - // create cublas handle - CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id])); - CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH)); - } +template +static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; +#if QK_K == 256 + dequantize_block_q6_K<<>>(vx, y); +#else + dequantize_block_q6_K<<>>(vx, y); +#endif +} - // configure logging to stdout - // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); +template +static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_xxs<<>>(vx, y); +} - initialized = true; - g_cublas_loaded = true; - } +template +static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_xs<<>>(vx, y); } -static void * ggml_cuda_host_malloc(size_t size) { - if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { - return nullptr; - } +template +static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_s<<>>(vx, y); +} - void * ptr = nullptr; - cudaError_t err = cudaMallocHost((void **) &ptr, size); - if (err != cudaSuccess) { - // clear the error - cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); - return nullptr; - } +template +static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq3_xxs<<>>(vx, y); +} - return ptr; +template +static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq3_s<<>>(vx, y); } -static void ggml_cuda_host_free(void * ptr) { - CUDA_CHECK(cudaFreeHost(ptr)); +template +static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq1_s<<>>(vx, y); } -static cudaError_t ggml_cuda_cpy_tensor_2d( - void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { +template +static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = (k + QK_K - 1) / QK_K; + dequantize_block_iq4_nl<<>>(vx, y); +} - cudaMemcpyKind kind; - char * src_ptr; - if (src->backend == GGML_BACKEND_TYPE_CPU) { - kind = cudaMemcpyHostToDevice; - src_ptr = (char *) src->data; - } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); - kind = cudaMemcpyDeviceToDevice; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; - int id; - CUDA_CHECK(cudaGetDevice(&id)); - src_ptr = (char *) extra->data_device[id]; - } else { - GGML_ASSERT(false); - } - char * dst_ptr = (char *) dst; +template +static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = (k + QK_K - 1) / QK_K; +#if QK_K == 64 + dequantize_block_iq4_nl<<>>(vx, y); +#else + dequantize_block_iq4_xs<<>>(vx, y); +#endif +} - const int64_t ne0 = src->ne[0]; - const int64_t nb0 = src->nb[0]; - const int64_t nb1 = src->nb[1]; - const int64_t nb2 = src->nb[2]; - const int64_t nb3 = src->nb[3]; - const enum ggml_type type = src->type; - const int64_t ts = ggml_type_size(type); - const int64_t bs = ggml_blck_size(type); - int64_t i1_diff = i1_high - i1_low; +template +static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; + convert_unary<<>>(vx, y, k); +} - const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; - if (nb0 == ts && nb1 == ts*ne0/bs) { - return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, kind, stream); - } else if (nb0 == ts) { - return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, kind, stream); - } else { - for (int64_t i1 = 0; i1 < i1_diff; i1++) { - const void * rx = (const void *) ((const char *) x + i1*nb1); - void * rd = (void *) (dst_ptr + i1*ts*ne0/bs); - // pretend the row is a matrix with cols=1 - cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, kind, stream); - if (r != cudaSuccess) return r; - } - return cudaSuccess; +static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { + int id; + switch (type) { + case GGML_TYPE_Q4_0: + return dequantize_row_q4_0_cuda; + case GGML_TYPE_Q4_1: + return dequantize_row_q4_1_cuda; + case GGML_TYPE_Q5_0: + return dequantize_block_cuda; + case GGML_TYPE_Q5_1: + return dequantize_block_cuda; + case GGML_TYPE_Q8_0: + CUDA_CHECK(cudaGetDevice(&id)); + if (get_cuda_global_info().devices[id].cc >= CC_PASCAL) { + return dequantize_block_q8_0_f16_cuda; + } + return dequantize_block_cuda; + case GGML_TYPE_Q2_K: + return dequantize_row_q2_K_cuda; + case GGML_TYPE_Q3_K: + return dequantize_row_q3_K_cuda; + case GGML_TYPE_Q4_K: + return dequantize_row_q4_K_cuda; + case GGML_TYPE_Q5_K: + return dequantize_row_q5_K_cuda; + case GGML_TYPE_Q6_K: + return dequantize_row_q6_K_cuda; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_cuda; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_cuda; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_cuda; + case GGML_TYPE_IQ4_NL: + return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_cuda; + case GGML_TYPE_F32: + return convert_unary_cuda; + default: + return nullptr; } } -static void ggml_cuda_op_get_rows( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t stream) { - - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); - - const int32_t * src1_i32 = (const int32_t *) src1_d; - - switch (src0->type) { - case GGML_TYPE_F16: - get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_F32: - get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; +static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { + switch (type) { case GGML_TYPE_Q4_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; + return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: - get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; + return dequantize_row_q4_1_cuda; case GGML_TYPE_Q5_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; + return dequantize_block_cuda; case GGML_TYPE_Q5_1: - get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; + return dequantize_block_cuda; case GGML_TYPE_Q8_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; + return dequantize_block_cuda; + case GGML_TYPE_Q2_K: + return dequantize_row_q2_K_cuda; + case GGML_TYPE_Q3_K: + return dequantize_row_q3_K_cuda; + case GGML_TYPE_Q4_K: + return dequantize_row_q4_K_cuda; + case GGML_TYPE_Q5_K: + return dequantize_row_q5_K_cuda; + case GGML_TYPE_Q6_K: + return dequantize_row_q6_K_cuda; + case GGML_TYPE_IQ2_XXS: + return dequantize_row_iq2_xxs_cuda; + case GGML_TYPE_IQ2_XS: + return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_cuda; + case GGML_TYPE_IQ1_S: + return dequantize_row_iq1_s_cuda; + case GGML_TYPE_IQ4_NL: + return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; + case GGML_TYPE_IQ3_S: + return dequantize_row_iq3_s_cuda; + case GGML_TYPE_F16: + return convert_unary_cuda; default: - // TODO: k-quants - fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); - GGML_ASSERT(false); - break; + return nullptr; } } -template -static void ggml_cuda_op_bin_bcast( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - op()(src0, src1, dst, (const half *) src0_dd, src1_dd, (half *) dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream); - } else { - fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, - ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); - GGML_ASSERT(false); - } +static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_repeat( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t main_stream) { - - ggml_cuda_op_bin_bcast>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream); - - (void) src1; - (void) src1_d; +static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_add( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_acc( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes - - acc_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); - - (void) dst; +static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_mul( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_div( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - ggml_cuda_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_gelu( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; +static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_silu( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; +static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_gelu_quick( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_quick_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; +static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const dim3 block_dims(32, 1, 1); + dequantize_mul_mat_vec_q5_k<<>>(vx, y, dst, ncols); } -static void ggml_cuda_op_tanh( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - tanh_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; +static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_relu( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; +static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + dequantize_mul_mat_vec<1, 1, convert_f16> + <<>>(vx, y, dst, ncols, nrows); } -static void ggml_cuda_op_hardsigmoid( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); +template +static void mul_mat_vec_q_cuda( + const void * vx, const void * vy, float * dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - (void) src1; - (void) dst; - (void) src1_dd; -} + GGML_ASSERT(ncols_x % qk == 0); + GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); -static void ggml_cuda_op_hardswish( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + int id; + CUDA_CHECK(cudaGetDevice(&id)); - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); + int64_t nwarps = 1; + int64_t rows_per_cuda_block = 1; - hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + if (get_cuda_global_info().devices[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2 + switch(ncols_y) { + case 1: + nwarps = 4; + rows_per_cuda_block = 1; + break; + case 2: + case 3: + case 4: + nwarps = 4; + rows_per_cuda_block = 2; + break; + case 5: + case 6: + case 7: + case 8: + nwarps = 2; + rows_per_cuda_block = 2; + break; + default: + GGML_ASSERT(false); + break; + } + } + const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block; + const dim3 block_nums(nblocks, 1, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + switch (ncols_y) { + case 1: + mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 5: + mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 6: + mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 7: + mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 8: + mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + default: + GGML_ASSERT(false); + break; + } } -static void ggml_cuda_op_leaky_relu( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - leaky_relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} +static void ggml_mul_mat_q4_0_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { -static void ggml_cuda_op_sqr( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q4_0_RDNA2; + mmq_y = MMQ_Y_Q4_0_RDNA2; + nwarps = NWARPS_Q4_0_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q4_0_RDNA1; + mmq_y = MMQ_Y_Q4_0_RDNA1; + nwarps = NWARPS_Q4_0_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q4_0_AMPERE; + mmq_y = MMQ_Y_Q4_0_AMPERE; + nwarps = NWARPS_Q4_0_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q4_0_PASCAL; + mmq_y = MMQ_Y_Q4_0_PASCAL; + nwarps = NWARPS_Q4_0_PASCAL; + } else { + GGML_ASSERT(false); + } - sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q4_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q4_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_norm( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); +static void ggml_mul_mat_q4_1_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - const int64_t ne00 = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q4_1_RDNA2; + mmq_y = MMQ_Y_Q4_1_RDNA2; + nwarps = NWARPS_Q4_1_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q4_1_RDNA1; + mmq_y = MMQ_Y_Q4_1_RDNA1; + nwarps = NWARPS_Q4_1_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q4_1_AMPERE; + mmq_y = MMQ_Y_Q4_1_AMPERE; + nwarps = NWARPS_Q4_1_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q4_1_PASCAL; + mmq_y = MMQ_Y_Q4_1_PASCAL; + nwarps = NWARPS_Q4_1_PASCAL; + } else { + GGML_ASSERT(false); + } - norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q4_1<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q4_1<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_group_norm( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int num_groups = dst->op_params[0]; - int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream); +static void ggml_mul_mat_q5_0_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - (void) src1; - (void) dst; - (void) src1_dd; -} + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; -static void ggml_cuda_op_concat( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q5_0_RDNA2; + mmq_y = MMQ_Y_Q5_0_RDNA2; + nwarps = NWARPS_Q5_0_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q5_0_RDNA1; + mmq_y = MMQ_Y_Q5_0_RDNA1; + nwarps = NWARPS_Q5_0_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q5_0_AMPERE; + mmq_y = MMQ_Y_Q5_0_AMPERE; + nwarps = NWARPS_Q5_0_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q5_0_PASCAL; + mmq_y = MMQ_Y_Q5_0_PASCAL; + nwarps = NWARPS_Q5_0_PASCAL; + } else { + GGML_ASSERT(false); + } - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - for (int i3 = 0; i3 < dst->ne[3]; i3++) { - concat_f32_cuda(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream); + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q5_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q5_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); } - - (void) src1; - (void) dst; } -static void ggml_cuda_op_upscale( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { +static void ggml_mul_mat_q5_1_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - const int scale_factor = dst->op_params[0]; + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q5_1_RDNA2; + mmq_y = MMQ_Y_Q5_1_RDNA2; + nwarps = NWARPS_Q5_1_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q5_1_RDNA1; + mmq_y = MMQ_Y_Q5_1_RDNA1; + nwarps = NWARPS_Q5_1_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q5_1_AMPERE; + mmq_y = MMQ_Y_Q5_1_AMPERE; + nwarps = NWARPS_Q5_1_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q5_1_PASCAL; + mmq_y = MMQ_Y_Q5_1_PASCAL; + nwarps = NWARPS_Q5_1_PASCAL; + } else { + GGML_ASSERT(false); + } - upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q5_1<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q5_1<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_pad( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { +static void ggml_mul_mat_q8_0_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - pad_f32_cuda(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q8_0_RDNA2; + mmq_y = MMQ_Y_Q8_0_RDNA2; + nwarps = NWARPS_Q8_0_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q8_0_RDNA1; + mmq_y = MMQ_Y_Q8_0_RDNA1; + nwarps = NWARPS_Q8_0_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q8_0_AMPERE; + mmq_y = MMQ_Y_Q8_0_AMPERE; + nwarps = NWARPS_Q8_0_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q8_0_PASCAL; + mmq_y = MMQ_Y_Q8_0_PASCAL; + nwarps = NWARPS_Q8_0_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q8_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q8_0<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_arange( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(dst->type == GGML_TYPE_F32); +static void ggml_mul_mat_q2_K_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - float start; - float stop; - float step; - memcpy(&start, (float *)dst->op_params + 0, sizeof(float)); - memcpy(&stop, (float *)dst->op_params + 1, sizeof(float)); - memcpy(&step, (float *)dst->op_params + 2, sizeof(float)); + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - int64_t steps = (int64_t)ceil((stop - start) / step); - GGML_ASSERT(ggml_nelements(dst) == steps); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q2_K_RDNA2; + mmq_y = MMQ_Y_Q2_K_RDNA2; + nwarps = NWARPS_Q2_K_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q2_K_RDNA1; + mmq_y = MMQ_Y_Q2_K_RDNA1; + nwarps = NWARPS_Q2_K_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q2_K_AMPERE; + mmq_y = MMQ_Y_Q2_K_AMPERE; + nwarps = NWARPS_Q2_K_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q2_K_PASCAL; + mmq_y = MMQ_Y_Q2_K_PASCAL; + nwarps = NWARPS_Q2_K_PASCAL; + } else { + GGML_ASSERT(false); + } - arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src0; - (void) src1; - (void) src0_dd; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q2_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q2_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_timestep_embedding( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { +static void ggml_mul_mat_q3_K_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); +#if QK_K == 256 - const int dim = dst->op_params[0]; - const int max_period = dst->op_params[1]; + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q3_K_RDNA2; + mmq_y = MMQ_Y_Q3_K_RDNA2; + nwarps = NWARPS_Q3_K_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q3_K_RDNA1; + mmq_y = MMQ_Y_Q3_K_RDNA1; + nwarps = NWARPS_Q3_K_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q3_K_AMPERE; + mmq_y = MMQ_Y_Q3_K_AMPERE; + nwarps = NWARPS_Q3_K_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q3_K_PASCAL; + mmq_y = MMQ_Y_Q3_K_PASCAL; + nwarps = NWARPS_Q3_K_PASCAL; + } else { + GGML_ASSERT(false); + } - (void) src1; - (void) dst; - (void) src1_dd; -} + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); -static void ggml_cuda_op_rms_norm( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q3_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q3_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } +#endif +} - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); +static void ggml_mul_mat_q4_K_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - const int64_t ne00 = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q4_K_RDNA2; + mmq_y = MMQ_Y_Q4_K_RDNA2; + nwarps = NWARPS_Q4_K_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q4_K_RDNA1; + mmq_y = MMQ_Y_Q4_K_RDNA1; + nwarps = NWARPS_Q4_K_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q4_K_AMPERE; + mmq_y = MMQ_Y_Q4_K_AMPERE; + nwarps = NWARPS_Q4_K_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q4_K_PASCAL; + mmq_y = MMQ_Y_Q4_K_PASCAL; + nwarps = NWARPS_Q4_K_PASCAL; + } else { + GGML_ASSERT(false); + } - rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream); + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - (void) src1; - (void) dst; - (void) src1_dd; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q4_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q4_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } } -static void ggml_cuda_op_mul_mat_q( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { +static void ggml_mul_mat_q5_K_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - const int64_t ne00 = src0->ne[0]; + int id; + CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - const int64_t ne10 = src1->ne[0]; - GGML_ASSERT(ne10 % QK8_1 == 0); + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q5_K_RDNA2; + mmq_y = MMQ_Y_Q5_K_RDNA2; + nwarps = NWARPS_Q5_K_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q5_K_RDNA1; + mmq_y = MMQ_Y_Q5_K_RDNA1; + nwarps = NWARPS_Q5_K_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q5_K_AMPERE; + mmq_y = MMQ_Y_Q5_K_AMPERE; + nwarps = NWARPS_Q5_K_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q5_K_PASCAL; + mmq_y = MMQ_Y_Q5_K_PASCAL; + nwarps = NWARPS_Q5_K_PASCAL; + } else { + GGML_ASSERT(false); + } - const int64_t ne0 = dst->ne[0]; + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); - const int64_t row_diff = row_high - row_low; + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q5_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q5_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } +} + +static void ggml_mul_mat_q6_K_q8_1_cuda( + const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { int id; CUDA_CHECK(cudaGetDevice(&id)); + const int compute_capability = get_cuda_global_info().devices[id].cc; - // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q4_1: - ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q5_0: - ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q5_1: - ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q8_0: - ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q2_K: - ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q3_K: - ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q4_K: - ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q5_K: - ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - case GGML_TYPE_Q6_K: - ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); - break; - default: - GGML_ASSERT(false); - break; + int mmq_x, mmq_y, nwarps; + if (compute_capability >= CC_RDNA2) { + mmq_x = MMQ_X_Q6_K_RDNA2; + mmq_y = MMQ_Y_Q6_K_RDNA2; + nwarps = NWARPS_Q6_K_RDNA2; + } else if (compute_capability >= CC_OFFSET_AMD) { + mmq_x = MMQ_X_Q6_K_RDNA1; + mmq_y = MMQ_Y_Q6_K_RDNA1; + nwarps = NWARPS_Q6_K_RDNA1; + } else if (compute_capability >= CC_VOLTA) { + mmq_x = MMQ_X_Q6_K_AMPERE; + mmq_y = MMQ_Y_Q6_K_AMPERE; + nwarps = NWARPS_Q6_K_AMPERE; + } else if (compute_capability >= MIN_CC_DP4A) { + mmq_x = MMQ_X_Q6_K_PASCAL; + mmq_y = MMQ_Y_Q6_K_PASCAL; + nwarps = NWARPS_Q6_K_PASCAL; + } else { + GGML_ASSERT(false); } - (void) src1; - (void) dst; - (void) src1_ddf_i; -} + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); -static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { - int64_t min_compute_capability = INT_MAX; - int64_t max_compute_capability = INT_MIN; - for (int id = 0; id < g_device_count; ++id) { - if (tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > g_device_caps[id].cc) { - min_compute_capability = g_device_caps[id].cc; - } - if (max_compute_capability < g_device_caps[id].cc) { - max_compute_capability = g_device_caps[id].cc; - } - } + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + mul_mat_q6_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); + } else { + const bool need_check = true; + mul_mat_q6_K<<>> + (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); } +} -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return max_compute_capability >= CC_RDNA2 ? 128 : 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - return max_compute_capability >= CC_RDNA2 ? 128 : 32; - case GGML_TYPE_Q3_K: - return min_compute_capability < CC_RDNA2 ? 128 : 64; - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - return max_compute_capability >= CC_RDNA2 ? 128 : 64; - default: - GGML_ASSERT(false); - } -#else - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - return max_compute_capability >= CC_VOLTA ? 128 : 64; - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - return max_compute_capability >= CC_VOLTA ? 128 : 64; - case GGML_TYPE_Q6_K: - return 64; - default: - GGML_ASSERT(false); - } -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +static void ggml_mul_mat_p021_f16_f32_cuda( + const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, + const int nchannels_x, const int nchannels_y, cudaStream_t stream) { + + const dim3 block_nums(1, nrows_x, nchannels_y); + const dim3 block_dims(WARP_SIZE, 1, 1); + mul_mat_p021_f16_f32<<>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y); } -static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { - const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor->type, tensor_split); +static void ggml_mul_mat_vec_nc_f16_f32_cuda( + const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, + const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) { - *row_low = id == 0 ? 0 : nrows*tensor_split[id]; - *row_low -= *row_low % rounding; + const dim3 block_nums(1, nrows_x, nchannels_y); + const dim3 block_dims(WARP_SIZE, 1, 1); + mul_mat_vec_nc_f16_f32<<>> + (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); +} - if (id == g_device_count - 1) { - *row_high = nrows; - } else { - *row_high = nrows*tensor_split[id + 1]; - *row_high -= *row_high % rounding; - } + +static void ggml_cpy_f16_f32_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } -static void ggml_cuda_op_mul_mat_vec_q( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { +static void ggml_cpy_f32_f32_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { - const int64_t ne00 = src0->ne[0]; - const int64_t row_diff = row_high - row_low; + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} - const int64_t ne10 = src1->ne[0]; - GGML_ASSERT(ne10 % QK8_1 == 0); +static void ggml_cpy_f32_f16_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { - const int64_t ne0 = dst->ne[0]; + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} - int id; - CUDA_CHECK(cudaGetDevice(&id)); +static void ggml_cpy_f32_q8_0_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { - // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; + GGML_ASSERT(ne % QK8_0 == 0); + const int num_blocks = ne / QK8_0; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} - switch (src0->type) { - case GGML_TYPE_Q4_0: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q4_1: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q5_0: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q5_1: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q8_0: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q2_K: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q3_K: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q4_K: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q5_K: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_Q6_K: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ2_XXS: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ2_XS: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ2_S: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ3_XXS: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ1_S: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ4_NL: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ4_XS: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - case GGML_TYPE_IQ3_S: - mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); - break; - default: - GGML_ASSERT(false); - break; - } +static void ggml_cpy_f32_q4_0_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + GGML_ASSERT(ne % QK4_0 == 0); + const int num_blocks = ne / QK4_0; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} + +static void ggml_cpy_f32_q4_1_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { - (void) src1; - (void) dst; - (void) src1_ddf_i; - (void) src1_ncols; - (void) src1_padded_row_size; + GGML_ASSERT(ne % QK4_1 == 0); + const int num_blocks = ne / QK4_1; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } -static void ggml_cuda_op_dequantize_mul_mat_vec( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { +static void ggml_cpy_f16_f16_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { - const int64_t ne00 = src0->ne[0]; - const int64_t row_diff = row_high - row_low; + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} - GGML_ASSERT(src1->type == GGML_TYPE_F32); - // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics -#ifdef GGML_CUDA_F16 - cuda_pool_alloc src1_dfloat_a; - half * src1_dfloat = nullptr; // dfloat == half - bool src1_convert_f16 = - src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || - src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || - src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; +static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE; + scale_f32<<>>(x, dst, scale, k); +} - if (src1_convert_f16) { - src1_dfloat = src1_dfloat_a.alloc(ne00); - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); - } -#else - const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion -#endif // GGML_CUDA_F16 +static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE; + clamp_f32<<>>(x, dst, min, max, k); +} - switch (src0->type) { - case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q2_K: - dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q3_K: - dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_K: - dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_K: - dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q6_K: - dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - default: - GGML_ASSERT(false); - break; +template +static void rope_cuda( + const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { + GGML_ASSERT(ncols % 2 == 0); + const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); + const dim3 block_nums(nrows, num_blocks_x, 1); + if (pos == nullptr) { + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); + } else { + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } - - (void) src1; - (void) dst; - (void) src1_ddq_i; - (void) src1_ncols; - (void) src1_padded_row_size; } -static void ggml_cuda_op_mul_mat_cublas( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { - - GGML_ASSERT(src0_dd_i != nullptr); - GGML_ASSERT(src1_ddf_i != nullptr); - GGML_ASSERT(dst_dd_i != nullptr); +template +static void rope_neox_cuda( + const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { + GGML_ASSERT(ncols % 2 == 0); + const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); + const dim3 block_nums(nrows, num_blocks_x, 1); - const int64_t ne00 = src0->ne[0]; - const int64_t ne10 = src1->ne[0]; + const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.0f / n_dims; - const int64_t ne0 = dst->ne[0]; + if (pos == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims + ); + } +} - const int64_t row_diff = row_high - row_low; +static void rope_glm_f32_cuda( + const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, int n_ctx, cudaStream_t stream +) { + GGML_ASSERT(ncols % 4 == 0); + const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1); + const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE; + const dim3 block_nums(num_blocks_x, nrows, 1); + rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx); +} - int id; - CUDA_CHECK(cudaGetDevice(&id)); +static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, + const int k_rows, const int n_heads_log2_floor, const float m0, + const float m1, cudaStream_t stream) { + const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1); + const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE); + const dim3 block_nums(num_blocks_x, nrows, 1); + alibi_f32<<>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1); +} - // the main device has a larger memory buffer to hold the results from all GPUs - // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; +static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + const dim3 block_dims(WARP_SIZE, 1, 1); + const dim3 block_nums(nrows, 1, 1); + k_sum_rows_f32<<>>(x, dst, ncols); +} - const int compute_capability = g_device_caps[id].cc; +static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) { + // bitonic sort requires ncols to be power of 2 + GGML_ASSERT((ncols & (ncols - 1)) == 0); - if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { - //printf("this branch\n"); - // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 - cuda_pool_alloc src0_as_f16; - if (src0->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - size_t ne = row_diff*ne00; - src0_as_f16.alloc(ne); - to_fp16_cuda(src0_dd_i, src0_as_f16.get(), ne, stream); - } - const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get(); + const dim3 block_dims(ncols, 1, 1); + const dim3 block_nums(1, nrows, 1); + if (order == GGML_SORT_ORDER_ASC) { + k_argsort_f32_i32<<>>(x, dst, ncols); + } else if (order == GGML_SORT_ORDER_DESC) { + k_argsort_f32_i32<<>>(x, dst, ncols); + } else { + GGML_ASSERT(false); + } +} - cuda_pool_alloc src1_as_f16; - if (src1->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - size_t ne = src1_ncols*ne10; - src1_as_f16.alloc(ne); - to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); - } - const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); - cuda_pool_alloc dst_f16(row_diff*src1_ncols); +static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) { + const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1); + const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE; + const dim3 block_nums(nrows_x, block_num_x, 1); + diag_mask_inf_f32<<>>(x, dst, ncols_x, rows_per_channel, n_past); +} - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; +static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { + int nth = WARP_SIZE; + while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; + const dim3 block_dims(nth, 1, 1); + const dim3 block_nums(nrows_x, 1, 1); + const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); + static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); - CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream)); - CUBLAS_CHECK( - cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, - row_diff, src1_ncols, ne10, - &alpha_f16, src0_ptr, CUDA_R_16F, ne00, - src1_ptr, CUDA_R_16F, ne10, - &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, - CUBLAS_COMPUTE_16F, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); - } else { - cuda_pool_alloc src0_ddq_as_f32; - cuda_pool_alloc src1_ddq_as_f32; + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - if (src0->type != GGML_TYPE_F32) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); - GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32.alloc(row_diff*ne00); - to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); - } - if (src1->type != GGML_TYPE_F32) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type); - GGML_ASSERT(to_fp32_cuda != nullptr); - src1_ddq_as_f32.alloc(src1_ncols*ne10); - to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + if (shmem < get_cuda_global_info().devices[ggml_cuda_get_device()].smpb) { + switch (ncols_x) { + case 32: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 64: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 128: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 256: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 512: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 1024: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 2048: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + case 4096: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; + default: + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + break; } - - const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); - const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); - - const float alpha = 1.0f; - const float beta = 0.0f; - - CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream)); - CUBLAS_CHECK( - cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, - row_diff, src1_ncols, ne10, - &alpha, src0_ddf_i, ne00, - src1_ddf1_i, ne10, - &beta, dst_dd_i, ldc)); + } else { + const size_t shmem_low = WARP_SIZE*sizeof(float); + soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); } - - (void) dst; - (void) src1_ddq_i; - (void) src1_padded_row_size; } -static void ggml_cuda_op_rope( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - GGML_ASSERT(src0->type == dst->type); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - - // RoPE alteration for extended context - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); +template +static void im2col_cuda(const float * x, T* dst, + int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC, + int64_t batch, int64_t batch_offset, int64_t offset_delta, + int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { + const int parallel_elements = OW * KW * KH; + const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; + dim3 block_nums(num_blocks, OH, batch * IC); + im2col_kernel<<>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); +} - const int32_t * pos = nullptr; - if ((mode & 1) == 0) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(src1->ne[0] == ne2); - pos = (const int32_t *) src1_dd; - } +static cudaError_t ggml_cuda_cpy_tensor_2d( + void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { - const bool is_neox = mode & 2; - const bool is_glm = mode & 4; + GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer)); + char * src_ptr = (char *) src->data; + char * dst_ptr = (char *) dst; - rope_corr_dims corr_dims; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + const int64_t ne0 = src->ne[0]; + const int64_t nb0 = src->nb[0]; + const int64_t nb1 = src->nb[1]; + const int64_t nb2 = src->nb[2]; + const int64_t nb3 = src->nb[3]; + const enum ggml_type type = src->type; + const int64_t ts = ggml_type_size(type); + const int64_t bs = ggml_blck_size(type); + int64_t i1_diff = i1_high - i1_low; - // compute - if (is_glm) { - GGML_ASSERT(false); - rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); - } else if (is_neox) { - if (src0->type == GGML_TYPE_F32) { - rope_neox_cuda( - (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream - ); - } else if (src0->type == GGML_TYPE_F16) { - rope_neox_cuda( - (const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream - ); - } else { - GGML_ASSERT(false); - } + const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; + if (nb0 == ts && nb1 == ts*ne0/bs) { + return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, cudaMemcpyDeviceToDevice, stream); + } else if (nb0 == ts) { + return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, cudaMemcpyDeviceToDevice, stream); } else { - if (src0->type == GGML_TYPE_F32) { - rope_cuda( - (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream - ); - } else if (src0->type == GGML_TYPE_F16) { - rope_cuda( - (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream - ); - } else { - GGML_ASSERT(false); + for (int64_t i1 = 0; i1 < i1_diff; i1++) { + const void * rx = (const void *) ((const char *) x + i1*nb1); + void * rd = (void *) (dst_ptr + i1*ts*ne0/bs); + // pretend the row is a matrix with cols=1 + cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyDeviceToDevice, stream); + if (r != cudaSuccess) { + return r; + } } + return cudaSuccess; } - - (void) src1; - (void) dst; - (void) src1_dd; } -static void ggml_cuda_op_alibi( +static void ggml_cuda_op_get_rows( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); + const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t stream) { - //GGML_ASSERT(ne01 + n_past == ne00); - GGML_ASSERT(n_head == ne02); + GGML_UNUSED(ctx); - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); + GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); - alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream); + const int32_t * src1_i32 = (const int32_t *) src1_d; - (void) src1; - (void) src1_dd; + switch (src0->type) { + case GGML_TYPE_F16: + get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_F32: + get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q4_0: + get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q4_1: + get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q5_0: + get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q5_1: + get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q8_0: + get_rows_cuda(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + default: + // TODO: k-quants + fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); + GGML_ASSERT(false); + break; + } } -static void ggml_cuda_op_pool2d( +template +static void ggml_cuda_op_bin_bcast( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_UNUSED(ctx); - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = static_cast(opts[0]); - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; + GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t IH = src0->ne[1]; - const int64_t IW = src0->ne[0]; + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + op()(src0, src1, dst, (const half *) src0_dd, src1_dd, (half *) dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream); + } else { + fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, + ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ASSERT(false); + } +} - const int64_t N = dst->ne[3]; - const int64_t OC = dst->ne[2]; - const int64_t OH = dst->ne[1]; - const int64_t OW = dst->ne[0]; +static void ggml_cuda_op_repeat( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t main_stream) { - const int parallel_elements = N * OC * OH * OW; - const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE; - dim3 block_nums(num_blocks); - pool2d_nchw_kernel<<>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op); + ggml_cuda_op_bin_bcast>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream); - (void) src1; - (void) src1_dd; + GGML_UNUSED(src1); + GGML_UNUSED(src1_d); } -static void ggml_cuda_op_im2col( +static void ggml_cuda_op_add( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; - - const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; + ggml_cuda_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} - const int64_t IC = src1->ne[is_2D ? 2 : 1]; - const int64_t IH = is_2D ? src1->ne[1] : 1; - const int64_t IW = src1->ne[0]; +static void ggml_cuda_op_acc( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - const int64_t KH = is_2D ? src0->ne[1] : 1; - const int64_t KW = src0->ne[0]; + GGML_UNUSED(ctx); - const int64_t OH = is_2D ? dst->ne[2] : 1; - const int64_t OW = dst->ne[1]; + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const int64_t batch = src1->ne[3]; - const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 + int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 + int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 + // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused + int offset = dst->op_params[3] / 4; // offset in bytes - if(dst->type == GGML_TYPE_F16) { - im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); - } else { - im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); - } + acc_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); - (void) src0; - (void) src0_dd; + GGML_UNUSED(dst); } -static void ggml_cuda_op_sum_rows( +static void ggml_cuda_op_mul( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ncols = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); + ggml_cuda_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} - sum_rows_f32_cuda(src0_dd, dst_dd, ncols, nrows, main_stream); +static void ggml_cuda_op_div( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - (void) src1; - (void) dst; - (void) src1_dd; + ggml_cuda_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -static void ggml_cuda_op_argsort( +static void ggml_cuda_op_gelu( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - + GGML_UNUSED(ctx); GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); - - const int64_t ncols = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + GGML_ASSERT( dst->type == GGML_TYPE_F32); - argsort_f32_i32_cuda(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream); + gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - (void) src1; - (void) dst; - (void) src1_dd; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_op_diag_mask_inf( +static void ggml_cuda_op_silu( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - + GGML_UNUSED(ctx); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int nrows0 = ggml_nrows(src0); - - const int n_past = ((int32_t *) dst->op_params)[0]; - - diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream); + silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - (void) src1; - (void) dst; - (void) src1_dd; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_op_soft_max( +static void ggml_cuda_op_gelu_quick( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - + GGML_UNUSED(ctx); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - - const int64_t ne00 = src0->ne[0]; - const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src0->ne[1]; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - - // positions tensor - float * src2_dd = nullptr; - - ggml_tensor * src2 = dst->src[2]; - const bool use_src2 = src2 != nullptr; - - if (use_src2) { - ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; - src2_dd = (float *) src2_extra->data_device[g_main_device]; - } + gelu_quick_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_op_scale( +static void ggml_cuda_op_tanh( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - + GGML_UNUSED(ctx); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - float scale; - memcpy(&scale, dst->op_params, sizeof(float)); - - scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); - CUDA_CHECK(cudaGetLastError()); + tanh_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - (void) src1; - (void) dst; - (void) src1_dd; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_op_clamp( +static void ggml_cuda_op_relu( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { - + GGML_UNUSED(ctx); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - float min; - float max; - memcpy(&min, dst->op_params, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); - CUDA_CHECK(cudaGetLastError()); + relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - (void) src1; - (void) dst; - (void) src1_dd; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) { - const int64_t nrows0 = ggml_nrows(src0); - - const bool use_src1 = src1 != nullptr; - const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; - - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - - // dd = data device - float * src0_ddf = nullptr; - float * src1_ddf = nullptr; - float * dst_ddf = nullptr; - - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; - - src0_ddf = (float *) src0_extra->data_device[g_main_device]; +static void ggml_cuda_op_hardsigmoid( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - if (use_src1) { - src1_ddf = (float *) src1_extra->data_device[g_main_device]; - } - dst_ddf = (float *) dst_extra->data_device[g_main_device]; + hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - // do the computation - op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); - CUDA_CHECK(cudaGetLastError()); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_set_peer_access(const int n_tokens) { - static bool peer_access_enabled = false; - - const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE; - - if (peer_access_enabled == enable_peer_access) { - return; - } - -#ifdef NDEBUG - for (int id = 0; id < g_device_count; ++id) { - ggml_cuda_set_device(id); - CUDA_CHECK(cudaDeviceSynchronize()); - } - - for (int id = 0; id < g_device_count; ++id) { - ggml_cuda_set_device(id); - - for (int id_other = 0; id_other < g_device_count; ++id_other) { - if (id == id_other) { - continue; - } - if (id != g_main_device && id_other != g_main_device) { - continue; - } +static void ggml_cuda_op_hardswish( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - int can_access_peer; - CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other)); - if (can_access_peer) { - if (enable_peer_access) { - cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); - if (err != cudaErrorPeerAccessAlreadyEnabled) { - CUDA_CHECK(err); - } - } else { - cudaError_t err = cudaDeviceDisablePeerAccess(id_other); - if (err != cudaErrorPeerAccessNotEnabled) { - CUDA_CHECK(err); - } - } - } - } - } -#endif // NDEBUG + hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - peer_access_enabled = enable_peer_access; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -// FIXME: move this somewhere else -struct ggml_backend_cuda_split_buffer_type_context { - std::array tensor_split; -}; - -static void ggml_cuda_op_mul_mat( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, - const bool convert_src1_to_q8_1) { - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int64_t nrows1 = ggml_nrows(src1); - - GGML_ASSERT(ne03 == ne13); - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); - - GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); - - const int64_t i02_divisor = ne12 / ne02; - - const size_t src0_ts = ggml_type_size(src0->type); - const size_t src0_bs = ggml_blck_size(src0->type); - const size_t q8_1_ts = sizeof(block_q8_1); - const size_t q8_1_bs = QK8_1; - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - - const bool src0_is_contiguous = ggml_is_contiguous(src0); - const bool src1_is_contiguous = ggml_is_contiguous(src1); - - const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - GGML_ASSERT(!(split && ne02 > 1)); - GGML_ASSERT(!(split && ne03 > 1)); - GGML_ASSERT(!(split && ne02 < ne12)); - - std::array tensor_split; - if (split) { - // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check - // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; - tensor_split = buft_ctx->tensor_split; - } - - struct dev_data { - cuda_pool_alloc src0_dd_alloc; - cuda_pool_alloc src1_ddf_alloc; - cuda_pool_alloc src1_ddq_alloc; - cuda_pool_alloc dst_dd_alloc; - - char * src0_dd = nullptr; - float * src1_ddf = nullptr; // float - char * src1_ddq = nullptr; // q8_1 - float * dst_dd = nullptr; - - int64_t row_low; - int64_t row_high; - }; - - dev_data dev[GGML_CUDA_MAX_DEVICES]; - - int used_devices = 0; - - for (int id = 0; id < g_device_count; ++id) { - // by default, use all rows - dev[id].row_low = 0; - dev[id].row_high = ne01; - - // for multi GPU, get the row boundaries from tensor split - // and round to mul_mat_q tile sizes - if (split) { - const int64_t rounding = get_row_rounding(src0->type, tensor_split); - - if (id != 0) { - dev[id].row_low = ne01*tensor_split[id]; - if (dev[id].row_low < ne01) { - dev[id].row_low -= dev[id].row_low % rounding; - } - } - - if (id != g_device_count - 1) { - dev[id].row_high = ne01*tensor_split[id + 1]; - if (dev[id].row_high < ne01) { - dev[id].row_high -= dev[id].row_high % rounding; - } - } - } - } - - for (int id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) { - continue; - } - - used_devices++; - - const bool src1_on_device = id == g_main_device; // TODO: check from buffer - const bool dst_on_device = id == g_main_device; - - ggml_cuda_set_device(id); - cudaStream_t stream = g_cudaStreams[id][0]; - - if (src0_is_contiguous) { - dev[id].src0_dd = (char *) src0_extra->data_device[id]; - } else { - dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ggml_nbytes(src0)); - } - - if (src1_on_device && src1_is_contiguous) { - dev[id].src1_ddf = (float *) src1_extra->data_device[id]; - } else { - dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ggml_nelements(src1)); - } +static void ggml_cuda_op_leaky_relu( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - if (convert_src1_to_q8_1) { - dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); - if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); - } - } + leaky_relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); - if (dst_on_device) { - dev[id].dst_dd = (float *) dst_extra->data_device[id]; - } else { - const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst); - dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(size_dst_ddf); - } - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - // if multiple devices are used they need to wait for the main device - // here an event is recorded that signals that the main device has finished calculating the input data - if (split && used_devices > 1) { - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0])); - } +static void ggml_cuda_op_sqr( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; - for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { - const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; - const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; + sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - for (int id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) { - continue; - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - const bool src1_on_device = id == g_main_device; // TODO: check from buffer - const bool dst_on_device = id == g_main_device; - const int64_t row_diff = dev[id].row_high - dev[id].row_low; +static void ggml_cuda_op_norm( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - ggml_cuda_set_device(id); - cudaStream_t stream = g_cudaStreams[id][is]; + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); - // wait for main GPU data if necessary - if (split && (id != g_main_device || is != 0)) { - CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0)); - } + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); - for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { - const int64_t i03 = i0 / ne12; - const int64_t i02 = i0 % ne12; + norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream); - const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = dev[id].src1_ddf + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = dev[id].src1_ddq + src1_ddq_i_offset; - float * dst_dd_i = dev[id].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); +static void ggml_cuda_op_group_norm( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - // the main device memory buffer can be on VRAM scratch, with space for all partial results - // in that case an offset on dst_ddf_i is needed - if (id == g_main_device) { - dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split - } + int num_groups = dst->op_params[0]; + int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); + group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream); - // copy src0, src1 to device if necessary - if (src1_is_contiguous) { - if (id != g_main_device) { - if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; - CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, g_main_device, - src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); - } else { - float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; - src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, g_main_device, - src1_ncols*ne10*sizeof(float), stream)); - } - } - } else if (src1_on_device && !src1_is_contiguous) { - CUDA_CHECK(ggml_cuda_cpy_tensor_2d( - src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); - } else { - GGML_ASSERT(false); - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - if (convert_src1_to_q8_1 && !src1_is_contiguous) { - quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); - } +static void ggml_cuda_op_concat( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); - if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream)); - } + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_cuda(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream); + } - // do the computation - op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); + GGML_UNUSED(src1); + GGML_UNUSED(dst); +} - // copy dst to host or other device if necessary - if (!dst_on_device) { - void * dst_off_device = dst_extra->data_device[g_main_device]; - if (split) { - // src0 = weight matrix is saved as a transposed matrix for better memory layout. - // dst is NOT transposed. - // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU. - // Instead they need to be copied to the correct slice in ne0 = dst row index. - // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; -#if !defined(GGML_USE_HIPBLAS) - // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices - cudaMemcpy3DPeerParms p = {}; - p.dstDevice = g_main_device; - p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); - p.srcDevice = id; - p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); - p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); - CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); -#else - // HIP does not support cudaMemcpy3DPeerAsync or vmm pools - CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), - dst_dd_i, row_diff*sizeof(float), - row_diff*sizeof(float), src1_ncols, - cudaMemcpyDeviceToDevice, stream)); -#endif - } else { - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0; - CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream)); - } - } +static void ggml_cuda_op_upscale( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - // add event for the main device to wait on until other device is done - if (split && (id != g_main_device || is != 0)) { - CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream)); - } - } - } - } + const int scale_factor = dst->op_params[0]; - // main device waits for all other devices to be finished - if (split && g_device_count > 1) { - int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; - is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; + upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream); - ggml_cuda_set_device(g_main_device); - for (int id = 0; id < g_device_count; ++id) { - if (dev[id].row_low == dev[id].row_high) { - continue; - } - for (int64_t is = 0; is < is_max; ++is) { - CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0)); - } - } - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat); -} +static void ggml_cuda_op_pad( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors -static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows); -} + pad_f32_cuda(src0_dd, dst_dd, + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream); -static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_acc); -} +static void ggml_cuda_op_arange( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(dst->type == GGML_TYPE_F32); -static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul); -} + float start; + float stop; + float step; + memcpy(&start, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *)dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *)dst->op_params + 2, sizeof(float)); -static void ggml_cuda_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_div); -} + int64_t steps = (int64_t)ceil((stop - start) / step); + GGML_ASSERT(ggml_nelements(dst) == steps); -static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu); -} + arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream); -static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu); + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(src0_dd); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu_quick); -} +static void ggml_cuda_op_timestep_embedding( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); -static void ggml_cuda_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_tanh); -} + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; -static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); -} + timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream); -static void ggml_cuda_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardsigmoid); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardswish); -} -static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu); -} +static void ggml_cuda_op_rms_norm( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); -static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr); -} + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); -static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm); -} + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); -static void ggml_cuda_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_group_norm); -} + rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream); -static void ggml_cuda_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_concat); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); } -static void ggml_cuda_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_upscale); -} +static void ggml_cuda_op_mul_mat_q( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { -static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad); -} + const int64_t ne00 = src0->ne[0]; -static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); - // dd = data device - float * src0_ddf = nullptr; - float * src1_ddf = nullptr; - float * dst_ddf = nullptr; + const int64_t ne0 = dst->ne[0]; - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + const int64_t row_diff = row_high - row_low; - dst_ddf = (float *) dst_extra->data_device[g_main_device]; + int id = ggml_cuda_get_device(); - // do the computation - ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); - CUDA_CHECK(cudaGetLastError()); -} + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; -static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_timestep_embedding); -} + switch (src0->type) { + case GGML_TYPE_Q4_0: + ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q4_1: + ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_0: + ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_1: + ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q8_0: + ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q2_K: + ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q3_K: + ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q4_K: + ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_K: + ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q6_K: + ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + default: + GGML_ASSERT(false); + break; + } -static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddf_i); } -static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); +static void ggml_cuda_op_mul_mat_vec_q( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; + const int64_t row_diff = row_high - row_low; - const int64_t ne12 = src1->ne[2]; + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + const int64_t ne0 = dst->ne[0]; - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; + int id; + CUDA_CHECK(cudaGetDevice(&id)); - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + switch (src0->type) { + case GGML_TYPE_Q4_0: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q4_1: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q5_0: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q5_1: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q8_0: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q2_K: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q3_K: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q4_K: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q5_K: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_Q6_K: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ2_XXS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ2_XS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ2_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ3_XXS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ1_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ4_NL: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ4_XS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + case GGML_TYPE_IQ3_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; + default: + GGML_ASSERT(false); + break; + } - ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddf_i); + GGML_UNUSED(src1_ncols); + GGML_UNUSED(src1_padded_row_size); } -static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - +static void ggml_cuda_op_dequantize_mul_mat_vec( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { + GGML_UNUSED(ctx); const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; + const int64_t row_diff = row_high - row_low; - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; + GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t ne12 = src1->ne[2]; + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics +#ifdef GGML_CUDA_F16 + cuda_pool_alloc src1_dfloat_a; + half * src1_dfloat = nullptr; // dfloat == half - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + bool src1_convert_f16 = + src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; + if (src1_convert_f16) { + src1_dfloat = src1_dfloat_a.alloc(ne00); + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); + } +#else + const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion +#endif // GGML_CUDA_F16 - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + switch (src0->type) { + case GGML_TYPE_Q4_0: + dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_1: + dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_0: + dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_1: + dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q8_0: + dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q2_K: + dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q3_K: + dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_K: + dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_K: + dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q6_K: + dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_F16: + convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + default: + GGML_ASSERT(false); + break; + } - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddq_i); + GGML_UNUSED(src1_ncols); + GGML_UNUSED(src1_padded_row_size); +} - const int64_t row_stride_x = nb01 / sizeof(half); - const int64_t channel_stride_x = nb02 / sizeof(half); +static void ggml_cuda_op_mul_mat_cublas( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { - ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); -} + GGML_ASSERT(src0_dd_i != nullptr); + GGML_ASSERT(src1_ddf_i != nullptr); + GGML_ASSERT(dst_dd_i != nullptr); -static __global__ void k_compute_batched_ptrs( - const half * src0_as_f16, const half * src1_as_f16, char * dst, - const void ** ptrs_src, void ** ptrs_dst, - int64_t ne12, int64_t ne13, - int64_t ne23, - size_t nb02, size_t nb03, - size_t nb12, size_t nb13, - size_t nbd2, size_t nbd3, - int64_t r2, int64_t r3) { - int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; - int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; + const int64_t ne00 = src0->ne[0]; + const int64_t ne10 = src1->ne[0]; - if (i13 >= ne13 || i12 >= ne12) { - return; - } + const int64_t ne0 = dst->ne[0]; - int64_t i03 = i13 / r3; - int64_t i02 = i12 / r2; + const int64_t row_diff = row_high - row_low; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; -} + int id = ggml_cuda_get_device(); -static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); + // the main device has a larger memory buffer to hold the results from all GPUs + // ldc == nrows of the matrix that cuBLAS writes into + int ldc = id == ctx.device ? ne0 : row_diff; - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->type == GGML_TYPE_F16); + const int compute_capability = get_cuda_global_info().devices[id].cc; - GGML_TENSOR_BINARY_OP_LOCALS + if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { + // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 + ggml_cuda_pool_alloc src0_as_f16(ctx.pool()); + if (src0->type != GGML_TYPE_F16) { + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + size_t ne = row_diff*ne00; + src0_as_f16.alloc(ne); + to_fp16_cuda(src0_dd_i, src0_as_f16.get(), ne, stream); + } + const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get(); + + ggml_cuda_pool_alloc src1_as_f16(ctx.pool()); + if (src1->type != GGML_TYPE_F16) { + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + size_t ne = src1_ncols*ne10; + src1_as_f16.alloc(ne); + to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); + } + const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); + ggml_cuda_pool_alloc dst_f16(ctx.pool(), row_diff*src1_ncols); - const int64_t ne_dst = ggml_nelements(dst); + const half alpha_f16 = 1.0f; + const half beta_f16 = 0.0f; - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); + CUBLAS_CHECK( + cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, + row_diff, src1_ncols, ne10, + &alpha_f16, src0_ptr, CUDA_R_16F, ne00, + src1_ptr, CUDA_R_16F, ne10, + &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, + CUBLAS_COMPUTE_16F, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream)); + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); + to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); + } else { + ggml_cuda_pool_alloc src0_ddq_as_f32(ctx.pool(id)); + ggml_cuda_pool_alloc src1_ddq_as_f32(ctx.pool(id)); - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; - half * src0_f16 = (half *) src0_ddq; + if (src0->type != GGML_TYPE_F32) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); + GGML_ASSERT(to_fp32_cuda != nullptr); + src0_ddq_as_f32.alloc(row_diff*ne00); + to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); + } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type); + GGML_ASSERT(to_fp32_cuda != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + const float alpha = 1.0f; + const float beta = 0.0f; - // convert src1 to fp16 - cuda_pool_alloc src1_f16_alloc; - if (src1->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - const int64_t ne_src1 = ggml_nelements(src1); - src1_f16_alloc.alloc(ne_src1); - GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); + CUBLAS_CHECK( + cublasSgemm(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, + row_diff, src1_ncols, ne10, + &alpha, src0_ddf_i, ne00, + src1_ddf1_i, ne10, + &beta, dst_dd_i, ldc)); } - half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); - - cuda_pool_alloc dst_f16; - char * dst_t; - - cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; - cudaDataType_t cu_data_type = CUDA_R_16F; - - // dst strides - size_t nbd2 = dst->nb[2]; - size_t nbd3 = dst->nb[3]; - - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; - const float alpha_f32 = 1.0f; - const float beta_f32 = 0.0f; + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddq_i); + GGML_UNUSED(src1_padded_row_size); +} - const void * alpha = &alpha_f16; - const void * beta = &beta_f16; +static void ggml_cuda_op_rope( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + GGML_ASSERT(src0->type == dst->type); - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne_dst); + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t nrows = ggml_nrows(src0); - nbd2 /= sizeof(float) / sizeof(half); - nbd3 /= sizeof(float) / sizeof(half); - } else { - dst_t = (char *) dst_ddf; + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - cu_compute_type = CUBLAS_COMPUTE_32F; - cu_data_type = CUDA_R_32F; + // RoPE alteration for extended context + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - alpha = &alpha_f32; - beta = &beta_f32; + const int32_t * pos = nullptr; + if ((mode & 1) == 0) { + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(src1->ne[0] == ne2); + pos = (const int32_t *) src1_dd; } - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; -#if 0 - // use cublasGemmEx - { - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; + rope_corr_dims corr_dims; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); - CUBLAS_CHECK( - cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), - beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } + // compute + if (is_glm) { + GGML_ASSERT(false); + rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); + } else if (is_neox) { + if (src0->type == GGML_TYPE_F32) { + rope_neox_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_neox_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else { + GGML_ASSERT(false); } - } -#else - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { - // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - // use cublasGemmStridedBatchedEx - CUBLAS_CHECK( - cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA - (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB - beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC - ne12*ne13, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } else { - // use cublasGemmBatchedEx - const int ne23 = ne12*ne13; + if (src0->type == GGML_TYPE_F32) { + rope_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else { + GGML_ASSERT(false); + } + } - cuda_pool_alloc ptrs_src(2*ne23); - cuda_pool_alloc< void *> ptrs_dst(1*ne23); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - dim3 block_dims(ne13, ne12); - k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_f16, src1_f16, dst_t, - ptrs_src.get(), ptrs_dst.get(), - ne12, ne13, - ne23, - nb02, nb03, - src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, - src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, - nbd2, nbd3, - r2, r3); - CUDA_CHECK(cudaGetLastError()); +static void ggml_cuda_op_alibi( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - CUBLAS_CHECK( - cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, - (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, - beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, - ne23, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } -#endif + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t nrows = ggml_nrows(src0); - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream); - } -} + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); -static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; + //GGML_ASSERT(ne01 + n_past == ne00); + GGML_ASSERT(n_head == ne02); - int64_t min_compute_capability = INT_MAX; + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - bool any_pascal_with_slow_fp16 = false; - if (split) { - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; - auto & tensor_split = buft_ctx->tensor_split; - for (int id = 0; id < g_device_count; ++id) { - // skip devices that are not going to do any work: - if (tensor_split[id] >= (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { - continue; - } + const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - if (min_compute_capability > g_device_caps[id].cc) { - min_compute_capability = g_device_caps[id].cc; - } - if (g_device_caps[id].cc == 610) { - any_pascal_with_slow_fp16 = true; - } - } - } else { - min_compute_capability = g_device_caps[g_main_device].cc; - any_pascal_with_slow_fp16 = g_device_caps[g_main_device].cc == 610; - } + alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream); - // check data types and tensor shapes for custom matrix multiplication kernels: - bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1; + GGML_UNUSED(src1); + GGML_UNUSED(src1_dd); +} - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; +static void ggml_cuda_op_pool2d( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; - bool use_mul_mat_q = ggml_cuda_supports_mmq(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; - const bool fp16_performance_good = min_compute_capability >= CC_RDNA1; + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE; + dim3 block_nums(num_blocks); + pool2d_nchw_kernel<<>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op); -#ifdef CUDA_USE_TENSOR_CORES - use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3; -#endif // CUDA_USE_TENSOR_CORES + GGML_UNUSED(src1); + GGML_UNUSED(src1_dd); +} -#else +static void ggml_cuda_op_im2col( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - // fp16 performance is good on Volta or newer and on P100 (compute capability 6.0) - const bool fp16_performance_good = min_compute_capability >= CC_PASCAL && !any_pascal_with_slow_fp16; + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; - // mmvq and mmq need the __dp4a instruction which on NVIDIA is only available for CC >= 6.1 - use_mul_mat_vec_q = use_mul_mat_vec_q && min_compute_capability >= MIN_CC_DP4A; - use_mul_mat_q = use_mul_mat_q && min_compute_capability >= MIN_CC_DP4A; + const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; -#ifdef CUDA_USE_TENSOR_CORES - // when tensor cores are available, use them for large batch size - // ref: https://github.com/ggerganov/llama.cpp/pull/3776 - use_mul_mat_q = use_mul_mat_q && (!fp16_performance_good || src1->ne[1] <= MMQ_MAX_BATCH_SIZE); -#endif // CUDA_USE_TENSOR_CORES + const int64_t IC = src1->ne[is_2D ? 2 : 1]; + const int64_t IH = is_2D ? src1->ne[1] : 1; + const int64_t IW = src1->ne[0]; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + const int64_t KH = is_2D ? src0->ne[1] : 1; + const int64_t KW = src0->ne[0]; - // if mmvq is available it's a better choice than dmmv: -#ifndef GGML_CUDA_FORCE_DMMV - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; -#endif // GGML_CUDA_FORCE_DMMV + const int64_t OH = is_2D ? dst->ne[2] : 1; + const int64_t OW = dst->ne[1]; - // debug helpers - //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); - //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); - //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); - //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); - //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); - //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); + const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[3]; + const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 - if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ single-batch - ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // KQV single-batch - ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { - // KQ + KQV multi-batch - ggml_cuda_mul_mat_batched_cublas(src0, src1, dst); - } else if (use_dequantize_mul_mat_vec) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); - } else if (use_mul_mat_vec_q) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); - } else if (use_mul_mat_q) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); + if(dst->type == GGML_TYPE_F16) { + im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); } else { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); + im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); } -} -#if 0 -template -static __global__ void k_compute_batched_ptrs_id( - const void ** ptrs_src, void ** ptrs_dst, - int ne12, int ne13, - int ne23, - int nb02, int nb03, - int nb12, int nb13, - int nb2, int nb3, - int r2, int r3, - ggml_type src0_type, half * src0_as_f16, int64_t src0_ne, - const half * src1_f16, half * dst_f16, - const int32_t * ids, const int id, - Srcs... src0s) { + GGML_UNUSED(src0); + GGML_UNUSED(src0_dd); +} - int i = ids[id]; +static void ggml_cuda_op_sum_rows( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - half * src0_f16; - const void * srcs_ar[] = { (const half *) src0s... }; - if (src0_type == GGML_TYPE_F16) { - src0_f16 = (half *) srcs_ar[i]; - } else { - src0_f16 = src0_as_f16; - if (threadIdx.x == 0 && threadIdx.y == 0) { - const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type); - to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget); - } - } + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); - int i13 = blockIdx.x * blockDim.x + threadIdx.x; - int i12 = blockIdx.y * blockDim.y + threadIdx.y; + sum_rows_f32_cuda(src0_dd, dst_dd, ncols, nrows, main_stream); - if (i13 >= ne13 || i12 >= ne12) { - return; - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - int i03 = i13 / r3; - int i02 = i12 / r2; +static void ggml_cuda_op_argsort( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; -} + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); -static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) { - const struct ggml_tensor * ids = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src00 = dst->src[2]; + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - const int id = dst->op_params[0]; + argsort_f32_i32_cuda(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream); - GGML_ASSERT(!ggml_is_transposed(src00)); - GGML_ASSERT(!ggml_is_transposed(src1)); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->type == GGML_TYPE_F32); +static void ggml_cuda_op_diag_mask_inf( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00); - const int64_t ne01 = src00->ne[1]; - const int64_t ne02 = src00->ne[2]; - const int64_t ne03 = src00->ne[3]; + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int nrows0 = ggml_nrows(src0); - //const int64_t nb01 = src00->nb[1]; - const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02); - const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03); + const int n_past = ((int32_t *) dst->op_params)[0]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; + diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream); - //const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); - const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); +static void ggml_cuda_op_soft_max( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream)); + const int64_t ne00 = src0->ne[0]; + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; - //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - //void * src0_ddq = src0_extra->data_device[g_main_device]; - //half * src0_as_f16 = (half *) src0_ddq; + float scale = 1.0f; + float max_bias = 0.0f; - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + // positions tensor + float * src2_dd = nullptr; - // convert src1 to fp16 - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); + ggml_tensor * src2 = dst->src[2]; + const bool use_src2 = src2 != nullptr; - size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); - to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); + if (use_src2) { + src2_dd = (float *)src2->data; + } - size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); +} - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); +static void ggml_cuda_op_scale( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; + scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); + CUDA_CHECK(cudaGetLastError()); - // use cublasGemmBatchedEx - const int ne23 = ne12*ne13; + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - const void ** ptrs_src = nullptr; - void ** ptrs_dst = nullptr; +static void ggml_cuda_op_clamp( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + GGML_UNUSED(ctx); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - size_t ptrs_src_s = 0; - size_t ptrs_dst_s = 0; + float min; + float max; + memcpy(&min, dst->op_params, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); + CUDA_CHECK(cudaGetLastError()); - int64_t src0_ne = ggml_nelements(src00); - half * src0_as_f16 = nullptr; - size_t src0_as = 0; - if (src00->type != GGML_TYPE_F16) { - src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as); - } + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_dd); +} - static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); - dim3 block_dims(ne13, ne12); - k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>( - ptrs_src, ptrs_dst, - ne12, ne13, - ne23, - ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half), - nb12, nb13, - dst->nb[2], dst->nb[3], - r2, r3, - src00->type, src0_as_f16, src0_ne, - src1_as_f16, dst_f16, - (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, - dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, - dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, - dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, - dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr - ); +// TODO: remove this function +static void ggml_cuda_op_flatten(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) { + GGML_ASSERT(!src0 || ggml_backend_buffer_is_cuda(src0->buffer)); + GGML_ASSERT(!src1 || ggml_backend_buffer_is_cuda(src1->buffer)); + GGML_ASSERT( ggml_backend_buffer_is_cuda(dst->buffer)); + + // dd = data device + float * src0_ddf = src0 ? (float *) src0->data : nullptr; + float * src1_ddf = src1 ? (float *) src1->data : nullptr; + float * dst_ddf = (float *) dst->data; + + ggml_cuda_set_device(ctx.device); + + // do the computation + op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, ctx.stream()); CUDA_CHECK(cudaGetLastError()); +} - CUBLAS_CHECK( - cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00, - (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10, - &beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01, - ne23, - CUBLAS_COMPUTE_16F, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); +static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { + static bool peer_access_enabled = false; - if (src0_as != 0) { - ggml_cuda_pool_free(src0_as_f16, src0_as); + const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE; + + if (peer_access_enabled == enable_peer_access) { + return; } - if (ptrs_src_s != 0) { - ggml_cuda_pool_free(ptrs_src, ptrs_src_s); + +#ifdef NDEBUG + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + ggml_cuda_set_device(id); + CUDA_CHECK(cudaDeviceSynchronize()); } - if (ptrs_dst_s != 0) { - ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s); + + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + ggml_cuda_set_device(id); + + for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) { + if (id == id_other) { + continue; + } + if (id != main_device && id_other != main_device) { + continue; + } + + int can_access_peer; + CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other)); + if (can_access_peer) { + if (enable_peer_access) { + cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); + if (err != cudaErrorPeerAccessAlreadyEnabled) { + CUDA_CHECK(err); + } + } else { + cudaError_t err = cudaDeviceDisablePeerAccess(id_other); + if (err != cudaErrorPeerAccessNotEnabled) { + CUDA_CHECK(err); + } + } + } + } } +#endif // NDEBUG - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); + peer_access_enabled = enable_peer_access; - ggml_cuda_pool_free(src1_as_f16, src1_as); - ggml_cuda_pool_free(dst_f16, dst_as); + GGML_UNUSED(main_device); } -#endif -static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#if 0 - ggml_cuda_mul_mat_id_cublas(dst); - // TODO: mmq/mmv support -#endif - cudaStream_t stream = g_cudaStreams[g_main_device][0]; +static void ggml_cuda_op_mul_mat( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, + const bool convert_src1_to_q8_1) { - const size_t nb11 = src1->nb[1]; - const size_t nb1 = dst->nb[1]; + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; - const struct ggml_tensor * ids = src0; - const int32_t id = ((int32_t *) dst->op_params)[0]; - const int32_t n_as = ((int32_t *) dst->op_params)[1]; + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + const int64_t nrows1 = ggml_nrows(src1); - std::vector ids_host(ggml_nbytes(ids)); - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; - CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); - CUDA_CHECK(cudaStreamSynchronize(stream)); + GGML_ASSERT(ne03 == ne13); + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; - const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra; - const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra; + const int nb2 = dst->nb[2]; + const int nb3 = dst->nb[3]; - ggml_tensor_extra_gpu src1_row_extra; - ggml_tensor_extra_gpu dst_row_extra; + GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer)); + GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer)); + ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context; + ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *) dst->buffer->context; - ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); - src1_row.backend = GGML_BACKEND_TYPE_GPU; - dst_row.backend = GGML_BACKEND_TYPE_GPU; + GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); - src1_row.extra = &src1_row_extra; - dst_row.extra = &dst_row_extra; + const int64_t i02_divisor = ne12 / ne02; - char * src1_original = (char *) src1_extra->data_device[g_main_device]; - char * dst_original = (char *) dst_extra->data_device[g_main_device]; + const size_t src0_ts = ggml_type_size(src0->type); + const size_t src0_bs = ggml_blck_size(src0->type); + const size_t q8_1_ts = sizeof(block_q8_1); + const size_t q8_1_bs = QK8_1; - if (src1->ne[1] == 1) { - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + const bool src0_is_contiguous = ggml_is_contiguous(src0); + const bool src1_is_contiguous = ggml_is_contiguous(src1); - GGML_ASSERT(row_id >= 0 && row_id < n_as); + const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); + GGML_ASSERT(!(split && ne02 > 1)); + GGML_ASSERT(!(split && ne03 > 1)); + GGML_ASSERT(!(split && ne02 < ne12)); - src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; - src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? + ggml_tensor_extra_gpu * src0_extra = split ? (ggml_tensor_extra_gpu *) src0->extra : nullptr; - dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; - dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? - ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); - } - } else { - cuda_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); - cuda_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); + std::array tensor_split; + if (split) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } - src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); - dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); + struct dev_data { + ggml_cuda_pool_alloc src0_dd_alloc; + ggml_cuda_pool_alloc src1_ddf_alloc; + ggml_cuda_pool_alloc src1_ddq_alloc; + ggml_cuda_pool_alloc dst_dd_alloc; - for (int32_t row_id = 0; row_id < n_as; ++row_id) { - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + char * src0_dd = nullptr; + float * src1_ddf = nullptr; // float + char * src1_ddq = nullptr; // q8_1 + float * dst_dd = nullptr; - int64_t num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + int64_t row_low; + int64_t row_high; + }; - if (row_id_i != row_id) { - continue; - } + dev_data dev[GGML_CUDA_MAX_DEVICES]; - GGML_ASSERT(row_id >= 0 && row_id < n_as); + int used_devices = 0; - CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11, - nb11, cudaMemcpyDeviceToDevice, stream)); - num_src1_rows++; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + // by default, use all rows + dev[id].row_low = 0; + dev[id].row_high = ne01; + + // for multi GPU, get the row boundaries from tensor split + // and round to mul_mat_q tile sizes + if (split) { + const int64_t rounding = get_row_rounding(src0->type, tensor_split); + + if (id != 0) { + dev[id].row_low = ne01*tensor_split[id]; + if (dev[id].row_low < ne01) { + dev[id].row_low -= dev[id].row_low % rounding; + } } - if (num_src1_rows == 0) { - continue; + if (id != ggml_backend_cuda_get_device_count() - 1) { + dev[id].row_high = ne01*tensor_split[id + 1]; + if (dev[id].row_high < ne01) { + dev[id].row_high -= dev[id].row_high % rounding; + } } + } + } - src1_row.ne[1] = num_src1_rows; - dst_row.ne[1] = num_src1_rows; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) { + continue; + } - src1_row.nb[1] = nb11; - src1_row.nb[2] = num_src1_rows*nb11; - src1_row.nb[3] = num_src1_rows*nb11; + used_devices++; - dst_row.nb[1] = nb1; - dst_row.nb[2] = num_src1_rows*nb1; - dst_row.nb[3] = num_src1_rows*nb1; + const bool src1_on_device = id == src1_ctx->device; + const bool dst_on_device = id == dst_ctx->device; - ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row); + ggml_cuda_set_device(id); + cudaStream_t stream = ctx.stream(id, 0); - num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + if (src0_is_contiguous) { + dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data; + } else { + dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0)); + } - if (row_id_i != row_id) { - continue; - } + if (src1_on_device && src1_is_contiguous) { + dev[id].src1_ddf = (float *) src1->data; + } else { + dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1)); + } - GGML_ASSERT(row_id >= 0 && row_id < n_as); + if (convert_src1_to_q8_1) { + dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); - CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1, - nb1, cudaMemcpyDeviceToDevice, stream)); - num_src1_rows++; + if (src1_on_device && src1_is_contiguous) { + quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); + CUDA_CHECK(cudaGetLastError()); } } + + if (dst_on_device) { + dev[id].dst_dd = (float *) dst->data; + } else { + const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst); + dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(ctx.pool(id), size_dst_ddf); + } } -} -static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale); -} + // if multiple devices are used they need to wait for the main device + // here an event is recorded that signals that the main device has finished calculating the input data + if (split && used_devices > 1) { + ggml_cuda_set_device(ctx.device); + CUDA_CHECK(cudaEventRecord(src0_extra->events[ctx.device][0], ctx.stream())); + } -static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp); -} + const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; + for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { + const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_CUDA_MAX_STREAMS : 0; + const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; -static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const int64_t ne = ggml_nelements(src0); - GGML_ASSERT(ne == ggml_nelements(src1)); + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) { + continue; + } - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); + const bool src1_on_device = id == src1_ctx->device; + const bool dst_on_device = id == dst_ctx->device; + const int64_t row_diff = dev[id].row_high - dev[id].row_low; - GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); - GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); + ggml_cuda_set_device(id); + cudaStream_t stream = ctx.stream(id, is); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; + // wait for main GPU data if necessary + if (split && (id != ctx.device || is != 0)) { + CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[ctx.device][0], 0)); + } - //GGML_ASSERT(src0->ne[3] == 1); + for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { + const int64_t i03 = i0 / ne12; + const int64_t i02 = i0 % ne12; - const int64_t nb00 = src0->nb[0]; - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - const int64_t nb03 = src0->nb[3]; + const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; + + // for split tensors the data begins at i0 == i0_offset_low + char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = dev[id].src1_ddf + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = dev[id].src1_ddq + src1_ddq_i_offset; + float * dst_dd_i = dev[id].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; + // the main device memory buffer can be on VRAM scratch, with space for all partial results + // in that case an offset on dst_ddf_i is needed + if (id == ctx.device) { + dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split + } - //GGML_ASSERT(src1->ne[3] == 1); + // copy src0, src1 to device if necessary + if (src1_is_contiguous) { + if (id != ctx.device) { + if (convert_src1_to_q8_1) { + char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset; + CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device, + src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); + } else { + float * src1_ddf_i_source = (float *) src1->data; + src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; + CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, ctx.device, + src1_ncols*ne10*sizeof(float), stream)); + } + } + } else if (src1_on_device && !src1_is_contiguous) { + CUDA_CHECK(ggml_cuda_cpy_tensor_2d( + src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); + } else { + GGML_ASSERT(false); + } - const int64_t nb10 = src1->nb[0]; - const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; - const int64_t nb13 = src1->nb[3]; + if (convert_src1_to_q8_1 && !src1_is_contiguous) { + quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); + CUDA_CHECK(cudaGetLastError()); + } - ggml_cuda_set_device(g_main_device); - cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { + CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream)); + } - const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + // do the computation + op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, + dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream); + CUDA_CHECK(cudaGetLastError()); - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; + // copy dst to host or other device if necessary + if (!dst_on_device) { + void * dst_off_device = dst->data; + if (split) { + // src0 = weight matrix is saved as a transposed matrix for better memory layout. + // dst is NOT transposed. + // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU. + // Instead they need to be copied to the correct slice in ne0 = dst row index. + // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. + float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); + GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); + dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; +#if !defined(GGML_USE_HIPBLAS) + // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices + cudaMemcpy3DPeerParms p = {}; + p.dstDevice = ctx.device; + p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); + p.srcDevice = id; + p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); + p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); + CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); +#else + // HIP does not support cudaMemcpy3DPeerAsync or vmm pools + CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), + dst_dd_i, row_diff*sizeof(float), + row_diff*sizeof(float), src1_ncols, + cudaMemcpyDeviceToDevice, stream)); +#endif + } else { + float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); + GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); + dhf_dst_i += src1_col_0*ne0; + CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream)); + } + } - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else { - fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, - ggml_type_name(src0->type), ggml_type_name(src1->type)); - GGML_ASSERT(false); + // add event for the main device to wait on until other device is done + if (split && (id != ctx.device || is != 0)) { + CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream)); + } + } + } } - (void) dst; -} + // main device waits for all other devices to be finished + if (split && ggml_backend_cuda_get_device_count() > 1) { + int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; + is_max = is_max <= GGML_CUDA_MAX_STREAMS ? is_max : GGML_CUDA_MAX_STREAMS; -static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - // TODO: why do we pass dst as src1 here? - ggml_cuda_cpy(src0, dst, nullptr); - (void) src1; + ggml_cuda_set_device(ctx.device); + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + if (dev[id].row_low == dev[id].row_high) { + continue; + } + for (int64_t is = 0; is < is_max; ++is) { + CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), src0_extra->events[id][is], 0)); + } + } + } } -static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf); +static void ggml_cuda_repeat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_repeat); } -static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max); +static void ggml_cuda_get_rows(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_get_rows); } -static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope); +static void ggml_cuda_add(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_add); } -static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); +static void ggml_cuda_acc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_acc); } -static void ggml_cuda_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pool2d); +static void ggml_cuda_mul(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_mul); } -static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); +static void ggml_cuda_div(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_div); } -static void ggml_cuda_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sum_rows); +static void ggml_cuda_gelu(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_gelu); } -static void ggml_cuda_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_argsort); +static void ggml_cuda_silu(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_silu); } -static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - (void) src0; - (void) src1; - (void) dst; +static void ggml_cuda_gelu_quick(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_gelu_quick); } -static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); +static void ggml_cuda_tanh(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_tanh); } -static void ggml_cuda_set_main_device(const int main_device) { - if (main_device >= g_device_count) { - fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", - main_device, g_device_count, g_main_device); - return; - } - - if (g_main_device != main_device && g_device_count > 1) { - g_main_device = main_device; - //cudaDeviceProp prop; - //CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device)); - //fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name); - } +static void ggml_cuda_relu(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_relu); } -static bool ggml_cuda_compute_forward(struct ggml_tensor * tensor) { - if (!g_cublas_loaded) return false; - - if (tensor->op == GGML_OP_MUL_MAT) { - if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { -#ifndef NDEBUG - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); -#endif - return false; - } - } - - ggml_cuda_func_t func; +static void ggml_cuda_hardsigmoid(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_hardsigmoid); +} - switch (tensor->op) { - case GGML_OP_REPEAT: - func = ggml_cuda_repeat; - break; - case GGML_OP_GET_ROWS: - func = ggml_cuda_get_rows; - break; - case GGML_OP_DUP: - func = ggml_cuda_dup; - break; - case GGML_OP_ADD: - func = ggml_cuda_add; - break; - case GGML_OP_ACC: - func = ggml_cuda_acc; - break; - case GGML_OP_MUL: - func = ggml_cuda_mul; - break; - case GGML_OP_DIV: - func = ggml_cuda_div; - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_GELU: - func = ggml_cuda_gelu; - break; - case GGML_UNARY_OP_SILU: - func = ggml_cuda_silu; - break; - case GGML_UNARY_OP_GELU_QUICK: - func = ggml_cuda_gelu_quick; - break; - case GGML_UNARY_OP_TANH: - func = ggml_cuda_tanh; - break; - case GGML_UNARY_OP_RELU: - func = ggml_cuda_relu; - break; - case GGML_UNARY_OP_HARDSIGMOID: - func = ggml_cuda_hardsigmoid; - break; - case GGML_UNARY_OP_HARDSWISH: - func = ggml_cuda_hardswish; - break; - default: - return false; - } - break; - case GGML_OP_NORM: - func = ggml_cuda_norm; - break; - case GGML_OP_GROUP_NORM: - func = ggml_cuda_group_norm; - break; - case GGML_OP_CONCAT: - func = ggml_cuda_concat; - break; - case GGML_OP_UPSCALE: - func = ggml_cuda_upscale; - break; - case GGML_OP_PAD: - func = ggml_cuda_pad; - break; - case GGML_OP_ARANGE: - func = ggml_cuda_arange; - break; - case GGML_OP_TIMESTEP_EMBEDDING: - func = ggml_cuda_timestep_embedding; - break; - case GGML_OP_LEAKY_RELU: - func = ggml_cuda_leaky_relu; - break; - case GGML_OP_RMS_NORM: - func = ggml_cuda_rms_norm; - break; - case GGML_OP_MUL_MAT: - func = ggml_cuda_mul_mat; - break; - case GGML_OP_MUL_MAT_ID: - func = ggml_cuda_mul_mat_id; - break; - case GGML_OP_SCALE: - func = ggml_cuda_scale; - break; - case GGML_OP_SQR: - func = ggml_cuda_sqr; - break; - case GGML_OP_CLAMP: - func = ggml_cuda_clamp; - break; - case GGML_OP_CPY: - func = ggml_cuda_cpy; - break; - case GGML_OP_CONT: - func = ggml_cuda_dup; - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - func = ggml_cuda_nop; - break; - case GGML_OP_DIAG_MASK_INF: - func = ggml_cuda_diag_mask_inf; - break; - case GGML_OP_SOFT_MAX: - func = ggml_cuda_soft_max; - break; - case GGML_OP_ROPE: - func = ggml_cuda_rope; - break; - case GGML_OP_ALIBI: - func = ggml_cuda_alibi; - break; - case GGML_OP_IM2COL: - func = ggml_cuda_im2col; - break; - case GGML_OP_POOL_2D: - func = ggml_cuda_pool2d; - break; - case GGML_OP_SUM_ROWS: - func = ggml_cuda_sum_rows; - break; - case GGML_OP_ARGSORT: - func = ggml_cuda_argsort; - break; - default: - return false; - } +static void ggml_cuda_hardswish(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_hardswish); +} +static void ggml_cuda_leaky_relu(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_leaky_relu); +} - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - ggml_cuda_set_peer_access(tensor->src[1]->ne[1]); - } +static void ggml_cuda_sqr(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_sqr); +} - func(tensor->src[0], tensor->src[1], tensor); - return true; +static void ggml_cuda_norm(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_norm); } -static int ggml_cuda_get_device_count() { - int device_count; - if (cudaGetDeviceCount(&device_count) != cudaSuccess) { - return 0; - } - return device_count; +static void ggml_cuda_group_norm(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_group_norm); } -static void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); - snprintf(description, description_size, "%s", prop.name); +static void ggml_cuda_concat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_concat); } -//////////////////////////////////////////////////////////////////////////////// +static void ggml_cuda_upscale(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_upscale); +} -// backend interface +static void ggml_cuda_pad(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_pad); +} -#define UNUSED GGML_UNUSED +static void ggml_cuda_arange(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_arange); +} -struct ggml_backend_cuda_context { - explicit ggml_backend_cuda_context(int device) : - device(device), - name(GGML_CUDA_NAME + std::to_string(device)) { - } +static void ggml_cuda_timestep_embedding(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_timestep_embedding); +} - ~ggml_backend_cuda_context() { - if (copy_event != nullptr) { - CUDA_CHECK(cudaEventDestroy(copy_event)); - } - } +static void ggml_cuda_rms_norm(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_rms_norm); +} - int device; - std::string name; - cudaEvent_t copy_event = nullptr; -}; +static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ + GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); + GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); + GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation + GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); -// cuda buffer + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; -struct ggml_backend_cuda_buffer_context { - int device; - void * dev_ptr = nullptr; - ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; - size_t temp_tensor_extra_index = 0; - std::string name; + const int64_t ne12 = src1->ne[2]; - ggml_backend_cuda_buffer_context(int device, void * dev_ptr) : - device(device), dev_ptr(dev_ptr), - name(GGML_CUDA_NAME + std::to_string(device)) { - } + ggml_cuda_set_device(ctx.device); + cudaStream_t main_stream = ctx.stream(); - ~ggml_backend_cuda_buffer_context() { - delete[] temp_tensor_extras; - } + void * src0_ddq = src0->data; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; - ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { - // TODO: remove GGML_CUDA_MAX_NODES, allocate dynamically and reuse in backend_buffer_reset - if (temp_tensor_extras == nullptr) { - temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; - } + ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); +} - size_t alloc_index = temp_tensor_extra_index; - temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES; - ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; - memset(extra, 0, sizeof(*extra)); +static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + GGML_ASSERT(!ggml_is_permuted(src0)); + GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); - return extra; - } -}; + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; -GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - return ctx->name.c_str(); -} + const int64_t nb01 = src0->nb[1]; + const int64_t nb02 = src0->nb[2]; -GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; -} + const int64_t ne12 = src1->ne[2]; -GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - CUDA_CHECK(cudaFree(ctx->dev_ptr)); - delete ctx; -} + ggml_cuda_set_device(ctx.device); + cudaStream_t main_stream = ctx.stream(); -GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - return ctx->dev_ptr; + void * src0_ddq = src0->data; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; + + const int64_t row_stride_x = nb01 / sizeof(half); + const int64_t channel_stride_x = nb02 / sizeof(half); + + ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); } -GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; +static __global__ void k_compute_batched_ptrs( + const half * src0_as_f16, const half * src1_as_f16, char * dst, + const void ** ptrs_src, void ** ptrs_dst, + int64_t ne12, int64_t ne13, + int64_t ne23, + size_t nb02, size_t nb03, + size_t nb12, size_t nb13, + size_t nbd2, size_t nbd3, + int64_t r2, int64_t r3) { + int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; + int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; - if (tensor->view_src != NULL && tensor->view_offs == 0) { - assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; + if (i13 >= ne13 || i12 >= ne12) { return; } - ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra(); + int64_t i03 = i13 / r3; + int64_t i02 = i12 / r2; - extra->data_device[ctx->device] = tensor->data; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; +} - tensor->backend = GGML_BACKEND_TYPE_GPU; - tensor->extra = extra; +static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); - if (ggml_is_quantized(tensor->type)) { - // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); + GGML_ASSERT(src0->type == GGML_TYPE_F16); - if (padded_size > original_size && tensor->view_src == nullptr) { - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size)); - } + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t ne_dst = ggml_nelements(dst); + + ggml_cuda_set_device(ctx.device); + cudaStream_t main_stream = ctx.stream(); + + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream)); + + void * src0_ddq = src0->data; + half * src0_f16 = (half *) src0_ddq; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; + + // convert src1 to fp16 + ggml_cuda_pool_alloc src1_f16_alloc(ctx.pool()); + if (src1->type != GGML_TYPE_F16) { + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); } -} + half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); -GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); + ggml_cuda_pool_alloc dst_f16(ctx.pool()); + char * dst_t; - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; + cudaDataType_t cu_data_type = CUDA_R_16F; - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread)); - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); -} + // dst strides + size_t nbd2 = dst->nb[2]; + size_t nbd3 = dst->nb[3]; -GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); + const half alpha_f16 = 1.0f; + const half beta_f16 = 0.0f; - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + const float alpha_f32 = 1.0f; + const float beta_f32 = 0.0f; - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); -} + const void * alpha = &alpha_f16; + const void * beta = &beta_f16; -GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { - if (ggml_backend_buffer_is_cuda(src->buffer)) { - ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; - ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; - if (src_ctx->device == dst_ctx->device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); - } else { - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); - } - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); - return true; + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + dst_t = (char *) dst_f16.alloc(ne_dst); + + nbd2 /= sizeof(float) / sizeof(half); + nbd3 /= sizeof(float) / sizeof(half); + } else { + dst_t = (char *) dst_ddf; + + cu_compute_type = CUBLAS_COMPUTE_32F; + cu_data_type = CUDA_R_32F; + + alpha = &alpha_f32; + beta = &beta_f32; } - return false; - UNUSED(buffer); -} + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); -GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); - CUDA_CHECK(cudaDeviceSynchronize()); -} +#if 0 + // use cublasGemmEx + { + for (int i13 = 0; i13 < ne13; ++i13) { + for (int i12 = 0; i12 < ne12; ++i12) { + int i03 = i13 / r3; + int i02 = i12 / r2; -static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_buffer_get_name, - /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, - /* .get_base = */ ggml_backend_cuda_buffer_get_base, - /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, - /* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cuda_buffer_clear, - /* .reset = */ NULL, -}; + CUBLAS_CHECK( + cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), + (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), + beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, + cu_compute_type, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + } + } + } +#else + if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + // there is no broadcast and src0, src1 are contiguous across dims 2, 3 + // use cublasGemmStridedBatchedEx + CUBLAS_CHECK( + cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA + (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB + beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC + ne12*ne13, + cu_compute_type, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + } else { + // use cublasGemmBatchedEx + const int ne23 = ne12*ne13; -// cuda buffer type -struct ggml_backend_cuda_buffer_type_context { - int device; - std::string name; -}; + ggml_cuda_pool_alloc ptrs_src(ctx.pool(), 2*ne23); + ggml_cuda_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23); + + dim3 block_dims(ne13, ne12); + k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( + src0_f16, src1_f16, dst_t, + ptrs_src.get(), ptrs_dst.get(), + ne12, ne13, + ne23, + nb02, nb03, + src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, + src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, + nbd2, nbd3, + r2, r3); + CUDA_CHECK(cudaGetLastError()); -GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { - ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + CUBLAS_CHECK( + cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, + (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, + beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, + ne23, + cu_compute_type, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + } +#endif - return ctx->name.c_str(); + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); + to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream); + } } -GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; +static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); - ggml_cuda_set_device(buft_ctx->device); + int64_t min_compute_capability = INT_MAX; - size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 + bool any_pascal_with_slow_fp16 = false; + if (split) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + auto & tensor_split = buft_ctx->tensor_split; + for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + // skip devices that are not going to do any work: + if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { + continue; + } - void * dev_ptr; - cudaError_t err = cudaMalloc(&dev_ptr, size); - if (err != cudaSuccess) { - fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); - return nullptr; + if (min_compute_capability > get_cuda_global_info().devices[id].cc) { + min_compute_capability = get_cuda_global_info().devices[id].cc; + } + if (get_cuda_global_info().devices[id].cc == 610) { + any_pascal_with_slow_fp16 = true; + } + } + } else { + min_compute_capability = get_cuda_global_info().devices[ctx.device].cc; + any_pascal_with_slow_fp16 = get_cuda_global_info().devices[ctx.device].cc == 610; } - ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr); + // check data types and tensor shapes for custom matrix multiplication kernels: + bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1; - return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); -} + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; -GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; + bool use_mul_mat_q = ggml_cuda_supports_mmq(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - UNUSED(buft); -} +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; + const bool fp16_performance_good = min_compute_capability >= CC_RDNA1; - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } +#ifdef CUDA_USE_TENSOR_CORES + use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3; +#endif // CUDA_USE_TENSOR_CORES - return size; +#else - UNUSED(buft); -} + // fp16 performance is good on Volta or newer and on P100 (compute capability 6.0) + const bool fp16_performance_good = min_compute_capability >= CC_PASCAL && !any_pascal_with_slow_fp16; -GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - if (!ggml_backend_is_cuda(backend)) { - return false; - } + // mmvq and mmq need the __dp4a instruction which on NVIDIA is only available for CC >= 6.1 + use_mul_mat_vec_q = use_mul_mat_vec_q && min_compute_capability >= MIN_CC_DP4A; + use_mul_mat_q = use_mul_mat_q && min_compute_capability >= MIN_CC_DP4A; - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; +#ifdef CUDA_USE_TENSOR_CORES + // when tensor cores are available, use them for large batch size + // ref: https://github.com/ggerganov/llama.cpp/pull/3776 + use_mul_mat_q = use_mul_mat_q && (!fp16_performance_good || src1->ne[1] <= MMQ_MAX_BATCH_SIZE); +#endif // CUDA_USE_TENSOR_CORES - return buft_ctx->device == cuda_ctx->device; -} +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { - /* .get_name = */ ggml_backend_cuda_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, - /* .is_host = */ NULL, -}; + // if mmvq is available it's a better choice than dmmv: +#ifndef GGML_CUDA_FORCE_DMMV + use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; +#endif // GGML_CUDA_FORCE_DMMV -GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { - ggml_init_cublas(); + // debug helpers + //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); + //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); + //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); + //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); + //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - // FIXME: this is not thread safe - if (device >= ggml_backend_cuda_get_device_count()) { - return nullptr; + if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + // KQ single-batch + ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst); + } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + // KQV single-batch + ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst); + } else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + // KQ + KQV multi-batch + ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); + } else if (use_dequantize_mul_mat_vec) { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); + } else if (use_mul_mat_vec_q) { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true); + } else { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); } +} - static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; +#if 0 +template +static __global__ void k_compute_batched_ptrs_id( + const void ** ptrs_src, void ** ptrs_dst, + int ne12, int ne13, + int ne23, + int nb02, int nb03, + int nb12, int nb13, + int nb2, int nb3, + int r2, int r3, + ggml_type src0_type, half * src0_as_f16, int64_t src0_ne, + const half * src1_f16, half * dst_f16, + const int32_t * ids, const int id, + Srcs... src0s) { - static bool ggml_backend_cuda_buffer_type_initialized = false; + int i = ids[id]; - if (!ggml_backend_cuda_buffer_type_initialized) { - for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) { - ggml_backend_cuda_buffer_types[i] = { - /* .iface = */ ggml_backend_cuda_buffer_type_interface, - /* .context = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)}, - }; + half * src0_f16; + const void * srcs_ar[] = { (const half *) src0s... }; + if (src0_type == GGML_TYPE_F16) { + src0_f16 = (half *) srcs_ar[i]; + } else { + src0_f16 = src0_as_f16; + if (threadIdx.x == 0 && threadIdx.y == 0) { + const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type); + to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget); } - ggml_backend_cuda_buffer_type_initialized = true; } - return &ggml_backend_cuda_buffer_types[device]; -} - -// cuda split buffer + int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i12 = blockIdx.y * blockDim.y + threadIdx.y; -struct ggml_backend_cuda_split_buffer_context { - ~ggml_backend_cuda_split_buffer_context() { - for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int id = 0; id < g_device_count; ++id) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); - } - } - if (extra->data_device[id] != nullptr) { - CUDA_CHECK(cudaFree(extra->data_device[id])); - } - } - delete extra; - } + if (i13 >= ne13 || i12 >= ne12) { + return; } - std::vector tensor_extras; -}; - -GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Split"; + int i03 = i13 / r3; + int i02 = i12 / r2; - UNUSED(buffer); + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; } -static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; - UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds -} +static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) { + const struct ggml_tensor * ids = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src00 = dst->src[2]; -GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; - delete ctx; -} + const int id = dst->op_params[0]; -GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { - // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced - return (void *)0x1000; + GGML_ASSERT(!ggml_is_transposed(src00)); + GGML_ASSERT(!ggml_is_transposed(src1)); - UNUSED(buffer); -} + GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32); -GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00); + const int64_t ne01 = src00->ne[1]; + const int64_t ne02 = src00->ne[2]; + const int64_t ne03 = src00->ne[3]; - ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + //const int64_t nb01 = src00->nb[1]; + const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02); + const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03); - const int64_t ne0 = tensor->ne[0]; + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; - ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + //const int64_t nb11 = src1->nb[1]; + const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); + const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); - ctx->tensor_extras.push_back(extra); + const int64_t ne1 = ggml_nelements(src1); + const int64_t ne = ggml_nelements(dst); - for (int id = 0; id < g_device_count; ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + ggml_cuda_set_device(g_main_device); + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } + CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream)); - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; + //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + //void * src0_ddq = src0_extra->data_device[g_main_device]; + //half * src0_as_f16 = (half *) src0_ddq; - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; - // FIXME: do not crash if cudaMalloc fails - // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first - ggml_cuda_set_device(id); - char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); - } + // convert src1 to fp16 + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); - extra->data_device[id] = buf; + size_t src1_as = 0; + half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); + to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); - } - } - tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; - tensor->extra = extra; -} + size_t dst_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); -GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + const half alpha_f16 = 1.0f; + const half beta_f16 = 0.0f; - for (int id = 0; id < g_device_count; ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + // use cublasGemmBatchedEx + const int ne23 = ne12*ne13; - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } + const void ** ptrs_src = nullptr; + void ** ptrs_dst = nullptr; - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; + size_t ptrs_src_s = 0; + size_t ptrs_dst_s = 0; - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } + ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); - const char * buf_host = (const char *)data + offset_split; - CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread)); + int64_t src0_ne = ggml_nelements(src00); + half * src0_as_f16 = nullptr; + size_t src0_as = 0; + if (src00->type != GGML_TYPE_F16) { + src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as); } - for (int id = 0; id < g_device_count; ++id) { - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); + static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); + dim3 block_dims(ne13, ne12); + k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>( + ptrs_src, ptrs_dst, + ne12, ne13, + ne23, + ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half), + nb12, nb13, + dst->nb[2], dst->nb[3], + r2, r3, + src00->type, src0_as_f16, src0_ne, + src1_as_f16, dst_f16, + (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, + dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, + dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, + dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, + dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr + ); + CUDA_CHECK(cudaGetLastError()); + + CUBLAS_CHECK( + cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00, + (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10, + &beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01, + ne23, + CUBLAS_COMPUTE_16F, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + + if (src0_as != 0) { + ggml_cuda_pool_free(src0_as_f16, src0_as); + } + if (ptrs_src_s != 0) { + ggml_cuda_pool_free(ptrs_src, ptrs_src_s); + } + if (ptrs_dst_s != 0) { + ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s); } + + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); + to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); + + ggml_cuda_pool_free(src1_as_f16, src1_as); + ggml_cuda_pool_free(dst_f16, dst_as); } +#endif -GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); +static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#if 0 + ggml_cuda_mul_mat_id_cublas(dst); + // TODO: mmq/mmv support +#endif + cudaStream_t stream = ctx.stream(); - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + const size_t nb11 = src1->nb[1]; + const size_t nb1 = dst->nb[1]; - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + const struct ggml_tensor * ids = src0; + const int32_t id = ((int32_t *) dst->op_params)[0]; + const int32_t n_as = ((int32_t *) dst->op_params)[1]; - for (int id = 0; id < g_device_count; ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + std::vector ids_host(ggml_nbytes(ids)); + const char * ids_dev = (const char *) ids->data; + CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } + ggml_tensor src1_row = *src1; + ggml_tensor dst_row = *dst; - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; + char * src1_original = (char *) src1->data; + char * dst_original = (char *) dst->data; - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } + if (src1->ne[1] == 1) { + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - char * buf_host = (char *)data + offset_split; - CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); - } + GGML_ASSERT(row_id >= 0 && row_id < n_as); - for (int id = 0; id < g_device_count; ++id) { - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); - } -} + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; -GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - UNUSED(buffer); - UNUSED(value); -} + src1_row.data = src1_original + i01*src1->nb[1]; + dst_row.data = dst_original + i01*dst->nb[1]; -static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, - /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, - /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, - /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, - /* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_cuda_split_buffer_clear, - /* .reset = */ NULL, -}; + ggml_cuda_mul_mat(ctx, src0_row, &src1_row, &dst_row); + } + } else { + ggml_cuda_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); + ggml_cuda_pool_alloc dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst)); -// cuda split buffer type + src1_row.data = src1_contiguous.get(); + dst_row.data = dst_contiguous.get(); -GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_CUDA_NAME "_Split"; + for (int32_t row_id = 0; row_id < n_as; ++row_id) { + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - UNUSED(buft); -} + int64_t num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); -GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point - // instead, we allocate them for each tensor separately in init_tensor - // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, - // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. - ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context(); + if (row_id_i != row_id) { + continue; + } - return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); -} + GGML_ASSERT(row_id >= 0 && row_id < n_as); -GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; + CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11, + nb11, cudaMemcpyDeviceToDevice, stream)); + num_src1_rows++; + } - UNUSED(buft); -} + if (num_src1_rows == 0) { + continue; + } -GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; + src1_row.ne[1] = num_src1_rows; + dst_row.ne[1] = num_src1_rows; - size_t total_size = 0; + src1_row.nb[1] = nb11; + src1_row.nb[2] = num_src1_rows*nb11; + src1_row.nb[3] = num_src1_rows*nb11; - const int64_t ne0 = tensor->ne[0]; + dst_row.nb[1] = nb1; + dst_row.nb[2] = num_src1_rows*nb1; + dst_row.nb[3] = num_src1_rows*nb1; - for (int id = 0; id < g_device_count; ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id); + ggml_cuda_mul_mat(ctx, src0_row, &src1_row, &dst_row); - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } + num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - total_size += ggml_nbytes_split(tensor, nrows_split); + if (row_id_i != row_id) { + continue; + } - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1, + nb1, cudaMemcpyDeviceToDevice, stream)); + num_src1_rows++; + } } } +} - return total_size; +static void ggml_cuda_scale(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_scale); } -GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_cuda(backend); +static void ggml_cuda_clamp(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_clamp); +} + +static void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const int64_t ne = ggml_nelements(src0); + GGML_ASSERT(ne == ggml_nelements(src1)); + + GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); + + GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); + GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; - UNUSED(buft); -} + //GGML_ASSERT(src0->ne[3] == 1); -GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; + const int64_t nb00 = src0->nb[0]; + const int64_t nb01 = src0->nb[1]; + const int64_t nb02 = src0->nb[2]; + const int64_t nb03 = src0->nb[3]; - UNUSED(buft); -} + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; -static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = { - /* .get_name = */ ggml_backend_cuda_split_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, - /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, -}; + //GGML_ASSERT(src1->ne[3] == 1); -GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { - ggml_init_cublas(); + const int64_t nb10 = src1->nb[0]; + const int64_t nb11 = src1->nb[1]; + const int64_t nb12 = src1->nb[2]; + const int64_t nb13 = src1->nb[3]; - // FIXME: this is not thread safe - static std::map, struct ggml_backend_buffer_type> buft_map; + ggml_cuda_set_device(ctx.device); + cudaStream_t main_stream = ctx.stream(); - std::array tensor_split_arr = {}; + char * src0_ddc = (char *) src0->data; + char * src1_ddc = (char *) src1->data; - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; }); - if (all_zero) { - tensor_split_arr = g_default_tensor_split; + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { + ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { + ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { + ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - tensor_split_arr[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - tensor_split_arr[i] /= split_sum; - } + fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, + ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ASSERT(false); } - auto it = buft_map.find(tensor_split_arr); - if (it != buft_map.end()) { - return &it->second; - } + GGML_UNUSED(dst); +} - struct ggml_backend_buffer_type buft { - /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, - /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, - }; +static void ggml_cuda_dup(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + // TODO: why do we pass dst as src1 here? + ggml_cuda_cpy(ctx, src0, dst, nullptr); + GGML_UNUSED(src1); +} - auto result = buft_map.emplace(tensor_split_arr, buft); - return &result.first->second; +static void ggml_cuda_diag_mask_inf(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_diag_mask_inf); } -// host buffer type +static void ggml_cuda_soft_max(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_soft_max); +} -GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_CUDA_NAME "_Host"; +static void ggml_cuda_rope(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_rope); +} - UNUSED(buft); +static void ggml_cuda_alibi(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_alibi); } -GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Host"; +static void ggml_cuda_pool2d(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_pool2d); +} - UNUSED(buffer); +static void ggml_cuda_im2col(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_im2col); } -GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_cuda_host_free(buffer->context); +static void ggml_cuda_sum_rows(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_sum_rows); } -GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_cuda_host_malloc(size); +static void ggml_cuda_argsort(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_cuda_op_flatten(ctx, src0, src1, dst, ggml_cuda_op_argsort); +} - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); +static void ggml_cuda_nop(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_UNUSED(ctx); + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(dst); +} + +static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * tensor) { + // FIXME: where should this be? + if (tensor->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(tensor->src[0]->buffer)) { + ggml_cuda_set_peer_access(tensor->src[1]->ne[1], ctx.device); } - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; - buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; + ggml_cuda_func_t func; - return buffer; + switch (tensor->op) { + case GGML_OP_REPEAT: + func = ggml_cuda_repeat; + break; + case GGML_OP_GET_ROWS: + func = ggml_cuda_get_rows; + break; + case GGML_OP_DUP: + func = ggml_cuda_dup; + break; + case GGML_OP_ADD: + func = ggml_cuda_add; + break; + case GGML_OP_ACC: + func = ggml_cuda_acc; + break; + case GGML_OP_MUL: + func = ggml_cuda_mul; + break; + case GGML_OP_DIV: + func = ggml_cuda_div; + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_GELU: + func = ggml_cuda_gelu; + break; + case GGML_UNARY_OP_SILU: + func = ggml_cuda_silu; + break; + case GGML_UNARY_OP_GELU_QUICK: + func = ggml_cuda_gelu_quick; + break; + case GGML_UNARY_OP_TANH: + func = ggml_cuda_tanh; + break; + case GGML_UNARY_OP_RELU: + func = ggml_cuda_relu; + break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_cuda_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_cuda_hardswish; + break; + default: + return false; + } + break; + case GGML_OP_NORM: + func = ggml_cuda_norm; + break; + case GGML_OP_GROUP_NORM: + func = ggml_cuda_group_norm; + break; + case GGML_OP_CONCAT: + func = ggml_cuda_concat; + break; + case GGML_OP_UPSCALE: + func = ggml_cuda_upscale; + break; + case GGML_OP_PAD: + func = ggml_cuda_pad; + break; + case GGML_OP_ARANGE: + func = ggml_cuda_arange; + break; + case GGML_OP_TIMESTEP_EMBEDDING: + func = ggml_cuda_timestep_embedding; + break; + case GGML_OP_LEAKY_RELU: + func = ggml_cuda_leaky_relu; + break; + case GGML_OP_RMS_NORM: + func = ggml_cuda_rms_norm; + break; + case GGML_OP_MUL_MAT: + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); + return false; + } else { + func = ggml_cuda_mul_mat; + } + break; + case GGML_OP_MUL_MAT_ID: + func = ggml_cuda_mul_mat_id; + break; + case GGML_OP_SCALE: + func = ggml_cuda_scale; + break; + case GGML_OP_SQR: + func = ggml_cuda_sqr; + break; + case GGML_OP_CLAMP: + func = ggml_cuda_clamp; + break; + case GGML_OP_CPY: + func = ggml_cuda_cpy; + break; + case GGML_OP_CONT: + func = ggml_cuda_dup; + break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + func = ggml_cuda_nop; + break; + case GGML_OP_DIAG_MASK_INF: + func = ggml_cuda_diag_mask_inf; + break; + case GGML_OP_SOFT_MAX: + func = ggml_cuda_soft_max; + break; + case GGML_OP_ROPE: + func = ggml_cuda_rope; + break; + case GGML_OP_ALIBI: + func = ggml_cuda_alibi; + break; + case GGML_OP_IM2COL: + func = ggml_cuda_im2col; + break; + case GGML_OP_POOL_2D: + func = ggml_cuda_pool2d; + break; + case GGML_OP_SUM_ROWS: + func = ggml_cuda_sum_rows; + break; + case GGML_OP_ARGSORT: + func = ggml_cuda_argsort; + break; + default: + return false; + } + + func(ctx, tensor->src[0], tensor->src[1], tensor); + return true; } -GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .context = */ nullptr, - }; - return &ggml_backend_cuda_buffer_type_host; -} +//////////////////////////////////////////////////////////////////////////////// -//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) { -// return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name; -//} // backend @@ -11139,9 +11133,8 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); + CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream())); } GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -11149,9 +11142,8 @@ GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); + CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream())); } GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { @@ -11186,19 +11178,19 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_ // copy on src stream if (cuda_ctx_src->device == cuda_ctx_dst->device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0])); + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream())); } else { - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), g_cudaStreams[cuda_ctx_src->device][0])); + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream())); } // record event on src stream - CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, g_cudaStreams[cuda_ctx_src->device][0])); + CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, cuda_ctx_src->stream())); // wait on dst stream for the copy to complete - CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx_dst->device][0], cuda_ctx_src->copy_event, 0)); + CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx_dst->stream(), cuda_ctx_src->copy_event, 0)); } else { // src and dst are on the same backend - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0])); + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream())); } return true; } @@ -11206,15 +11198,15 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0])); + CUDA_CHECK(cudaStreamSynchronize(cuda_ctx->stream())); - UNUSED(backend); + GGML_UNUSED(backend); } GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - ggml_cuda_set_main_device(cuda_ctx->device); + ggml_cuda_set_device(cuda_ctx->device); for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -11224,20 +11216,15 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } #ifndef NDEBUG - assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); - assert(node->extra != nullptr); - for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); - assert(node->src[j]->extra != nullptr); } } #endif - bool ok = ggml_cuda_compute_forward(node); + bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } @@ -11371,7 +11358,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons return false; } - UNUSED(backend); + GGML_UNUSED(backend); } GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) { @@ -11379,7 +11366,7 @@ GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; - UNUSED(backend); + GGML_UNUSED(backend); } static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) { @@ -11405,14 +11392,14 @@ static void ggml_backend_cuda_event_free(ggml_backend_event_t event) { static void ggml_backend_cuda_event_record(ggml_backend_event_t event) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)event->backend->context; - CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, g_cudaStreams[cuda_ctx->device][0])); + CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, cuda_ctx->stream())); } static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; if (ggml_backend_is_cuda(event->backend)) { - CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx->device][0], (cudaEvent_t)event->context, 0)); + CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx->stream(), (cudaEvent_t)event->context, 0)); } else { #if 0 // untested @@ -11421,7 +11408,7 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev ggml_backend_event_synchronize(event); }; - CUDA_CHECK(cudaLaunchHostFunc(g_cudaStreams[cuda_ctx->device][0], wait_fn, event)); + CUDA_CHECK(cudaLaunchHostFunc(cuda_ctx->stream(), wait_fn, event)); #endif GGML_ASSERT(false); } @@ -11458,16 +11445,11 @@ static ggml_guid_t ggml_backend_cuda_guid() { } GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { - ggml_init_cublas(); - - if (device < 0 || device >= ggml_cuda_get_device_count()) { + if (device < 0 || device >= ggml_backend_cuda_get_device_count()) { fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); return nullptr; } - // not strictly necessary, but it may reduce the overhead of the first graph_compute - ggml_cuda_set_main_device(device); - ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); if (ctx == nullptr) { fprintf(stderr, "%s: error: failed to allocate context\n", __func__); @@ -11488,11 +11470,13 @@ GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) { } GGML_CALL int ggml_backend_cuda_get_device_count() { - return ggml_cuda_get_device_count(); + return get_cuda_global_info().device_count; } GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { - ggml_cuda_get_device_description(device, description, description_size); + cudaDeviceProp prop; + CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); + snprintf(description, description_size, "%s", prop.name); } GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { @@ -11531,13 +11515,13 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); return cuda_backend; - UNUSED(params); + GGML_UNUSED(params); } extern "C" GGML_CALL int ggml_backend_cuda_reg_devices(); GGML_CALL int ggml_backend_cuda_reg_devices() { - int device_count = ggml_cuda_get_device_count(); + int device_count = ggml_backend_cuda_get_device_count(); //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization for (int i = 0; i < device_count; i++) { char name[128]; From 272935b281fee5c683e3d6d1eb580b84553cf503 Mon Sep 17 00:00:00 2001 From: Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> Date: Wed, 20 Mar 2024 23:02:32 +0800 Subject: [PATCH 310/401] llava : add MobileVLM_V2 backup (#6175) * Add MobileVLM_V2 backup * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov * Update examples/llava/convert-image-encoder-to-gguf.py Co-authored-by: Georgi Gerganov * clip : fix whitespace * fix deifinition mistake in clip.cpp --------- Co-authored-by: Georgi Gerganov --- examples/llava/MobileVLM-README.md | 14 +++++- examples/llava/clip.cpp | 50 ++++++++++++++++++- .../llava/convert-image-encoder-to-gguf.py | 9 ++-- 3 files changed, 67 insertions(+), 6 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 9eba791dadfef..c1f361d1704f4 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -1,11 +1,13 @@ # MobileVLM -Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants. +Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants. for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM) The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. +Notice: The overall process of model inference for both **MobilVLM** and **MobilVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown. + ## Usage Build with cmake or run `make llava-cli` to build it. @@ -34,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B ``` -3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF: +3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh python ./examples/llava/convert-image-encoder-to-gguf \ @@ -44,6 +46,14 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldp ``` +```sh +python ./examples/llava/convert-image-encoder-to-gguf \ + -m path/to/clip-vit-large-patch14-336 \ + --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ + --output-dir path/to/MobileVLM-1.7B_V2 \ + --projector-type ldpv2 +``` + 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 690bca2eb7732..48caafa872aed 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -119,6 +119,7 @@ static std::string format(const char * fmt, ...) { #define TN_LLAVA_PROJ "mm.%d.%s" #define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" +#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" #define TN_IMAGE_NEWLINE "model.image_newline" @@ -126,12 +127,14 @@ enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, + PROJECTOR_TYPE_LDPV2, PROJECTOR_TYPE_UNKNOWN, }; static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MLP, "mlp" }, { PROJECTOR_TYPE_LDP, "ldp" }, + { PROJECTOR_TYPE_LDPV2, "ldpv2"}, }; @@ -475,6 +478,14 @@ struct clip_vision_model { struct ggml_tensor * mm_model_block_2_block_2_0_w; struct ggml_tensor * mm_model_block_2_block_2_1_w; struct ggml_tensor * mm_model_block_2_block_2_1_b; + + // MobileVLM_V2 projection + struct ggml_tensor * mm_model_mlp_0_w; + struct ggml_tensor * mm_model_mlp_0_b; + struct ggml_tensor * mm_model_mlp_2_w; + struct ggml_tensor * mm_model_mlp_2_b; + struct ggml_tensor * mm_model_peg_0_w; + struct ggml_tensor * mm_model_peg_0_b; }; struct clip_ctx { @@ -807,6 +818,29 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } embeddings = block_1; } + else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } else { GGML_ASSERT(false); } @@ -1177,7 +1211,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } else { + } + else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2) + { + // MobilVLM_V2 projection + vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight")); + vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias")); + vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight")); + vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias")); + vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight")); + vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias")); + } + else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } @@ -1966,6 +2011,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { if (ctx->proj_type == PROJECTOR_TYPE_LDP) { return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0]; } + if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) { + return ctx->vision_model.mm_model_peg_0_b->ne[0]; + } if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; } diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index c69f89ac2b60e..b00bf7c6d0b59 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -1,6 +1,7 @@ import argparse import os import json +import re import torch import numpy as np @@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b def get_tensor_name(name: str) -> str: if "projection" in name: return name - if "mm_projector" in name: - return name.replace("model.mm_projector", "mm") + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") @@ -83,7 +86,7 @@ def bytes_to_unicode(): ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, help="The clip model is from openclip (for ViT-SO400M type))") ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") -ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 From f9c7ba34476ffc4f13ae2cdb1aec493a16eb8d47 Mon Sep 17 00:00:00 2001 From: Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> Date: Wed, 20 Mar 2024 23:29:51 +0800 Subject: [PATCH 311/401] llava : update MobileVLM-README.md (#6180) --- examples/llava/MobileVLM-README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index c1f361d1704f4..4d5fef020f406 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -6,7 +6,7 @@ for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. -Notice: The overall process of model inference for both **MobilVLM** and **MobilVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown. +Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown. ## Usage Build with cmake or run `make llava-cli` to build it. From 1c51f98adcbad40e3c41f0a6ffadeb723190b417 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 20 Mar 2024 21:03:26 +0100 Subject: [PATCH 312/401] cuda : print the returned error when CUDA initialization fails (#6185) --- ggml-cuda.cu | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e70b79c300a26..24af9f671f97a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -294,8 +294,9 @@ static ggml_cuda_device_info ggml_cuda_init() { ggml_cuda_device_info info = {}; - if (cudaGetDeviceCount(&info.device_count) != cudaSuccess) { - fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__); + cudaError_t err = cudaGetDeviceCount(&info.device_count); + if (err != cudaSuccess) { + fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } From 42e21c68826f2e56b9592dccd9f3c43895b6890d Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 21 Mar 2024 01:47:46 +0100 Subject: [PATCH 313/401] cuda : fix conflict with std::swap (#6186) --- ggml-cuda.cu | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 24af9f671f97a..27816a7be6ec2 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -370,12 +370,6 @@ struct ggml_cuda_pool { virtual void * alloc(size_t size, size_t * actual_size) = 0; virtual void free(void * ptr, size_t size) = 0; - - ggml_cuda_pool() = default; - ggml_cuda_pool(const ggml_cuda_pool &) = delete; - ggml_cuda_pool(ggml_cuda_pool &&) = delete; - ggml_cuda_pool& operator=(const ggml_cuda_pool &) = delete; - ggml_cuda_pool& operator=(ggml_cuda_pool &&) = delete; }; struct ggml_cuda_pool_leg : public ggml_cuda_pool { @@ -6969,7 +6963,7 @@ static __global__ void k_sum_rows_f32(const float * x, float * dst, const int nc } template -static inline __device__ void swap(T & a, T & b) { +static inline __device__ void ggml_cuda_swap(T & a, T & b) { T tmp = a; a = b; b = tmp; @@ -6998,11 +6992,11 @@ static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int n if (ixj > col) { if ((col & k) == 0) { if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { - swap(dst_row[col], dst_row[ixj]); + ggml_cuda_swap(dst_row[col], dst_row[ixj]); } } else { if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { - swap(dst_row[col], dst_row[ixj]); + ggml_cuda_swap(dst_row[col], dst_row[ixj]); } } } From c5b8595e3f4f4ed319ef71c9c9d868d1b7a27626 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Thu, 21 Mar 2024 06:10:52 +0000 Subject: [PATCH 314/401] Add nvidia and amd backends (#6157) --- ggml-sycl.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index d51f23b410595..cc9ee0762b903 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -977,8 +977,10 @@ namespace dpct static int convert_backend_index(std::string & backend) { if (backend == "ext_oneapi_level_zero:gpu") return 0; if (backend == "opencl:gpu") return 1; - if (backend == "opencl:cpu") return 2; - if (backend == "opencl:acc") return 3; + if (backend == "ext_oneapi_cuda:gpu") return 2; + if (backend == "ext_oneapi_hip:gpu") return 3; + if (backend == "opencl:cpu") return 4; + if (backend == "opencl:acc") return 5; printf("convert_backend_index: can't handle backend=%s\n", backend.c_str()); GGML_ASSERT(false); } From 76aa30a26353f597e4fbe3cf776772ae812af89a Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 21 Mar 2024 08:27:57 +0100 Subject: [PATCH 315/401] Add ability to use Q5_0, Q5_1, and IQ4_NL for quantized K cache (#6183) * k_cache: be able to use Q5_0 * k_cache: be able to use Q5_1 on CODA * k_cache: be able to use Q5_0 on Metal * k_cache: be able to use Q5_1 on Metal * k_cache: be able to use IQ4_NL - just CUDA for now * k_cache: be able to use IQ4_NL on Metal * k_cache: add newly added supported types to llama-bench and CUDA supports_op --------- Co-authored-by: Iwan Kawrakow --- common/common.cpp | 3 + examples/llama-bench/llama-bench.cpp | 3 + ggml-cuda.cu | 165 ++++++++++++++++++ ggml-metal.m | 28 ++-- ggml-metal.metal | 240 ++++++++++++++++++++++++++- 5 files changed, 424 insertions(+), 15 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 5f10718ece90c..192182d05b747 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1590,6 +1590,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) { if (s == "q4_1") { return GGML_TYPE_Q4_1; } + if (s == "iq4_nl") { + return GGML_TYPE_IQ4_NL; + } if (s == "q5_0") { return GGML_TYPE_Q5_0; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 4cb2308044732..82413b79d0255 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -249,6 +249,9 @@ static ggml_type ggml_type_from_name(const std::string & s) { if (s == "q5_1") { return GGML_TYPE_Q5_1; } + if (s == "iq4_nl") { + return GGML_TYPE_IQ4_NL; + } return GGML_TYPE_COUNT; } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 27816a7be6ec2..280839ea475cd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6757,6 +6757,123 @@ static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { } } +static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q5_0 * dsti = (block_q5_0 *) cdsti; + + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK5_0; ++j) { + const float v = xi[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; + } + } + + const float d = vmax / -16; + const float id = d ? 1.0f/d : 0.0f; + + dsti->d = d; + + uint32_t qh = 0; + for (int j = 0; j < QK5_0/2; ++j) { + const float x0 = xi[0 + j]*id; + const float x1 = xi[QK5_0/2 + j]*id; + + const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f)); + const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f)); + + dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); + } + memcpy(dsti->qh, &qh, sizeof(qh)); +} + +static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q5_1 * dsti = (block_q5_1 *) cdsti; + + float min = xi[0]; + float max = xi[0]; + + for (int j = 1; j < QK5_1; ++j) { + const float v = xi[j]; + min = v < min ? v : min; + max = v > max ? v : max; + } + + const float d = (max - min) / 31; + const float id = d ? 1.0f/d : 0.0f; + + dsti->dm.x = d; + dsti->dm.y = min; + + uint32_t qh = 0; + for (int j = 0; j < QK5_1/2; ++j) { + const float x0 = (xi[0 + j] - min)*id; + const float x1 = (xi[QK5_1/2 + j] - min)*id; + + const uint8_t xi0 = (uint8_t)(x0 + 0.5f); + const uint8_t xi1 = (uint8_t)(x1 + 0.5f); + + dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); + } + memcpy(dsti->qh, &qh, sizeof(qh)); +} + +static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) { + if (x <= val[0]) return 0; + if (x >= val[n-1]) return n-1; + int ml = 0, mu = n-1; + while (mu-ml > 1) { + int mav = (ml+mu)/2; + if (x < val[mav]) mu = mav; else ml = mav; + } + return x - val[mu-1] < val[mu] - x ? mu-1 : mu; +} + +static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_iq4_nl * dsti = (block_iq4_nl *) cdsti; + + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK4_NL; ++j) { + const float v = xi[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; + } + } + + float d = vmax / kvalues_iq4nl[0]; + const float id = d ? 1.0f/d : 0.0f; + + float sumqx = 0, sumq2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + const float x0 = xi[0 + j]*id; + const float x1 = xi[QK4_NL/2 + j]*id; + const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0); + const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1); + dsti->qs[j] = xi0 | (xi1 << 4); + const float v0 = kvalues_iq4nl[xi0]; + const float v1 = kvalues_iq4nl[xi1]; + const float w0 = xi[0 + j]*xi[0 + j]; + const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j]; + sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j]; + sumq2 += w0*v0*v0 + w1*v1*v1; + } + + dsti->d = sumq2 > 0 ? sumqx/sumq2 : d; +} + + template static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, @@ -8490,6 +8607,39 @@ static void ggml_cpy_f32_q4_1_cuda( (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } +static void ggml_cpy_f32_q5_0_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + GGML_ASSERT(ne % QK5_0 == 0); + const int num_blocks = ne / QK5_0; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} + +static void ggml_cpy_f32_q5_1_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + GGML_ASSERT(ne % QK5_1 == 0); + const int num_blocks = ne / QK5_1; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} + +static void ggml_cpy_f32_iq4_nl_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + GGML_ASSERT(ne % QK4_NL == 0); + const int num_blocks = ne / QK4_NL; + cpy_f32_q<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} + static void ggml_cpy_f16_f16_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, @@ -10888,6 +11038,12 @@ static void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * s ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { + ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) { + ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) { + ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { @@ -11304,6 +11460,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) { return true; } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_0) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_1) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) { + return true; + } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { return true; } diff --git a/ggml-metal.m b/ggml-metal.m index 109e5fe6ba13d..54d1f1d5a3cee 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -173,8 +173,9 @@ GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, - //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, - //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, + GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, GGML_METAL_KERNEL_TYPE_CPY_F16_F16, GGML_METAL_KERNEL_TYPE_CPY_F16_F32, GGML_METAL_KERNEL_TYPE_CONCAT, @@ -598,8 +599,9 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); @@ -739,6 +741,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_IQ4_NL: return true; default: return false; @@ -2431,13 +2436,14 @@ static enum ggml_status ggml_metal_graph_compute( GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); switch (dstt) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; - //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; - //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 63de563251d89..5552c8b28834a 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2388,6 +2388,242 @@ kernel void kernel_cpy_f32_q4_1( } } +kernel void kernel_cpy_f32_q5_0( + device const float * src0, + device void * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i03 = tgpig[2]; + const int64_t i02 = tgpig[1]; + const int64_t i01 = tgpig[0]; + + const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + const int64_t i3 = n / (ne2*ne1*ne0); + const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); + const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; + const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0; + + device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) { + device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int j = 0; j < QK5_0; j++) { + const float v = src[j]; + if (amax < fabs(v)) { + amax = fabs(v); + max = v; + } + } + + const float d = max / -16; + const float id = d ? 1.0f/d : 0.0f; + + dst_data[i00/QK5_0].d = d; + + uint32_t qh = 0; + for (int j = 0; j < QK5_0/2; ++j) { + const float x0 = src[0 + j]*id; + const float x1 = src[QK5_0/2 + j]*id; + + const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f)); + const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f)); + + dst_data[i00/QK5_0].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); + } + thread const uint8_t * qh8 = (thread const uint8_t *)&qh; + for (int j = 0; j < 4; ++j) { + dst_data[i00/QK5_0].qh[j] = qh8[j]; + } + } +} + +kernel void kernel_cpy_f32_q5_1( + device const float * src0, + device void * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i03 = tgpig[2]; + const int64_t i02 = tgpig[1]; + const int64_t i01 = tgpig[0]; + + const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + const int64_t i3 = n / (ne2*ne1*ne0); + const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); + const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; + const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1; + + device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) { + device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + + float max = src[0]; + float min = src[0]; + + for (int j = 1; j < QK5_1; j++) { + const float v = src[j]; + min = v < min ? v : min; + max = v > max ? v : max; + } + + const float d = (max - min) / 31; + const float id = d ? 1.0f/d : 0.0f; + + dst_data[i00/QK5_1].d = d; + dst_data[i00/QK5_1].m = min; + + uint32_t qh = 0; + for (int j = 0; j < QK5_1/2; ++j) { + const float x0 = (src[0 + j] - min)*id; + const float x1 = (src[QK5_1/2 + j] - min)*id; + + const uint8_t xi0 = (uint8_t)(x0 + 0.5f); + const uint8_t xi1 = (uint8_t)(x1 + 0.5f); + + dst_data[i00/QK5_1].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); + } + thread const uint8_t * qh8 = (thread const uint8_t *)&qh; + for (int j = 0; j < 4; ++j) { + dst_data[i00/QK5_1].qh[j] = qh8[j]; + } + } +} + +static inline int best_index_int8(int n, constant float * val, float x) { + if (x <= val[0]) return 0; + if (x >= val[n-1]) return n-1; + int ml = 0, mu = n-1; + while (mu-ml > 1) { + int mav = (ml+mu)/2; + if (x < val[mav]) mu = mav; else ml = mav; + } + return x - val[mu-1] < val[mu] - x ? mu-1 : mu; +} + +constexpr constant static float kvalues_iq4nl_f[16] = { + -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f +}; + +kernel void kernel_cpy_f32_iq4_nl( + device const float * src0, + device void * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i03 = tgpig[2]; + const int64_t i02 = tgpig[1]; + const int64_t i01 = tgpig[0]; + + const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + const int64_t i3 = n / (ne2*ne1*ne0); + const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); + const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; + const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL; + + device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) { + device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int j = 0; j < QK4_0; j++) { + const float v = src[j]; + if (amax < fabs(v)) { + amax = fabs(v); + max = v; + } + } + + const float d = max / kvalues_iq4nl_f[0]; + const float id = d ? 1.0f/d : 0.0f; + + float sumqx = 0, sumq2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + const float x0 = src[0 + j]*id; + const float x1 = src[QK4_NL/2 + j]*id; + + const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0); + const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1); + + dst_data[i00/QK4_NL].qs[j] = xi0 | (xi1 << 4); + + const float v0 = kvalues_iq4nl_f[xi0]; + const float v1 = kvalues_iq4nl_f[xi1]; + const float w0 = src[0 + j]*src[0 + j]; + const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j]; + sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j]; + sumq2 += w0*v0*v0 + w1*v1*v1; + + } + + dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d; + + } +} + kernel void kernel_concat( device const char * src0, device const char * src1, @@ -4220,10 +4456,6 @@ void kernel_mul_mv_iq1_s_f32_impl( } } -constexpr constant static float kvalues_iq4nl_f[16] = { - -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f -}; - void kernel_mul_mv_iq4_nl_f32_impl( device const void * src0, device const float * src1, From 5e43ba87429d85acbde97d16b2f48d9de992cc80 Mon Sep 17 00:00:00 2001 From: Vaibhav Srivastav Date: Thu, 21 Mar 2024 10:13:12 +0100 Subject: [PATCH 316/401] build : add mac pre-build binaries (#6182) * Initial commit - add mac prebuilds. * forward contribution credits for building the workflow. * minor : remove trailing whitespaces --------- Co-authored-by: Nicolas Patry Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 114 ++++++++++++++++++++++++++++++++++++ 1 file changed, 114 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 992c34a0302cc..334a48824b33a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -21,6 +21,118 @@ env: GGML_N_THREADS: 1 jobs: + macOS-latest-cmake-arm64: + runs-on: macos-14 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + mkdir build + cd build + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON .. + cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v3 + with: + path: | + llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip + + macOS-latest-cmake-x64: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + mkdir build + cd build + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON .. + cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v3 + with: + path: | + llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip + ubuntu-focal-make: runs-on: ubuntu-20.04 @@ -748,6 +860,8 @@ jobs: - macOS-latest-cmake - windows-latest-cmake - windows-latest-cmake-cublas + - macOS-latest-cmake-arm64 + - macOS-latest-cmake-x64 steps: - name: Clone From 1943c0198125a0da1a200390e82cf461f9080d99 Mon Sep 17 00:00:00 2001 From: Vaibhav Srivastav Date: Thu, 21 Mar 2024 10:30:40 +0100 Subject: [PATCH 317/401] ci : fix indentation error (#6195) --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 334a48824b33a..0f68e4cc382bd 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -121,7 +121,7 @@ jobs: - name: Pack artifacts id: pack_artifacts - if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* From 5b7b0ac8dfdd800c0fd0dc69b69991e8cb19fb46 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Thu, 21 Mar 2024 11:50:43 +0000 Subject: [PATCH 318/401] json-schema-to-grammar improvements (+ added to server) (#5978) * json: fix arrays (disallow `[,1]`) * json: support tuple types (`[number, string]`) * json: support additionalProperties (`{[k: string]: [string,number][]}`) * json: support required / optional properties * json: add support for pattern * json: resolve $ref (and support https schema urls) * json: fix $ref resolution * join: support union types (mostly for nullable types I think) * json: support allOf + nested anyOf * json: support any (`{}` or `{type: object}`) * json: fix merge * json: temp fix for escapes * json: spaces in output and unrestricted output spaces * json: add typings * json:fix typo * Create ts-type-to-grammar.sh * json: fix _format_literal (json.dumps already escapes quotes) * json: merge lit sequences and handle negatives {"type": "string", "pattern": "^({\"question\": \"[^\"]+\", \"response\": \"[^\"]+\"}\\n)+$"} * json: handle pattern repetitions * Update json-schema-to-grammar.mjs * Create regex-to-grammar.py * json: extract repeated regexp patterns to subrule * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * json: handle schema from pydantic Optional fields * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * Update ts-type-to-grammar.sh * Update ts-type-to-grammar.sh * json: simplify nullable fields handling * json: accept duplicate identical rules * json: revert space to 1 at most * json: reuse regexp pattern subrules * json: handle uuid string format * json: fix literal escapes * json: add --allow-fetch * json: simplify range escapes * json: support negative ranges in patterns * Delete commit.txt * json: custom regex parser, adds dot support & JS-portable * json: rm trailing spaces * Update json-schema-to-grammar.mjs * json: updated server & chat `( cd examples/server && ./deps.sh )` * json: port fixes from mjs to python * Update ts-type-to-grammar.sh * json: support prefixItems alongside array items * json: add date format + fix uuid * json: add date, time, date-time formats * json: preserve order of props from TS defs * json: port schema converter to C++, wire in ./server * json: nits * Update json-schema-to-grammar.cpp * Update json-schema-to-grammar.cpp * Update json-schema-to-grammar.cpp * json: fix mjs implementation + align outputs * Update json-schema-to-grammar.mjs.hpp * json: test C++, JS & Python versions * json: nits + regen deps * json: cleanup test * json: revert from c++17 to 11 * json: nit fixes * json: dirty include for test * json: fix zig build * json: pass static command to std::system in tests (fixed temp files) * json: fix top-level $refs * json: don't use c++20 designated initializers * nit * json: basic support for reserved names `{number:{number:{root:number}}}` * Revamp test cmake to allow args (WORKING_DIRECTORY needed for JSON test) * json: re-ran server deps.sh * json: simplify test * json: support mix of additional props & required/optional * json: add tests for some expected failures * json: fix type=const in c++, add failure expectations for non-str const&enum * json: test (& simplify output of) empty schema * json: check parsing in test + fix value & string refs * json: add server tests for OAI JSON response_format * json: test/fix top-level anyOf * json: improve grammar parsing failures * json: test/fix additional props corner cases * json: fix string patterns (was missing quotes) * json: ws nit * json: fix json handling in server when there's no response_format * json: catch schema conversion errors in server * json: don't complain about unknown format type in server if unset * json: cleaner build of test * json: create examples/json-schema-pydantic-example.py * json: fix date pattern * json: move json.hpp & json-schema-to-grammar.{cpp,h} to common * json: indent 4 spaces * json: fix naming of top-level c++ function (+ drop unused one) * json: avoid using namespace std * json: fix zig build * Update server.feature * json: iostream -> fprintf * json: space before & refs for consistency * json: nits --- .gitignore | 1 + Makefile | 12 +- build.zig | 3 +- common/CMakeLists.txt | 3 + common/json-schema-to-grammar.cpp | 725 +++ common/json-schema-to-grammar.h | 4 + {examples/server => common}/json.hpp | 0 examples/json-schema-pydantic-example.py | 74 + examples/json-schema-to-grammar.py | 510 +- examples/regex-to-grammar.py | 20 + examples/server/CMakeLists.txt | 8 +- examples/server/chat.mjs | 5 +- examples/server/completion.js.hpp | 2 +- examples/server/index.html.hpp | 4532 +++++++++-------- examples/server/index.js.hpp | 1680 +++--- .../server/json-schema-to-grammar.mjs.hpp | 1940 +++++-- examples/server/public/index.html | 14 +- examples/server/public/index.js | 2 +- .../server/public/json-schema-to-grammar.mjs | 528 +- examples/server/server.cpp | 14 +- .../server/tests/features/security.feature | 16 + examples/server/tests/features/server.feature | 16 + examples/server/tests/features/steps/steps.py | 17 + examples/server/utils.hpp | 13 +- examples/ts-type-to-grammar.sh | 28 + tests/CMakeLists.txt | 109 +- tests/run-json-schema-to-grammar.mjs | 10 + tests/test-json-schema-to-grammar.cpp | 824 +++ 28 files changed, 7573 insertions(+), 3537 deletions(-) create mode 100644 common/json-schema-to-grammar.cpp create mode 100644 common/json-schema-to-grammar.h rename {examples/server => common}/json.hpp (100%) create mode 100644 examples/json-schema-pydantic-example.py create mode 100644 examples/regex-to-grammar.py create mode 100755 examples/ts-type-to-grammar.sh create mode 100644 tests/run-json-schema-to-grammar.mjs create mode 100755 tests/test-json-schema-to-grammar.cpp diff --git a/.gitignore b/.gitignore index fbeb9223c203a..51aa84222d667 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ *.gcda *.dot *.bat +*.tmp *.metallib *.etag *.lastModified diff --git a/Makefile b/Makefile index 1daad45ed8e4a..9b72e1dbd5493 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,8 @@ TEST_TARGETS = \ tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \ tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \ tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \ - tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease + tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease \ + tests/test-json-schema-to-grammar # Code coverage output files COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report @@ -666,6 +667,9 @@ console.o: common/console.cpp common/console.h grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h $(CXX) $(CXXFLAGS) -c $< -o $@ +json-schema-to-grammar.o: common/json-schema-to-grammar.cpp common/json-schema-to-grammar.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + train.o: common/train.cpp common/train.h $(CXX) $(CXXFLAGS) -c $< -o $@ @@ -745,7 +749,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp json-schema-to-grammar.o common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) @@ -865,6 +869,10 @@ tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp json-schema-to-grammar.o ggml.o llama.o grammar-parser.o $(OBJS) + $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/build.zig b/build.zig index 90609359bfe0b..a1de7083a07a8 100644 --- a/build.zig +++ b/build.zig @@ -122,6 +122,7 @@ pub fn build(b: *std.build.Builder) !void { const console = make.obj("console", "common/console.cpp"); const sampling = make.obj("sampling", "common/sampling.cpp"); const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); + const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp"); const train = make.obj("train", "common/train.cpp"); const clip = make.obj("clip", "examples/llava/clip.cpp"); const llava = make.obj("llava", "examples/llava/llava.cpp"); @@ -133,7 +134,7 @@ pub fn build(b: *std.build.Builder) !void { _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index af2629a460b93..10951693ab0a2 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -47,6 +47,8 @@ if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() +set(TARGET json-schema-to-grammar) +add_library(${TARGET} OBJECT json-schema-to-grammar.cpp json-schema-to-grammar.h) set(TARGET common) @@ -60,6 +62,7 @@ add_library(${TARGET} STATIC console.cpp grammar-parser.h grammar-parser.cpp + json.hpp train.h train.cpp ) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp new file mode 100644 index 0000000000000..dc6d30ef15ca2 --- /dev/null +++ b/common/json-schema-to-grammar.cpp @@ -0,0 +1,725 @@ +#include "json-schema-to-grammar.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::json; + +const std::string SPACE_RULE = "\" \"?"; + +std::unordered_map PRIMITIVE_RULES = { + {"boolean", "(\"true\" | \"false\") space"}, + {"number", "(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space"}, + {"integer", "(\"-\"? ([0-9] | [1-9] [0-9]*)) space"}, + {"value", "object | array | string | number | boolean"}, + {"object", "\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space"}, + {"array", "\"[\" space ( value (\",\" space value)* )? \"]\" space"}, + {"uuid", "\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space"}, + {"string", " \"\\\"\" (\n" + " [^\"\\\\] |\n" + " \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n" + " )* \"\\\"\" space"}, + {"null", "\"null\" space"} +}; +std::vector OBJECT_RULE_NAMES = {"object", "array", "string", "number", "boolean", "null", "value"}; + +std::unordered_map DATE_RULES = { + {"date", "[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )"}, + {"time", "([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )"}, + {"date-time", "date \"T\" time"}, + {"date-string", "\"\\\"\" date \"\\\"\" space"}, + {"time-string", "\"\\\"\" time \"\\\"\" space"}, + {"date-time-string", "\"\\\"\" date-time \"\\\"\" space"} +}; + +static bool is_reserved_name(const std::string & name) { + static std::unordered_set RESERVED_NAMES; + if (RESERVED_NAMES.empty()) { + RESERVED_NAMES.insert("root"); + for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first); + for (const auto &p : DATE_RULES) RESERVED_NAMES.insert(p.first); + } + return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); +} + +std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+"); +std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]"); +std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]"); +std::unordered_map GRAMMAR_LITERAL_ESCAPES = { + {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"} +}; + +std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; +std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; + +template +std::string join(Iterator begin, Iterator end, const std::string & separator) { + std::ostringstream result; + if (begin != end) { + result << *begin; + for (Iterator it = begin + 1; it != end; ++it) { + result << separator << *it; + } + } + return result.str(); +} + +static std::vector split(const std::string & str, const std::string & delimiter) { + std::vector tokens; + size_t start = 0; + size_t end = str.find(delimiter); + + while (end != std::string::npos) { + tokens.push_back(str.substr(start, end - start)); + start = end + delimiter.length(); + end = str.find(delimiter, start); + } + + tokens.push_back(str.substr(start)); + + return tokens; +} + +static std::string repeat(const std::string & str, size_t n) { + if (n == 0) { + return ""; + } + + std::string result; + result.reserve(str.length() * n); + + for (size_t i = 0; i < n; ++i) { + result += str; + } + + return result; +} + +static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function & replacement) { + std::smatch match; + std::string result; + + std::string::const_iterator searchStart(input.cbegin()); + std::string::const_iterator searchEnd(input.cend()); + + while (std::regex_search(searchStart, searchEnd, match, regex)) { + result.append(searchStart, searchStart + match.position()); + result.append(replacement(match)); + searchStart = match.suffix().first; + } + + result.append(searchStart, searchEnd); + + return result; +} + +static std::string format_literal(const std::string & literal) { + std::string escaped = replacePattern(json(literal).dump(), GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) { + char c = match.str()[0]; + return GRAMMAR_LITERAL_ESCAPES.at(c); + }); + return "\"" + escaped + "\""; +} + + +class SchemaConverter { +private: + std::function _fetch_json; + bool _dotall; + std::map _rules; + std::unordered_map _refs; + std::unordered_set _refs_being_resolved; + std::vector _errors; + std::vector _warnings; + + std::string _add_rule(const std::string & name, const std::string & rule) { + std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-"); + if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) { + _rules[esc_name] = rule; + return esc_name; + } else { + int i = 0; + while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) { + i++; + } + std::string key = esc_name + std::to_string(i); + _rules[key] = rule; + return key; + } + } + + std::string _generate_union_rule(const std::string & name, const std::vector & alt_schemas) { + std::vector rules; + for (size_t i = 0; i < alt_schemas.size(); i++) { + rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i))); + } + return join(rules.begin(), rules.end(), " | "); + } + + std::string _visit_pattern(const std::string & pattern, const std::string & name) { + if (!(pattern.front() == '^' && pattern.back() == '$')) { + _errors.push_back("Pattern must start with '^' and end with '$'"); + return ""; + } + std::string sub_pattern = pattern.substr(1, pattern.length() - 2); + std::unordered_map sub_rule_ids; + + size_t i = 0; + size_t length = sub_pattern.length(); + + using literal_or_rule = std::pair; + auto to_rule = [&](const literal_or_rule & ls) { + auto is_literal = ls.second; + auto s = ls.first; + return is_literal ? "\"" + s + "\"" : s; + }; + std::function transform = [&]() -> literal_or_rule { + size_t start = i; + std::vector seq; + + auto get_dot = [&]() { + std::string rule; + if (_dotall) { + rule = "[\\U00000000-\\U0010FFFF]"; + } else { + rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]"; + } + return _add_rule("dot", rule); + }; + + // Joins the sequence, merging consecutive literals together. + auto join_seq = [&]() { + std::vector ret; + + std::string literal; + auto flush_literal = [&]() { + if (literal.empty()) { + return false; + } + ret.push_back(std::make_pair(literal, true)); + literal.clear(); + return true; + }; + + for (const auto & item : seq) { + auto is_literal = item.second; + if (is_literal) { + literal += item.first; + } else { + flush_literal(); + ret.push_back(item); + } + } + flush_literal(); + + std::vector results; + for (const auto & item : ret) { + results.push_back(to_rule(item)); + } + return std::make_pair(join(results.begin(), results.end(), " "), false); + }; + + while (i < length) { + char c = sub_pattern[i]; + if (c == '.') { + seq.push_back(std::make_pair(get_dot(), false)); + i++; + } else if (c == '(') { + i++; + if (i < length) { + if (sub_pattern[i] == '?') { + _warnings.push_back("Unsupported pattern syntax"); + } + } + seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false)); + } else if (c == ')') { + i++; + if (start > 0 && sub_pattern[start - 1] != '(') { + _errors.push_back("Unbalanced parentheses"); + } + return join_seq(); + } else if (c == '[') { + std::string square_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != ']') { + if (sub_pattern[i] == '\\') { + square_brackets += sub_pattern.substr(i, 2); + i += 2; + } else { + square_brackets += sub_pattern[i]; + i++; + } + } + if (i >= length) { + _errors.push_back("Unbalanced square brackets"); + } + square_brackets += ']'; + i++; + seq.push_back(std::make_pair(square_brackets, false)); + } else if (c == '|') { + seq.push_back(std::make_pair("|", false)); + i++; + } else if (c == '*' || c == '+' || c == '?') { + seq.back() = std::make_pair(to_rule(seq.back()) + c, false); + i++; + } else if (c == '{') { + std::string curly_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != '}') { + curly_brackets += sub_pattern[i]; + i++; + } + if (i >= length) { + _errors.push_back("Unbalanced curly brackets"); + } + curly_brackets += '}'; + i++; + auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ","); + int min_times = 0; + int max_times = std::numeric_limits::max(); + try { + if (nums.size() == 1) { + min_times = max_times = std::stoi(nums[0]); + } else if (nums.size() != 2) { + _errors.push_back("Wrong number of values in curly brackets"); + } else { + if (!nums[0].empty()) { + min_times = std::stoi(nums[0]); + } + if (!nums[1].empty()) { + max_times = std::stoi(nums[1]); + } + } + } catch (const std::invalid_argument & e) { + _errors.push_back("Invalid number in curly brackets"); + return std::make_pair("", false); + } + auto &last = seq.back(); + auto &sub = last.first; + auto sub_is_literal = last.second; + + if (min_times == 0 && max_times == std::numeric_limits::max()) { + sub += "*"; + } else if (min_times == 0 && max_times == 1) { + sub += "?"; + } else if (min_times == 1 && max_times == std::numeric_limits::max()) { + sub += "+"; + } else { + if (!sub_is_literal) { + std::string & sub_id = sub_rule_ids[sub]; + if (sub_id.empty()) { + sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub); + } + sub = sub_id; + } + std::string result; + if (sub_is_literal && min_times > 0) { + result = "\"" + repeat(sub.substr(1, sub.length() - 2), min_times) + "\""; + } else { + for (int j = 0; j < min_times; j++) { + if (j > 0) { + result += " "; + } + result += sub; + } + } + if (min_times > 0 && min_times < max_times) { + result += " "; + } + if (max_times == std::numeric_limits::max()) { + result += sub + "*"; + } else { + for (int j = min_times; j < max_times; j++) { + if (j > min_times) { + result += " "; + } + result += sub + "?"; + } + } + seq.back().first = result; + seq.back().second = false; + } + } else { + std::string literal; + auto is_non_literal = [&](char c) { + return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end(); + }; + while (i < length) { + if (sub_pattern[i] == '\\' && i < length - 1) { + char next = sub_pattern[i + 1]; + if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) { + i++; + literal += sub_pattern[i]; + i++; + } else { + literal += sub_pattern.substr(i, 2); + i += 2; + } + } else if (sub_pattern[i] == '"') { + literal += "\\\""; + i++; + } else if (!is_non_literal(sub_pattern[i]) && + (i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) { + literal += sub_pattern[i]; + i++; + } else { + break; + } + } + if (!literal.empty()) { + seq.push_back(std::make_pair(literal, true)); + } + } + } + return join_seq(); + }; + return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space"); + } + + std::string _resolve_ref(const std::string & ref) { + std::string ref_name = ref.substr(ref.find_last_of('/') + 1); + if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) { + _refs_being_resolved.insert(ref); + json resolved = _refs[ref]; + ref_name = visit(resolved, ref_name); + _refs_being_resolved.erase(ref); + } + return ref_name; + } + + std::string _build_object_rule( + const std::vector> & properties, + const std::unordered_set & required, + const std::string & name, + const json & additional_properties) + { + std::vector required_props; + std::vector optional_props; + std::unordered_map prop_kv_rule_names; + for (const auto & kv : properties) { + const auto &prop_name = kv.first; + const auto &prop_schema = kv.second; + + std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name); + prop_kv_rule_names[prop_name] = _add_rule( + name + (name.empty() ? "" : "-") + prop_name + "-kv", + format_literal(prop_name) + " space \":\" space " + prop_rule_name + ); + if (required.find(prop_name) != required.end()) { + required_props.push_back(prop_name); + } else { + optional_props.push_back(prop_name); + } + } + if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get())) { + std::string sub_name = name + (name.empty() ? "" : "-") + "additional"; + std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value"); + std::string kv_rule = _add_rule(sub_name + "-kv", _add_rule("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule); + prop_kv_rule_names["*"] = kv_rule; + optional_props.push_back("*"); + } + + std::string rule = "\"{\" space "; + for (size_t i = 0; i < required_props.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += prop_kv_rule_names[required_props[i]]; + } + + if (!optional_props.empty()) { + rule += " ("; + if (!required_props.empty()) { + rule += " \",\" space ( "; + } + + std::function &, bool)> get_recursive_refs = [&](const std::vector & ks, bool first_is_optional) { + std::string res; + if (ks.empty()) { + return res; + } + std::string k = ks[0]; + std::string kv_rule_name = prop_kv_rule_names[k]; + if (k == "*") { + res = _add_rule( + name + (name.empty() ? "" : "-") + "additional-kvs", + kv_rule_name + " ( \",\" space " + kv_rule_name + " )*" + ); + } else if (first_is_optional) { + res = "( \",\" space " + kv_rule_name + " )?"; + } else { + res = kv_rule_name; + } + if (ks.size() > 1) { + res += " " + _add_rule( + name + (name.empty() ? "" : "-") + k + "-rest", + get_recursive_refs(std::vector(ks.begin() + 1, ks.end()), true) + ); + } + return res; + }; + + for (size_t i = 0; i < optional_props.size(); i++) { + if (i > 0) { + rule += " | "; + } + rule += get_recursive_refs(std::vector(optional_props.begin() + i, optional_props.end()), false); + } + if (!required_props.empty()) { + rule += " )"; + } + rule += " )?"; + } + + rule += " \"}\" space"; + + return rule; + } + +public: + SchemaConverter( + const std::function & fetch_json, + bool dotall) + : _fetch_json(fetch_json), _dotall(dotall) + { + _rules["space"] = SPACE_RULE; + } + + void resolve_refs(nlohmann::json & schema, const std::string & url) { + /* + * Resolves all $ref fields in the given schema, fetching any remote schemas, + * replacing each $ref with absolute reference URL and populates _refs with the + * respective referenced (sub)schema dictionaries. + */ + std::function visit_refs = [&](json & n) { + if (n.is_array()) { + for (auto & x : n) { + visit_refs(x); + } + } else if (n.is_object()) { + if (n.contains("$ref")) { + std::string ref = n["$ref"]; + if (_refs.find(ref) == _refs.end()) { + json target; + if (ref.find("https://") == 0) { + std::string base_url = ref.substr(0, ref.find('#')); + auto it = _refs.find(base_url); + if (it != _refs.end()) { + target = it->second; + } else { + // Fetch the referenced schema and resolve its refs + auto referenced = _fetch_json(ref); + resolve_refs(referenced, base_url); + _refs[base_url] = referenced; + } + if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) { + return; + } + } else if (ref.find("#/") == 0) { + target = schema; + n["$ref"] = url + ref; + ref = url + ref; + } else { + _errors.push_back("Unsupported ref: " + ref); + return; + } + std::string pointer = ref.substr(ref.find('#') + 1); + std::vector tokens = split(pointer, "/"); + for (size_t i = 1; i < tokens.size(); ++i) { + std::string sel = tokens[i]; + if (target.is_null() || !target.contains(sel)) { + _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump()); + return; + } + target = target[sel]; + } + _refs[ref] = target; + } + } else { + for (auto & kv : n.items()) { + visit_refs(kv.value()); + } + } + } + }; + + visit_refs(schema); + } + + std::string _generate_constant_rule(const json & value) { + if (!value.is_string()) { + _errors.push_back("Only std::string constants are supported, got " + value.dump()); + return ""; + } + return format_literal(value.get()); + } + + std::string visit(const json & schema, const std::string & name) { + json schema_type = schema.contains("type") ? schema["type"] : json(); + std::string schema_format = schema.contains("format") ? schema["format"].get() : ""; + std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name; + + if (schema.contains("$ref")) { + return _add_rule(rule_name, _resolve_ref(schema["$ref"])); + } else if (schema.contains("oneOf") || schema.contains("anyOf")) { + std::vector alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get>() : schema["anyOf"].get>(); + return _add_rule(rule_name, _generate_union_rule(name, alt_schemas)); + } else if (schema_type.is_array()) { + std::vector schema_types; + for (const auto & t : schema_type) { + schema_types.push_back({{"type", t}}); + } + return _add_rule(rule_name, _generate_union_rule(name, schema_types)); + } else if (schema.contains("const")) { + return _add_rule(rule_name, _generate_constant_rule(schema["const"])); + } else if (schema.contains("enum")) { + std::vector enum_values; + for (const auto & v : schema["enum"]) { + enum_values.push_back(_generate_constant_rule(v)); + } + return _add_rule(rule_name, join(enum_values.begin(), enum_values.end(), " | ")); + } else if ((schema_type.is_null() || schema_type == "object") + && (schema.contains("properties") || + (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) { + std::unordered_set required; + if (schema.contains("required") && schema["required"].is_array()) { + for (const auto & item : schema["required"]) { + if (item.is_string()) { + required.insert(item.get()); + } + } + } + std::vector> properties; + if (schema.contains("properties")) { + for (const auto & prop : schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + } + } + return _add_rule(rule_name, + _build_object_rule( + properties, required, name, + schema.contains("additionalProperties") ? schema["additionalProperties"] : json())); + } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) { + std::unordered_set required; + std::vector> properties; + std::string hybrid_name = name; + std::function add_component = [&](const json & comp_schema, bool is_required) { + if (comp_schema.contains("$ref")) { + add_component(_refs[comp_schema["$ref"]], is_required); + } else if (comp_schema.contains("properties")) { + for (const auto & prop : comp_schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + if (is_required) { + required.insert(prop.key()); + } + } + } else { + // todo warning + } + }; + for (auto & t : schema["allOf"]) { + if (t.contains("anyOf")) { + for (auto & tt : t["anyOf"]) { + add_component(tt, false); + } + } else { + add_component(t, true); + } + } + return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json())); + } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) { + json items = schema.contains("items") ? schema["items"] : schema["prefixItems"]; + if (items.is_array()) { + std::string rule = "\"[\" space "; + for (size_t i = 0; i < items.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i)); + } + rule += " \"]\" space"; + return _add_rule(rule_name, rule); + } else { + std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); + std::string list_item_operator = "( \",\" space " + item_rule_name + " )"; + std::string successive_items; + int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; + json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : -1; + if (min_items > 0) { + successive_items += repeat(list_item_operator, min_items - 1); + min_items--; + } + if (max_items >= 0 && max_items > min_items) { + successive_items += repeat(list_item_operator + "?", max_items - min_items - 1); + } else { + successive_items += list_item_operator + "*"; + } + std::string rule; + if (min_items == 0) { + rule = "\"[\" space ( " + item_rule_name + " " + successive_items + " )? \"]\" space"; + } else { + rule = "\"[\" space " + item_rule_name + " " + successive_items + " \"]\" space"; + } + return _add_rule(rule_name, rule); + } + } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { + return _visit_pattern(schema["pattern"], rule_name); + } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { + return _add_rule(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); + } else if ((schema_type.is_null() || schema_type == "string") && DATE_RULES.find(schema_format) != DATE_RULES.end()) { + for (const auto & kv : DATE_RULES) { + _add_rule(kv.first, kv.second); + } + return schema_format + "-string"; + } else if (schema.empty() || schema_type == "object") { + for (const auto & n : OBJECT_RULE_NAMES) { + _add_rule(n, PRIMITIVE_RULES.at(n)); + } + return _add_rule(rule_name, "object"); + } else { + if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { + _errors.push_back("Unrecognized schema: " + schema.dump()); + return ""; + } + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return _add_rule(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); + } + } + + void check_errors() { + if (!_errors.empty()) { + throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n")); + } + if (!_warnings.empty()) { + fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str()); + } + } + + std::string format_grammar() { + std::stringstream ss; + for (const auto & kv : _rules) { + ss << kv.first << " ::= " << kv.second << std::endl; + } + return ss.str(); + } +}; + +std::string json_schema_to_grammar(const json & schema) { + SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false); + auto copy = schema; + converter.resolve_refs(copy, "input"); + converter.visit(copy, ""); + converter.check_errors(); + return converter.format_grammar(); +} diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h new file mode 100644 index 0000000000000..2e4c6ade3e7b2 --- /dev/null +++ b/common/json-schema-to-grammar.h @@ -0,0 +1,4 @@ +#pragma once +#include "json.hpp" + +std::string json_schema_to_grammar(const nlohmann::json& schema); diff --git a/examples/server/json.hpp b/common/json.hpp similarity index 100% rename from examples/server/json.hpp rename to common/json.hpp diff --git a/examples/json-schema-pydantic-example.py b/examples/json-schema-pydantic-example.py new file mode 100644 index 0000000000000..69ebfd4093824 --- /dev/null +++ b/examples/json-schema-pydantic-example.py @@ -0,0 +1,74 @@ +# Usage: +#! ./server -m some-model.gguf & +#! pip install pydantic +#! python json-schema-pydantic-example.py + +from pydantic import BaseModel, TypeAdapter +from annotated_types import MinLen +from typing import Annotated, List, Optional +import json, requests + +if True: + + def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs): + ''' + Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support + (llama.cpp server, llama-cpp-python, Anyscale / Together...) + + The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below) + ''' + if response_model: + type_adapter = TypeAdapter(response_model) + schema = type_adapter.json_schema() + messages = [{ + "role": "system", + "content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}" + }] + messages + response_format={"type": "json_object", "schema": schema} + + data = requests.post(endpoint, headers={"Content-Type": "application/json"}, + json=dict(messages=messages, response_format=response_format, **kwargs)).json() + if 'error' in data: + raise Exception(data['error']['message']) + + content = data["choices"][0]["message"]["content"] + return type_adapter.validate_json(content) if type_adapter else content + +else: + + # This alternative branch uses Instructor + OpenAI client lib. + # Instructor support streamed iterable responses, retry & more. + # (see https://python.useinstructor.com/) + #! pip install instructor openai + import instructor, openai + client = instructor.patch( + openai.OpenAI(api_key="123", base_url="http://localhost:8080"), + mode=instructor.Mode.JSON_SCHEMA) + create_completion = client.chat.completions.create + + +if __name__ == '__main__': + + class QAPair(BaseModel): + question: str + concise_answer: str + justification: str + + class PyramidalSummary(BaseModel): + title: str + summary: str + question_answers: Annotated[List[QAPair], MinLen(2)] + sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]] + + print("# Summary\n", create_completion( + model="...", + response_model=PyramidalSummary, + messages=[{ + "role": "user", + "content": f""" + You are a highly efficient corporate document summarizer. + Create a pyramidal summary of an imaginary internal document about our company processes + (starting high-level, going down to each sub sections). + Keep questions short, and answers even shorter (trivia / quizz style). + """ + }])) diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py index 6a977f031662e..9eead557f2644 100755 --- a/examples/json-schema-to-grammar.py +++ b/examples/json-schema-to-grammar.py @@ -1,8 +1,10 @@ #!/usr/bin/env python3 import argparse +import itertools import json import re import sys +from typing import Any, Dict, List, Set, Tuple, Union # whitespace is constrained to a single space char to prevent model "running away" in # whitespace. Also maybe improves generation quality? @@ -12,22 +14,50 @@ 'boolean': '("true" | "false") space', 'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', 'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space', + 'value' : 'object | array | string | number | boolean', + 'object' : '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', + 'array' : '"[" space ( value ("," space value)* )? "]" space', + 'uuid' : '"\\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + ' "\\"" space', 'string': r''' "\"" ( [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\"" space ''', + )* "\"" space''', 'null': '"null" space', } +OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value'] + +# TODO: support "uri", "email" string formats +DATE_RULES = { + 'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', + 'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', + 'date-time': 'date "T" time', + 'date-string': '"\\"" date "\\"" space', + 'time-string': '"\\"" time "\\"" space', + 'date-time-string': '"\\"" date-time "\\"" space', +} + +RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()]) INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') -GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'} +GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]') +GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'} +NON_LITERAL_SET = set('|.()[]{}*+?') +ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?') + +DATE_PATTERN = '[0-9]{4}-(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])' +TIME_PATTERN = '([01][0-9]|2[0-3])(:[0-5][0-9]){2}(\\.[0-9]{1,3})?(Z|[+-](([01][0-9]|2[0-3]):[0-5][0-9]))' # Cap millisecond precision w/ 3 digits class SchemaConverter: - def __init__(self, prop_order): + def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): self._prop_order = prop_order + self._allow_fetch = allow_fetch + self._dotall = dotall + self._raw_pattern = raw_pattern self._rules = {'space': SPACE_RULE} + self._refs = {} + self._refs_being_resolved = set() def _format_literal(self, literal): escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( @@ -41,78 +71,421 @@ def _add_rule(self, name, rule): key = esc_name else: i = 0 - while f'{esc_name}{i}' in self._rules: + while f'{esc_name}{i}' in self._rules and self._rules[f'{esc_name}{i}'] != rule: i += 1 key = f'{esc_name}{i}' self._rules[key] = rule return key + def resolve_refs(self, schema: dict, url: str): + ''' + Resolves all $ref fields in the given schema, fetching any remote schemas, + replacing $ref with absolute reference URL and populating self._refs with the + respective referenced (sub)schema dictionaries. + ''' + def visit(n: dict): + if isinstance(n, list): + return [visit(x) for x in n] + elif isinstance(n, dict): + ref = n.get('$ref') + if ref is not None and ref not in self._refs: + if ref.startswith('https://'): + assert self._allow_fetch, 'Fetching remote schemas is not allowed (use --allow-fetch for force)' + import requests + + frag_split = ref.split('#') + base_url = frag_split[0] + + target = self._refs.get(base_url) + if target is None: + target = self.resolve_refs(requests.get(ref).json(), base_url) + self._refs[base_url] = target + + if len(frag_split) == 1 or frag_split[-1] == '': + return target + elif ref.startswith('#/'): + target = schema + ref = f'{url}{ref}' + n['$ref'] = ref + else: + raise ValueError(f'Unsupported ref {ref}') + + for sel in ref.split('#')[-1].split('/')[1:]: + assert target is not None and sel in target, f'Error resolving ref {ref}: {sel} not in {target}' + target = target[sel] + + self._refs[ref] = target + else: + for v in n.values(): + visit(v) + + return n + return visit(schema) + + def _generate_union_rule(self, name, alt_schemas): + return ' | '.join(( + self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}') + for i, alt_schema in enumerate(alt_schemas) + )) + + def _visit_pattern(self, pattern, name): + ''' + Transforms a regular expression pattern into a GBNF rule. + + Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions + Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md + + Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers. + + Mostly a 1:1 translation, except for {x} / {x,} / {x,y} quantifiers for which + we define sub-rules to keep the output lean. + ''' + + assert pattern.startswith('^') and pattern.endswith('$'), 'Pattern must start with "^" and end with "$"' + pattern = pattern[1:-1] + sub_rule_ids = {} + + i = 0 + length = len(pattern) + + def to_rule(s: Tuple[str, bool]) -> str: + (txt, is_literal) = s + return "\"" + txt + "\"" if is_literal else txt + + def transform() -> Tuple[str, bool]: + ''' + Parse a unit at index i (advancing it), and return its string representation + whether it's a literal. + ''' + nonlocal i + nonlocal pattern + nonlocal sub_rule_ids + + start = i + # For each component of this sequence, store its string representation and whether it's a literal. + # We only need a flat structure here to apply repetition operators to the last item, and + # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially + # (GBNF's syntax is luckily very close to regular expressions!) + seq: list[Tuple[str, bool]] = [] + + def get_dot(): + if self._dotall: + rule = '[\\U00000000-\\U0010FFFF]' + else: + # Accept any character... except \n and \r line break chars (\x0A and \xOD) + rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]' + return self._add_rule(f'dot', rule) + + def join_seq(): + nonlocal seq + ret = [] + for is_literal, g in itertools.groupby(seq, lambda x: x[1]): + if is_literal: + ret.append((''.join(x[0] for x in g), True)) + else: + ret.extend(g) + if len(ret) == 1: + return ret[0] + return (' '.join(to_rule(x) for x in seq), False) + + while i < length: + c = pattern[i] + if c == '.': + seq.append((get_dot(), False)) + i += 1 + elif c == '(': + i += 1 + if i < length: + assert pattern[i] != '?', f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + seq.append((f'({to_rule(transform())})', False)) + elif c == ')': + i += 1 + assert start > 0 and pattern[start-1] == '(', f'Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}' + return join_seq() + elif c == '[': + square_brackets = c + i += 1 + while i < length and pattern[i] != ']': + if pattern[i] == '\\': + square_brackets += pattern[i:i+2] + i += 2 + else: + square_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}' + square_brackets += ']' + i += 1 + seq.append((square_brackets, False)) + elif c == '|': + seq.append(('|', False)) + i += 1 + elif c in ('*', '+', '?'): + seq[-1] = (to_rule(seq[-1]) + c, False) + i += 1 + elif c == '{': + curly_brackets = c + i += 1 + while i < length and pattern[i] != '}': + curly_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}' + curly_brackets += '}' + i += 1 + nums = [s.strip() for s in curly_brackets[1:-1].split(',')] + min_times = 0 + max_times = None + try: + if len(nums) == 1: + min_times = int(nums[0]) + max_times = min_times + else: + assert len(nums) == 2 + min_times = int(nums[0]) if nums[0] else 0 + max_times = int(nums[1]) if nums[1] else None + except ValueError: + raise ValueError(f'Invalid quantifier {curly_brackets} in /{pattern}/') + + (sub, sub_is_literal) = seq[-1] + + if min_times == 0 and max_times is None: + seq[-1] = (f'{sub}*', False) + elif min_times == 0 and max_times == 1: + seq[-1] = (f'{sub}?', False) + elif min_times == 1 and max_times is None: + seq[-1] = (f'{sub}+', False) + else: + if not sub_is_literal: + id = sub_rule_ids.get(sub) + if id is None: + id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub) + sub_rule_ids[sub] = id + sub = id + + seq[-1] = ( + ' '.join( + ([f'"{sub[1:-1] * min_times}"'] if sub_is_literal else [sub] * min_times) + + ([f'{sub}?'] * (max_times - min_times) if max_times is not None else [f'{sub}*'])), + False + ) + else: + literal = '' + while i < length: + if pattern[i] == '\\' and i < length - 1: + next = pattern[i + 1] + if next in ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS: + i += 1 + literal += pattern[i] + i += 1 + else: + literal += pattern[i:i+2] + i += 2 + elif pattern[i] == '"' and not self._raw_pattern: + literal += '\\"' + i += 1 + elif pattern[i] not in NON_LITERAL_SET and \ + (i == length - 1 or literal == '' or pattern[i+1] == '.' or pattern[i+1] not in NON_LITERAL_SET): + literal += pattern[i] + i += 1 + else: + break + if literal: + seq.append((literal, True)) + + return join_seq() + + return self._add_rule( + name, + to_rule(transform()) if self._raw_pattern \ + else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space") + + + def _resolve_ref(self, ref): + ref_name = ref.split('/')[-1] + if ref_name not in self._rules and ref not in self._refs_being_resolved: + self._refs_being_resolved.add(ref) + resolved = self._refs[ref] + ref_name = self.visit(resolved, ref_name) + self._refs_being_resolved.remove(ref) + return ref_name + + def _generate_constant_rule(self, value): + assert isinstance(value, str), f'Only string constants are supported, got {value}' + return self._format_literal(value) + def visit(self, schema, name): schema_type = schema.get('type') - rule_name = name or 'root' + schema_format = schema.get('format') + rule_name = name + '-' if name in RESERVED_NAMES else name or 'root' - if 'oneOf' in schema or 'anyOf' in schema: - rule = ' | '.join(( - self.visit(alt_schema, f'{name}{"-" if name else ""}{i}') - for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf']) - )) - return self._add_rule(rule_name, rule) + if (ref := schema.get('$ref')) is not None: + return self._add_rule(rule_name, self._resolve_ref(ref)) + + elif 'oneOf' in schema or 'anyOf' in schema: + return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf'])) + + elif isinstance(schema_type, list): + return self._add_rule(rule_name, self._generate_union_rule(name, [{'type': t} for t in schema_type])) elif 'const' in schema: - return self._add_rule(rule_name, self._format_literal(schema['const'])) + return self._add_rule(rule_name, self._generate_constant_rule(schema['const'])) elif 'enum' in schema: - rule = ' | '.join((self._format_literal(v) for v in schema['enum'])) + rule = ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) return self._add_rule(rule_name, rule) - elif schema_type == 'object' and 'properties' in schema: - # TODO: `required` keyword - prop_order = self._prop_order - prop_pairs = sorted( - schema['properties'].items(), - # sort by position in prop_order (if specified) then by key - key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]), - ) + elif schema_type in (None, 'object') and \ + ('properties' in schema or \ + ('additionalProperties' in schema and schema['additionalProperties'] is not True)): + required = set(schema.get('required', [])) + properties = list(schema.get('properties', {}).items()) + return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties'))) - rule = '"{" space' - for i, (prop_name, prop_schema) in enumerate(prop_pairs): - prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') - if i > 0: - rule += ' "," space' - rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}' - rule += ' "}" space' + elif schema_type in (None, 'object') and 'allOf' in schema: + required = set() + properties = [] + hybrid_name = name + def add_component(comp_schema, is_required): + if (ref := comp_schema.get('$ref')) is not None: + comp_schema = self._refs[ref] - return self._add_rule(rule_name, rule) + if 'properties' in comp_schema: + for prop_name, prop_schema in comp_schema['properties'].items(): + properties.append((prop_name, prop_schema)) + if is_required: + required.add(prop_name) - elif schema_type == 'array' and 'items' in schema: - # TODO `prefixItems` keyword - item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item') - list_item_operator = f'("," space {item_rule_name})' - successive_items = "" - min_items = schema.get("minItems", 0) - if min_items > 0: - first_item = f"({item_rule_name})" - successive_items = list_item_operator * (min_items - 1) - min_items -= 1 - else: - first_item = f"({item_rule_name})?" - max_items = schema.get("maxItems") - if max_items is not None and max_items > min_items: - successive_items += (list_item_operator + "?") * (max_items - min_items - 1) + for t in schema['allOf']: + if 'anyOf' in t: + for tt in t['anyOf']: + add_component(tt, is_required=False) + else: + add_component(t, is_required=True) + + return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=[])) + + elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema): + items = schema.get('items') or schema['prefixItems'] + if isinstance(items, list): + return self._add_rule( + rule_name, + '"[" space ' + + ' "," space '.join( + self.visit(item, f'{name}{"-" if name else ""}tuple-{i}') + for i, item in enumerate(items)) + + ' "]" space') else: - successive_items += list_item_operator + "*" - rule = f'"[" space {first_item} {successive_items} "]" space' - return self._add_rule(rule_name, rule) + item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') + list_item_operator = f'( "," space {item_rule_name} )' + successive_items = "" + min_items = schema.get("minItems", 0) + max_items = schema.get("maxItems") + if min_items > 0: + successive_items = list_item_operator * (min_items - 1) + min_items -= 1 + if max_items is not None and max_items > min_items: + successive_items += (list_item_operator + "?") * (max_items - min_items - 1) + else: + successive_items += list_item_operator + "*" + if min_items == 0: + rule = f'"[" space ( {item_rule_name} {successive_items} )? "]" space' + else: + rule = f'"[" space {item_rule_name} {successive_items} "]" space' + return self._add_rule(rule_name, rule) + + elif schema_type in (None, 'string') and 'pattern' in schema: + return self._visit_pattern(schema['pattern'], rule_name) + + elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''): + return self._add_rule( + 'root' if rule_name == 'root' else schema_format, + PRIMITIVE_RULES['uuid'] + ) + + elif schema_type in (None, 'string') and schema_format in DATE_RULES: + for t, r in DATE_RULES.items(): + self._add_rule(t, r) + return schema_format + '-string' + + elif (schema_type == 'object') or (len(schema) == 0): + for n in OBJECT_RULE_NAMES: + self._add_rule(n, PRIMITIVE_RULES[n]) + return self._add_rule(rule_name, 'object') else: assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' + # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero return self._add_rule( 'root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type] ) + def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]): + prop_order = self._prop_order + # sort by position in prop_order (if specified) then by original order + sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))] + + prop_kv_rule_names = {} + for prop_name, prop_schema in properties: + prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') + prop_kv_rule_names[prop_name] = self._add_rule( + f'{name}{"-" if name else ""}{prop_name}-kv', + fr'{self._format_literal(prop_name)} space ":" space {prop_rule_name}' + ) + required_props = [k for k in sorted_props if k in required] + optional_props = [k for k in sorted_props if k not in required] + + if additional_properties == True or isinstance(additional_properties, dict): + sub_name = f'{name}{"-" if name else ""}additional' + value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value') + prop_kv_rule_names["*"] = self._add_rule( + f'{sub_name}-kv', + self._add_rule('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}' + ) + optional_props.append("*") + + rule = '"{" space ' + rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props) + + if optional_props: + rule += ' (' + if required_props: + rule += ' "," space ( ' + + def get_recursive_refs(ks, first_is_optional): + [k, *rest] = ks + kv_rule_name = prop_kv_rule_names[k] + if k == '*': + res = self._add_rule( + f'{name}{"-" if name else ""}additional-kvs', + f'{kv_rule_name} ( "," space ' + kv_rule_name + ' )*' + ) + elif first_is_optional: + res = f'( "," space {kv_rule_name} )?' + else: + res = kv_rule_name + if len(rest) > 0: + res += ' ' + self._add_rule( + f'{name}{"-" if name else ""}{k}-rest', + get_recursive_refs(rest, first_is_optional=True) + ) + return res + + rule += ' | '.join( + get_recursive_refs(optional_props[i:], first_is_optional=False) + for i in range(len(optional_props)) + ) + if required_props: + rule += ' )' + rule += ' )?' + + rule += ' "}" space' + + return rule + def format_grammar(self): - return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items())) + return '\n'.join( + f'{name} ::= {rule}' + for name, rule in sorted(self._rules.items(), key=lambda kv: kv[0]) + ) def main(args_in = None): @@ -129,16 +502,47 @@ def main(args_in = None): type=lambda s: s.split(','), help=''' comma-separated property names defining the order of precedence for object properties; - properties not specified here are given lower precedence than those that are, and are - sorted alphabetically + properties not specified here are given lower precedence than those that are, and + are kept in their original order from the schema. Required properties are always + given precedence over optional properties. ''' ) + parser.add_argument( + '--allow-fetch', + action='store_true', + default=False, + help='Whether to allow fetching referenced schemas over HTTPS') + parser.add_argument( + '--dotall', + action='store_true', + default=False, + help='Whether to treat dot (".") as matching all chars including line breaks in regular expression patterns') + parser.add_argument( + '--raw-pattern', + action='store_true', + default=False, + help='Treats string patterns as raw patterns w/o quotes (or quote escapes)') + parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)') args = parser.parse_args(args_in) - schema = json.load(sys.stdin if args.schema == '-' else open(args.schema)) - prop_order = {name: idx for idx, name in enumerate(args.prop_order)} - converter = SchemaConverter(prop_order) + if args.schema.startswith('https://'): + url = args.schema + import requests + schema = requests.get(url).json() + elif args.schema == '-': + url = 'stdin' + schema = json.load(sys.stdin) + else: + url = f'file://{args.schema}' + with open(args.schema) as f: + schema = json.load(f) + converter = SchemaConverter( + prop_order={name: idx for idx, name in enumerate(args.prop_order)}, + allow_fetch=args.allow_fetch, + dotall=args.dotall, + raw_pattern=args.raw_pattern) + schema = converter.resolve_refs(schema, url) converter.visit(schema, '') print(converter.format_grammar()) diff --git a/examples/regex-to-grammar.py b/examples/regex-to-grammar.py new file mode 100644 index 0000000000000..c98118e95a858 --- /dev/null +++ b/examples/regex-to-grammar.py @@ -0,0 +1,20 @@ +import json, subprocess, sys, os + +assert len(sys.argv) >= 2 +[_, pattern, *rest] = sys.argv + +print(subprocess.check_output( + [ + "python", + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "json-schema-to-grammar.py"), + *rest, + "-", + "--raw-pattern", + ], + text=True, + input=json.dumps({ + "type": "string", + "pattern": pattern, + }, indent=2))) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index f94de1e99b7e9..d2ee47d01d767 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -2,12 +2,16 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h) +add_executable(${TARGET} + server.cpp + utils.hpp + httplib.h +) install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common json-schema-to-grammar ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) find_package(OpenSSL REQUIRED) target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 219ebb51a1c9b..a79c8a3cd09c2 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -26,8 +26,9 @@ const propOrder = grammarJsonSchemaPropOrder let grammar = null if (grammarJsonSchemaFile) { - const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8')) - const converter = new SchemaConverter(propOrder) + let schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8')) + const converter = new SchemaConverter({prop_order: propOrder, allow_fetch: true}) + schema = await converter.resolveRefs(schema, grammarJsonSchemaFile) converter.visit(schema, '') grammar = converter.formatGrammar() } diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 10734091eeaf6..f8bb4b0f2c128 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -483,4 +483,4 @@ unsigned char completion_js[] = { 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; -unsigned int completion_js_len = 5796; +size_t completion_js_len = 5796; diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 20551520e29cd..ae8c8898028f9 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -417,2375 +417,2405 @@ unsigned char index_html[] = { 0x20, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x61, 0x67, 0x65, 0x5f, - 0x64, 0x61, 0x74, 0x61, 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x61, 0x63, 0x68, 0x65, 0x5f, 0x70, 0x72, - 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x74, 0x72, 0x75, 0x65, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x70, 0x69, 0x5f, 0x6b, 0x65, - 0x79, 0x3a, 0x20, 0x27, 0x27, 0x0a, 0x20, 0x20, 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b/examples/server/index.js.hpp index e09b3c8c59060..24d0b33fa6f03 100644 --- a/examples/server/index.js.hpp +++ b/examples/server/index.js.hpp @@ -12,7 +12,7 @@ unsigned char index_js[] = { 0x74, 0x2c, 0x6e, 0x3d, 0x21, 0x31, 0x3b, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6f, 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x5f, 0x3d, 0x6f, 0x3b, 0x6f, 0x3d, 0x76, - 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x73, 0x2b, 0x2b, 0x3b, 0x77, 0x68, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x6c, 0x2b, 0x2b, 0x3b, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x5f, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x69, 0x3d, 0x5f, 0x2e, 0x6f, 0x3b, 0x5f, 0x2e, 0x6f, 0x3d, 0x76, 0x6f, 0x69, 0x64, @@ -21,7 +21,7 @@ unsigned char index_js[] = { 0x70, 0x28, 0x5f, 0x29, 0x29, 0x74, 0x72, 0x79, 0x7b, 0x5f, 0x2e, 0x63, 0x28, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x65, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x21, 0x6e, 0x29, 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=> { + const convertJSONSchemaGrammar = async () => { try { - const schema = JSON.parse(params.value.grammar) - const converter = new SchemaConverter( - grammarJsonSchemaPropOrder.value + let schema = JSON.parse(params.value.grammar) + const converter = new SchemaConverter({ + prop_order: grammarJsonSchemaPropOrder.value .split(',') - .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}) - ) + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}), + allow_fetch: true, + }) + schema = await converter.resolveRefs(schema, 'input') converter.visit(schema, '') params.value = { ...params.value, diff --git a/examples/server/public/index.js b/examples/server/public/index.js index 9db5a9d9faf29..aa3ffb5e06be5 100644 --- a/examples/server/public/index.js +++ b/examples/server/public/index.js @@ -1 +1 @@ -function t(){throw new Error("Cycle detected")}function n(){if(u>1){u--;return}let t,n=!1;while(void 0!==_){let i=_;_=void 0;f++;while(void 0!==i){const _=i.o;i.o=void 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createContext,L as createElement,R as createRef,w as effect,L as h,un as html,ct as hydrate,U as isValidElement,C as options,st as render,a as signal,Y as toChildArray,u as untracked,Ft as useCallback,nn as useComputed,Mt as useContext,Wt as useDebugValue,$t as useEffect,Lt as useErrorBoundary,Ot as useId,Vt as useImperativeHandle,Dt as useLayoutEffect,At as useMemo,Nt as useReducer,Tt as useRef,tn as useSignal,en as useSignalEffect,Pt as useState}; diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index 3f1b255c262a3..baa6761c8cc2c 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -1,25 +1,50 @@ +// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first. const SPACE_RULE = '" "?'; const PRIMITIVE_RULES = { boolean: '("true" | "false") space', number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', + value: 'object | array | string | number | boolean', + object: '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', + array: '"[" space ( value ("," space value)* )? "]" space', + uuid: '"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', string: ` "\\"" ( [^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) )* "\\"" space`, null: '"null" space', }; +const OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value']; + +// TODO: support "uri", "email" string formats +const DATE_RULES = { + 'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', + 'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', + 'date-time': 'date "T" time', + 'date-string': '"\\"" date "\\"" space', + 'time-string': '"\\"" time "\\"" space', + 'date-time-string': '"\\"" date-time "\\"" space', +}; + +const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES}; const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; -const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; +const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g; +const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' }; + +const NON_LITERAL_SET = new Set('|.()[]{}*+?'); +const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('[]()|{}*+?'); export class SchemaConverter { - constructor(propOrder) { - this._propOrder = propOrder || {}; - this._rules = new Map(); - this._rules.set('space', SPACE_RULE); + constructor(options) { + this._propOrder = options.prop_order || {}; + this._allowFetch = options.allow_fetch || false; + this._dotall = options.dotall || false; + this._rules = {'space': SPACE_RULE}; + this._refs = {}; + this._refsBeingResolved = new Set(); } _formatLiteral(literal) { @@ -30,83 +55,490 @@ export class SchemaConverter { return `"${escaped}"`; } + _formatRangeChar(literal) { + return JSON.stringify(literal).slice(1, -1).replace( + GRAMMAR_RANGE_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + } + _addRule(name, rule) { let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); let key = escName; - if (this._rules.has(escName)) { - if (this._rules.get(escName) === rule) { + if (escName in this._rules) { + if (this._rules[escName] === rule) { return key; } let i = 0; - while (this._rules.has(`${escName}${i}`)) { + while ((`${escName}${i}` in this._rules) && (this._rules[`${escName}${i}`] !== rule)) { i += 1; } key = `${escName}${i}`; } - this._rules.set(key, rule); + this._rules[key] = rule; return key; } + async resolveRefs(schema, url) { + const visit = async (n) => { + if (Array.isArray(n)) { + return Promise.all(n.map(visit)); + } else if (typeof n === 'object' && n !== null) { + let ref = n.$ref; + let target; + if (ref !== undefined && !this._refs[ref]) { + if (ref.startsWith('https://')) { + if (!this._allowFetch) { + throw new Error('Fetching remote schemas is not allowed (use --allow-fetch for force)'); + } + const fetch = (await import('node-fetch')).default; + + const fragSplit = ref.split('#'); + const baseUrl = fragSplit[0]; + + target = this._refs[baseUrl]; + if (!target) { + target = await this.resolveRefs(await fetch(ref).then(res => res.json()), baseUrl); + this._refs[baseUrl] = target; + } + + if (fragSplit.length === 1 || fragSplit[fragSplit.length - 1] === '') { + return target; + } + } else if (ref.startsWith('#/')) { + target = schema; + ref = `${url}${ref}`; + n.$ref = ref; + } else { + throw new Error(`Unsupported ref ${ref}`); + } + + const selectors = ref.split('#')[1].split('/').slice(1); + for (const sel of selectors) { + if (!target || !(sel in target)) { + throw new Error(`Error resolving ref ${ref}: ${sel} not in ${JSON.stringify(target)}`); + } + target = target[sel]; + } + + this._refs[ref] = target; + } else { + await Promise.all(Object.values(n).map(visit)); + } + } + + return n; + }; + + return visit(schema); + } + + _generateUnionRule(name, altSchemas) { + return altSchemas + .map((altSchema, i) => this.visit(altSchema, `${name ?? ''}${name ? '-' : 'alternative-'}${i}`)) + .join(' | '); + } + + _visitPattern(pattern, name) { + if (!pattern.startsWith('^') || !pattern.endsWith('$')) { + throw new Error('Pattern must start with "^" and end with "$"'); + } + pattern = pattern.slice(1, -1); + const subRuleIds = {}; + + let i = 0; + const length = pattern.length; + + const getDot = () => { + let rule; + if (this._dotall) { + rule = '[\\U00000000-\\U0010FFFF]'; + } else { + // Accept any character... except \n and \r line break chars (\x0A and \xOD) + rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'; + } + return this._addRule('dot', rule); + }; + + + const toRule = ([s, isLiteral]) => isLiteral ? "\"" + s + "\"" : s; + + const transform = () => { + const start = i; + // For each component of this sequence, store its string representation and whether it's a literal. + // We only need a flat structure here to apply repetition operators to the last item, and + // to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially + // (GBNF's syntax is luckily very close to regular expressions!) + const seq = []; + + const joinSeq = () => { + const ret = []; + for (const [isLiteral, g] of groupBy(seq, x => x[1])) { + if (isLiteral) { + ret.push([[...g].map(x => x[0]).join(''), true]); + } else { + ret.push(...g); + } + } + if (ret.length === 1) { + return ret[0]; + } + return [ret.map(x => toRule(x)).join(' '), false]; + }; + + while (i < length) { + const c = pattern[i]; + if (c === '.') { + seq.push([getDot(), false]); + i += 1; + } else if (c === '(') { + i += 1; + if (i < length) { + if (pattern[i] === '?') { + throw new Error(`Unsupported pattern syntax "${pattern[i]}" at index ${i} of /${pattern}/`); + } + } + seq.push([`(${toRule(transform())})`, false]); + } else if (c === ')') { + i += 1; + if (start <= 0 || pattern[start - 1] !== '(') { + throw new Error(`Unbalanced parentheses; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + return joinSeq(); + } else if (c === '[') { + let squareBrackets = c; + i += 1; + while (i < length && pattern[i] !== ']') { + if (pattern[i] === '\\') { + squareBrackets += pattern.slice(i, i + 2); + i += 2; + } else { + squareBrackets += pattern[i]; + i += 1; + } + } + if (i >= length) { + throw new Error(`Unbalanced square brackets; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + squareBrackets += ']'; + i += 1; + seq.push([squareBrackets, false]); + } else if (c === '|') { + seq.push(['|', false]); + i += 1; + } else if (c === '*' || c === '+' || c === '?') { + seq[seq.length - 1] = [toRule(seq[seq.length - 1]) + c, false]; + i += 1; + } else if (c === '{') { + let curlyBrackets = c; + i += 1; + while (i < length && pattern[i] !== '}') { + curlyBrackets += pattern[i]; + i += 1; + } + if (i >= length) { + throw new Error(`Unbalanced curly brackets; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + curlyBrackets += '}'; + i += 1; + const nums = curlyBrackets.slice(1, -1).split(',').map(s => s.trim()); + let minTimes, maxTimes; + if (nums.length === 1) { + minTimes = parseInt(nums[0], 10); + maxTimes = minTimes; + } else { + if (nums.length !== 2) { + throw new Error(`Invalid quantifier ${curlyBrackets}`); + } + minTimes = nums[0] ? parseInt(nums[0], 10) : 0; + maxTimes = nums[1] ? parseInt(nums[1], 10) : Infinity; + } + + let [sub, subIsLiteral] = seq[seq.length - 1]; + + if (minTimes === 0 && maxTimes === Infinity) { + seq[seq.length - 1] = [`${sub}*`, false]; + } else if (minTimes === 0 && maxTimes === 1) { + seq[seq.length - 1] = [`${sub}?`, false]; + } else if (minTimes === 1 && maxTimes === Infinity) { + seq[seq.length - 1] = [`${sub}+`, false]; + } else { + if (!subIsLiteral) { + let id = subRuleIds[sub]; + if (id === undefined) { + id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub); + subRuleIds[sub] = id; + } + sub = id; + } + + const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${sub.slice(1, -1).repeat(minTimes)}"` : sub); + const optionalSub = maxTimes !== undefined ? Array.from({ length: maxTimes - minTimes }, () => `${sub}?`) : [`${sub}*`]; + seq[seq.length - 1] = [repeatedSub.concat(optionalSub).join(' '), false]; + } + } else { + let literal = ''; + while (i < length) { + if (pattern[i] === '\\' && i < length - 1) { + const next = pattern[i + 1]; + if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.has(next)) { + i += 1; + literal += pattern[i]; + i += 1; + } else { + literal += pattern.slice(i, i + 2); + i += 2; + } + } else if (pattern[i] === '"') { + literal += '\\"'; + i += 1; + } else if (!NON_LITERAL_SET.has(pattern[i]) && + (i === length - 1 || literal === '' || pattern[i + 1] === '.' || !NON_LITERAL_SET.has(pattern[i+1]))) { + literal += pattern[i]; + i += 1; + } else { + break; + } + } + if (literal !== '') { + seq.push([literal, true]); + } + } + } + + return joinSeq(); + }; + + return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space") + } + + _resolveRef(ref) { + let refName = ref.split('/').pop(); + if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) { + this._refsBeingResolved.add(ref); + const resolved = this._refs[ref]; + refName = this.visit(resolved, refName); + this._refsBeingResolved.delete(ref); + } + return refName; + } + + _generateConstantRule(value) { + if (typeof value !== 'string') { + throw new Error('Only string constants are supported, got ' + JSON.stringify(value)); + } + return this._formatLiteral(value); + } + visit(schema, name) { const schemaType = schema.type; - const ruleName = name || 'root'; - - if (schema.oneOf || schema.anyOf) { - const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => - this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) - ).join(' | '); + const schemaFormat = schema.format; + const ruleName = name in RESERVED_NAMES ? name + '-' : name == '' ? 'root' : name; - return this._addRule(ruleName, rule); + const ref = schema.$ref; + if (ref !== undefined) { + return this._addRule(ruleName, this._resolveRef(ref)); + } else if (schema.oneOf || schema.anyOf) { + return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf)); + } else if (Array.isArray(schemaType)) { + return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t })))); } else if ('const' in schema) { - return this._addRule(ruleName, this._formatLiteral(schema.const)); + if (typeof schema.const !== 'string') { + throw new Error('Only string constants are supported, got ' + JSON.stringify(schema.const)); + } + return this._addRule(ruleName, this._generateConstantRule(schema.const)); } else if ('enum' in schema) { - const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); + const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | '); return this._addRule(ruleName, rule); - } else if (schemaType === 'object' && 'properties' in schema) { - // TODO: `required` keyword (from python implementation) - const propOrder = this._propOrder; - const propPairs = Object.entries(schema.properties).sort((a, b) => { - // sort by position in prop_order (if specified) then by key - const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; - const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; - return orderA - orderB || a[0].localeCompare(b[0]); - }); - - let rule = '"{" space'; - propPairs.forEach(([propName, propSchema], i) => { - const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); - if (i > 0) { - rule += ' "," space'; + } else if ((schemaType === undefined || schemaType === 'object') && + ('properties' in schema || + ('additionalProperties' in schema && schema.additionalProperties !== true))) { + const required = new Set(schema.required || []); + const properties = Object.entries(schema.properties ?? {}); + return this._addRule(ruleName, this._buildObjectRule(properties, required, name, schema.additionalProperties)); + } else if ((schemaType === undefined || schemaType === 'object') && 'allOf' in schema) { + const required = new Set(); + const properties = []; + const addComponent = (compSchema, isRequired) => { + const ref = compSchema.$ref; + if (ref !== undefined) { + compSchema = this._refs[ref]; } - rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; - }); - rule += ' "}" space'; - return this._addRule(ruleName, rule); - } else if (schemaType === 'array' && 'items' in schema) { - // TODO `prefixItems` keyword (from python implementation) - const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); - const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; - return this._addRule(ruleName, rule); + if ('properties' in compSchema) { + for (const [propName, propSchema] of Object.entries(compSchema.properties)) { + properties.push([propName, propSchema]); + if (isRequired) { + required.add(propName); + } + } + } + }; + + for (const t of schema.allOf) { + if ('anyOf' in t) { + for (const tt of t.anyOf) { + addComponent(tt, false); + } + } else { + addComponent(t, true); + } + } + + return this._addRule(ruleName, this._buildObjectRule(properties, required, name, /* additionalProperties= */ false)); + } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) { + const items = schema.items ?? schema.prefixItems; + if (Array.isArray(items)) { + return this._addRule( + ruleName, + '"[" space ' + + items.map((item, i) => this.visit(item, `${name ?? ''}${name ? '-' : ''}tuple-${i}`)).join(' "," space ') + + ' "]" space' + ); + } else { + const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`); + const listItemOperator = `( "," space ${itemRuleName} )`; + let successiveItems = ''; + let minItems = schema.minItems || 0; + const maxItems = schema.maxItems; + if (minItems > 0) { + successiveItems = listItemOperator.repeat(minItems - 1); + minItems--; + } + if (maxItems !== undefined && maxItems > minItems) { + successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1); + } else { + successiveItems += `${listItemOperator}*`; + } + const rule = minItems === 0 + ? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space` + : `"[" space ${itemRuleName} ${successiveItems} "]" space`; + return this._addRule(ruleName, rule); + } + } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) { + return this._visitPattern(schema.pattern, ruleName); + } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) { + return this._addRule( + ruleName === 'root' ? 'root' : schemaFormat, + PRIMITIVE_RULES['uuid']) + } else if ((schemaType === undefined || schemaType === 'string') && schema.format in DATE_RULES) { + for (const [t, r] of Object.entries(DATE_RULES)) { + this._addRule(t, r); + } + return schemaFormat + '-string'; + } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) { + for (const n of OBJECT_RULE_NAMES) { + this._addRule(n, PRIMITIVE_RULES[n]); + } + return this._addRule(ruleName, 'object'); } else { - if (!PRIMITIVE_RULES[schemaType]) { + if (!(schemaType in PRIMITIVE_RULES)) { throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); } - return this._addRule( - ruleName === 'root' ? 'root' : schemaType, - PRIMITIVE_RULES[schemaType] + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); + } + } + + _buildObjectRule(properties, required, name, additionalProperties) { + const propOrder = this._propOrder; + // sort by position in prop_order (if specified) then by original order + const sortedProps = properties.map(([k]) => k).sort((a, b) => { + const orderA = propOrder[a] || Infinity; + const orderB = propOrder[b] || Infinity; + return orderA - orderB || properties.findIndex(([k]) => k === a) - properties.findIndex(([k]) => k === b); + }); + + const propKvRuleNames = {}; + for (const [propName, propSchema] of properties) { + const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`); + propKvRuleNames[propName] = this._addRule( + `${name ?? ''}${name ? '-' : ''}${propName}-kv`, + `${this._formatLiteral(propName)} space ":" space ${propRuleName}` ); } + const requiredProps = sortedProps.filter(k => required.has(k)); + const optionalProps = sortedProps.filter(k => !required.has(k)); + + if (typeof additionalProperties === 'object' || additionalProperties === true) { + const subName = `${name ?? ''}${name ? '-' : ''}additional`; + const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`); + propKvRuleNames['*'] = this._addRule( + `${subName}-kv`, + `${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`); + optionalProps.push('*'); + } + + let rule = '"{" space '; + rule += requiredProps.map(k => propKvRuleNames[k]).join(' "," space '); + + if (optionalProps.length > 0) { + rule += ' ('; + if (requiredProps.length > 0) { + rule += ' "," space ( '; + } + + const getRecursiveRefs = (ks, firstIsOptional) => { + const [k, ...rest] = ks; + const kvRuleName = propKvRuleNames[k]; + let res; + if (k === '*') { + res = this._addRule( + `${name ?? ''}${name ? '-' : ''}additional-kvs`, + `${kvRuleName} ( "," space ` + kvRuleName + ` )*` + ) + } else if (firstIsOptional) { + res = `( "," space ${kvRuleName} )?`; + } else { + res = kvRuleName; + } + if (rest.length > 0) { + res += ' ' + this._addRule( + `${name ?? ''}${name ? '-' : ''}${k}-rest`, + getRecursiveRefs(rest, true) + ); + } + return res; + }; + + rule += optionalProps.map((_, i) => getRecursiveRefs(optionalProps.slice(i), false)).join(' | '); + if (requiredProps.length > 0) { + rule += ' )'; + } + rule += ' )?'; + } + + rule += ' "}" space'; + + return rule; } formatGrammar() { let grammar = ''; - this._rules.forEach((rule, name) => { + for (const [name, rule] of Object.entries(this._rules).sort(([a], [b]) => a.localeCompare(b))) { grammar += `${name} ::= ${rule}\n`; - }); + } return grammar; } } + +// Helper function to group elements by a key function +function* groupBy(iterable, keyFn) { + let lastKey = null; + let group = []; + for (const element of iterable) { + const key = keyFn(element); + if (lastKey !== null && key !== lastKey) { + yield [lastKey, group]; + group = []; + } + group.push(element); + lastKey = key; + } + if (group.length > 0) { + yield [lastKey, group]; + } +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d2a8e541d3305..cf075d6c41aa4 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,7 @@ #include "utils.hpp" #include "common.h" +#include "json-schema-to-grammar.h" #include "llama.h" #include "grammar-parser.h" @@ -178,6 +179,7 @@ struct server_slot { llama_token sampled; struct llama_sampling_params sparams; llama_sampling_context * ctx_sampling = nullptr; + json json_schema; int32_t ga_i = 0; // group-attention state int32_t ga_n = 1; // group-attention factor @@ -845,7 +847,17 @@ struct server_context { slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); slot.params.seed = json_value(data, "seed", default_params.seed); - slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + if (data.contains("json_schema") && !data.contains("grammar")) { + try { + auto schema = json_value(data, "json_schema", json::object()); + slot.sparams.grammar = json_schema_to_grammar(schema); + } catch (const std::exception & e) { + send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST); + return false; + } + } else { + slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + } slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index 1d6aa40ea6985..eb82e7aca3cd9 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -37,6 +37,22 @@ Feature: Security | llama.cpp | no | | hackme | raised | + Scenario Outline: OAI Compatibility (invalid response formats) + Given a system prompt test + And a user prompt test + And a response format + And a model test + And 2 max tokens to predict + And streaming is disabled + Given an OAI compatible chat completions request with raised api error + + Examples: Prompts + | response_format | + | {"type": "sound"} | + | {"type": "json_object", "schema": 123} | + | {"type": "json_object", "schema": {"type": 123}} | + | {"type": "json_object", "schema": {"type": "hiccup"}} | + Scenario Outline: CORS Options Given a user api key llama.cpp diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 45a988db67f5f..a2e0e5b3532b5 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -70,6 +70,22 @@ Feature: llama.cpp server | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | + Scenario Outline: OAI Compatibility w/ response format + Given a model test + And a system prompt test + And a user prompt test + And a response format + And 10 max tokens to predict + Given an OAI compatible chat completions request with no api error + Then tokens are predicted matching + + Examples: Prompts + | response_format | n_predicted | re_content | + | {"type": "json_object", "schema": {"const": "42"}} | 5 | "42" | + | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | + | {"type": "json_object"} | 10 | \{ " Jacky. | + + Scenario: Tokenize / Detokenize When tokenizing: """ diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 42cdd89850398..03f55f65910de 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -59,6 +59,7 @@ def step_server_config(context, server_fqdn, server_port): context.seed = None context.server_seed = None context.user_api_key = None + context.response_format = None context.tasks_result = [] context.concurrent_tasks = [] @@ -269,6 +270,11 @@ def step_max_tokens(context, max_tokens): context.n_predict = max_tokens +@step('a response format {response_format}') +def step_response_format(context, response_format): + context.response_format = json.loads(response_format) + + @step('streaming is {enable_streaming}') def step_streaming(context, enable_streaming): context.enable_streaming = enable_streaming == 'enabled' @@ -384,6 +390,9 @@ async def step_oai_chat_completions(context, api_error): enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, + response_format=context.response_format + if hasattr(context, 'response_format') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key @@ -443,6 +452,8 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, + response_format=context.response_format + if hasattr(context, 'response_format') else None, seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -463,6 +474,8 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, + response_format=context.response_format + if hasattr(context, 'response_format') else None, seed=context.seed if hasattr(context, 'seed') else context.server_seed @@ -745,6 +758,7 @@ async def oai_chat_completions(user_prompt, model=None, n_predict=None, enable_streaming=None, + response_format=None, seed=None, user_api_key=None, expect_api_error=None): @@ -770,6 +784,8 @@ async def oai_chat_completions(user_prompt, "stream": enable_streaming, "seed": seed } + if response_format is not None: + payload['response_format'] = response_format completion_response = { 'content': '', 'timings': { @@ -830,6 +846,7 @@ async def oai_chat_completions(user_prompt, model=model, max_tokens=n_predict, stream=enable_streaming, + response_format=payload.get('response_format'), seed=seed ) except openai.error.AuthenticationError as e: diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index cc5fce6918b07..ff146893d720b 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -373,10 +373,21 @@ static json oaicompat_completion_params_parse( llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); llama_params["n_keep"] = json_value(body, "n_keep", 0); - if (body.count("grammar") != 0) { + if (body.contains("grammar")) { llama_params["grammar"] = json_value(body, "grammar", json::object()); } + if (body.contains("response_format")) { + auto response_format = json_value(body, "response_format", json::object()); + if (response_format.contains("type")) { + if (response_format["type"] == "json_object") { + llama_params["json_schema"] = json_value(response_format, "schema", json::object()); + } else { + throw std::runtime_error("response_format type not supported: " + response_format["type"].dump()); + } + } + } + // Handle 'stop' field if (body.contains("stop") && body["stop"].is_string()) { llama_params["stop"] = json::array({body["stop"].get()}); diff --git a/examples/ts-type-to-grammar.sh b/examples/ts-type-to-grammar.sh new file mode 100755 index 0000000000000..a7ad001737c25 --- /dev/null +++ b/examples/ts-type-to-grammar.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# +# ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}" +# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json +# +set -euo pipefail + +readonly type="$1" + +# Create a temporary directory +TMPDIR="" +trap 'rm -fR "$TMPDIR"' EXIT +TMPDIR=$(mktemp -d) + +DTS_FILE="$TMPDIR/type.d.ts" +SCHEMA_FILE="$TMPDIR/schema.json" + +echo "export type MyType = $type" > "$DTS_FILE" + +# This is a fork of typescript-json-schema, actively maintained as of March 2024: +# https://github.com/vega/ts-json-schema-generator +npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type MyType -e none > "$SCHEMA_FILE" + +# Alternative, not actively maintained as of March 2024: +# https://github.com/YousefED/typescript-json-schema +# npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2 + +./examples/json-schema-to-grammar.py "$SCHEMA_FILE" diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 10326d531e9fb..a43439aed96e7 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,66 +1,75 @@ -function(llama_build_executable source) - get_filename_component(TEST_TARGET ${source} NAME_WE) - add_executable(${TEST_TARGET} ${source} get-model.cpp) - install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE common) -endfunction() +# Builds and runs a test source file. +# Optional args: +# - NAME: name of the executable & test target (defaults to the source file name without extension) +# - LABEL: label for the test (defaults to main) +# - ARGS: arguments to pass to the test executable +# - WORKING_DIRECTORY +function(llama_test source) + include(CMakeParseArguments) + set(options) + set(oneValueArgs NAME LABEL WORKING_DIRECTORY) + set(multiValueArgs ARGS) + cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) -function(llama_test_executable name source) - get_filename_component(TEST_TARGET ${source} NAME_WE) - add_test(NAME ${name} COMMAND $ ${ARGN}) - set_property(TEST ${name} PROPERTY LABELS "main") -endfunction() + if (NOT DEFINED LLAMA_TEST_LABEL) + set(LLAMA_TEST_LABEL "main") + endif() + if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY) + set(LLAMA_TEST_WORKING_DIRECTORY .) + endif() + if (DEFINED LLAMA_TEST_NAME) + set(TEST_TARGET ${LLAMA_TEST_NAME}) + else() + get_filename_component(TEST_TARGET ${source} NAME_WE) + endif() -function(llama_build_and_test_executable source) - llama_build_and_test_executable_with_label(${source} "main") -endfunction() - -function(llama_build_and_test_executable_with_label source label) - get_filename_component(TEST_TARGET ${source} NAME_WE) add_executable(${TEST_TARGET} ${source} get-model.cpp) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE common) - add_test(NAME ${TEST_TARGET} COMMAND $ ${ARGN}) - set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${label}) + target_link_libraries(${TEST_TARGET} PRIVATE common json-schema-to-grammar) + add_test( + NAME ${TEST_TARGET} + WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY} + COMMAND $ + ${LLAMA_TEST_ARGS}) + + set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${LLAMA_TEST_LABEL}) endfunction() -# llama_build_and_test_executable(test-double-float.cpp) # SLOW -llama_build_and_test_executable(test-quantize-fns.cpp) -llama_build_and_test_executable(test-quantize-perf.cpp) -llama_build_and_test_executable(test-sampling.cpp) -llama_build_and_test_executable(test-chat-template.cpp) +# llama_test(test-double-float.cpp) # SLOW +llama_test(test-quantize-fns.cpp) +llama_test(test-quantize-perf.cpp) +llama_test(test-sampling.cpp) +llama_test(test-chat-template.cpp) -llama_build_executable(test-tokenizer-0-llama.cpp) -llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_test(test-tokenizer-0-llama.cpp NAME test-tokenizer-0-llama ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_test(test-tokenizer-0-falcon.cpp NAME test-tokenizer-0-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) -llama_build_executable(test-tokenizer-0-falcon.cpp) -llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) +llama_test(test-tokenizer-1-llama.cpp NAME test-tokenizer-1-llama ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_test(test-tokenizer-1-llama.cpp NAME test-tokenizer-1-baichuan ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) -llama_build_executable(test-tokenizer-1-llama.cpp) -llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) -llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-aquila ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-stablelm-3b-4e1t ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-gpt-neox ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) +llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-gpt2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf) +#llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-bloom ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG -llama_build_executable(test-tokenizer-1-bpe.cpp) -llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) -llama_test_executable (test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) -llama_test_executable (test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) -llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf) -llama_test_executable (test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf) -llama_test_executable (test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) -llama_test_executable (test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) -llama_test_executable (test-tokenizer-1-gpt2 test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf) -# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG +llama_test(test-grammar-parser.cpp) +llama_test(test-llama-grammar.cpp) +llama_test(test-grad0.cpp) +# llama_test(test-opt.cpp) # SLOW +llama_test(test-backend-ops.cpp) -llama_build_and_test_executable(test-grammar-parser.cpp) -llama_build_and_test_executable(test-llama-grammar.cpp) -llama_build_and_test_executable(test-grad0.cpp) -# llama_build_and_test_executable(test-opt.cpp) # SLOW -llama_build_and_test_executable(test-backend-ops.cpp) +llama_test(test-rope.cpp) -llama_build_and_test_executable(test-rope.cpp) +llama_test(test-model-load-cancel.cpp LABEL "model") +llama_test(test-autorelease.cpp LABEL "model") -llama_build_and_test_executable_with_label(test-model-load-cancel.cpp "model") -llama_build_and_test_executable_with_label(test-autorelease.cpp "model") +llama_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) +target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs new file mode 100644 index 0000000000000..71bf62ed34a6e --- /dev/null +++ b/tests/run-json-schema-to-grammar.mjs @@ -0,0 +1,10 @@ +import { readFileSync } from "fs" +import { SchemaConverter } from "../examples/server/public/json-schema-to-grammar.mjs" + +const [, , file] = process.argv +const url = `file://${file}` +let schema = JSON.parse(readFileSync(file, "utf8")); +const converter = new SchemaConverter({}) +schema = await converter.resolveRefs(schema, url) +converter.visit(schema, '') +console.log(converter.formatGrammar()) diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp new file mode 100755 index 0000000000000..a13bb9d7f4c16 --- /dev/null +++ b/tests/test-json-schema-to-grammar.cpp @@ -0,0 +1,824 @@ +#ifdef NDEBUG +#undef NDEBUG +#endif + +#include +#include +#include + +#include "json-schema-to-grammar.h" +#include "grammar-parser.h" + +static std::string trim(const std::string & source) { + std::string s(source); + s.erase(0,s.find_first_not_of(" \n\r\t")); + s.erase(s.find_last_not_of(" \n\r\t")+1); + return std::regex_replace(s, std::regex("(^|\n)[ \t]+"), "$1"); +} + +enum TestCaseStatus { + SUCCESS, + FAILURE +}; + +struct TestCase { + TestCaseStatus expected_status; + std::string name; + std::string schema; + std::string expected_grammar; + + void _print_failure_header() const { + fprintf(stderr, "#\n# Test '%s' failed.\n#\n%s\n", name.c_str(), schema.c_str()); + } + void verify(const std::string & actual_grammar) const { + if (trim(actual_grammar) != trim(expected_grammar)) { + _print_failure_header(); + fprintf(stderr, "# EXPECTED:\n%s\n# ACTUAL:\n%s\n", expected_grammar.c_str(), actual_grammar.c_str()); + assert(false); + } + } + void verify_expectation_parseable() const { + try { + auto state = grammar_parser::parse(expected_grammar.c_str()); + if (state.symbol_ids.find("root") == state.symbol_ids.end()) { + throw std::runtime_error("Grammar failed to parse:\n" + expected_grammar); + } + } catch (const std::runtime_error & ex) { + _print_failure_header(); + fprintf(stderr, "# GRAMMAR ERROR: %s\n", ex.what()); + assert(false); + } + } + void verify_status(TestCaseStatus status) const { + if (status != expected_status) { + _print_failure_header(); + fprintf(stderr, "# EXPECTED STATUS: %s\n", expected_status == SUCCESS ? "SUCCESS" : "FAILURE"); + fprintf(stderr, "# ACTUAL STATUS: %s\n", status == SUCCESS ? "SUCCESS" : "FAILURE"); + assert(false); + } + } +}; + +static void write(const std::string & file, const std::string & content) { + std::ofstream f; + f.open(file.c_str()); + f << content.c_str(); + f.close(); +} + +static std::string read(const std::string & file) { + std::ostringstream actuals; + actuals << std::ifstream(file.c_str()).rdbuf(); + return actuals.str(); +} + +static void test_all(const std::string & lang, std::function runner) { + fprintf(stderr, "#\n# Testing JSON schema conversion (%s)\n#\n", lang.c_str()); + auto test = [&](const TestCase & tc) { + fprintf(stderr, "- %s%s\n", tc.name.c_str(), tc.expected_status == FAILURE ? " (failure expected)" : ""); + runner(tc); + }; + + test({ + FAILURE, + "unknown type", + R"""({ + "type": "kaboom" + })""", + "" + }); + + test({ + FAILURE, + "invalid type type", + R"""({ + "type": 123 + })""", + "" + }); + + test({ + SUCCESS, + "empty schema (object)", + "{}", + R"""( + array ::= "[" space ( value ("," space value)* )? "]" space + boolean ::= ("true" | "false") space + null ::= "null" space + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space + root ::= object + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + value ::= object | array | string | number | boolean + )""" + }); + + test({ + SUCCESS, + "exotic formats", + R"""({ + "items": [ + { "format": "date" }, + { "format": "uuid" }, + { "format": "time" }, + { "format": "date-time" } + ] + })""", + R"""( + date ::= [0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] ) + date-string ::= "\"" date "\"" space + date-time ::= date "T" time + date-time-string ::= "\"" date-time "\"" space + root ::= "[" space date-string "," space uuid "," space time-string "," space date-time-string "]" space + space ::= " "? + time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] ) + time-string ::= "\"" time "\"" space + uuid ::= "\"" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "\"" space + )""" + }); + + test({ + SUCCESS, + "string", + R"""({ + "type": "string" + })""", + R"""( + root ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "boolean", + R"""({ + "type": "boolean" + })""", + R"""( + root ::= ("true" | "false") space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "integer", + R"""({ + "type": "integer" + })""", + R"""( + root ::= ("-"? ([0-9] | [1-9] [0-9]*)) space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "string const", + R"""({ + "const": "foo" + })""", + R"""( + root ::= "\"foo\"" + space ::= " "? + )""" + }); + + test({ + FAILURE, + "non-string const", + R"""({ + "const": 123 + })""", + "" + }); + + test({ + FAILURE, + "non-string enum", + R"""({ + "enum": [123] + })""", + "" + }); + + test({ + SUCCESS, + "tuple1", + R"""({ + "prefixItems": [{ "type": "string" }] + })""", + R"""( + root ::= "[" space string "]" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "tuple2", + R"""({ + "prefixItems": [{ "type": "string" }, { "type": "number" }] + })""", + R"""( + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "[" space string "," space number "]" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "number", + R"""({ + "type": "number" + })""", + R"""( + root ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "minItems", + R"""({ + "items": { + "type": "boolean" + }, + "minItems": 2 + })""", + R"""( + boolean ::= ("true" | "false") space + root ::= "[" space boolean ( "," space boolean )( "," space boolean )* "]" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "maxItems 1", + R"""({ + "items": { + "type": "boolean" + }, + "maxItems": 1 + })""", + R"""( + boolean ::= ("true" | "false") space + root ::= "[" space ( boolean )? "]" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "maxItems 2", + R"""({ + "items": { + "type": "boolean" + }, + "maxItems": 2 + })""", + R"""( + boolean ::= ("true" | "false") space + root ::= "[" space ( boolean ( "," space boolean )? )? "]" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "min + maxItems", + R"""({ + "items": { + "type": ["number", "integer"] + }, + "minItems": 3, + "maxItems": 5 + })""", + R"""( + integer ::= ("-"? ([0-9] | [1-9] [0-9]*)) space + item ::= number | integer + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "[" space item ( "," space item )( "," space item )( "," space item )?( "," space item )? "]" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "simple regexp", + R"""({ + "type": "string", + "pattern": "^abc?d*efg+(hij)?kl$" + })""", + R"""( + root ::= "\"" "ab" "c"? "d"* "ef" "g"+ ("hij")? "kl" "\"" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "regexp escapes", + R"""({ + "type": "string", + "pattern": "^\\[\\]\\{\\}\\(\\)\\|\\+\\*\\?$" + })""", + R"""( + root ::= "\"" "[]{}()|+*?" "\"" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "regexp quote", + R"""({ + "type": "string", + "pattern": "^\"$" + })""", + R"""( + root ::= "\"" "\"" "\"" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "regexp", + R"""({ + "type": "string", + "pattern": "^(\\([0-9]{1,3}\\))?[0-9]{3}-[0-9]{4} and...$" + })""", + R"""( + dot ::= [\U00000000-\x09\x0B\x0C\x0E-\U0010FFFF] + root ::= "\"" ("(" root-1 root-1? root-1? ")")? root-1 root-1 root-1 "-" root-1 root-1 root-1 root-1 " and" dot dot dot "\"" space + root-1 ::= [0-9] + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "required props", + R"""({ + "type": "object", + "properties": { + "a": { + "type": "string" + }, + "b": { + "type": "string" + } + }, + "required": [ + "a", + "b" + ], + "additionalProperties": false, + "definitions": {} + })""", + R"""( + a-kv ::= "\"a\"" space ":" space string + b-kv ::= "\"b\"" space ":" space string + root ::= "{" space a-kv "," space b-kv "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "1 optional prop", + R"""({ + "properties": { + "a": { + "type": "string" + } + }, + "additionalProperties": false + })""", + R"""( + a-kv ::= "\"a\"" space ":" space string + root ::= "{" space (a-kv )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "N optional props", + R"""({ + "properties": { + "a": {"type": "string"}, + "b": {"type": "string"}, + "c": {"type": "string"} + }, + "additionalProperties": false + })""", + R"""( + a-kv ::= "\"a\"" space ":" space string + a-rest ::= ( "," space b-kv )? b-rest + b-kv ::= "\"b\"" space ":" space string + b-rest ::= ( "," space c-kv )? + c-kv ::= "\"c\"" space ":" space string + root ::= "{" space (a-kv a-rest | b-kv b-rest | c-kv )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "required + optional props", + R"""({ + "properties": { + "a": {"type": "string"}, + "b": {"type": "string"}, + "c": {"type": "string"}, + "d": {"type": "string"} + }, + "required": ["a", "b"], + "additionalProperties": false + })""", + R"""( + a-kv ::= "\"a\"" space ":" space string + b-kv ::= "\"b\"" space ":" space string + c-kv ::= "\"c\"" space ":" space string + c-rest ::= ( "," space d-kv )? + d-kv ::= "\"d\"" space ":" space string + root ::= "{" space a-kv "," space b-kv ( "," space ( c-kv c-rest | d-kv ) )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "additional props", + R"""({ + "type": "object", + "additionalProperties": {"type": "array", "items": {"type": "number"}} + })""", + R"""( + additional-kv ::= string ":" space additional-value + additional-kvs ::= additional-kv ( "," space additional-kv )* + additional-value ::= "[" space ( number ( "," space number )* )? "]" space + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "{" space (additional-kvs )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "additional props (true)", + R"""({ + "type": "object", + "additionalProperties": true + })""", + R"""( + array ::= "[" space ( value ("," space value)* )? "]" space + boolean ::= ("true" | "false") space + null ::= "null" space + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space + root ::= object + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + value ::= object | array | string | number | boolean + )""" + }); + + test({ + SUCCESS, + "additional props (implicit)", + R"""({ + "type": "object" + })""", + R"""( + array ::= "[" space ( value ("," space value)* )? "]" space + boolean ::= ("true" | "false") space + null ::= "null" space + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space + root ::= object + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + value ::= object | array | string | number | boolean + )""" + }); + + test({ + SUCCESS, + "empty w/o additional props", + R"""({ + "type": "object", + "additionalProperties": false + })""", + R"""( + root ::= "{" space "}" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "required + additional props", + R"""({ + "type": "object", + "properties": { + "a": {"type": "number"} + }, + "required": ["a"], + "additionalProperties": {"type": "string"} + })""", + R"""( + a-kv ::= "\"a\"" space ":" space number + additional-kv ::= string ":" space string + additional-kvs ::= additional-kv ( "," space additional-kv )* + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "{" space a-kv ( "," space ( additional-kvs ) )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "optional + additional props", + R"""({ + "type": "object", + "properties": { + "a": {"type": "number"} + }, + "additionalProperties": {"type": "number"} + })""", + R"""( + a-kv ::= "\"a\"" space ":" space number + a-rest ::= additional-kvs + additional-kv ::= string ":" space number + additional-kvs ::= additional-kv ( "," space additional-kv )* + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "{" space (a-kv a-rest | additional-kvs )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "required + optional + additional props", + R"""({ + "type": "object", + "properties": { + "a": {"type": "number"}, + "b": {"type": "number"} + }, + "required": ["a"], + "additionalProperties": {"type": "number"} + })""", + R"""( + a-kv ::= "\"a\"" space ":" space number + additional-kv ::= string ":" space number + additional-kvs ::= additional-kv ( "," space additional-kv )* + b-kv ::= "\"b\"" space ":" space number + b-rest ::= additional-kvs + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "{" space a-kv ( "," space ( b-kv b-rest | additional-kvs ) )? "}" space + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "top-level $ref", + R"""({ + "$ref": "#/definitions/MyType", + "definitions": { + "MyType": { + "type": "object", + "properties": { + "a": { + "type": "string" + } + }, + "required": [ + "a" + ], + "additionalProperties": false + } + } + })""", + R"""( + MyType ::= "{" space MyType-a-kv "}" space + MyType-a-kv ::= "\"a\"" space ":" space string + root ::= MyType + space ::= " "? + string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space + )""" + }); + + test({ + SUCCESS, + "anyOf", + R"""({ + "anyOf": [ + {"$ref": "#/definitions/foo"}, + {"$ref": "#/definitions/bar"} + ], + "definitions": { + "foo": { + "properties": {"a": {"type": "number"}} + }, + "bar": { + "properties": {"b": {"type": "number"}} + } + }, + "type": "object" + })""", + R"""( + alternative-0 ::= foo + alternative-1 ::= bar + bar ::= "{" space (bar-b-kv )? "}" space + bar-b-kv ::= "\"b\"" space ":" space number + foo ::= "{" space (foo-a-kv )? "}" space + foo-a-kv ::= "\"a\"" space ":" space number + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= alternative-0 | alternative-1 + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "mix of allOf, anyOf and $ref (similar to https://json.schemastore.org/tsconfig.json)", + R"""({ + "allOf": [ + {"$ref": "#/definitions/foo"}, + {"$ref": "#/definitions/bar"}, + { + "anyOf": [ + {"$ref": "#/definitions/baz"}, + {"$ref": "#/definitions/bam"} + ] + } + ], + "definitions": { + "foo": { + "properties": {"a": {"type": "number"}} + }, + "bar": { + "properties": {"b": {"type": "number"}} + }, + "bam": { + "properties": {"c": {"type": "number"}} + }, + "baz": { + "properties": {"d": {"type": "number"}} + } + }, + "type": "object" + })""", + R"""( + a-kv ::= "\"a\"" space ":" space number + b-kv ::= "\"b\"" space ":" space number + c-kv ::= "\"c\"" space ":" space number + d-kv ::= "\"d\"" space ":" space number + d-rest ::= ( "," space c-kv )? + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space + space ::= " "? + )""" + }); + + test({ + SUCCESS, + "conflicting names", + R"""({ + "type": "object", + "properties": { + "number": { + "type": "object", + "properties": { + "number": { + "type": "object", + "properties": { + "root": { + "type": "number" + } + }, + "required": [ + "root" + ], + "additionalProperties": false + } + }, + "required": [ + "number" + ], + "additionalProperties": false + } + }, + "required": [ + "number" + ], + "additionalProperties": false, + "definitions": {} + })""", + R"""( + number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space + number- ::= "{" space number-number-kv "}" space + number-kv ::= "\"number\"" space ":" space number- + number-number ::= "{" space number-number-root-kv "}" space + number-number-kv ::= "\"number\"" space ":" space number-number + number-number-root-kv ::= "\"root\"" space ":" space number + root ::= "{" space number-kv "}" space + space ::= " "? + )""" + }); +} + +int main() { + test_all("C++", [](const TestCase & tc) { + try { + tc.verify(json_schema_to_grammar(nlohmann::json::parse(tc.schema))); + tc.verify_status(SUCCESS); + } catch (const std::runtime_error & ex) { + fprintf(stderr, "Error: %s\n", ex.what()); + tc.verify_status(FAILURE); + } + }); + test_all("Python", [](const TestCase & tc) { + write("test-json-schema-input.tmp", tc.schema); + tc.verify_status(std::system( + "python ./examples/json-schema-to-grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + tc.verify(read("test-grammar-output.tmp")); + }); + test_all("JavaScript", [](const TestCase & tc) { + write("test-json-schema-input.tmp", tc.schema); + tc.verify_status(std::system( + "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + tc.verify(read("test-grammar-output.tmp")); + }); + + test_all("Check Expectations Validity", [](const TestCase & tc) { + if (tc.expected_status == SUCCESS) { + tc.verify_expectation_parseable(); + } + }); +} From cfd3be76e37dab92c846d75a2421178f20db4a11 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:59:38 +0100 Subject: [PATCH 319/401] ggml : same IQ4_NL quantization for CPU/CUDA/Metal (#6196) * Make quantize_row_iq4_nl do the same thing is quantization on CUDA * Make quantize_row_iq4_nl do the same thing is quantization on CUDA This time for real. backend-ops tests pass. * Now fix test-quantize-fns --------- Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 38 +++++++++++++++++++++++++------------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 109dd6660d856..2eaca0593fd8b 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -11705,9 +11705,8 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l, float * scales, float * weight, uint8_t * L, const int8_t * values, - const float * quant_weights) { - - const int ntry = 7; + const float * quant_weights, + const int ntry) { float sigma2 = 0; for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j]; @@ -11719,6 +11718,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block float max_scale = 0, amax_scale = 0; for (int ib = 0; ib < super_block_size/block_size; ++ib) { const float * xb = x + ib*block_size; + uint8_t * Lb = L + ib*block_size; if (quant_weights) { const float * qw = quant_weights + ib*block_size; for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]); @@ -11736,12 +11736,13 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block scales[ib] = 0; continue; } - float d = -max/values[0]; + float d = ntry > 0 ? -max/values[0] : max/values[0]; float id = 1/d; float sumqx = 0, sumq2 = 0; for (int j = 0; j < block_size; ++j) { float al = id*xb[j]; int l = best_index_int8(16, values, al); + Lb[j] = l; float q = values[l]; float w = weight[j]; sumqx += w*q*xb[j]; @@ -11796,9 +11797,11 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block } } else { dh[0] = GGML_FP32_TO_FP16(scales[0]); - float id = scales[0] ? 1/scales[0] : 0; - for (int j = 0; j < super_block_size; ++j) { - L[j] = best_index_int8(16, values, id*x[j]); + if (ntry > 0) { + float id = scales[0] ? 1/scales[0] : 0; + for (int j = 0; j < super_block_size; ++j) { + L[j] = best_index_int8(16, values, id*x[j]); + } } } @@ -11823,7 +11826,7 @@ size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL; quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, - &scale, weight, L, kvalues_iq4nl, qw); + &scale, weight, L, kvalues_iq4nl, qw, 7); } src += n_per_row; qrow += nblock*sizeof(block_iq4_nl); @@ -11832,14 +11835,23 @@ size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow } void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { - assert(k % QK4_NL == 0); - block_iq4_nl * restrict y = vy; - quantize_row_iq4_nl_reference(x, y, k); + GGML_ASSERT(k%QK4_NL == 0); + int nblock = k/QK4_NL; + uint8_t L[QK4_NL]; + float weight[QK4_NL]; + uint16_t unused_h; + uint8_t * unused_l = NULL; + float scale; + block_iq4_nl * iq4 = (block_iq4_nl *)vy; + for (int ibl = 0; ibl < nblock; ++ibl) { + quantize_row_iq4_nl_impl(QK4_NL, 32, x + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, + &scale, weight, L, kvalues_iq4nl, NULL, -1); + } } void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) { assert(k % QK4_NL == 0); - quantize_iq4_nl(x, y, 1, k, NULL); + quantize_row_iq4_nl(x, y, k); } size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { @@ -11857,7 +11869,7 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL; quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l, - scales, weight, L, kvalues_iq4nl, qw); + scales, weight, L, kvalues_iq4nl, qw, 7); } src += n_per_row; qrow += nblock*sizeof(block_iq4_xs); From 03a8f8fafec2e21009be9fe7297d92e5d4538964 Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 21 Mar 2024 13:59:53 +0100 Subject: [PATCH 320/401] cuda : fix LLAMA_CUDA_F16 build (#6197) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 280839ea475cd..04c6f5d07d4ca 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -9453,7 +9453,7 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_CUDA_F16 - cuda_pool_alloc src1_dfloat_a; + ggml_cuda_pool_alloc src1_dfloat_a(ctx.pool()); half * src1_dfloat = nullptr; // dfloat == half bool src1_convert_f16 = From 924ce1dce7fdf54894b462d03e7b608a6e574c32 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 21 Mar 2024 16:20:05 +0200 Subject: [PATCH 321/401] tests : disable system() calls (#6198) ggml-ci --- tests/test-json-schema-to-grammar.cpp | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index a13bb9d7f4c16..3c40e7563c479 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -803,18 +803,18 @@ int main() { tc.verify_status(FAILURE); } }); - test_all("Python", [](const TestCase & tc) { - write("test-json-schema-input.tmp", tc.schema); - tc.verify_status(std::system( - "python ./examples/json-schema-to-grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); - tc.verify(read("test-grammar-output.tmp")); - }); - test_all("JavaScript", [](const TestCase & tc) { - write("test-json-schema-input.tmp", tc.schema); - tc.verify_status(std::system( - "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); - tc.verify(read("test-grammar-output.tmp")); - }); + //test_all("Python", [](const TestCase & tc) { + // write("test-json-schema-input.tmp", tc.schema); + // tc.verify_status(std::system( + // "python ./examples/json-schema-to-grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + // tc.verify(read("test-grammar-output.tmp")); + //}); + //test_all("JavaScript", [](const TestCase & tc) { + // write("test-json-schema-input.tmp", tc.schema); + // tc.verify_status(std::system( + // "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + // tc.verify(read("test-grammar-output.tmp")); + //}); test_all("Check Expectations Validity", [](const TestCase & tc) { if (tc.expected_status == SUCCESS) { From f372c49ccdc561ab96fb3c7d2b7cbc0f89a4b359 Mon Sep 17 00:00:00 2001 From: semidark Date: Thu, 21 Mar 2024 11:52:35 -0600 Subject: [PATCH 322/401] Corrected typo to wrong file (#6199) The stated file `./devops/main-server.Dockerfile` does not exist. I figure that `.devops/server-intel.Dockerfile` was meant. --- README-sycl.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README-sycl.md b/README-sycl.md index 32adfda47769c..501b9d481a026 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -115,7 +115,7 @@ You can choose between **F16** and **F32** build. F16 is faster for long-prompt # Or, for F32: docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . -# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example +# Note: you can also use the ".devops/server-intel.Dockerfile", which compiles the "server" example ``` ### Run From d0a71233fbf8ade8ef06ad8e6b81d1d7b254895f Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 21 Mar 2024 19:54:28 +0100 Subject: [PATCH 323/401] cuda : disable host register by default (#6206) --- ggml-cuda.cu | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 04c6f5d07d4ca..14f409eb1dd6a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -11646,7 +11646,7 @@ GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, si } GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) { - if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { + if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) { return false; } @@ -11663,6 +11663,10 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size } GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) { + if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) { + return; + } + cudaError_t err = cudaHostUnregister(buffer); if (err != cudaSuccess) { // clear the error From be07a03217376c53b1d95e5f658adbbbb2831555 Mon Sep 17 00:00:00 2001 From: Jan Boon Date: Fri, 22 Mar 2024 06:41:24 +0800 Subject: [PATCH 324/401] server : update readme doc from `slot_id` to `id_slot` (#6213) --- examples/server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 755e1d5384f55..355601ff4dec2 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -260,7 +260,7 @@ node index.js `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. - `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) + `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false) From fa046eafbc70bf97dcf39843af0323f19a8c9ac3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Thu, 21 Mar 2024 21:32:42 -0400 Subject: [PATCH 325/401] Fix params underscore convert to dash. (#6203) * Fix params underscore convert to dash. * Update common/common.cpp --------- Co-authored-by: slaren --- common/common.cpp | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 192182d05b747..11bf554d28ba2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -154,8 +154,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { return result; } -static bool gpt_params_find_arg(int argc, char ** argv, gpt_params & params, int & i, bool & invalid_param) { - std::string arg = argv[i]; +static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { llama_sampling_params& sparams = params.sparams; if (arg == "-s" || arg == "--seed") { @@ -1201,7 +1200,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { std::replace(arg.begin(), arg.end(), '_', '-'); } - if (!gpt_params_find_arg(argc, argv, params, i, invalid_param)) { + if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { throw std::invalid_argument("error: unknown argument: " + arg); } } From 59c17f02de8fdf7b084d6100b875b7e2bc07a83b Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 22 Mar 2024 15:19:37 +0800 Subject: [PATCH 326/401] add blog link (#6222) --- README-sycl.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README-sycl.md b/README-sycl.md index 501b9d481a026..cbf14f2da7c07 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -29,6 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). ## News - 2024.3 + - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd). - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437). - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. From 95d576b48ebf582b112d1c9cf4eed7142fa4e464 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Mar 2024 09:36:03 +0200 Subject: [PATCH 327/401] metal : pad n_ctx by 32 (#6177) * metal : require ne00 >= 128 for mat-mat kernels ggml-ci * llama : pad n_ctx by 32 ggml-ci --- common/common.cpp | 2 +- examples/batched/batched.cpp | 4 +++- llama.cpp | 3 +++ tests/test-backend-ops.cpp | 7 +++++++ 4 files changed, 14 insertions(+), 2 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 11bf554d28ba2..cc230c9ff55b1 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -101,7 +101,7 @@ int32_t get_num_physical_cores() { return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; } -void process_escapes(std::string& input) { +void process_escapes(std::string & input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index ee1f8f1bf5dd2..7aaf63ceb1a7c 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -48,6 +48,8 @@ int main(int argc, char ** argv) { params.prompt = "Hello my name is"; } + process_escapes(params.prompt); + // init LLM llama_backend_init(); @@ -78,7 +80,7 @@ int main(int argc, char ** argv) { llama_context_params ctx_params = llama_context_default_params(); ctx_params.seed = 1234; - ctx_params.n_ctx = n_kv_req; + ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_len, n_parallel); ctx_params.n_seq_max = n_parallel; ctx_params.n_threads = params.n_threads; diff --git a/llama.cpp b/llama.cpp index 1a9fe0c4d2cea..9de4a86022b1a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13044,6 +13044,9 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; + // this is necessary due to kv_self.n being padded later during inference + cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32); + // with causal attention, the batch size is limited by the context size cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index c2916c3e480e0..1998e1cbc4703 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2091,6 +2091,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 128, { 8, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 128, { 8, 1}, {4, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 64, { 8, 1}, {4, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 64, { 8, 1}, {4, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1})); + for (ggml_type type_a : all_types) { for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { for (int n_mats : {2, 4, 8}) { From b2075fd6a578f5685060df4baa90ae9e48e98c70 Mon Sep 17 00:00:00 2001 From: Vaibhav Srivastav Date: Fri, 22 Mar 2024 08:53:43 +0100 Subject: [PATCH 328/401] ci : add CURL flag for the mac builds (#6214) --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0f68e4cc382bd..b8d66a837989b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -41,7 +41,7 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON .. + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -97,7 +97,7 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON .. + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test From b3e94f26bab8e3b5338b5902e5af5bb01894cb4a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Mar 2024 11:35:53 +0200 Subject: [PATCH 329/401] metal : proper assert for mat-mat memory alignment (#6225) * metal : proper assert for mat-mat memory alignment ggml-ci * readme : add notice about the bug fix * metal : fix the fix ggml-ci --- README.md | 1 + ggml-metal.m | 16 ++++++++++++++++ ggml-metal.metal | 48 ++++++++++++++++++++++++------------------------ 3 files changed, 41 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index c2f3342f0ff42..aa41b9e760c9b 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225 - Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017 - Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981 - Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962 diff --git a/ggml-metal.m b/ggml-metal.m index 54d1f1d5a3cee..416b24532f0d0 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1392,6 +1392,14 @@ static enum ggml_status ggml_metal_graph_compute( (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + // some Metal matrix data types require aligned pointers + // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + default: break; + } + id pipeline = nil; switch (src0->type) { @@ -1706,6 +1714,14 @@ static enum ggml_status ggml_metal_graph_compute( ne20 % 32 == 0 && ne20 >= 64 && ne11 > ne11_mm_min) { + // some Metal matrix data types require aligned pointers + // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + default: break; + } + id pipeline = nil; switch (src2->type) { diff --git a/ggml-metal.metal b/ggml-metal.metal index 5552c8b28834a..748f0acef27b1 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -5760,18 +5760,18 @@ typedef void (mat_mm_t)( threadgroup uchar *, uint3, uint, uint); -template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -5820,18 +5820,18 @@ typedef void (mat_mm_id_t)( threadgroup uchar *, uint3, uint, uint); -template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; From 68e210b3543e0cc71268bee0920441747679ee13 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Mar 2024 13:08:28 +0200 Subject: [PATCH 330/401] server : enable continuous batching by default (#6231) --- common/common.h | 2 +- examples/server/server.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/common/common.h b/common/common.h index 8dd8a3edc9c94..31fd401b6957b 100644 --- a/common/common.h +++ b/common/common.h @@ -139,7 +139,7 @@ struct gpt_params { bool interactive_first = false; // wait for user input immediately bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles - bool cont_batching = false; // insert new sequences for decoding on-the-fly + bool cont_batching = true; // insert new sequences for decoding on-the-fly bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens diff --git a/examples/server/server.cpp b/examples/server/server.cpp index cf075d6c41aa4..c918fabe4a9ed 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1758,7 +1758,7 @@ struct server_context { } // process in chunks of params.n_batch - int32_t n_batch = llama_n_batch(ctx); + int32_t n_batch = llama_n_batch(ctx); int32_t n_ubatch = llama_n_ubatch(ctx); // next, batch any pending prompts without exceeding n_batch @@ -2225,7 +2225,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); printf(" -ctk TYPE, --cache-type-k TYPE\n"); From 6b8bb3a31d260a01020497c5f01025c34222fcb9 Mon Sep 17 00:00:00 2001 From: Jan Boon Date: Fri, 22 Mar 2024 19:12:05 +0800 Subject: [PATCH 331/401] server : fix n_keep always showing as 0 in response (#6211) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c918fabe4a9ed..421fa6d41f7f3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1247,7 +1247,7 @@ struct server_context { {"penalize_nl", slot.sparams.penalize_nl}, {"stop", slot.params.antiprompt}, {"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict - {"n_keep", params.n_keep}, + {"n_keep", slot.params.n_keep}, {"ignore_eos", ignore_eos}, {"stream", slot.params.stream}, {"logit_bias", slot.sparams.logit_bias}, From 29ab270e65975785cdca3243a3de71ccebc1252a Mon Sep 17 00:00:00 2001 From: Xiaoyi Chen Date: Fri, 22 Mar 2024 04:29:49 -0700 Subject: [PATCH 332/401] readme : add RecurseChat to the list of UIs (#6219) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index aa41b9e760c9b..368489caa98d0 100644 --- a/README.md +++ b/README.md @@ -166,6 +166,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [RecurseChat](https://recurse.chat/) (proprietary) - [semperai/amica](https://github.com/semperai/amica) - [withcatai/catai](https://github.com/withcatai/catai) - [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) From 2f0e81e053b41ca28e73a841e7bdbf9820baaa57 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 22 Mar 2024 14:05:31 +0100 Subject: [PATCH 333/401] cuda : add LLAMA_CUDA_NO_PEER_COPY to workaround broken ROCm p2p copy (#6208) * cuda : add LLAMA_CUDA_NO_PEER_COPY to workaround broken ROCm p2p copy * add LLAMA_CUDA_NO_PEER_COPY to HIP build --- CMakeLists.txt | 8 ++++++++ Makefile | 9 ++++++--- ggml-cuda.cu | 22 +++++++++++++++++----- 3 files changed, 31 insertions(+), 8 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index fc4cff28f44ac..3333ee1c9d501 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -99,6 +99,7 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") +option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF) option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) @@ -387,6 +388,9 @@ if (LLAMA_CUBLAS) endif() add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE}) + if (LLAMA_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() if (LLAMA_STATIC) if (WIN32) @@ -531,6 +535,10 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_FORCE_MMQ) endif() + if (LLAMA_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) diff --git a/Makefile b/Makefile index 9b72e1dbd5493..fa112e7081fa7 100644 --- a/Makefile +++ b/Makefile @@ -452,9 +452,9 @@ ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE else MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE -#ifdef LLAMA_CUDA_CUBLAS -# MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS -#endif # LLAMA_CUDA_CUBLAS +ifdef LLAMA_CUDA_NO_PEER_COPY + MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # LLAMA_CUDA_NO_PEER_COPY ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) endif @@ -535,6 +535,9 @@ endif # LLAMA_HIP_UMA ifdef LLAMA_CUDA_FORCE_DMMV HIPFLAGS += -DGGML_CUDA_FORCE_DMMV endif # LLAMA_CUDA_FORCE_DMMV +ifdef LLAMA_CUDA_NO_PEER_COPY + HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # LLAMA_CUDA_NO_PEER_COPY OBJS += ggml-cuda.o ggml-cuda.o: ggml-cuda.cu ggml-cuda.h $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 14f409eb1dd6a..adf930478f5ff 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -771,7 +771,11 @@ GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t if (src_ctx->device == dst_ctx->device) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); } else { +#ifdef GGML_CUDA_NO_PEER_COPY + return false; +#else CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); +#endif } CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); return true; @@ -11322,19 +11326,23 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_ GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device); GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device); - if (!cuda_ctx_src->copy_event) { - ggml_cuda_set_device(cuda_ctx_src->device); - CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming)); - } - // copy on src stream if (cuda_ctx_src->device == cuda_ctx_dst->device) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream())); } else { +#ifdef GGML_CUDA_NO_PEER_COPY + return false; +#else CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream())); +#endif } // record event on src stream + if (!cuda_ctx_src->copy_event) { + ggml_cuda_set_device(cuda_ctx_src->device); + CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming)); + } + CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, cuda_ctx_src->stream())); // wait on dst stream for the copy to complete @@ -11530,6 +11538,9 @@ GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const } static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) { +#ifdef GGML_CUDA_NO_PEER_COPY + return nullptr; +#else ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_device(cuda_ctx->device); @@ -11541,6 +11552,7 @@ static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) /* .backend = */ backend, /* .context = */ event, }; +#endif } static void ggml_backend_cuda_event_free(ggml_backend_event_t event) { From 72114edf068a9b2f54d3b09e9a198f611be397e8 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Fri, 22 Mar 2024 13:07:44 +0000 Subject: [PATCH 334/401] json-schema-to-grammar : fix order of props + non-str const/enum (#6232) * json: ordered json in server/schema converter to respect orig order * json: ws nits * json: support non-string const / enums --- common/json-schema-to-grammar.cpp | 16 +- common/json-schema-to-grammar.h | 2 +- examples/json-schema-to-grammar.py | 7 +- .../server/json-schema-to-grammar.mjs.hpp | 2791 ++++++++--------- .../server/public/json-schema-to-grammar.mjs | 12 +- examples/server/server.cpp | 2 +- examples/server/utils.hpp | 2 +- tests/test-json-schema-to-grammar.cpp | 101 +- 8 files changed, 1452 insertions(+), 1481 deletions(-) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index dc6d30ef15ca2..0e468034642f7 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -9,7 +9,7 @@ #include #include -using json = nlohmann::json; +using json = nlohmann::ordered_json; const std::string SPACE_RULE = "\" \"?"; @@ -124,7 +124,7 @@ static std::string replacePattern(const std::string & input, const std::regex & } static std::string format_literal(const std::string & literal) { - std::string escaped = replacePattern(json(literal).dump(), GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) { + std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) { char c = match.str()[0]; return GRAMMAR_LITERAL_ESCAPES.at(c); }); @@ -137,7 +137,7 @@ class SchemaConverter { std::function _fetch_json; bool _dotall; std::map _rules; - std::unordered_map _refs; + std::unordered_map _refs; std::unordered_set _refs_being_resolved; std::vector _errors; std::vector _warnings; @@ -413,7 +413,7 @@ class SchemaConverter { std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name); prop_kv_rule_names[prop_name] = _add_rule( name + (name.empty() ? "" : "-") + prop_name + "-kv", - format_literal(prop_name) + " space \":\" space " + prop_rule_name + format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name ); if (required.find(prop_name) != required.end()) { required_props.push_back(prop_name); @@ -495,7 +495,7 @@ class SchemaConverter { _rules["space"] = SPACE_RULE; } - void resolve_refs(nlohmann::json & schema, const std::string & url) { + void resolve_refs(json & schema, const std::string & url) { /* * Resolves all $ref fields in the given schema, fetching any remote schemas, * replacing each $ref with absolute reference URL and populates _refs with the @@ -557,11 +557,7 @@ class SchemaConverter { } std::string _generate_constant_rule(const json & value) { - if (!value.is_string()) { - _errors.push_back("Only std::string constants are supported, got " + value.dump()); - return ""; - } - return format_literal(value.get()); + return format_literal(value.dump()); } std::string visit(const json & schema, const std::string & name) { diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h index 2e4c6ade3e7b2..e1abed3037582 100644 --- a/common/json-schema-to-grammar.h +++ b/common/json-schema-to-grammar.h @@ -1,4 +1,4 @@ #pragma once #include "json.hpp" -std::string json_schema_to_grammar(const nlohmann::json& schema); +std::string json_schema_to_grammar(const nlohmann::ordered_json& schema); diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py index 9eead557f2644..91dd734cc26ff 100755 --- a/examples/json-schema-to-grammar.py +++ b/examples/json-schema-to-grammar.py @@ -61,7 +61,7 @@ def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): def _format_literal(self, literal): escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( - lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal) + lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), literal ) return f'"{escaped}"' @@ -308,8 +308,7 @@ def _resolve_ref(self, ref): return ref_name def _generate_constant_rule(self, value): - assert isinstance(value, str), f'Only string constants are supported, got {value}' - return self._format_literal(value) + return self._format_literal(json.dumps(value)) def visit(self, schema, name): schema_type = schema.get('type') @@ -428,7 +427,7 @@ def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[st prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') prop_kv_rule_names[prop_name] = self._add_rule( f'{name}{"-" if name else ""}{prop_name}-kv', - fr'{self._format_literal(prop_name)} space ":" space {prop_rule_name}' + fr'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}' ) required_props = [k for k in sorted_props if k in required] optional_props = [k for k in sorted_props if k not in required] diff --git a/examples/server/json-schema-to-grammar.mjs.hpp b/examples/server/json-schema-to-grammar.mjs.hpp index 72fb6b80f43da..ef1d5dcf3a2eb 100644 --- a/examples/server/json-schema-to-grammar.mjs.hpp +++ b/examples/server/json-schema-to-grammar.mjs.hpp @@ -189,1479 +189,1456 @@ unsigned char json_schema_to_grammar_mjs[] = { 0x66, 0x6f, 0x72, 0x6d, 0x61, 0x74, 0x4c, 0x69, 0x74, 0x65, 0x72, 0x61, 0x6c, 0x28, 0x6c, 0x69, 0x74, 0x65, 0x72, 0x61, 0x6c, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, - 0x73, 0x63, 0x61, 0x70, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x4a, 0x53, 0x4f, + 0x73, 0x63, 0x61, 0x70, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x6c, 0x69, 0x74, + 0x65, 0x72, 0x61, 0x6c, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x47, 0x52, 0x41, 0x4d, + 0x4d, 0x41, 0x52, 0x5f, 0x4c, 0x49, 0x54, 0x45, 0x52, 0x41, 0x4c, 0x5f, + 0x45, 0x53, 0x43, 0x41, 0x50, 0x45, 0x5f, 0x52, 0x45, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x20, 0x3d, 0x3e, 0x20, 0x47, 0x52, + 0x41, 0x4d, 0x4d, 0x41, 0x52, 0x5f, 0x4c, 0x49, 0x54, 0x45, 0x52, 0x41, + 0x4c, 0x5f, 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json_schema_to_grammar_mjs_len = 19690; diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index baa6761c8cc2c..17bd60fdedb8b 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -48,7 +48,7 @@ export class SchemaConverter { } _formatLiteral(literal) { - const escaped = JSON.stringify(literal).replace( + const escaped = literal.replace( GRAMMAR_LITERAL_ESCAPE_RE, m => GRAMMAR_LITERAL_ESCAPES[m] ); @@ -327,10 +327,7 @@ export class SchemaConverter { } _generateConstantRule(value) { - if (typeof value !== 'string') { - throw new Error('Only string constants are supported, got ' + JSON.stringify(value)); - } - return this._formatLiteral(value); + return this._formatLiteral(JSON.stringify(value)); } visit(schema, name) { @@ -346,9 +343,6 @@ export class SchemaConverter { } else if (Array.isArray(schemaType)) { return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t })))); } else if ('const' in schema) { - if (typeof schema.const !== 'string') { - throw new Error('Only string constants are supported, got ' + JSON.stringify(schema.const)); - } return this._addRule(ruleName, this._generateConstantRule(schema.const)); } else if ('enum' in schema) { const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | '); @@ -457,7 +451,7 @@ export class SchemaConverter { const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`); propKvRuleNames[propName] = this._addRule( `${name ?? ''}${name ? '-' : ''}${propName}-kv`, - `${this._formatLiteral(propName)} space ":" space ${propRuleName}` + `${this._formatLiteral(JSON.stringify(propName))} space ":" space ${propRuleName}` ); } const requiredProps = sortedProps.filter(k => required.has(k)); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 421fa6d41f7f3..27bd2dd70c5f7 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -30,7 +30,7 @@ #include #include -using json = nlohmann::json; +using json = nlohmann::ordered_json; bool server_verbose = false; bool server_log_json = true; diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index ff146893d720b..d7eef556a4632 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -12,7 +12,7 @@ #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" -using json = nlohmann::json; +using json = nlohmann::ordered_json; // https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 enum error_type { diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 3c40e7563c479..8aa7c37d8977b 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -90,7 +90,7 @@ static void test_all(const std::string & lang, std::function Date: Fri, 22 Mar 2024 13:09:07 +0000 Subject: [PATCH 335/401] tests : conditional python & node json schema tests (#6207) * json: only attempt python & node schema conversion tests if their bins are present Tests introduced in https://github.com/ggerganov/llama.cpp/pull/5978 disabled in https://github.com/ggerganov/llama.cpp/pull/6198 * json: orange warnings when tests skipped * json: ensure py/js schema conv tested on ubuntu-focal-make * json: print env vars in test --- .github/workflows/build.yml | 11 ++++++++ tests/test-json-schema-to-grammar.cpp | 37 ++++++++++++++++++--------- 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b8d66a837989b..469d8116562e7 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -135,6 +135,9 @@ jobs: ubuntu-focal-make: runs-on: ubuntu-20.04 + env: + LLAMA_NODE_AVAILABLE: true + LLAMA_PYTHON_AVAILABLE: true steps: - name: Clone @@ -147,6 +150,14 @@ jobs: sudo apt-get update sudo apt-get install build-essential gcc-8 + - uses: actions/setup-node@v4 + with: + node-version: "20" + + - uses: actions/setup-python@v4 + with: + python-version: "3.11" + - name: Build id: make_build env: diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 8aa7c37d8977b..4d110cf5aab86 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -799,6 +799,9 @@ static void test_all(const std::string & lang, std::function test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); - // tc.verify(read("test-grammar-output.tmp")); - //}); - //test_all("JavaScript", [](const TestCase & tc) { - // write("test-json-schema-input.tmp", tc.schema); - // tc.verify_status(std::system( - // "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); - // tc.verify(read("test-grammar-output.tmp")); - //}); + + if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) { + test_all("Python", [](const TestCase & tc) { + write("test-json-schema-input.tmp", tc.schema); + tc.verify_status(std::system( + "python ./examples/json-schema-to-grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + tc.verify(read("test-grammar-output.tmp")); + }); + } else { + fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m"); + } + + if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) { + test_all("JavaScript", [](const TestCase & tc) { + write("test-json-schema-input.tmp", tc.schema); + tc.verify_status(std::system( + "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + tc.verify(read("test-grammar-output.tmp")); + }); + } else { + fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m"); + } test_all("Check Expectations Validity", [](const TestCase & tc) { if (tc.expected_status == SUCCESS) { From e80f06d2a194be62ab5b1cd7ef7c7a5b241dd4fb Mon Sep 17 00:00:00 2001 From: Nexesenex <124105151+Nexesenex@users.noreply.github.com> Date: Fri, 22 Mar 2024 14:32:02 +0100 Subject: [PATCH 336/401] llama : correction of the attn.v.weight quantization for IQ3_XS (#6209) IQ3_XS was not mentioned, IQ3_S and IQ3_M were present twice. That PR corrects this in the manner which was probably intended initially. --- llama.cpp | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/llama.cpp b/llama.cpp index 9de4a86022b1a..91bd6b8d070e9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12027,13 +12027,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) { - new_type = GGML_TYPE_Q4_K; - } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { - new_type = GGML_TYPE_Q4_K; - } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) { + else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { From 80bd33bc2c4be352697dc8473339f25e1085d117 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Mar 2024 15:33:38 +0200 Subject: [PATCH 337/401] common : add HF arg helpers (#6234) * common : add HF arg helpers * common : remove defaults --- common/common.cpp | 85 +++++++++++++++++++++++++++++++++++++++-------- common/common.h | 10 +++--- 2 files changed, 78 insertions(+), 17 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index cc230c9ff55b1..0cc4859f1148b 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -647,6 +647,22 @@ static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, params.model = argv[i]; return true; } + if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.model_draft = argv[i]; + return true; + } + if (arg == "-a" || arg == "--alias") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.model_alias = argv[i]; + return true; + } if (arg == "-mu" || arg == "--model-url") { if (++i >= argc) { invalid_param = true; @@ -655,20 +671,20 @@ static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, params.model_url = argv[i]; return true; } - if (arg == "-md" || arg == "--model-draft") { + if (arg == "-hfr" || arg == "--hf-repo") { if (++i >= argc) { invalid_param = true; return true; } - params.model_draft = argv[i]; + params.hf_repo = argv[i]; return true; } - if (arg == "-a" || arg == "--alias") { + if (arg == "-hff" || arg == "--hf-file") { if (++i >= argc) { invalid_param = true; return true; } - params.model_alias = argv[i]; + params.hf_file = argv[i]; return true; } if (arg == "--lora") { @@ -1403,10 +1419,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" layer range to apply the control vector(s) to, start and end inclusive\n"); printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); - printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); - printf(" model download url (default: %s)\n", params.model_url.c_str()); printf(" -md FNAME, --model-draft FNAME\n"); - printf(" draft model for speculative decoding\n"); + printf(" draft model for speculative decoding (default: unused)\n"); + printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); + printf(" model download url (default: unused)\n"); + printf(" -hfr REPO, --hf-repo REPO\n"); + printf(" Hugging Face model repository (default: unused)\n"); + printf(" -hff FILE, --hf-file FILE\n"); + printf(" Hugging Face model file (default: unused)\n"); printf(" -ld LOGDIR, --logdir LOGDIR\n"); printf(" path under which to save YAML logs (no logging if unset)\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); @@ -1655,8 +1675,10 @@ void llama_batch_add( #ifdef LLAMA_USE_CURL -struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, - struct llama_model_params params) { +struct llama_model * llama_load_model_from_url( + const char * model_url, + const char * path_model, + const struct llama_model_params & params) { // Basic validation of the model_url if (!model_url || strlen(model_url) == 0) { fprintf(stderr, "%s: invalid model_url\n", __func__); @@ -1850,25 +1872,62 @@ struct llama_model * llama_load_model_from_url(const char * model_url, const cha return llama_load_model_from_file(path_model, params); } +struct llama_model * llama_load_model_from_hf( + const char * repo, + const char * model, + const char * path_model, + const struct llama_model_params & params) { + // construct hugging face model url: + // + // --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf + // https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf + // + // --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf + // https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf + // + + std::string model_url = "https://huggingface.co/"; + model_url += repo; + model_url += "/resolve/main/"; + model_url += model; + + return llama_load_model_from_url(model_url.c_str(), path_model, params); +} + #else -struct llama_model * llama_load_model_from_url(const char * /*model_url*/, const char * /*path_model*/, - struct llama_model_params /*params*/) { +struct llama_model * llama_load_model_from_url( + const char * /*model_url*/, + const char * /*path_model*/, + const struct llama_model_params & /*params*/) { fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); return nullptr; } +struct llama_model * llama_load_model_from_hf( + const char * /*repo*/, + const char * /*model*/, + const char * /*path_model*/, + const struct llama_model_params & /*params*/) { + fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); + return nullptr; +} + #endif // LLAMA_USE_CURL std::tuple llama_init_from_gpt_params(gpt_params & params) { auto mparams = llama_model_params_from_gpt_params(params); llama_model * model = nullptr; - if (!params.model_url.empty()) { + + if (!params.hf_repo.empty() && !params.hf_file.empty()) { + model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); + } else if (!params.model_url.empty()) { model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); } else { model = llama_load_model_from_file(params.model.c_str(), mparams); } + if (model == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); return std::make_tuple(nullptr, nullptr); @@ -1908,7 +1967,7 @@ std::tuple llama_init_from_gpt_par } for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { - const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); + const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); float lora_scale = std::get<1>(params.lora_adapter[i]); int err = llama_model_apply_lora_from_file(model, lora_adapter.c_str(), diff --git a/common/common.h b/common/common.h index 31fd401b6957b..d827d4df78547 100644 --- a/common/common.h +++ b/common/common.h @@ -89,9 +89,11 @@ struct gpt_params { struct llama_sampling_params sparams; std::string model = "models/7B/ggml-model-f16.gguf"; // model path - std::string model_url = ""; // model url to download - std::string model_draft = ""; // draft model for speculative decoding + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias + std::string model_url = ""; // model url to download + std::string hf_repo = ""; // HF repo + std::string hf_file = ""; // HF file std::string prompt = ""; std::string prompt_file = ""; // store the external prompt file name std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state @@ -192,8 +194,8 @@ std::tuple llama_init_from_gpt_par struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); -struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, - struct llama_model_params params); +struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const struct llama_model_params & params); +struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const struct llama_model_params & params); // Batch utils From ee804f6223777019cf921e0d99cc24669313ab98 Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Sat, 23 Mar 2024 02:15:06 +0900 Subject: [PATCH 338/401] ci: apply concurrency limit for github workflows (#6243) --- .github/workflows/build.yml | 4 ++++ .github/workflows/code-coverage.yml | 4 ++++ .github/workflows/docker.yml | 4 ++++ .github/workflows/editorconfig.yml | 4 ++++ .github/workflows/nix-ci-aarch64.yml | 4 ++++ .github/workflows/nix-ci.yml | 4 ++++ .github/workflows/python-check-requirements.yml | 4 ++++ .github/workflows/python-lint.yml | 4 ++++ .github/workflows/server.yml | 4 ++++ .github/workflows/zig-build.yml | 4 ++++ 10 files changed, 40 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 469d8116562e7..7711bd8d8c470 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -15,6 +15,10 @@ on: types: [opened, synchronize, reopened] paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m'] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml index 392db8a089ac5..4112518bb813c 100644 --- a/.github/workflows/code-coverage.yml +++ b/.github/workflows/code-coverage.yml @@ -5,6 +5,10 @@ env: GGML_NLOOP: 3 GGML_N_THREADS: 1 +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: run: runs-on: ubuntu-20.04 diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 94f9161fc5547..9591bfc2a5d3b 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -15,6 +15,10 @@ on: branches: - master +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: push_to_registry: name: Push Docker image to Docker Hub diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index 0e0993cd41c16..7b2a00c904237 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -14,6 +14,10 @@ on: branches: - master +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: editorconfig: runs-on: ubuntu-latest diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml index 8d0a3fd7fd313..109a793ea4387 100644 --- a/.github/workflows/nix-ci-aarch64.yml +++ b/.github/workflows/nix-ci-aarch64.yml @@ -17,6 +17,10 @@ on: types: [opened, synchronize, reopened] paths: ['**/*.nix', 'flake.lock'] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: nix-build-aarch64: runs-on: ubuntu-latest diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index 01c5a9d5aaca2..8b5b99c8f8e17 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -8,6 +8,10 @@ on: pull_request: types: [opened, synchronize, reopened] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: nix-eval: strategy: diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml index b822059923ebe..4092b12fa9930 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -16,6 +16,10 @@ on: - 'requirements.txt' - 'requirements/*.txt' +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: python-check-requirements: runs-on: ubuntu-latest diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index ea0a05ea1a014..4bdd79c4a0b19 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -2,6 +2,10 @@ name: flake8 Lint on: [push, pull_request] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: flake8-lint: runs-on: ubuntu-latest diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 65ca7d9ca3ae9..b74dc5e2114d8 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -18,6 +18,10 @@ on: schedule: - cron: '0 0 * * *' +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: server: runs-on: ubuntu-latest diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml index 68a698ab94f55..cb43954eb452d 100644 --- a/.github/workflows/zig-build.yml +++ b/.github/workflows/zig-build.yml @@ -6,6 +6,10 @@ on: branches: - master +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: build: strategy: From dba1af612926cbd4ebe2d876277af1e3305177e0 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 22 Mar 2024 19:00:01 +0100 Subject: [PATCH 339/401] llama_model_loader: support multiple split/shard GGUFs (#6187) * split: support in llama_model_loader * avoid copying the entire vector Co-authored-by: slaren * split: move llama_tensor_offset to llama_model_loader * llama_model_loader: PR feedbacks: - use only one gguf_context for metadata only - store all ggml_context in a vector as the files and mappings - store all weights in a vector along with the source tensor - rename ctx_gguf to meta - rename ctx_meta to contexts * avoid copying the entire vector * Simplify this by making these optional, switch some layer creation tensor optional Co-authored-by: Georgi Gerganov * Handle optional tensors Co-authored-by: Georgi Gerganov * llama_model_loader: fail if backend cannot allocate buffer * fix mmap buffer management * llama_model_loader: map file to backend buffer if the allocation succeeds only * llama_model_loader: only map tensors included in the context * llama_model_loader: minor, use same variable name for consistency, fix spacing in types cast * llama_model_loader: fail if any of backend buffer cannot be allocated * spacing Co-authored-by: slaren * fix loop over pointer Co-authored-by: slaren * llama_model_loader: if n_tensors declared not equals to loaded tensors in split, throw an exception instead of asserting * llama_model_loader: ensure mappings vector has the expected size * llama_model_loader: use at instead of operator[] if this should never add to the map. * llama_model_loader: immediately add the backend buffer to the model buffers in order to free them if an error occurs in the next allocation. Reserve the expected size. * llama_model_loader: be sure the model mappings has enough capacity before allocating backend buffer * llama_model_loader: fix map -> unordered map * llama_split_prefix: use a clearer version, not pass split path len but dest max len. Co-authored-by: Xuan Son Nguyen * llama : minor ggml-ci * llama : introduce some typedef helpers * docs: add model shard in hot topic * llama_model_loader: put mapping in a unique_ptr from the moment it is allocated Co-authored-by: slaren * fix llama_split_prefix --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov Co-authored-by: Xuan Son Nguyen --- README.md | 1 + examples/gguf-split/gguf-split.cpp | 149 ++++----- llama.cpp | 474 ++++++++++++++++++++--------- llama.h | 10 + 4 files changed, 411 insertions(+), 223 deletions(-) diff --git a/README.md b/README.md index 368489caa98d0..f9cf1961629d0 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) - Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981 - Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962 - Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328 +- Support loading sharded model, using `gguf-split` CLI https://github.com/ggerganov/llama.cpp/pull/6187 ---- diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 8e12e64937bd7..f703588e164f6 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -1,31 +1,34 @@ #include "llama.h" -#include "ggml.h" #include "common.h" #include #include -#include #include #include -#include #include #include #include -#include #include +#include +#include + +#if defined(_WIN32) + #include + #ifndef PATH_MAX + #define PATH_MAX MAX_PATH + #endif + #include +#endif enum split_operation : uint8_t { SPLIT_OP_SPLIT, SPLIT_OP_MERGE, }; -static const char * const LLM_KV_GENERAL_SPLIT_I_SPLIT = "general.split"; -static const char * const LLM_KV_GENERAL_SPLIT_N_SPLIT = "general.split_count"; - -static const int SPLIT_FILENAME_MAX = 256; - -static const char * const SPLIT_FILENAME_FORMAT = "%s-%05d-of-%05d.gguf"; +static const char * const LLM_KV_SPLIT_NO = "split.no"; +static const char * const LLM_KV_SPLIT_COUNT = "split.count"; +static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; struct split_params { split_operation operation = SPLIT_OP_SPLIT; @@ -116,13 +119,13 @@ static bool split_params_parse(int argc, const char ** argv, split_params & para try { if (!split_params_parse_ex(argc, argv, params)) { split_print_usage(argv[0]); - exit(1); + exit(EXIT_FAILURE); } } catch (const std::invalid_argument & ex) { fprintf(stderr, "%s\n", ex.what()); split_print_usage(argv[0]); - exit(1); + exit(EXIT_FAILURE); } return result; } @@ -134,12 +137,6 @@ static void zeros(std::ofstream & file, size_t n) { } } -static std::string split_file_name(const std::string & path, int i_split, int n_split) { - char f_split[SPLIT_FILENAME_MAX] = {0}; - snprintf(f_split, sizeof(f_split), SPLIT_FILENAME_FORMAT, path.c_str(), i_split + 1, n_split); - return std::string(f_split); -} - struct split_strategy { const split_params params; std::ifstream & f_input; @@ -180,8 +177,9 @@ struct split_strategy { if (i_split == 0) { gguf_set_kv(ctx_out, ctx_gguf); } - gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_I_SPLIT, i_split); - gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, n_split); + gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split); + gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, n_split); + gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors); // populate the original tensors, so we get an initial metadata for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) { @@ -189,10 +187,11 @@ struct split_strategy { gguf_add_tensor(ctx_out, meta); } - auto split_name = split_file_name(params.output, i_split, n_split); + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split); - fprintf(stderr, "%s: %s ...", __func__, split_name.c_str()); - fout = std::ofstream(split_name, std::ios::binary); + fprintf(stderr, "%s: %s ...", __func__, split_path); + fout = std::ofstream(split_path, std::ios::binary); fout.exceptions(std::ofstream::failbit); // fail fast on write errors auto meta_size = gguf_get_meta_size(ctx_out); @@ -250,19 +249,23 @@ static void gguf_split(const split_params & split_params) { std::ifstream f_input(split_params.input.c_str(), std::ios::binary); if (!f_input.is_open()) { fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str()); - exit(1); + exit(EXIT_FAILURE); } auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params); if (!ctx_gguf) { fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); - exit(1); + exit(EXIT_FAILURE); } split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta); + + char first_split_path[PATH_MAX] = {0}; + llama_split_path(first_split_path, sizeof(first_split_path), + split_params.output.c_str(), strategy.i_split, strategy.n_split); fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n", __func__, split_params.input.c_str(), - split_file_name(split_params.output, strategy.i_split, strategy.n_split).c_str(), + first_split_path, split_params.n_split_tensors); strategy.split_start(); @@ -298,7 +301,9 @@ static void gguf_merge(const split_params & split_params) { std::vector ctx_metas; std::vector ctx_ggufs; - std::string split_prefix; + char split_path[PATH_MAX] = {0}; + strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1); + char split_prefix[PATH_MAX] = {0}; // First pass to find KV and tensors metadata for (int i_split = 0; i_split < n_split; i_split++) { @@ -309,87 +314,64 @@ static void gguf_merge(const split_params & split_params) { /*.ctx = */ &ctx_meta, }; - auto split_name = split_params.input; if (i_split > 0) { - split_name = split_file_name(split_prefix, i_split, n_split); + llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split); } - fprintf(stderr, "%s: reading metadata %s ...", __func__, split_name.c_str()); + fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path); - auto * ctx_gguf = gguf_init_from_file(split_name.c_str(), params); + auto * ctx_gguf = gguf_init_from_file(split_path, params); if (!ctx_gguf) { fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); - exit(1); + exit(EXIT_FAILURE); } ctx_ggufs.push_back(ctx_gguf); ctx_metas.push_back(ctx_meta); if (i_split == 0) { - auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_GENERAL_SPLIT_N_SPLIT); + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT); if (key_n_split < 0) { fprintf(stderr, "\n%s: input file does not contain %s metadata\n", __func__, - LLM_KV_GENERAL_SPLIT_N_SPLIT); + LLM_KV_SPLIT_COUNT); gguf_free(ctx_gguf); + ggml_free(ctx_meta); gguf_free(ctx_out); fout.close(); - exit(1); + exit(EXIT_FAILURE); } - n_split = gguf_get_val_u8(ctx_gguf, key_n_split); + n_split = gguf_get_val_u16(ctx_gguf, key_n_split); if (n_split < 1) { fprintf(stderr, "\n%s: input file does not contain a valid split count %d\n", __func__, n_split); gguf_free(ctx_gguf); + ggml_free(ctx_meta); gguf_free(ctx_out); fout.close(); - exit(1); + exit(EXIT_FAILURE); } - // Do not trigger merge if we try to merge again the output - gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, 0); - - // Set metadata from the first split - gguf_set_kv(ctx_out, ctx_gguf); - } - - // Verify the file naming - { - int i_split_file = 0; - int n_split_file = 0; - const char * i_split_format = "-00000-of-00000.gguf"; - - if (split_name.size() < strlen(i_split_format)) { - fprintf(stderr, "\n%s: unexpected input file name: %s\n", __func__, split_params.input.c_str()); - for (auto * _ctx_gguf : ctx_ggufs) { - gguf_free(_ctx_gguf); - } + // Verify the file naming and extract split_prefix + if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) { + fprintf(stderr, "\n%s: unexpected input file name: %s" + " i_split=%d" + " n_split=%d\n", __func__, + split_path, i_split, n_split); + gguf_free(ctx_gguf); + ggml_free(ctx_meta); gguf_free(ctx_out); fout.close(); - exit(1); + exit(EXIT_FAILURE); } - split_prefix = split_name.substr(0, split_name.size() - strlen(i_split_format)); - - const char * split_name_c_str = split_name.c_str(); - int n_part = sscanf(&split_name_c_str[0] + split_prefix.size(), "-%d-of-%d", &i_split_file, &n_split_file); + // Do not trigger merge if we try to merge again the output + gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0); - if (n_part != 2 || i_split_file - 1 != i_split || n_split_file != n_split) { - fprintf(stderr, "\n%s: unexpected input file name: %s" - " i_split=%d i_split_file=%d" - " n_split=%d n_split_file=%d\n", __func__, - split_params.input.c_str(), - i_split, i_split_file, - n_split, n_split_file); - for (auto * _ctx_gguf : ctx_ggufs) { - gguf_free(_ctx_gguf); - } - gguf_free(ctx_out); - fout.close(); - exit(1); - } + // Set metadata from the first split + gguf_set_kv(ctx_out, ctx_gguf); } auto n_tensors = gguf_get_n_tensors(ctx_gguf); @@ -411,18 +393,19 @@ static void gguf_merge(const split_params & split_params) { // Write tensors data for (int i_split = 0; i_split < n_split; i_split++) { - auto split_name = split_file_name(split_prefix, i_split, n_split); - std::ifstream f_input(split_name.c_str(), std::ios::binary); + llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split); + std::ifstream f_input(split_path, std::ios::binary); if (!f_input.is_open()) { - fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_name.c_str()); - for (auto * _ctx_gguf : ctx_ggufs) { - gguf_free(_ctx_gguf); + fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_path); + for (uint32_t i = 0; i < ctx_ggufs.size(); i++) { + gguf_free(ctx_ggufs[i]); + ggml_free(ctx_metas[i]); } gguf_free(ctx_out); fout.close(); - exit(1); + exit(EXIT_FAILURE); } - fprintf(stderr, "%s: writing tensors %s ...", __func__, split_name.c_str()); + fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path); auto * ctx_gguf = ctx_ggufs[i_split]; auto * ctx_meta = ctx_metas[i_split]; @@ -481,8 +464,8 @@ int main(int argc, const char ** argv) { break; case SPLIT_OP_MERGE: gguf_merge(params); break; - default:split_print_usage(argv[0]); - exit(1); + default: split_print_usage(argv[0]); + exit(EXIT_FAILURE); } return 0; diff --git a/llama.cpp b/llama.cpp index 91bd6b8d070e9..aa6c8924660b0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -52,6 +52,9 @@ #define NOMINMAX #endif #include + #ifndef PATH_MAX + #define PATH_MAX MAX_PATH + #endif #include #endif @@ -290,6 +293,10 @@ enum llm_kv { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_SPLIT_NO, + LLM_KV_SPLIT_COUNT, + LLM_KV_SPLIT_TENSORS_COUNT, + LLM_KV_SSM_INNER_SIZE, LLM_KV_SSM_CONV_KERNEL, LLM_KV_SSM_STATE_SIZE, @@ -355,6 +362,10 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_SPLIT_NO, "split.no" }, + { LLM_KV_SPLIT_COUNT, "split.count" }, + { LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" }, + { LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" }, { LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" }, { LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" }, @@ -1099,6 +1110,7 @@ struct llama_file { } } }; +using llama_files = std::vector>; struct llama_mmap { void * addr; @@ -1299,6 +1311,7 @@ struct llama_mmap { } #endif }; +using llama_mmaps = std::vector>; // Represents some region of memory being locked using mlock or VirtualLock; // will automatically unlock on destruction. @@ -1448,6 +1461,7 @@ struct llama_mlock { static void raw_unlock(const void * addr, size_t len) {} #endif }; +using llama_mlocks = std::vector>; static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { std::vector result(8, 0); @@ -2023,12 +2037,12 @@ struct llama_model { // the model memory buffers for the tensor data std::vector bufs; - // model memory mapped file - std::unique_ptr mapping; + // model memory mapped files + llama_mmaps mappings; // objects representing data potentially being locked in memory - std::vector> mlock_bufs; - llama_mlock mlock_mmap; + llama_mlocks mlock_bufs; + llama_mlocks mlock_mmaps; // for quantize-stats only std::vector> tensors_by_name; @@ -2792,6 +2806,8 @@ namespace GGUFMeta { }; } +using llama_buf_map = std::unordered_map; + struct llama_model_loader { int n_kv = 0; int n_tensors = 0; @@ -2802,54 +2818,133 @@ struct llama_model_loader { bool use_mmap = false; - llama_file file; + llama_files files; llama_ftype ftype; llama_fver fver; - std::unique_ptr mapping; + llama_mmaps mappings; + + // Holds information on a model weights + struct llama_tensor_weights { + uint16_t idx; // source file index + size_t offs; // tensor data offset in the original file + + ggml_tensor * tensor; + + llama_tensor_weights(uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { + const int tensor_idx = gguf_find_tensor(gguf_ctx, name); + offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); + } + }; + std::vector weights; + std::unordered_map kv_overrides; - struct gguf_context * ctx_gguf = NULL; - struct ggml_context * ctx_meta = NULL; + struct gguf_context * meta = NULL; + std::vector contexts; std::string arch_name; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); - llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") { + llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) { int trace = 0; if (getenv("LLAMA_TRACE")) { trace = atoi(getenv("LLAMA_TRACE")); } - struct gguf_init_params params = { - /*.no_alloc = */ true, - /*.ctx = */ &ctx_meta, - }; - if (param_overrides_p != nullptr) { for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) { kv_overrides.insert({std::string(p->key), *p}); } } - ctx_gguf = gguf_init_from_file(fname.c_str(), params); - if (!ctx_gguf) { + struct ggml_context * ctx = NULL; + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx, + }; + + meta = gguf_init_from_file(fname.c_str(), params); + if (!meta) { throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str())); } get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false); llm_kv = LLM_KV(llm_arch_from_string(arch_name)); - n_kv = gguf_get_n_kv(ctx_gguf); - n_tensors = gguf_get_n_tensors(ctx_gguf); + // Save tensors data offset of the main file. + // For subsidiary files, `meta` tensor data offset must not be used, + // so we build a unified tensors index for weights. + for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + weights.emplace_back(llama_tensor_weights(0, cur->name, meta, cur)); + } + files.emplace_back(new llama_file(fname.c_str(), "rb")); + contexts.emplace_back(ctx); + + uint16_t n_split = 0; + get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); + + // Load additional GGML contexts + if (n_split > 1) { + uint16_t idx = 0; + get_key(llm_kv(LLM_KV_SPLIT_NO), idx); + if (idx != 0) { + throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx)); + } + + char split_prefix[PATH_MAX] = {0}; + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) { + throw std::runtime_error(format("invalid split file: %s", fname.c_str())); + } - fver = (enum llama_fver ) gguf_get_version(ctx_gguf); + if (trace > 0) { + LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split); + } - for (int i = 0; i < n_tensors; i++) { - const char * name = gguf_get_tensor_name(ctx_gguf, i); - struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name); - n_elements += ggml_nelements(t); - n_bytes += ggml_nbytes(t); + char split_path[PATH_MAX] = {0}; + for (idx = 1; idx < n_split; idx++) { + llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split); + + struct gguf_init_params split_params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params); + if (!ctx_gguf) { + throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path)); + } + + // Save tensors data offset info of the shard. + for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + weights.emplace_back(llama_tensor_weights(idx, cur->name, ctx_gguf, cur)); + } + files.emplace_back(new llama_file(split_path, "rb")); + contexts.emplace_back(ctx); + + gguf_free(ctx_gguf); + } + + get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors); + + // sanity check + { + const int n_tensors_loaded = (int) weights.size(); + if (n_tensors != n_tensors_loaded) { + throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); + } + } + + LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split); + } + + n_kv = gguf_get_n_kv(meta); + n_tensors = weights.size(); + + fver = (enum llama_fver) gguf_get_version(meta); + + for (auto & w : weights) { + n_elements += ggml_nelements(w.tensor); + n_bytes += ggml_nbytes(w.tensor); } LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", @@ -2864,7 +2959,8 @@ struct llama_model_loader { enum ggml_type type_max = GGML_TYPE_F32; for (int i = 0; i < n_tensors; i++) { - enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i); + const ggml_tensor * tensor = weights.at(i).tensor; + enum ggml_type type = tensor->type; n_type[type]++; @@ -2874,8 +2970,8 @@ struct llama_model_loader { } if (trace > 0) { - struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); - LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); + const uint16_t sid = weights.at(i).idx; + LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); } } @@ -2911,22 +3007,23 @@ struct llama_model_loader { ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED); { - const int kid = gguf_find_key(ctx_gguf, "general.file_type"); + const int kid = gguf_find_key(meta, "general.file_type"); if (kid >= 0) { - ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid); + ftype = (llama_ftype) gguf_get_val_u32(meta, kid); } } LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); + for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(ctx_gguf, i); - const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); + const char * name = gguf_get_key(meta, i); + const enum gguf_type type = gguf_get_kv_type(meta, i); const std::string type_name = type == GGUF_TYPE_ARRAY - ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i)) + ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i)) : gguf_type_name(type); - std::string value = gguf_kv_to_str(ctx_gguf, i); + std::string value = gguf_kv_to_str(meta, i); const size_t MAX_VALUE_LEN = 40; if (value.size() > MAX_VALUE_LEN) { value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); @@ -2955,18 +3052,18 @@ struct llama_model_loader { } ~llama_model_loader() { - if (ctx_gguf) { - gguf_free(ctx_gguf); + if (meta) { + gguf_free(meta); } - if (ctx_meta) { - ggml_free(ctx_meta); + for (auto * ctx : contexts) { + ggml_free(ctx); } } template typename std::enable_if::value, bool>::type get_arr_n(const std::string & key, T & result, const bool required = true) { - const int kid = gguf_find_key(ctx_gguf, key.c_str()); + const int kid = gguf_find_key(meta, key.c_str()); if (kid < 0) { if (required) { @@ -2976,7 +3073,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(ctx_gguf, kid); + GGUFMeta::GKV::get_kv(meta, kid); result = arr_info.length; @@ -2996,7 +3093,7 @@ struct llama_model_loader { const struct llama_model_kv_override * override = it != kv_overrides.end() ? &it->second : nullptr; - const bool found = GGUFMeta::GKV::set(ctx_gguf, key, result, override); + const bool found = GGUFMeta::GKV::set(meta, key, result, override); if (required && !found) { throw std::runtime_error(format("key not found in model: %s", key.c_str())); @@ -3019,20 +3116,33 @@ struct llama_model_loader { } const char * get_tensor_name(int i) const { - return gguf_get_tensor_name(ctx_gguf, i); + return weights.at(i).tensor->name; + } + + const llama_tensor_weights & get_weights(const char * name) const { + for (const auto & weight : weights) { + if (strcmp(name, weight.tensor->name) == 0) { + return weight; + } + } + throw std::runtime_error(format("tensor %s not found", name)); } struct ggml_tensor * get_tensor_meta(const char * name) const { - return ggml_get_tensor(ctx_meta, name); + try { + return get_weights(name).tensor; + } catch (const std::runtime_error & e) { + return NULL; + } } struct ggml_tensor * get_tensor_meta(int i) const { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta) { - struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta); - ggml_set_name(tensor, ggml_get_name(meta)); + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) { + struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); + ggml_set_name(tensor, ggml_get_name(cur)); n_created++; @@ -3040,7 +3150,7 @@ struct llama_model_loader { } struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { - struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str()); + const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); if (cur == NULL) { if (!required) { @@ -3075,76 +3185,79 @@ struct llama_model_loader { } } - size_t file_offset(const char * name) const { - const int idx = gguf_find_tensor(ctx_gguf, name); - - if (idx < 0) { - throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name)); - } - - return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx); - } - - void init_mapping(bool prefetch = true, llama_mlock * lmlock = nullptr) { - // prefetch the whole file - all the data is needed anyway + void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) { if (use_mmap) { - mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa())); + mappings.reserve(files.size()); + mmaps_used.reserve(files.size()); + for (const auto & file : files) { + std::unique_ptr mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa())); + mmaps_used.emplace_back(std::make_pair(mapping->size, 0)); + if (mlock_mmaps) { + std::unique_ptr mlock_mmap(new llama_mlock()); + mlock_mmap->init(mapping->addr); + mlock_mmaps->emplace_back(std::move(mlock_mmap)); + } + mappings.emplace_back(std::move(mapping)); + } } // compute the total size of all tensors for progress reporting - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); - size_data += ggml_nbytes(cur); - } - - if (use_mmap && mapping) { - if (lmlock) { - lmlock->init(mapping->addr); - } - mmap_used_first = mapping->size; + for (auto & w : weights) { + size_data += ggml_nbytes(w.tensor); } } - void get_mapping_range(size_t * first, size_t * last, ggml_context * ctx) const { - GGML_ASSERT(mapping); + void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const { + GGML_ASSERT(!mappings.empty()); + const auto & mapping = mappings.at(idx); *first = mapping->size; *last = 0; + *addr = mapping->addr; for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { - const size_t offs = file_offset(ggml_get_name(tensor)); - *first = std::min(*first, offs); - *last = std::max(*last, offs + ggml_nbytes(tensor)); + const auto & w = get_weights(ggml_get_name(tensor)); + if (w.idx != idx) { + continue; + } + *first = std::min(*first, w.offs); + *last = std::max(*last, w.offs + ggml_nbytes(tensor)); } } // for backwards compatibility, does not support ggml-backend void load_data_for(struct ggml_tensor * cur) const { - const size_t offs = file_offset(ggml_get_name(cur)); + const auto & w = get_weights(ggml_get_name(cur)); - if (use_mmap && mapping) { + if (use_mmap) { + const auto & mapping = mappings.at(w.idx); if (cur->data == nullptr) { - cur->data = (uint8_t *)mapping->addr + offs; + cur->data = (uint8_t *)mapping->addr + w.offs; } else { - memcpy(cur->data, (uint8_t *)mapping->addr + offs, ggml_nbytes(cur)); + memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur)); } } else { GGML_ASSERT(cur->data != nullptr); - file.seek(offs, SEEK_SET); - file.read_raw(cur->data, ggml_nbytes(cur)); + GGML_ASSERT(w.idx < files.size()); + const auto & file = files.at(w.idx); + file->seek(w.offs, SEEK_SET); + file->read_raw(cur->data, ggml_nbytes(cur)); } } size_t size_done = 0; size_t size_data = 0; - size_t mmap_used_first = -1; - size_t mmap_used_last = 0; + std::vector> mmaps_used; // Returns false if cancelled by progress_callback - bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) { - GGML_ASSERT(size_data != 0 && "call init_mapping() first"); + bool load_all_data( + struct ggml_context * ctx, + llama_buf_map & bufs_mmap, + llama_mlocks * lmlocks, + llama_progress_callback progress_callback, + void * progress_callback_user_data) { + GGML_ASSERT(size_data != 0 && "call init_mappings() first"); std::vector> read_buf; - for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { if (progress_callback) { if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) { @@ -3152,41 +3265,57 @@ struct llama_model_loader { } } - const size_t offs = file_offset(ggml_get_name(cur)); + const auto & w = get_weights(ggml_get_name(cur)); + size_t n_size = ggml_nbytes(cur); - if (use_mmap && mapping) { + if (use_mmap) { + const auto & mapping = mappings.at(w.idx); + ggml_backend_buffer_t buf_mmap = nullptr; + if (bufs_mmap.count(w.idx)) { + buf_mmap = bufs_mmap.at(w.idx); + } + GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated if (buf_mmap && cur->data == nullptr) { - ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); - if (lmlock) { - lmlock->grow_to(offs + ggml_nbytes(cur)); + ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + w.offs); + if (lmlocks) { + const auto & lmlock = lmlocks->at(w.idx); + lmlock->grow_to(w.offs + ggml_nbytes(cur)); } - mmap_used_first = std::min(mmap_used_first, offs); - mmap_used_last = std::max(mmap_used_last, offs + ggml_nbytes(cur)); + + auto & mmap_used = mmaps_used[w.idx]; + mmap_used.first = std::min(mmap_used.first, w.offs); + mmap_used.second = std::max(mmap_used.second, w.offs + n_size); } else { - ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); + ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + w.offs, 0, n_size); } } else { + GGML_ASSERT(w.idx < files.size()); + const auto & file = files.at(w.idx); if (ggml_backend_buffer_is_host(cur->buffer)) { - file.seek(offs, SEEK_SET); - file.read_raw(cur->data, ggml_nbytes(cur)); + file->seek(w.offs, SEEK_SET); + file->read_raw(cur->data, ggml_nbytes(cur)); } else { read_buf.resize(ggml_nbytes(cur)); - file.seek(offs, SEEK_SET); - file.read_raw(read_buf.data(), ggml_nbytes(cur)); - ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); + file->seek(w.offs, SEEK_SET); + file->read_raw(read_buf.data(), ggml_nbytes(cur)); + ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size); } } - size_done += ggml_nbytes(cur); + size_done += n_size; } // check if this is the last call and do final cleanup if (size_done >= size_data) { // unmap offloaded tensors and metadata - if (use_mmap && mapping) { - mapping->unmap_fragment(0, mmap_used_first); - if (mmap_used_last != 0) { - mapping->unmap_fragment(mmap_used_last, mapping->size); + if (use_mmap) { + for (uint32_t idx = 0; idx < mappings.size(); idx++) { + const auto & mmap_used = mmaps_used.at(idx); + auto & mapping = mappings.at(idx); + mapping->unmap_fragment(0, mmap_used.first); + if (mmap_used.second != 0) { + mapping->unmap_fragment(mmap_used.second, mapping->size); + } } } if (progress_callback) { @@ -3319,7 +3448,7 @@ static void llm_load_hparams( llama_model_loader & ml, llama_model & model) { auto & hparams = model.hparams; - const gguf_context * ctx = ml.ctx_gguf; + const gguf_context * ctx = ml.meta; // get metadata as string for (int i = 0; i < gguf_get_n_kv(ctx); i++) { @@ -3709,7 +3838,7 @@ static void llm_load_vocab( llama_model & model) { auto & vocab = model.vocab; - struct gguf_context * ctx = ml.ctx_gguf; + struct gguf_context * ctx = ml.meta; const auto kv = LLM_KV(model.arch); @@ -4319,10 +4448,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) { - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); - } + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); @@ -5024,56 +5151,97 @@ static bool llm_load_tensors( ml.done_getting_tensors(); - ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr); + ml.init_mappings(true, &model.mlock_mmaps); + model.mappings.reserve(ml.mappings.size()); // create the backend buffers - std::vector> ctx_bufs; + std::vector> ctx_bufs; + ctx_bufs.reserve(ctx_map.size()); + + // Ensure we have enough capacity for the maximum backend buffer we will potentially create + size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); + model.bufs.reserve(n_max_backend_buffer); for (auto & it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; - ggml_context * ctx = it.second; - ggml_backend_buffer_t buf = nullptr; + ggml_context * ctx = it.second; + + llama_buf_map bufs; + bufs.reserve(n_max_backend_buffer); // only the mmap region containing the tensors in the model is mapped to the backend buffer // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) { - size_t first, last; - ml.get_mapping_range(&first, &last, ctx); - buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first); + for (uint32_t idx = 0; idx < ml.files.size(); idx++) { + void * addr = nullptr; + size_t first, last; + ml.get_mapping_range(&first, &last, &addr, idx, ctx); + if (first >= last) { + continue; + } + ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first); + if (buf == nullptr) { + throw std::runtime_error("unable to allocate backend CPU buffer"); + } + model.bufs.push_back(buf); + bufs.emplace(idx, buf); #ifdef GGML_USE_CUBLAS - if (n_layer >= n_gpu_layers) { - ggml_backend_cuda_register_host_buffer( + if (n_layer >= n_gpu_layers) { + ggml_backend_cuda_register_host_buffer( ggml_backend_buffer_get_base(buf), ggml_backend_buffer_get_size(buf)); - } + } #endif + } } #ifdef GGML_USE_METAL else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) { - const size_t max_size = ggml_get_max_tensor_size(ctx); - size_t first, last; - ml.get_mapping_range(&first, &last, ctx); - buf = ggml_backend_metal_buffer_from_ptr((char *) ml.mapping->addr + first, last - first, max_size); + for (uint32_t idx = 0; idx < ml.files.size(); idx++) { + const size_t max_size = ggml_get_max_tensor_size(ctx); + void * addr = nullptr; + size_t first, last; + ml.get_mapping_range(&first, &last, &addr, idx, ctx); + if (first >= last) { + continue; + } + ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size); + if (buf == nullptr) { + throw std::runtime_error("unable to allocate backend metal buffer"); + } + model.bufs.push_back(buf); + bufs.emplace(idx, buf); + } } #endif else { - buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); - if (buf != nullptr && use_mlock && ggml_backend_buffer_is_host(buf)) { + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (buf == nullptr) { + throw std::runtime_error("unable to allocate backend buffer"); + } + model.bufs.push_back(buf); + if (use_mlock && ggml_backend_buffer_is_host(buf)) { model.mlock_bufs.emplace_back(new llama_mlock); auto & mlock_buf = model.mlock_bufs.back(); mlock_buf->init (ggml_backend_buffer_get_base(buf)); mlock_buf->grow_to(ggml_backend_buffer_get_size(buf)); } + for (uint32_t idx = 0; idx < ml.files.size(); idx++) { + bufs.emplace(idx, buf); + } } - if (buf == nullptr) { + + if (bufs.empty()) { throw std::runtime_error("failed to allocate buffer"); } - // indicate that this buffer contains weights - // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight - ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); - model.bufs.push_back(buf); - ctx_bufs.emplace_back(ctx, buf); + + for (auto & buf : bufs) { + // indicate that this buffer contains weights + // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight + ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); + } + + ctx_bufs.emplace_back(ctx, bufs); } if (llama_supports_gpu_offload()) { @@ -5105,13 +5273,15 @@ static bool llm_load_tensors( // load tensor data for (auto & it : ctx_bufs) { ggml_context * ctx = it.first; - ggml_backend_buffer_t buf = it.second; - if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf, use_mlock ? &model.mlock_mmap : NULL)) { + auto & bufs = it.second; + if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) { return false; } } - model.mapping = std::move(ml.mapping); + for (auto & mapping : ml.mappings) { + model.mappings.emplace_back(std::move(mapping)); + } // loading time will be recalculate after the first eval, so // we take page faults deferred by mmap() into consideration @@ -12302,7 +12472,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s #endif llama_model_loader ml(fname_inp, use_mmap, NULL); - ml.init_mapping(false); // no prefetching? + ml.init_mappings(false); // no prefetching? llama_model model; llm_load_arch(ml, model); @@ -12326,12 +12496,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s struct gguf_context * ctx_out = gguf_init_empty(); // copy the KV pairs from the input file - gguf_set_kv (ctx_out, ml.ctx_gguf); + gguf_set_kv (ctx_out, ml.meta); gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); gguf_set_val_u32(ctx_out, "general.file_type", ftype); for (int i = 0; i < ml.n_tensors; ++i) { - struct ggml_tensor * meta = ml.get_tensor_meta(i); + const struct ggml_tensor * meta = ml.get_tensor_meta(i); const std::string name = ggml_get_name(meta); @@ -12371,7 +12541,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // populate the original tensors so we get an initial meta data for (int i = 0; i < ml.n_tensors; ++i) { - struct ggml_tensor * meta = ml.get_tensor_meta(i); + const struct ggml_tensor * meta = ml.get_tensor_meta(i); gguf_add_tensor(ctx_out, meta); } @@ -12576,7 +12746,7 @@ static int llama_apply_lora_from_file_internal( if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr)); - ml->init_mapping(/*prefetch*/ false); // no prefetching + ml->init_mappings(/*prefetch*/ false); // no prefetching } struct tensor_meta { @@ -12697,7 +12867,7 @@ static int llama_apply_lora_from_file_internal( ggml_tensor * base_t; if (ml) { - if (gguf_find_tensor(ml->ctx_gguf, base_name.c_str()) < 0) { + if (!ml->get_tensor_meta(base_name.c_str())) { LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); return 1; } @@ -14645,6 +14815,30 @@ LLAMA_API int32_t llama_chat_apply_template( return res; } +LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) { + static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf"; + if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) { + return strlen(split_path); + } + return 0; +} + +int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) { + std::string str_split_path(split_path); + char postfix[32]; + snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count); + std::string str_postfix(postfix); + + // check if dest ends with postfix + int size_prefix = str_split_path.size() - str_postfix.size(); + if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) { + snprintf(dest, std::min((size_t) size_prefix, maxlen), "%s", split_path); + return size_prefix; + } + + return 0; +} + struct llama_timings llama_get_timings(struct llama_context * ctx) { struct llama_timings result = { /*.t_start_ms =*/ 1e-3 * ctx->t_start_us, diff --git a/llama.h b/llama.h index 40dcf54e394f8..7e8ac4b62beb5 100644 --- a/llama.h +++ b/llama.h @@ -960,6 +960,16 @@ extern "C" { int32_t n_past, int32_t n_predict); + /// @details Build a split GGUF final path for this chunk. + /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf" + // Returns the split_path length. + LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count); + + /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match. + /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0" + // Returns the split_prefix length. + LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count); + // Performance information LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); From 1d0331c12a2f2a6296b471232bd4e66fbf06e6a1 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 22 Mar 2024 19:47:14 +0100 Subject: [PATCH 340/401] quantize: options for output and token embedding tensors qtype (#6239) * quantize: be able to specify the output tensor type * quantize: be able to specify the token embedding tensor type --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 24 +++++++++++++++++ llama.cpp | 47 ++++++++++++++++++++-------------- llama.h | 16 +++++++----- 3 files changed, 61 insertions(+), 26 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 7662ec80c5e0f..79e60ea7ba6b9 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -189,6 +189,18 @@ static void prepare_imatrix(const std::string& imatrix_file, } } +static ggml_type parse_ggml_type(const char * arg) { + ggml_type result = GGML_TYPE_COUNT; + for (int j = 0; j < GGML_TYPE_COUNT; ++j) { + auto type = ggml_type(j); + const auto * name = ggml_type_name(type); + if (name && strcmp(arg, name) == 0) { + result = type; break; + } + } + return result; +} + int main(int argc, char ** argv) { if (argc < 3) { usage(argv[0]); @@ -203,6 +215,18 @@ int main(int argc, char ** argv) { for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { params.quantize_output_tensor = false; + } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) { + if (arg_idx < argc-1) { + params.output_tensor_type = parse_ggml_type(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) { + if (arg_idx < argc-1) { + params.token_embedding_type = parse_ggml_type(argv[++arg_idx]); + } else { + usage(argv[0]); + } } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { params.allow_requantize = true; } else if (strcmp(argv[arg_idx], "--pure") == 0) { diff --git a/llama.cpp b/llama.cpp index aa6c8924660b0..eedca802b86a7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12141,27 +12141,34 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings // with the quantization of the output tensor if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) { - int nx = tensor->ne[0]; - if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { - new_type = GGML_TYPE_Q8_0; - } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || - ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { - new_type = GGML_TYPE_Q5_K; - } - else if (new_type != GGML_TYPE_Q8_0) { - new_type = GGML_TYPE_Q6_K; + if (qs.params->output_tensor_type < GGML_TYPE_COUNT) { + new_type = qs.params->output_tensor_type; + } else { + int nx = tensor->ne[0]; + if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { + new_type = GGML_TYPE_Q8_0; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || + ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { + new_type = GGML_TYPE_Q5_K; + } + else if (new_type != GGML_TYPE_Q8_0) { + new_type = GGML_TYPE_Q6_K; + } } } else if (name == "token_embd.weight") { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || - ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { - new_type = GGML_TYPE_Q2_K; - } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { - new_type = GGML_TYPE_IQ3_S; - } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - new_type = GGML_TYPE_IQ3_S; + if (qs.params->token_embedding_type < GGML_TYPE_COUNT) { + new_type = qs.params->token_embedding_type; + } else { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + new_type = GGML_TYPE_Q2_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { + new_type = GGML_TYPE_IQ3_S; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = GGML_TYPE_IQ3_S; + } } } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { @@ -13051,6 +13058,8 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { struct llama_model_quantize_params result = { /*.nthread =*/ 0, /*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1, + /*.output_tensor_type =*/ GGML_TYPE_COUNT, + /*.token_embedding_type =*/ GGML_TYPE_COUNT, /*.allow_requantize =*/ false, /*.quantize_output_tensor =*/ true, /*.only_copy =*/ false, diff --git a/llama.h b/llama.h index 7e8ac4b62beb5..74f0e56de71c6 100644 --- a/llama.h +++ b/llama.h @@ -275,13 +275,15 @@ extern "C" { // model quantization parameters typedef struct llama_model_quantize_params { - int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() - enum llama_ftype ftype; // quantize to this llama_ftype - bool allow_requantize; // allow quantizing non-f32/f16 tensors - bool quantize_output_tensor; // quantize output.weight - bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored - bool pure; // quantize all tensors to the default type - void * imatrix; // pointer to importance matrix data + int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() + enum llama_ftype ftype; // quantize to this llama_ftype + enum ggml_type output_tensor_type; // output tensor type + enum ggml_type token_embedding_type; // itoken embeddings tensor type + bool allow_requantize; // allow quantizing non-f32/f16 tensors + bool quantize_output_tensor; // quantize output.weight + bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored + bool pure; // quantize all tensors to the default type + void * imatrix; // pointer to importance matrix data } llama_model_quantize_params; // grammar types From 92397d87a45a09b5449d845a64856f177cd7a920 Mon Sep 17 00:00:00 2001 From: fraxy-v <65565042+fraxy-v@users.noreply.github.com> Date: Fri, 22 Mar 2024 20:49:06 +0200 Subject: [PATCH 341/401] convert-llama2c-to-ggml : enable conversion of GQA models (#6237) * convert-llama2c-to-ggml: enable conversion of multiqueries, #5608 * add test in build action * Update build.yml * Update build.yml * Update build.yml * gg patch --- .github/workflows/build.yml | 11 + examples/convert-llama2c-to-ggml/README.md | 2 + .../convert-llama2c-to-ggml.cpp | 389 ++++++++---------- 3 files changed, 194 insertions(+), 208 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 7711bd8d8c470..bf42df8fe58d4 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -225,6 +225,17 @@ jobs: cd build ctest -L main --verbose --timeout 900 + - name: Test llama2c conversion + id: llama2c_test + run: | + cd build + echo "Fetch tokenizer" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin + echo "Fetch llama2c model" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin + ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf + ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + # ubuntu-latest-cmake-sanitizer: # runs-on: ubuntu-latest # diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 0f37d295bd9ee..6da2d7e1809a9 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -21,6 +21,8 @@ An example command using a model from [karpathy/tinyllamas](https://huggingface. `$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` +Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K). + Now you can use the model with a command like: `$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 8209dcb642bee..6b5c6653011b1 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -1,6 +1,7 @@ #include "ggml.h" #include "llama.h" #include "common.h" +#include "log.h" #include #include @@ -78,111 +79,101 @@ typedef struct { struct TransformerWeights { // token embedding table - float* token_embedding_table; // (vocab_size, dim) + std::vector token_embedding_table; // (vocab_size, dim) // weights for rmsnorms - float* rms_att_weight; // (layer, dim) rmsnorm weights - float* rms_ffn_weight; // (layer, dim) + std::vector rms_att_weight; // (layer, dim) rmsnorm weights + std::vector rms_ffn_weight; // (layer, dim) // weights for matmuls - float* wq; // (layer, dim, dim) - float* wk; // (layer, dim, dim) - float* wv; // (layer, dim, dim) - float* wo; // (layer, dim, dim) + std::vector wq; // (layer, dim, dim) + std::vector wk; // (layer, dim, dim) + std::vector wv; // (layer, dim, dim) + std::vector wo; // (layer, dim, dim) // weights for ffn - float* w1; // (layer, hidden_dim, dim) - float* w2; // (layer, dim, hidden_dim) - float* w3; // (layer, hidden_dim, dim) + std::vector w1; // (layer, hidden_dim, dim) + std::vector w2; // (layer, dim, hidden_dim) + std::vector w3; // (layer, hidden_dim, dim) // final rmsnorm - float* rms_final_weight; // (dim,) + std::vector rms_final_weight; // (dim,) // freq_cis for RoPE relatively positional embeddings - // float* freq_cis_real; // (seq_len, dim/2) - // float* freq_cis_imag; // (seq_len, dim/2) + // std::vector freq_cis_real; // (seq_len, dim/2) + // std::vector freq_cis_imag; // (seq_len, dim/2) // (optional) classifier weights for the logits, on the last layer - float* wcls; - - ~TransformerWeights() { - delete[] token_embedding_table; - delete[] rms_att_weight; - delete[] rms_ffn_weight; - delete[] wq; - delete[] wk; - delete[] wv; - delete[] wo; - delete[] w1; - delete[] w2; - delete[] w3; - delete[] rms_final_weight; - delete[] wcls; - } + std::vector wcls; }; -static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { - // we calloc instead of malloc to keep valgrind happy - w->token_embedding_table = new float[p->vocab_size * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); +static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) { + const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads; + try { + w->token_embedding_table.resize(p->vocab_size * p->dim); + LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); - w->rms_att_weight = new float[p->n_layers * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim); + w->rms_att_weight.resize(p->n_layers * p->dim); + LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim); - w->rms_ffn_weight = new float[p->n_layers * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim); + w->rms_ffn_weight.resize(p->n_layers * p->dim); + LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim); - w->wq = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wq.resize(p->n_layers * p->dim * p->dim); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); - w->wk = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); - w->wv = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); - w->wo = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wo.resize(p->n_layers * p->dim * p->dim); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); - w->w1 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + w->w1.resize(p->n_layers * p->hidden_dim * p->dim); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); - w->w2 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim); + w->w2.resize(p->n_layers * p->hidden_dim * p->dim); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim); - w->w3 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + w->w3.resize(p->n_layers * p->hidden_dim * p->dim); + LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); - w->rms_final_weight = new float[p->dim](); - printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim); + w->rms_final_weight.resize(p->dim); + LOG("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim); - if (shared_weights) { - w->wcls = NULL; - } else { - w->wcls = new float[p->vocab_size * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + if (shared_weights) { + w->wcls = {}; + } else { + w->wcls.resize(p->vocab_size * p->dim); + LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + } + } + catch (std::length_error &) { + die("Invalid configuration. Failed to allocate memory for weights"); } } -static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) { - if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1; - if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1; - if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1; - if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1; - if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast(p->n_layers * p->hidden_dim * p->dim)) return 1; - if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1; - if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast(p->dim)) return 1; +static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) { + if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1; + if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1; + if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1; + if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1; + if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1; + if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1; + if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1; + if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1; + if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1; + if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1; + if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1; // Skip freq_cis_real & freq_cis_imag int head_size = p->dim / p->n_heads; fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR); - if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1; + if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1; // Check we didn't forget to read anything auto curr = ftell(f); fseek(f, 0, SEEK_END); auto end = ftell(f); if (curr != end) { - printf("Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", curr, end); + LOG("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", __func__, curr, end); return 1; } @@ -190,20 +181,20 @@ static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bo } static void print_sample_weights(TransformerWeights *w){ - printf("----- Quick print of first of the weight vales of all the variables\n"); - printf("%f\n", w->token_embedding_table[0]); - printf("%f\n", w->rms_att_weight[0]); - printf("%f\n", w->rms_ffn_weight[0]); - - printf("%f\n", w->wq[0]); - printf("%f\n", w->wk[0]); - printf("%f\n", w->wv[0]); - printf("%f\n", w->wo[0]); - printf("%f\n", w->w1[0]); - printf("%f\n", w->w2[0]); - printf("%f\n", w->w3[0]); - printf("%f\n", w->rms_att_weight[0]); - if (w->wcls) printf("%f\n", w->wcls[0]); + LOG("----- Quick print of first of the weight vales of all the variables\n"); + LOG("%f\n", w->token_embedding_table[0]); + LOG("%f\n", w->rms_att_weight[0]); + LOG("%f\n", w->rms_ffn_weight[0]); + + LOG("%f\n", w->wq[0]); + LOG("%f\n", w->wk[0]); + LOG("%f\n", w->wv[0]); + LOG("%f\n", w->wo[0]); + LOG("%f\n", w->w1[0]); + LOG("%f\n", w->w2[0]); + LOG("%f\n", w->w3[0]); + LOG("%f\n", w->rms_att_weight[0]); + if (!w->wcls.empty()) LOG("%f\n", w->wcls[0]); } //////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -225,14 +216,16 @@ struct llama_vocab { }; struct my_llama_hparams { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? - uint32_t n_embd = 4096; - uint32_t n_ff = 11008; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; + uint32_t n_vocab = 32000; + uint32_t n_ctx = 512; // this is provided as user input? + uint32_t n_embd = 4096; + uint32_t n_ff = 11008; + uint32_t n_mult = 4; + uint32_t n_head = 32; + uint32_t n_head_kv = 32; + uint32_t n_layer = 32; + uint32_t n_rot = 64; + bool operator!=(const my_llama_hparams& other) const { return memcmp(this, &other, sizeof(my_llama_hparams)); } @@ -325,14 +318,30 @@ struct train_params { }; static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab: %u\n", __func__, params->n_vocab); - printf("%s: n_ctx: %u\n", __func__, params->n_ctx); - printf("%s: n_embd: %u\n", __func__, params->n_embd); - printf("%s: n_mult: %u\n", __func__, params->n_mult); - printf("%s: n_head: %u\n", __func__, params->n_head); - printf("%s: n_ff: %u\n", __func__, params->n_ff); - printf("%s: n_layer: %u\n", __func__, params->n_layer); - printf("%s: n_rot: %u\n", __func__, params->n_rot); + LOG("%s: n_vocab: %u\n", __func__, params->n_vocab); + LOG("%s: n_ctx: %u\n", __func__, params->n_ctx); + LOG("%s: n_embd: %u\n", __func__, params->n_embd); + LOG("%s: n_mult: %u\n", __func__, params->n_mult); + LOG("%s: n_head: %u\n", __func__, params->n_head); + LOG("%s: n_head_kv: %u\n", __func__, params->n_head_kv); + LOG("%s: n_ff: %u\n", __func__, params->n_ff); + LOG("%s: n_layer: %u\n", __func__, params->n_layer); + LOG("%s: n_rot: %u\n", __func__, params->n_rot); +} + +static void print_tensor_info(const struct ggml_context * ctx) { + for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + LOG("%s: Allocating ", __func__); + int64_t total = 1; + int i = 0; + for (; i < ggml_n_dims(t); ++i) { + if (i > 0) LOG("x "); + LOG("[%" PRId64 "] ", t->ne[i]); + total *= t->ne[i]; + } + if (i > 1) LOG("= [%" PRId64 "] ", total); + LOG("float space for %s\n", ggml_get_name(t)); + } } static void init_model(struct my_llama_model * model) { @@ -342,6 +351,8 @@ static void init_model(struct my_llama_model * model) { const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; + const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv; + const uint32_t n_ff = hparams.n_ff; struct ggml_context * ctx = model->ctx; @@ -350,25 +361,8 @@ static void init_model(struct my_llama_model * model) { model->train_tokens = 0; model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%u] x [%u] = [%u] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - printf("[%s:GG] Allocating [%u] float space for model->norm\n",__func__,n_embd); - model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab); - - // printing the per-layer allocations here so we dont print in the for loop. - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wq for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wk for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wv for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wo for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - - printf("[%s:GG] Allocating [%u] float space for layer.ffn_norm for [%u] layers\n",__func__,n_embd, n_layer); - - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w1 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w2 for [%u] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w3 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); ggml_set_name(model->tok_embeddings, "tok_embeddings.weight"); ggml_set_name(model->norm, "norm.weight"); @@ -383,8 +377,8 @@ static void init_model(struct my_llama_model * model) { layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); - layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); - layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); + layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); @@ -406,6 +400,8 @@ static void init_model(struct my_llama_model * model) { ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str()); ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str()); } + + print_tensor_info(ctx); } static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { @@ -421,9 +417,9 @@ static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { static void print_row(struct ggml_tensor * probs, int i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = get_f32_2d(probs, k, i); - printf(" %f", p); + LOG(" %f", p); } - printf("\n"); + LOG("\n"); } static void print_matrix(struct ggml_tensor * probs) { @@ -431,33 +427,12 @@ static void print_matrix(struct ggml_tensor * probs) { for (int i = 0; i < probs->ne[1]; ++i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = get_f32_2d(probs, k, i); - printf(" %.2f", p); + LOG(" %.2f", p); } - printf("\n"); + LOG("\n"); } } -#ifdef __GNUC__ -#ifdef __MINGW32__ -__attribute__((format(gnu_printf, 1, 2))) -#else -__attribute__((format(printf, 1, 2))) -#endif -#endif -static std::string format(const char * fmt, ...) { - va_list ap, ap2; - va_start(ap, fmt); - va_copy(ap2, ap); - int size = vsnprintf(NULL, 0, fmt, ap); - GGML_ASSERT(size >= 0 && size < INT_MAX); - std::vector buf(size + 1); - int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); - GGML_ASSERT(size2 == size); - va_end(ap2); - va_end(ap); - return std::string(buf.data(), size); -} - struct llama_file { // use FILE * so we don't have to re-open the file to mmap FILE * fp; @@ -549,8 +524,9 @@ static std::string llama_escape_whitespaces(const std::string & text) { return out.str(); } -static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { +static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) { if (is_ggml_file(filename)) { + LOG("%s: Loading vocabulary from gguf file %s\n", __func__, filename); struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { @@ -578,6 +554,9 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx); + if (n_vocab != static_cast(config->vocab_size)) { + die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size); + } vocab->id_to_token.resize(n_vocab); @@ -595,7 +574,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab gguf_free(ctx); } else { // assume llama2.c vocabulary - printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); + LOG("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename); llama_file file(filename, "rb"); if (!file.fp) { die_fmt("%s: %s", strerror(errno), filename); @@ -638,38 +617,15 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab } static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { - int ct; - switch (ggml_n_dims(gg_weights)) { - case 1: - ct = 0; - for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){ - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]); - *ptr = karpathy_weights[ct]; - ct++; - } - break; - case 2: - ct = 0; - for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { - for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]); - *ptr = karpathy_weights[ct]; - ct++; - } - } - break; - case 3: - ct = 0; - for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) { - for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { - for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]); - *ptr = karpathy_weights[ct]; - ct++; - } - } - } - break; + int size = 1; + for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) { + size *= gg_weights->ne[dim]; + } + for (int ct = 0; ct < size; ++ct) { + int64_t i0 = 0; int64_t i1 = 0; + int64_t i2 = 0; int64_t i3 = 0; + ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3); + ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]); } } @@ -679,16 +635,18 @@ static void save_as_llama_model( // convert AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor - convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table); - convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); + convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data()); + convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data()); - convert_weights_ak_to_gg(model->norm, w->rms_final_weight); + convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data()); //print_row(model->norm, 0); // for rms-att-weight int row_length = model->hparams.n_embd; int n_ff = model->hparams.n_ff; + const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv; + for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ auto & layer = model->layers[i]; // 1d @@ -697,9 +655,10 @@ static void save_as_llama_model( // from 3d matrix layer x dim x dim to 2d matrix dim x dim convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]); - convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]); - convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]); convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]); + // from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries + convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length/n_multiqueries]); + convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length/n_multiqueries]); convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]); convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]); @@ -736,8 +695,8 @@ static void save_as_llama_model( gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd); gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff); gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); - // n_head_kv is optional, default to n_head - // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv); gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer); gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot); gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); @@ -789,12 +748,12 @@ static void save_as_llama_model( static struct train_params get_default_train_params() { struct train_params params; - params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; + params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; params.fn_llama2c_output_model = "ak_llama_model.bin"; - params.fn_train_data = "shakespeare.txt"; - params.fn_checkpoint_in = "checkpoint.bin"; - params.fn_checkpoint_out = "checkpoint.bin"; - params.fn_model_out = "ggml-checkpoint-f32.bin"; + params.fn_train_data = "shakespeare.txt"; + params.fn_checkpoint_in = "checkpoint.bin"; + params.fn_checkpoint_out = "checkpoint.bin"; + params.fn_model_out = "ggml-checkpoint-f32.bin"; params.seed = -1; @@ -829,8 +788,8 @@ static struct train_params get_default_train_params() { params.adam_alpha = 1e-3f; params.adam_decay = 1e-3f; - params.mem_model_gb = 2; - params.mem_compute_gb = 24; + params.mem_model_gb = 2; + params.mem_compute_gb = 24; params.mem_compute0_gb = 8; params.mem_compute1_gb = 2; @@ -916,19 +875,30 @@ int main(int argc, char ** argv) { if (!params_parse(argc, argv, ¶ms)) { return 1; } + log_set_target(stdout); Config config; TransformerWeights weights = {}; { - FILE *file = fopen(params.fn_llama2c_model, "rb"); - if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; } + LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model); + FILE *file = fopen(params.fn_llama2c_model, "r"); + if (!file) { + LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model); + return 1; + } // read in the config header - if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; } + if (fread(&config, sizeof(Config), 1, file) != 1) { + LOG("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model); + return 1; + } auto shared_weights = config.vocab_size > 0; config.vocab_size = abs(config.vocab_size); // read in the Transformer weights - malloc_weights(&weights, &config, shared_weights); - if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; } + alloc_weights(&weights, &config, shared_weights); + if (checkpoint_init_weights(&weights, &config, file, shared_weights)) { + LOG("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model); + return 1; + } fclose(file); } @@ -936,15 +906,18 @@ int main(int argc, char ** argv) { load_vocab(params.fn_vocab_model, &config, &vocab); struct my_llama_model model; - model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); - model.hparams.n_ctx = params.n_ctx; - model.hparams.n_embd = config.dim; //params.n_embd; - model.hparams.n_ff = config.hidden_dim; - model.hparams.n_mult = 32;//params.n_mult; - model.hparams.n_head = config.n_heads; //params.n_head; - model.hparams.n_layer = config.n_layers; //params.n_layer; - model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); + model.hparams.n_ctx = params.n_ctx; + model.hparams.n_embd = config.dim; //params.n_embd; + model.hparams.n_ff = config.hidden_dim; + model.hparams.n_mult = 32;//params.n_mult; + model.hparams.n_head = config.n_heads; //params.n_head; + model.hparams.n_head_kv = config.n_kv_heads; + model.hparams.n_layer = config.n_layers; //params.n_layer; + model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + print_params(&model.hparams); + struct ggml_init_params lcparams; lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb); lcparams.mem_buffer = NULL; @@ -956,7 +929,7 @@ int main(int argc, char ** argv) { model.name = basename(params.fn_llama2c_model); save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model); - printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); + LOG("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model); ggml_free(model.ctx); return 0; From 56a00f0a2f48a85376f48b5ce77699df781631ae Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 22 Mar 2024 21:10:39 +0200 Subject: [PATCH 342/401] common : default --hf-file to --model (#6234) --- common/common.cpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 0cc4859f1148b..de6eb960a7b56 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1220,9 +1220,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { throw std::invalid_argument("error: unknown argument: " + arg); } } + if (invalid_param) { throw std::invalid_argument("error: invalid parameter for argument: " + arg); } + if (params.prompt_cache_all && (params.interactive || params.interactive_first || params.instruct)) { @@ -1230,6 +1232,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } + // short-hand to avoid specifying --hf-file -> default it to --model + if (!params.hf_repo.empty() && params.hf_file.empty()) { + params.hf_file = params.model; + } + if (params.escape) { process_escapes(params.prompt); process_escapes(params.input_prefix); From 50ccaf5eacb50a2ca378a4ef0dc7aeb45fead652 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 23 Mar 2024 01:24:36 +0100 Subject: [PATCH 343/401] lookup: complement data from context with general text statistics (#5479) * lookup: evaluation tools, use corpus/previous gens * fixup! lookup: evaluation tools, use corpus/previous gens * fixup! lookup: evaluation tools, use corpus/previous gens * fixup! lookup: evaluation tools, use corpus/previous gens * fixup! lookup: evaluation tools, use corpus/previous gens --- .gitignore | 3 + Makefile | 13 +- common/CMakeLists.txt | 2 + common/common.cpp | 20 +++ common/common.h | 28 +-- common/ngram-cache.cpp | 280 ++++++++++++++++++++++++++++++ common/ngram-cache.h | 94 ++++++++++ examples/lookup/CMakeLists.txt | 18 ++ examples/lookup/lookup-create.cpp | 43 +++++ examples/lookup/lookup-merge.cpp | 47 +++++ examples/lookup/lookup-stats.cpp | 163 +++++++++++++++++ examples/lookup/lookup.cpp | 116 ++++++++----- scripts/get-wikitext-103.sh | 10 ++ 13 files changed, 774 insertions(+), 63 deletions(-) create mode 100644 common/ngram-cache.cpp create mode 100644 common/ngram-cache.h create mode 100644 examples/lookup/lookup-create.cpp create mode 100644 examples/lookup/lookup-merge.cpp create mode 100644 examples/lookup/lookup-stats.cpp create mode 100755 scripts/get-wikitext-103.sh diff --git a/.gitignore b/.gitignore index 51aa84222d667..27562f6d7b9b3 100644 --- a/.gitignore +++ b/.gitignore @@ -58,6 +58,9 @@ models-mnt /llava-cli /lookahead /lookup +/lookup-create +/lookup-merge +/lookup-stats /main /metal /passkey diff --git a/Makefile b/Makefile index fa112e7081fa7..b8b261abade6a 100644 --- a/Makefile +++ b/Makefile @@ -676,6 +676,9 @@ json-schema-to-grammar.o: common/json-schema-to-grammar.cpp common/json-schema-t train.o: common/train.cpp common/train.h $(CXX) $(CXXFLAGS) -c $< -o $@ +ngram-cache.o: common/ngram-cache.cpp common/ngram-cache.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) @@ -683,7 +686,7 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: - rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) find examples pocs -type f -name "*.o" -delete # @@ -813,9 +816,15 @@ lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-create.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) -o lookup-create $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-merge.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) -o lookup-merge $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-stats.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) -o lookup-stats $(LDFLAGS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 10951693ab0a2..1d840e5f73877 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -65,6 +65,8 @@ add_library(${TARGET} STATIC json.hpp train.h train.cpp + ngram-cache.h + ngram-cache.cpp ) if (BUILD_SHARED_LIBS) diff --git a/common/common.cpp b/common/common.cpp index de6eb960a7b56..69c2d5bf79f91 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -963,6 +963,22 @@ static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, } return true; } + if (arg == "-lcs" || arg == "--lookup-cache-static") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.lookup_cache_static = argv[i]; + return true; + } + if (arg == "-lcd" || arg == "--lookup-cache-dynamic") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.lookup_cache_dynamic = argv[i]; + return true; + } if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { if (++i >= argc) { invalid_param = true; @@ -1436,6 +1452,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" Hugging Face model file (default: unused)\n"); printf(" -ld LOGDIR, --logdir LOGDIR\n"); printf(" path under which to save YAML logs (no logging if unset)\n"); + printf(" -lcs FNAME, --lookup-cache-static FNAME\n"); + printf(" path to static lookup cache to use for lookup decoding (not updated by generation)\n"); + printf(" -lcd FNAME, --lookup-cache-dynamic FNAME\n"); + printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); diff --git a/common/common.h b/common/common.h index d827d4df78547..afa4cf6d7d528 100644 --- a/common/common.h +++ b/common/common.h @@ -88,20 +88,22 @@ struct gpt_params { // // sampling parameters struct llama_sampling_params sparams; - std::string model = "models/7B/ggml-model-f16.gguf"; // model path - std::string model_draft = ""; // draft model for speculative decoding - std::string model_alias = "unknown"; // model alias - std::string model_url = ""; // model url to download - std::string hf_repo = ""; // HF repo - std::string hf_file = ""; // HF file - std::string prompt = ""; - std::string prompt_file = ""; // store the external prompt file name - std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state - std::string input_prefix = ""; // string to prefix user inputs with - std::string input_suffix = ""; // string to suffix user inputs with + std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding + std::string model_alias = "unknown"; // model alias + std::string model_url = ""; // model url to download + std::string hf_repo = ""; // HF repo + std::string hf_file = ""; // HF file + std::string prompt = ""; + std::string prompt_file = ""; // store the external prompt file name + std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state + std::string input_prefix = ""; // string to prefix user inputs with + std::string input_suffix = ""; // string to suffix user inputs with std::vector antiprompt; // string upon seeing which more user input is prompted - std::string logdir = ""; // directory in which to save YAML log files - std::string logits_file = ""; // file for saving *all* logits + std::string logdir = ""; // directory in which to save YAML log files + std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding + std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding + std::string logits_file = ""; // file for saving *all* logits std::vector kv_overrides; diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp new file mode 100644 index 0000000000000..20703d3063822 --- /dev/null +++ b/common/ngram-cache.cpp @@ -0,0 +1,280 @@ +#include "ngram-cache.h" +#include "log.h" + +#include + +void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, + std::vector & inp, int nnew, bool print_progress) { + const int64_t t_start_ms = ggml_time_ms(); + const int64_t inp_size = inp.size(); + + const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1); + int64_t n_done = 0; + + for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) { + const int64_t i_start = std::max(inp_size - nnew, ngram_size); + for (int64_t i = i_start; i < inp_size; ++i) { + const int64_t ngram_start = i - ngram_size; + llama_ngram ngram(&inp[ngram_start], ngram_size); + const llama_token token = inp[i]; + + llama_ngram_cache::iterator part_it = ngram_cache.find(ngram); + if (part_it == ngram_cache.end()) { + llama_ngram_cache_part part; + part.emplace(token, 1); + ngram_cache.emplace(ngram, part); + } else { + llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token); + if (token_count_it == part_it->second.end()) { + part_it->second.emplace(token, 1); + } else { + token_count_it->second++; + } + } + ++n_done; + + if (print_progress && n_done % 10000000 == 0) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s); + } + } + } +} + +// Helper function to get a token from the combined, speculative sequence of inp and draft. +static llama_token get_token(const std::vector & inp, const std::vector & draft, const size_t i) { + return i < inp.size() ? inp[i] : draft[1 + i - inp.size()]; +} + +// If sample size or percentage are below these thresholds the draft is aborted early: +constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1}; +constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50}; +constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2}; +constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; + +// Helper function that tries to draft a token from only the static ngram cache: +static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) { + llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + if (part_static_it == nc_static.end()) { + return -1; + } + const llama_ngram_cache_part part_static = part_static_it->second; + + int max_count_static = 0; + int sum_count_static = 0; + llama_token max_token = -1; + + for (std::pair token_count_static : part_static) { + const llama_token token = token_count_static.first; + const int32_t count_static = token_count_static.second; + + if (count_static > max_count_static) { + max_token = token; + max_count_static = count_static; + } + sum_count_static += count_static; + } + + if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { + return -1; + } + if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { + return -1; + } + return max_token; +} + +// Try to draft a token from primary cache (context/dynamic), validate with static cache: +static llama_token try_draft( + llama_ngram_cache & nc_primary, const std::vector & ngrams_primary, llama_ngram_cache_part & part_static, + const int * min_sample_size, const int * min_percent) { + + llama_token drafted_token = -1; + + for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) { + const llama_ngram ngram_primary = ngrams_primary[i]; + + llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); + if (part_primary_it == nc_primary.end()) { + continue; + } + const llama_ngram_cache_part part_primary = part_primary_it->second; + + int max_count_primary = 0; + int max_count_static = 0; + int sum_count_primary = 0; + llama_token max_token = -1; + + for (std::pair token_count_primary : part_primary) { + const llama_token token = token_count_primary.first; + + llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token); + + const int32_t count_primary = token_count_primary.second; + const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1; + + if (count_primary*count_static > max_count_primary*max_count_static) { + max_token = token; + max_count_primary = count_primary; + max_count_static = count_static; + } + sum_count_primary += count_primary; + } + + if (sum_count_primary < min_sample_size[i]) { + continue; + } + if (100*max_count_primary < min_percent[i]*sum_count_primary) { + continue;; + } + drafted_token = max_token; + } + + return drafted_token; +} + +void llama_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static +) { + GGML_ASSERT(draft.size() == 1); + const int inp_size = inp.size(); + + if (inp_size < LLAMA_NGRAM_STATIC) { + return; + } + + while ((int) draft.size()-1 < n_draft) { + llama_token drafted_token = -1; + + const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; + llama_ngram ngram_static; + for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) { + ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j); + } + llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + llama_ngram_cache_part part_static; + if (part_static_it != nc_static.end()) { + part_static = part_static_it->second; + } + + // cd = context + dynamic + std::vector ngrams_cd; + for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) { + const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1; + llama_ngram ngram_cd; + for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) { + ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j); + } + ngrams_cd.push_back(ngram_cd); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_static, ngram_static); + } + + if (drafted_token == -1) { + break; + } + + LOG(" - draft candidate: token=%d\n", drafted_token); + draft.push_back(drafted_token); + } +} + +void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) { + std::ofstream file_out(filename, std::ios::binary); + for (std::pair item : ngram_cache) { + const llama_ngram ngram = item.first; + llama_ngram_cache_part token_counts = item.second; + GGML_ASSERT(!token_counts.empty()); + const int32_t ntokens = token_counts.size(); + GGML_ASSERT(ntokens > 0); + + file_out.write(reinterpret_cast(&ngram), sizeof(llama_ngram)); + file_out.write(reinterpret_cast(&ntokens), sizeof(int32_t)); + for (std::pair item2 : token_counts) { + const llama_token token = item2.first; + const int32_t count = item2.second; + GGML_ASSERT(count > 0); + + file_out.write(reinterpret_cast(&token), sizeof(llama_token)); + file_out.write(reinterpret_cast(&count), sizeof(int32_t)); + } + } + +} + +llama_ngram_cache llama_ngram_cache_load(std::string & filename) { + std::ifstream hashmap_file(filename, std::ios::binary); + if (!hashmap_file) { + throw std::ifstream::failure("Unable to open file " + filename); + } + llama_ngram_cache ngram_cache; + + llama_ngram ngram; + int32_t ntokens; + llama_token token; + int32_t count; + + char * ngramc = reinterpret_cast(&ngram); + char * ntokensc = reinterpret_cast(&ntokens); + char * tokenc = reinterpret_cast(&token); + char * countc = reinterpret_cast(&count); + while(hashmap_file.read(ngramc, sizeof(llama_ngram))) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t))); + GGML_ASSERT(ntokens > 0); + llama_ngram_cache_part token_counts; + + for (int i = 0; i < ntokens; ++i) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token))); + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t))); + GGML_ASSERT(count > 0); + token_counts.emplace(token, count); + } + + ngram_cache.emplace(ngram, token_counts); + } + GGML_ASSERT(hashmap_file.eof()); + + return ngram_cache; +} + +void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) { + for (std::pair ngram_part : ngram_cache_add) { + const llama_ngram ngram = ngram_part.first; + llama_ngram_cache_part part = ngram_part.second; + + llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram); + if (part_merged_it == ngram_cache_target.end()) { + ngram_cache_target.emplace(ngram, part); + continue; + } + + for (std::pair token_count : part) { + const llama_token token = token_count.first; + const int32_t count = token_count.second; + GGML_ASSERT(count > 0); + + llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token); + if (token_count_merged_it == part_merged_it->second.end()) { + part_merged_it->second.emplace(token, count); + continue; + } + + token_count_merged_it->second += count; + } + } +} diff --git a/common/ngram-cache.h b/common/ngram-cache.h new file mode 100644 index 0000000000000..e4fa4cbd12f11 --- /dev/null +++ b/common/ngram-cache.h @@ -0,0 +1,94 @@ +#pragma once + +#include "llama.h" + +#include +#include +#include + +#define LLAMA_NGRAM_MIN 1 +#define LLAMA_NGRAM_MAX 4 +#define LLAMA_NGRAM_STATIC 2 + +// Data structures to map n-grams to empirical token probabilities: + +struct llama_ngram { + llama_token tokens[LLAMA_NGRAM_MAX]; + + llama_ngram() { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = -1; + } + } + + llama_ngram(const llama_token * input, const int ngram_size) { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = i < ngram_size ? input[i] : -1; + } + } + + bool operator==(const llama_ngram & other) const { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + if (tokens[i] != other.tokens[i]) { + return false; + } + } + return true; + } +}; + +struct llama_ngram_hash_function { + size_t operator()(const llama_ngram & ngram) const { + size_t hash = 0; + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + hash ^= std::hash{}(ngram.tokens[i]); + } + return hash; + } +}; + +// token -> number of times token has been seen +typedef std::unordered_map llama_ngram_cache_part; + +// n-gram -> empirical distribution of following tokens +typedef std::unordered_map llama_ngram_cache; + + +// Update an ngram cache with tokens. +// ngram_cache: the cache to modify. +// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data. +// inp_data: the token sequence with which to update ngram_cache. +// nnew: how many new tokens have been appended to inp_data since the last call to this function. +// print_progress: whether to print progress to stderr. +// +// In order to get correct results inp_data can ONLY BE APPENDED TO. +// Changes in the middle need a complete rebuild. +void llama_ngram_cache_update( + llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector & inp_data, int nnew, bool print_progress); + +// Try to draft tokens from ngram caches. +// inp: the tokens generated so far. +// draft: the token sequence to draft. Expected to initially contain the previously sampled token. +// n_draft: maximum number of tokens to add to draft. +// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic. +// nc_context: ngram cache based on current context. +// nc_dynamic: ngram cache based on previous user generations. +// nc_static: ngram cache generated from a large text corpus, used for validation. +void llama_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static); + +// Save an ngram cache to a file. +// ngram_cache: the ngram cache to save. +// filename: the path under which to save the ngram cache. +void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename); + +// Load an ngram cache saved with llama_ngram_cache_save. +// filename: the path from which to load the ngram cache. +// returns: an ngram cache containing the information saved to filename. +llama_ngram_cache llama_ngram_cache_load(std::string & filename); + +// Merge two ngram caches. +// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add. +// ngram_cache_add: the ngram cache to add to ngram_cache_target. +void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add); diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index c060b8f56d436..b91633f63e4ee 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -3,3 +3,21 @@ add_executable(${TARGET} lookup.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) + +set(TARGET lookup-create) +add_executable(${TARGET} lookup-create.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) + +set(TARGET lookup-merge) +add_executable(${TARGET} lookup-merge.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) + +set(TARGET lookup-stats) +add_executable(${TARGET} lookup-stats.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp new file mode 100644 index 0000000000000..46a6bed078e71 --- /dev/null +++ b/examples/lookup/lookup-create.cpp @@ -0,0 +1,43 @@ +#include "ggml.h" +#include "llama.h" +#include "common.h" +#include "ngram-cache.h" + +#include +#include +#include +#include +#include +#include + +int main(int argc, char ** argv){ + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + // init llama.cpp + llama_backend_init(); + llama_numa_init(params.numa); + + llama_model * model = NULL; + llama_context * ctx = NULL; + + // load the model + std::tie(model, ctx) = llama_init_from_gpt_params(params); + GGML_ASSERT(model != nullptr); + + // tokenize the prompt + const bool add_bos = llama_should_add_bos_token(model); + + std::vector inp; + inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + fprintf(stderr, "%s: tokenization done\n", __func__); + + + llama_ngram_cache ngram_cache; + llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); + fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str()); + + llama_ngram_cache_save(ngram_cache, params.lookup_cache_static); +} diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp new file mode 100644 index 0000000000000..07c93eb8d057b --- /dev/null +++ b/examples/lookup/lookup-merge.cpp @@ -0,0 +1,47 @@ +#include "ggml.h" +#include "llama.h" +#include "common.h" +#include "ngram-cache.h" + +#include +#include +#include +#include +#include +#include +#include + +static void print_usage() { + fprintf(stderr, "Merges multiple lookup cache files into a single one.\n"); + fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n"); +} + +int main(int argc, char ** argv){ + if (argc < 3) { + print_usage(); + exit(1); + } + + std::vector args; + args.resize(argc-1); + for (int i = 0; i < argc-1; ++i) { + args[i] = argv[i+1]; + if (args[i] == "-h" || args[i] == "--help") { + print_usage(); + exit(0); + } + } + + fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str()); + llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]); + + for (size_t i = 1; i < args.size()-1; ++i) { + fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str()); + llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]); + + llama_ngram_cache_merge(ngram_cache_merged, ngram_cache); + } + + fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str()); + llama_ngram_cache_save(ngram_cache_merged, args.back()); +} diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp new file mode 100644 index 0000000000000..31f227773de6f --- /dev/null +++ b/examples/lookup/lookup-stats.cpp @@ -0,0 +1,163 @@ +#include "ggml.h" +#include "common.h" +#include "llama.h" +#include "log.h" +#include "ngram-cache.h" + +#include +#include +#include +#include +#include +#include +#include + +int main(int argc, char ** argv){ + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + + const int n_draft = params.n_draft; + + // init llama.cpp + llama_backend_init(); + llama_numa_init(params.numa); + + llama_model * model = NULL; + llama_context * ctx = NULL; + + // load the model + std::tie(model, ctx) = llama_init_from_gpt_params(params); + llama_set_rng_seed(ctx, params.seed); + GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); + + // tokenize the prompt + const bool add_bos = llama_should_add_bos_token(model); + LOG("add_bos tgt: %d\n", add_bos); + + std::vector inp; + inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + + llama_ngram_cache ngram_cache_context; + llama_ngram_cache ngram_cache_dynamic; + llama_ngram_cache ngram_cache_static; + int64_t t_draft_flat_us = 0; + int64_t t_draft_us = 0; + + { + const int64_t t_start_draft_us = ggml_time_us(); + + if (!params.lookup_cache_static.empty()) { + try { + ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static); + } catch (std::ifstream::failure const &) { + fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + exit(1); + } + } + + if (!params.lookup_cache_dynamic.empty()) { + try { + ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic); + } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program + } + + t_draft_flat_us += ggml_time_us() - t_start_draft_us; + } + + const int n_input = inp.size(); + const int n_ctx = params.n_ctx; + + int n_drafted = 0; + int n_accept = 0; + + const int64_t t_start_ms = ggml_time_ms(); + + // Iterate over input tokens in chunks of size n_ctx. + // Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility. + for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) { + const std::vector inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx); + std::vector pseudo_output; + pseudo_output.push_back(inp_slice[0]); + + while ((int) pseudo_output.size() < n_ctx) { + // Simulate drafting and decoding from draft: + std::vector draft; + draft.push_back(pseudo_output.back()); + + { + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + + n_drafted += draft.size() - 1; + + for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) { + const llama_token ground_truth = inp_slice[pseudo_output.size()]; + const llama_token drafted = draft[j]; + + if (ground_truth != drafted) { + break; + } + + ++n_accept; + pseudo_output.push_back(ground_truth); + + { + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + } + + // After each simulated batch decoding simulate the sampling of a single token: + if ((int) pseudo_output.size() < n_ctx) { + pseudo_output.push_back(inp_slice[pseudo_output.size()]); + { + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + } + + draft.erase(draft.begin()); + + } + if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + LOG_TEE("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s); + } + + // After each chunk, update the dynamic ngram cache with the context ngram cache: + llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context); + ngram_cache_context.clear(); + } + + LOG_TEE("\n"); + + LOG_TEE("\n"); + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_input - n_input % n_ctx); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3); + LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", + t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us)); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index b53fae11045b8..2e8c35de31427 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -1,12 +1,15 @@ -#include "common.h" #include "ggml.h" #include "llama.h" +#include "common.h" +#include "ngram-cache.h" #include #include #include +#include #include #include +#include int main(int argc, char ** argv){ gpt_params params; @@ -15,11 +18,7 @@ int main(int argc, char ** argv){ return 1; } - // max/min n-grams size to search for in prompt - const int ngram_max = 4; - const int ngram_min = 1; - - // length of the candidate / draft sequence, if match is found + // max. number of additional tokens to draft if match is found const int n_draft = params.n_draft; const bool dump_kv_cache = params.dump_kv_cache; @@ -39,6 +38,8 @@ int main(int argc, char ** argv){ // load the model std::tie(model, ctx) = llama_init_from_gpt_params(params); + llama_set_rng_seed(ctx, params.seed); + GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); // tokenize the prompt const bool add_bos = llama_should_add_bos_token(model); @@ -47,6 +48,35 @@ int main(int argc, char ** argv){ std::vector inp; inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + llama_ngram_cache ngram_cache_context; + llama_ngram_cache ngram_cache_dynamic; + llama_ngram_cache ngram_cache_static; + int64_t t_draft_flat_us = 0; + int64_t t_draft_us = 0; + + { + // Fill up context ngram cache with tokens from user input: + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false); + + if (!params.lookup_cache_static.empty()) { + try { + ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static); + } catch (std::ifstream::failure const &) { + fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + exit(1); + } + } + + if (!params.lookup_cache_dynamic.empty()) { + try { + ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic); + } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program + } + + t_draft_flat_us += ggml_time_us() - t_start_draft_us; + } + const int max_context_size = llama_n_ctx(ctx); const int max_tokens_list_size = max_context_size - 4; @@ -76,8 +106,6 @@ int main(int argc, char ** argv){ int n_drafted = 0; int n_accept = 0; - int64_t t_draft_us = 0; - int n_past = inp.size(); bool has_eos = false; @@ -129,6 +157,12 @@ int main(int argc, char ** argv){ ++n_past; ++i_dft; inp.push_back(id); + { + // Update context ngram cache with the newly accepted token: + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } if (params.use_color) { // color accepted draft token @@ -149,6 +183,12 @@ int main(int argc, char ** argv){ draft.clear(); draft.push_back(id); inp.push_back(id); + { + // Update context ngram cache with the newly accepted token: + const int64_t t_start_draft_us = ggml_time_us(); + llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } break; } @@ -163,44 +203,19 @@ int main(int argc, char ** argv){ llama_batch_clear(batch_tgt); llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true); - // generate n_pred tokens through prompt lookup - auto prompt_lookup = [&]() -> void { - const int inp_size = inp.size(); - for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){ - const llama_token * ngram = &inp[inp_size - ngram_size]; - - for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) { - bool match = true; - for (int j = 0; j < ngram_size; ++j) { - if (inp[i + j] != ngram[j]) { - match = false; - break; - } - } - - if (match) { - const int startIdx = i + ngram_size; - const int endIdx = startIdx + n_draft; - if (endIdx < inp_size) { - for (int j = startIdx; j < endIdx; ++j) { - LOG(" - draft candidate %d: %d\n", j, inp[j]); - draft.push_back(inp[j]); - llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true); - ++n_drafted; - } - return; - } - } - } - } - return; - }; - + // Draft already contains a single token sampled from the model: + GGML_ASSERT(draft.size() == 1); + GGML_ASSERT(draft[0] == inp.back()); const int64_t t_start_draft_us = ggml_time_us(); - prompt_lookup(); + llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static); + + for (size_t i = 1; i < draft.size(); ++i) { + llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true); + } t_draft_us += ggml_time_us() - t_start_draft_us; + n_drafted += draft.size() - 1; llama_decode(ctx, batch_tgt); ++n_past; @@ -210,19 +225,24 @@ int main(int argc, char ** argv){ auto t_dec_end = ggml_time_us(); + // Update dynamic ngram cache with context ngram cache and save it to disk: + llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context); + llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic); + LOG_TEE("\n\n"); LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); LOG_TEE("\n"); - LOG_TEE("n_draft = %d\n", n_draft); - LOG_TEE("n_predict = %d\n", n_predict); - LOG_TEE("n_drafted = %d\n", n_drafted); - LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_predict); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3); + LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us)); - LOG_TEE("n_accept = %d\n", n_accept); - LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); LOG_TEE("\ntarget:\n"); llama_print_timings(ctx); diff --git a/scripts/get-wikitext-103.sh b/scripts/get-wikitext-103.sh new file mode 100755 index 0000000000000..880dd5cbe2230 --- /dev/null +++ b/scripts/get-wikitext-103.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip + +echo "Usage:" +echo "" +echo " ./perplexity -m model.gguf -f wiki.test.raw [other params]" +echo "" + +exit 0 From 1b26aebe4de4f048ac99996efd8a2c9af150904d Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 23 Mar 2024 13:18:45 +0100 Subject: [PATCH 344/401] server: flush stdout after logging in both text and json layout (#6253) --- examples/server/utils.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d7eef556a4632..8f20ff61454e9 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -95,8 +95,8 @@ static inline void server_log(const char *level, const char *function, int line, const std::string str = ss.str(); printf("%.*s\n", (int)str.size(), str.data()); - fflush(stdout); } + fflush(stdout); } // From 21cad01b6e6e1a96f99391f95e8ea8ae25c8288e Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 23 Mar 2024 17:18:13 +0100 Subject: [PATCH 345/401] split: add gguf-split in the make build target (#6262) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index b8b261abade6a..4f260cc3da6e5 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf gguf-split llama-bench libllava.a llava-cli baby-llama beam-search \ speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o # Binaries only useful for tests From 476b0251b27fb64c575507024a671e639d675594 Mon Sep 17 00:00:00 2001 From: Julius Arkenberg Date: Sat, 23 Mar 2024 17:41:53 +0100 Subject: [PATCH 346/401] llama : add grok-1 support (#6204) * Add support for Grok model architecture * Revert convert-hf-to-gguf to default options * Fixed f_norm_rms_eps bug * Fix whitespaces * llama : fix grok rope type * llama : minor --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 26 +++ gguf-py/gguf/constants.py | 24 +++ gguf-py/gguf/tensor_mapping.py | 55 +++--- llama.cpp | 299 +++++++++++++++++++++++++++++++++ 4 files changed, 384 insertions(+), 20 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 1e49d56c19514..723ea18e34c65 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -93,31 +93,42 @@ def set_gguf_parameters(self): if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) + print(f"gguf: context length = {n_ctx}") n_embd = self.find_hparam(["hidden_size", "n_embd"]) self.gguf_writer.add_embedding_length(n_embd) + print(f"gguf: embedding length = {n_embd}") if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) + print(f"gguf: feed forward length = {n_ff}") n_head = self.find_hparam(["num_attention_heads", "n_head"]) self.gguf_writer.add_head_count(n_head) + print(f"gguf: head count = {n_head}") if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) + print(f"gguf: key-value head count = {n_head_kv}") if (rope_theta := self.hparams.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) + print(f"gguf: rope theta = {rope_theta}") if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + print(f"gguf: rms norm epsilon = {f_rms_eps}") if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) + print(f"gguf: layer norm epsilon = {f_norm_eps}") if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) + print(f"gguf: expert count = {n_experts}") if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: self.gguf_writer.add_expert_used_count(n_experts_used) + print(f"gguf: experts used count = {n_experts_used}") self.gguf_writer.add_file_type(self.ftype) + print(f"gguf: file type = {self.ftype}") def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) @@ -1051,6 +1062,21 @@ def set_vocab(self): self._set_vocab_sentencepiece() +@Model.register("GrokForCausalLM") +class GrokModel(Model): + model_arch = gguf.MODEL_ARCH.GROK + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_name("Grok") + + @Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 4a4facb06ea14..e47896e2a9d3e 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -100,6 +100,7 @@ class MODEL_ARCH(IntEnum): LLAMA = auto() FALCON = auto() BAICHUAN = auto() + GROK = auto() GPT2 = auto() GPTJ = auto() GPTNEOX = auto() @@ -167,6 +168,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.LLAMA: "llama", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GROK: "grok", MODEL_ARCH.GPT2: "gpt2", MODEL_ARCH.GPTJ: "gptj", MODEL_ARCH.GPTNEOX: "gptneox", @@ -251,6 +253,28 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.GROK: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], MODEL_ARCH.GPTNEOX: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index ed89955d8970f..11fd34b8b9103 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -23,6 +23,7 @@ class TensorNameMap: "model.embedding", # mamba-qbert "backbone.embedding", # mamba "backbone.embeddings", # mamba-hf + "transformer.in_out_embed", # Grok ), # Token type embeddings @@ -66,6 +67,7 @@ class TensorNameMap: "lm_head.ln", # phi2 "model.norm_f", # mamba-qbert "backbone.norm_f", # mamba + "transformer.rms_norm", # Grok ), # Rope frequencies @@ -93,6 +95,7 @@ class TensorNameMap: "model.layers.{bid}.attention_norm", # internlm2 "model.layers.{bid}.norm", # mamba-qbert "backbone.layers.{bid}.norm", # mamba + "transformer.decoder_layer.{bid}.rms_norm", # Grok ), # Attention norm 2 @@ -116,32 +119,35 @@ class TensorNameMap: # Attention query MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf - "layers.{bid}.attention.wq", # llama-pth - "encoder.layer.{bid}.attention.self.query", # bert - "transformer.h.{bid}.attn.q_proj", # gpt-j - "model.layers.layers.{bid}.self_attn.q_proj", # plamo - "model.layers.{bid}.attention.wq" # internlm2 + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.h.{bid}.attn.q_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.q_proj", # plamo + "model.layers.{bid}.attention.wq", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok ), # Attention key MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf - "layers.{bid}.attention.wk", # llama-pth - "encoder.layer.{bid}.attention.self.key", # bert - "transformer.h.{bid}.attn.k_proj", # gpt-j - "model.layers.layers.{bid}.self_attn.k_proj", # plamo - "model.layers.{bid}.attention.wk" # internlm2 + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.h.{bid}.attn.k_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.k_proj", # plamo + "model.layers.{bid}.attention.wk", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok ), # Attention value MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf - "layers.{bid}.attention.wv", # llama-pth - "encoder.layer.{bid}.attention.self.value", # bert - "transformer.h.{bid}.attn.v_proj", # gpt-j - "model.layers.layers.{bid}.self_attn.v_proj", # plamo - "model.layers.{bid}.attention.wv" # internlm2 + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.h.{bid}.attn.v_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.v_proj", # plamo + "model.layers.{bid}.attention.wv", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok ), # Attention output @@ -162,12 +168,14 @@ class TensorNameMap: "model.layers.layers.{bid}.self_attn.o_proj", # plamo "model.layers.{bid}.attention.wo", # internlm2 "encoder.layers.{bid}.attn.out_proj", # nomic-bert + "transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok ), # Attention output norm MODEL_TENSOR.ATTN_OUT_NORM: ( "encoder.layer.{bid}.attention.output.LayerNorm", # bert "encoder.layers.{bid}.norm1", # nomic-bert + "transformer.decoder_layer.{bid}.rms_norm_1", # Grok ), # Rotary embeddings @@ -190,11 +198,13 @@ class TensorNameMap: "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 "model.layers.{bid}.ffn_norm", # internlm2 + "transformer.decoder_layer.{bid}.rms_norm_2", # Grok ), MODEL_TENSOR.FFN_GATE_INP: ( "layers.{bid}.feed_forward.gate", # mixtral "model.layers.{bid}.block_sparse_moe.gate", # mixtral + "transformer.decoder_layer.{bid}.router" # Grok ), # Feed-forward up @@ -223,6 +233,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_UP_EXP: ( "layers.{bid}.feed_forward.experts.{xid}.w3", # mixtral "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral + "transformer.decoder_layer.{bid}.moe.{xid}.linear_v", # Grok ), # AWQ-activation gate @@ -243,6 +254,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_GATE_EXP: ( "layers.{bid}.feed_forward.experts.{xid}.w1", # mixtral "model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral + "transformer.decoder_layer.{bid}.moe.{xid}.linear" # Grok ), # Feed-forward down @@ -270,6 +282,8 @@ class TensorNameMap: MODEL_TENSOR.FFN_DOWN_EXP: ( "layers.{bid}.feed_forward.experts.{xid}.w2", # mixtral "model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral + "transformer.decoder_layer.{bid}.moe.{xid}.linear_1", # Grok + ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -287,8 +301,9 @@ class TensorNameMap: ), MODEL_TENSOR.LAYER_OUT_NORM: ( - "encoder.layer.{bid}.output.LayerNorm", # bert - "encoder.layers.{bid}.norm2", # nomic-bert + "encoder.layer.{bid}.output.LayerNorm", # bert + "encoder.layers.{bid}.norm2", # nomic-bert + "transformer.decoder_layer.{bid}.rms_norm_3", # Grok ), MODEL_TENSOR.SSM_IN: ( diff --git a/llama.cpp b/llama.cpp index eedca802b86a7..4e08be18d7c27 100644 --- a/llama.cpp +++ b/llama.cpp @@ -195,6 +195,7 @@ enum llm_arch { LLM_ARCH_LLAMA, LLM_ARCH_FALCON, LLM_ARCH_BAICHUAN, + LLM_ARCH_GROK, LLM_ARCH_GPT2, LLM_ARCH_GPTJ, LLM_ARCH_GPTNEOX, @@ -224,6 +225,7 @@ enum llm_arch { static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GROK, "grok" }, { LLM_ARCH_GPT2, "gpt2" }, { LLM_ARCH_GPTJ, "gptj" }, { LLM_ARCH_GPTNEOX, "gptneox" }, @@ -494,6 +496,28 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_GROK, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + }, + }, { LLM_ARCH_GPT2, { @@ -1635,6 +1659,7 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_314B, MODEL_SMALL, MODEL_MEDIUM, MODEL_LARGE, @@ -3419,6 +3444,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_40B: return "40B"; case MODEL_65B: return "65B"; case MODEL_70B: return "70B"; + case MODEL_314B: return "314B"; case MODEL_SMALL: return "0.1B"; case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; @@ -3557,6 +3583,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GROK: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 64: model.type = e_model::MODEL_314B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_FALCON: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -4394,6 +4429,54 @@ static bool llm_load_tensors( } } } break; + case LLM_ARCH_GROK: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}); + + GGML_ASSERT(hparams.n_expert > 0); + GGML_ASSERT(hparams.n_expert_used > 0); + + // MoE branch + for (uint32_t x = 0; x < hparams.n_expert; ++x) { + layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}); + layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}); + layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}); + } + + layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + } + } break; case LLM_ARCH_BAICHUAN: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -5621,6 +5704,20 @@ static struct ggml_tensor * llm_build_kqv( ggml_mul_mat_set_prec(kq, GGML_PREC_F32); } + if (model.arch == LLM_ARCH_GROK) { + // need to do the following: + // multiply by attn_output_multiplyer of 0.08838834764831845 + // and then : + // kq = 30 * tanh(kq / 30) + // before the softmax below + + //try from phi2 + //ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + + kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f)); + kq = ggml_scale(ctx, kq, 30); + } + #if defined(GGML_USE_KOMPUTE) #pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") #pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") @@ -6395,6 +6492,203 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_grok() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // multiply by embedding_multiplier_scale of 78.38367176906169 + inpL = ggml_scale(ctx0, inpL, 78.38367176906169f); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + } + + // Grok + // if attn_out_norm is present then apply it before adding the input + if (model.layers[il].attn_out_norm) { + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_out_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_out_norm", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + // MoE branch + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts] + cb(logits, "ffn_moe_logits", il); + + ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts] + cb(probs, "ffn_moe_probs", il); + + // select experts + ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok] + cb(selected_experts->src[0], "ffn_moe_argsort", il); + + ggml_tensor * weights = ggml_get_rows(ctx0, + ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); + cb(weights, "ffn_moe_weights", il); + + weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok] + + ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); + cb(weights_sum, "ffn_moe_weights_sum", il); + + weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok] + cb(weights, "ffn_moe_weights_norm", il); + + // compute expert outputs + ggml_tensor * moe_out = nullptr; + + for (int i = 0; i < n_expert_used; ++i) { + ggml_tensor * cur_expert; + + ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur); + cb(cur_up, "ffn_moe_up", il); + + ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur); + cb(cur_gate, "ffn_moe_gate", il); + + //GeLU + cur_gate = ggml_gelu(ctx0, cur_gate); + cb(cur_gate, "ffn_moe_gelu", il); + + cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd] + cb(cur_expert, "ffn_moe_gate_par", il); + + cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd] + cb(cur_expert, "ffn_moe_down", il); + + cur_expert = ggml_mul(ctx0, cur_expert, + ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0])); + cb(cur_expert, "ffn_moe_weighted", il); + + if (i == 0) { + moe_out = cur_expert; + } else { + moe_out = ggml_add(ctx0, moe_out, cur_expert); + cb(moe_out, "ffn_moe_out", il); + } + } + + cur = moe_out; + + // Grok + // if layer_out_norm is present then apply it before adding the input + // Idea: maybe ffn_out_norm is a better name + if (model.layers[il].layer_out_norm) { + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].layer_out_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "layer_out_norm", il); + } + + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + + // Grok + // multiply logits by output_multiplier_scale of 0.5773502691896257 + + cur = ggml_scale(ctx0, cur, 0.5773502691896257f); + + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_starcoder() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -8818,6 +9112,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_falcon(); } break; + case LLM_ARCH_GROK: + { + result = llm.build_grok(); + } break; case LLM_ARCH_STARCODER: { result = llm.build_starcoder(); @@ -13561,6 +13859,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { // the pairs of head values are offset by n_rot/2 case LLM_ARCH_FALCON: + case LLM_ARCH_GROK: case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: From 1997577d5e121568ae39f538021733ccd4278c23 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 23 Mar 2024 18:00:38 +0100 Subject: [PATCH 347/401] server: docs: `--threads` and `--threads`, `--ubatch-size`, `--log-disable` (#6254) --- examples/server/README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 355601ff4dec2..8aa4eac9db262 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -16,8 +16,8 @@ The project is under active development, and we are [looking for feedback and co **Command line options:** -- `--threads N`, `-t N`: Set the number of threads to use during generation. -- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching, this parameter is used only if one token is to be processed on CPU backend. +- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU. - `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf). @@ -26,7 +26,8 @@ The project is under active development, and we are [looking for feedback and co - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. -- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`. +- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`. +- `-ub N`, `--ubatch-size N`: physical maximum batch size. Default: `512`. - `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. - `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. - `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. @@ -57,7 +58,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437 - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. - `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled) - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) -- `--log-disable`: Output logs to stdout only, default: enabled. +- `--log-disable`: Output logs to stdout only, not to `llama.log`. default: enabled. - `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json) **If compiled with `LLAMA_SERVER_SSL=ON`** From f482bb2e4920e544651fb832f2e0bcb4d2ff69ab Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 23 Mar 2024 18:07:00 +0100 Subject: [PATCH 348/401] common: llama_load_model_from_url split support (#6192) * llama: llama_split_prefix fix strncpy does not include string termination common: llama_load_model_from_url: - fix header name case sensitive - support downloading additional split in parallel - hide password in url * common: EOL EOF * common: remove redundant LLAMA_CURL_MAX_PATH_LENGTH definition * common: change max url max length * common: minor comment * server: support HF URL options * llama: llama_model_loader fix log * common: use a constant for max url length * common: clean up curl if file cannot be loaded in gguf * server: tests: add split tests, and HF options params * common: move llama_download_hide_password_in_url inside llama_download_file as a lambda * server: tests: enable back Release test on PR * spacing Co-authored-by: Georgi Gerganov * spacing Co-authored-by: Georgi Gerganov * spacing Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- .github/workflows/server.yml | 1 - common/common.cpp | 203 ++++++++++++++---- common/common.h | 7 + examples/gguf-split/gguf-split.cpp | 4 - examples/server/README.md | 4 +- examples/server/server.cpp | 18 +- .../server/tests/features/parallel.feature | 3 +- examples/server/tests/features/server.feature | 4 +- examples/server/tests/features/steps/steps.py | 13 +- llama.cpp | 4 +- 10 files changed, 199 insertions(+), 62 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index b74dc5e2114d8..f07d2553669af 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -35,7 +35,6 @@ jobs: include: - build_type: Release sanitizer: "" - disabled_on_pr: true fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken container: diff --git a/common/common.cpp b/common/common.cpp index 69c2d5bf79f91..fb80d4bf72283 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -39,6 +39,9 @@ #endif #if defined(LLAMA_USE_CURL) #include +#include +#include +#include #endif #if defined(_MSC_VER) @@ -61,7 +64,7 @@ #else #include #endif -#define LLAMA_CURL_MAX_PATH_LENGTH PATH_MAX +#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 #define LLAMA_CURL_MAX_HEADER_LENGTH 256 #endif // LLAMA_USE_CURL @@ -1702,27 +1705,13 @@ void llama_batch_add( #ifdef LLAMA_USE_CURL -struct llama_model * llama_load_model_from_url( - const char * model_url, - const char * path_model, - const struct llama_model_params & params) { - // Basic validation of the model_url - if (!model_url || strlen(model_url) == 0) { - fprintf(stderr, "%s: invalid model_url\n", __func__); - return NULL; - } - - // Initialize libcurl globally - auto curl = curl_easy_init(); - - if (!curl) { - fprintf(stderr, "%s: error initializing libcurl\n", __func__); - return NULL; - } +static bool llama_download_file(CURL * curl, const char * url, const char * path) { + bool force_download = false; // Set the URL, allow to follow http redirection - curl_easy_setopt(curl, CURLOPT_URL, model_url); + curl_easy_setopt(curl, CURLOPT_URL, url); curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); + #if defined(_WIN32) // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of // operating system. Currently implemented under MS-Windows. @@ -1731,16 +1720,16 @@ struct llama_model * llama_load_model_from_url( // Check if the file already exists locally struct stat model_file_info; - auto file_exists = (stat(path_model, &model_file_info) == 0); + auto file_exists = (stat(path, &model_file_info) == 0); // If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; - char etag_path[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; - snprintf(etag_path, sizeof(etag_path), "%s.etag", path_model); + char etag_path[PATH_MAX] = {0}; + snprintf(etag_path, sizeof(etag_path), "%s.etag", path); char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; - char last_modified_path[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; - snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path_model); + char last_modified_path[PATH_MAX] = {0}; + snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path); if (file_exists) { auto * f_etag = fopen(etag_path, "r"); @@ -1748,7 +1737,7 @@ struct llama_model * llama_load_model_from_url( if (!fgets(etag, sizeof(etag), f_etag)) { fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path); } else { - fprintf(stderr, "%s: previous model file found %s: %s\n", __func__, etag_path, etag); + fprintf(stderr, "%s: previous file found %s: %s\n", __func__, etag_path, etag); } fclose(f_etag); } @@ -1758,7 +1747,7 @@ struct llama_model * llama_load_model_from_url( if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) { fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path); } else { - fprintf(stderr, "%s: previous model file found %s: %s\n", __func__, last_modified_path, + fprintf(stderr, "%s: previous file found %s: %s\n", __func__, last_modified_path, last_modified); } fclose(f_last_modified); @@ -1776,6 +1765,11 @@ struct llama_model * llama_load_model_from_url( auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata; + // Convert header field name to lowercase + for (size_t i = 0; i < n_items && buffer[i] != ':'; ++i) { + buffer[i] = tolower(buffer[i]); + } + const char * etag_prefix = "etag: "; if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) { strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF @@ -1798,7 +1792,7 @@ struct llama_model * llama_load_model_from_url( if (res != CURLE_OK) { curl_easy_cleanup(curl); fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); - return NULL; + return false; } long http_code = 0; @@ -1806,30 +1800,34 @@ struct llama_model * llama_load_model_from_url( if (http_code != 200) { // HEAD not supported, we don't know if the file has changed // force trigger downloading - file_exists = false; + force_download = true; fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code); } } // If the ETag or the Last-Modified headers are different: trigger a new download - if (!file_exists || strcmp(etag, headers.etag) != 0 || strcmp(last_modified, headers.last_modified) != 0) { - char path_model_temporary[LLAMA_CURL_MAX_PATH_LENGTH] = {0}; - snprintf(path_model_temporary, sizeof(path_model_temporary), "%s.downloadInProgress", path_model); + bool should_download = !file_exists + || force_download + || (strlen(headers.etag) > 0 && strcmp(etag, headers.etag) != 0) + || (strlen(headers.last_modified) > 0 && strcmp(last_modified, headers.last_modified) != 0); + if (should_download) { + char path_temporary[PATH_MAX] = {0}; + snprintf(path_temporary, sizeof(path_temporary), "%s.downloadInProgress", path); if (file_exists) { - fprintf(stderr, "%s: deleting previous downloaded model file: %s\n", __func__, path_model); - if (remove(path_model) != 0) { + fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path); + if (remove(path) != 0) { curl_easy_cleanup(curl); - fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path_model); - return NULL; + fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path); + return false; } } // Set the output file - auto * outfile = fopen(path_model_temporary, "wb"); + auto * outfile = fopen(path_temporary, "wb"); if (!outfile) { curl_easy_cleanup(curl); - fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path_model); - return NULL; + fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path); + return false; } typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd); @@ -1843,15 +1841,30 @@ struct llama_model * llama_load_model_from_url( // display download progress curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); + // helper function to hide password in URL + auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string { + std::size_t protocol_pos = url.find("://"); + if (protocol_pos == std::string::npos) { + return url; // Malformed URL + } + + std::size_t at_pos = url.find('@', protocol_pos + 3); + if (at_pos == std::string::npos) { + return url; // No password in URL + } + + return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos); + }; + // start the download - fprintf(stderr, "%s: downloading model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, - model_url, path_model, headers.etag, headers.last_modified); + fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, + llama_download_hide_password_in_url(url).c_str(), path, headers.etag, headers.last_modified); auto res = curl_easy_perform(curl); if (res != CURLE_OK) { fclose(outfile); curl_easy_cleanup(curl); fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); - return NULL; + return false; } long http_code = 0; @@ -1860,7 +1873,7 @@ struct llama_model * llama_load_model_from_url( fclose(outfile); curl_easy_cleanup(curl); fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code); - return NULL; + return false; } // Clean up @@ -1872,7 +1885,7 @@ struct llama_model * llama_load_model_from_url( if (etag_file) { fputs(headers.etag, etag_file); fclose(etag_file); - fprintf(stderr, "%s: model etag saved %s: %s\n", __func__, etag_path, headers.etag); + fprintf(stderr, "%s: file etag saved %s: %s\n", __func__, etag_path, headers.etag); } } @@ -1882,20 +1895,118 @@ struct llama_model * llama_load_model_from_url( if (last_modified_file) { fputs(headers.last_modified, last_modified_file); fclose(last_modified_file); - fprintf(stderr, "%s: model last modified saved %s: %s\n", __func__, last_modified_path, + fprintf(stderr, "%s: file last modified saved %s: %s\n", __func__, last_modified_path, headers.last_modified); } } - if (rename(path_model_temporary, path_model) != 0) { + if (rename(path_temporary, path) != 0) { + curl_easy_cleanup(curl); + fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary, path); + return false; + } + } + + return true; +} + +struct llama_model * llama_load_model_from_url( + const char * model_url, + const char * path_model, + const struct llama_model_params & params) { + // Basic validation of the model_url + if (!model_url || strlen(model_url) == 0) { + fprintf(stderr, "%s: invalid model_url\n", __func__); + return NULL; + } + + // Initialize libcurl + auto * curl = curl_easy_init(); + + if (!curl) { + fprintf(stderr, "%s: error initializing libcurl\n", __func__); + return NULL; + } + + if (!curl) { + fprintf(stderr, "%s: error initializing libcurl\n", __func__); + return NULL; + } + + if (!llama_download_file(curl, model_url, path_model)) { + return NULL; + } + + // check for additional GGUFs split to download + int n_split = 0; + { + struct gguf_init_params gguf_params = { + /*.no_alloc = */ true, + /*.ctx = */ NULL, + }; + auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params); + if (!ctx_gguf) { + fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model); curl_easy_cleanup(curl); - fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_model_temporary, path_model); return NULL; } + + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT); + if (key_n_split >= 0) { + n_split = gguf_get_val_u16(ctx_gguf, key_n_split); + } + + gguf_free(ctx_gguf); } curl_easy_cleanup(curl); + if (n_split > 1) { + char split_prefix[PATH_MAX] = {0}; + char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + + // Verify the first split file format + // and extract split URL and PATH prefixes + { + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) { + fprintf(stderr, "\n%s: unexpected model file name: %s" + " n_split=%d\n", __func__, path_model, n_split); + return NULL; + } + + if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) { + fprintf(stderr, "\n%s: unexpected model url: %s" + " n_split=%d\n", __func__, model_url, n_split); + return NULL; + } + } + + // Prepare download in parallel + std::vector> futures_download; + for (int idx = 1; idx < n_split; idx++) { + futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool { + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split); + + char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split); + + auto * curl = curl_easy_init(); + bool res = llama_download_file(curl, split_url, split_path); + curl_easy_cleanup(curl); + + return res; + }, idx)); + } + + // Wait for all downloads to complete + for (auto & f : futures_download) { + if (!f.get()) { + return NULL; + } + } + } + return llama_load_model_from_file(path_model, params); } diff --git a/common/common.h b/common/common.h index afa4cf6d7d528..a223eceaad87e 100644 --- a/common/common.h +++ b/common/common.h @@ -306,3 +306,10 @@ struct llama_control_vector_load_info { // Load control vectors, scale each by strength, and add them together. // On error, returns {-1, empty} llama_control_vector_data llama_control_vector_load(const std::vector & load_infos); + +// +// Split utils +// +static const char * const LLM_KV_SPLIT_NO = "split.no"; +static const char * const LLM_KV_SPLIT_COUNT = "split.count"; +static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index f703588e164f6..b1af599923809 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -26,10 +26,6 @@ enum split_operation : uint8_t { SPLIT_OP_MERGE, }; -static const char * const LLM_KV_SPLIT_NO = "split.no"; -static const char * const LLM_KV_SPLIT_COUNT = "split.count"; -static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; - struct split_params { split_operation operation = SPLIT_OP_SPLIT; int n_split_tensors = 128; diff --git a/examples/server/README.md b/examples/server/README.md index 8aa4eac9db262..dfea2b9050210 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -20,7 +20,9 @@ The project is under active development, and we are [looking for feedback and co - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU. - `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). -- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf). +- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (default: unused). +- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository (default: unused). +- `-hff FILE, --hf-file FILE`: Hugging Face model file (default: unused). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 27bd2dd70c5f7..b02c2546eb4c6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2208,7 +2208,11 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); - printf(" model download url (default: %s)\n", params.model_url.c_str()); + printf(" model download url (default: unused)\n"); + printf(" -hfr REPO, --hf-repo REPO\n"); + printf(" Hugging Face model repository (default: unused)\n"); + printf(" -hff FILE, --hf-file FILE\n"); + printf(" Hugging Face model file (default: unused)\n"); printf(" -a ALIAS, --alias ALIAS\n"); printf(" set an alias for the model, will be added as `model` field in completion response\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); @@ -2337,6 +2341,18 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } params.model_url = argv[i]; + } else if (arg == "-hfr" || arg == "--hf-repo") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.hf_repo = argv[i]; + } else if (arg == "-hff" || arg == "--hf-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.hf_file = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index a66fed626619d..6cd306a2bcf7c 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -4,7 +4,8 @@ Feature: Parallel Background: Server startup Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models + And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models + And a model file test-model-00001-of-00003.gguf And 42 as server seed And 128 as batch size And 256 KV cache size diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index a2e0e5b3532b5..646a4e49d0d56 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -4,8 +4,8 @@ Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models + And a model file test-model.gguf And a model alias tinyllama-2 And 42 as server seed # KV Cache corresponds to the total amount of tokens diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 03f55f65910de..86c3339dc7183 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -16,7 +16,6 @@ import openai from behave import step from behave.api.async_step import async_run_until_complete -from huggingface_hub import hf_hub_download from prometheus_client import parser @@ -39,6 +38,8 @@ def step_server_config(context, server_fqdn, server_port): context.model_alias = None context.model_file = None + context.model_hf_repo = None + context.model_hf_file = None context.model_url = None context.n_batch = None context.n_ubatch = None @@ -68,9 +69,9 @@ def step_server_config(context, server_fqdn, server_port): @step('a model file {hf_file} from HF repo {hf_repo}') def step_download_hf_model(context, hf_file, hf_repo): - context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) - if context.debug: - print(f"model file: {context.model_file}") + context.model_hf_repo = hf_repo + context.model_hf_file = hf_file + context.model_file = os.path.basename(hf_file) @step('a model file {model_file}') @@ -1079,6 +1080,10 @@ def start_server_background(context): server_args.extend(['--model', context.model_file]) if context.model_url: server_args.extend(['--model-url', context.model_url]) + if context.model_hf_repo: + server_args.extend(['--hf-repo', context.model_hf_repo]) + if context.model_hf_file: + server_args.extend(['--hf-file', context.model_hf_file]) if context.n_batch: server_args.extend(['--batch-size', context.n_batch]) if context.n_ubatch: diff --git a/llama.cpp b/llama.cpp index 4e08be18d7c27..b385ae360fbdb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2959,7 +2959,7 @@ struct llama_model_loader { } } - LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split); + LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1); } n_kv = gguf_get_n_kv(meta); @@ -15140,7 +15140,7 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int // check if dest ends with postfix int size_prefix = str_split_path.size() - str_postfix.size(); if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) { - snprintf(dest, std::min((size_t) size_prefix, maxlen), "%s", split_path); + snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path); return size_prefix; } From 95562175f83a49755ff6fd3bad09409417c8e6f9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 23 Mar 2024 21:35:23 +0200 Subject: [PATCH 349/401] gitignore : gguf-split --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 27562f6d7b9b3..072945180bf05 100644 --- a/.gitignore +++ b/.gitignore @@ -50,6 +50,7 @@ models-mnt /embedding /gguf /gguf-llama-simple +/gguf-split /gritlm /imatrix /infill From 94d1b3b4119209efcdd08df0dceaecbd1fe7f85c Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 23 Mar 2024 18:48:02 -0400 Subject: [PATCH 350/401] use _wfopen instead of fopen on Windows (#6248) also fix missing #defines before windows.h, and BPE LF token on MSVC --- ggml.c | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++----- ggml.h | 8 +++++-- llama.cpp | 4 ++-- 3 files changed, 69 insertions(+), 9 deletions(-) diff --git a/ggml.c b/ggml.c index 1d5854960a51b..54365b7ae26c6 100644 --- a/ggml.c +++ b/ggml.c @@ -3,6 +3,7 @@ #include "ggml-impl.h" #include "ggml-quants.h" +#include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -43,6 +44,10 @@ #if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif #include typedef volatile LONG atomic_int; @@ -430,6 +435,57 @@ int64_t ggml_cycles_per_ms(void) { #define ggml_perf_cycles_per_ms() 0 #endif +// +// cross-platform UTF-8 file paths +// + +#ifdef _WIN32 +static wchar_t * ggml_mbstowcs(const char * mbs) { + int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0); + if (!wlen) { + errno = EINVAL; + return NULL; + } + + wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t)); + wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen); + if (!wlen) { + GGML_FREE(wbuf); + errno = EINVAL; + return NULL; + } + + return wbuf; +} +#endif + +FILE * ggml_fopen(const char * fname, const char * mode) { +#ifdef _WIN32 + FILE * file = NULL; + + // convert fname (UTF-8) + wchar_t * wfname = ggml_mbstowcs(fname); + if (wfname) { + // convert mode (ANSI) + wchar_t * wmode = GGML_MALLOC(strlen(mode) + 1); + wchar_t * wmode_p = wmode; + do { + *wmode_p++ = (wchar_t)*mode; + } while (*mode++); + + // open file + file = _wfopen(wfname, wmode); + + GGML_FREE(wfname); + GGML_FREE(wmode); + } + + return file; +#else + return fopen(fname, mode); +#endif +} + // // cache line // @@ -18739,7 +18795,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { // write binary data { - FILE * fout = fopen(fname, "wb"); + FILE * fout = ggml_fopen(fname, "wb"); if (!fout) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); @@ -18877,7 +18933,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context * // read file into data { - FILE * fin = fopen(fname, "rb"); + FILE * fin = ggml_fopen(fname, "rb"); if (!fin) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); return result; @@ -19213,7 +19269,7 @@ static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; - FILE * fp = fopen(filename, "w"); + FILE * fp = ggml_fopen(filename, "w"); GGML_ASSERT(fp); fprintf(fp, "digraph G {\n"); @@ -20531,7 +20587,7 @@ struct gguf_context * gguf_init_empty(void) { } struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { - FILE * file = fopen(fname, "rb"); + FILE * file = ggml_fopen(fname, "rb"); if (!file) { return NULL; } @@ -21486,7 +21542,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * } void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) { - FILE * file = fopen(fname, "wb"); + FILE * file = ggml_fopen(fname, "wb"); if (!file) { GGML_ASSERT(false && "failed to open file for writing"); } diff --git a/ggml.h b/ggml.h index c937d4a535adb..0a5af72058815 100644 --- a/ggml.h +++ b/ggml.h @@ -214,9 +214,10 @@ # define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) #endif -#include -#include #include +#include +#include +#include #define GGML_FILE_MAGIC 0x67676d6c // "ggml" #define GGML_FILE_VERSION 1 @@ -708,6 +709,9 @@ extern "C" { GGML_API void ggml_print_backtrace(void); + // accepts a UTF-8 path, even on Windows + GGML_API FILE * ggml_fopen(const char * fname, const char * mode); + GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node diff --git a/llama.cpp b/llama.cpp index b385ae360fbdb..9614cdb17400f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1065,7 +1065,7 @@ struct llama_file { size_t size; llama_file(const char * fname, const char * mode) { - fp = std::fopen(fname, mode); + fp = ggml_fopen(fname, mode); if (fp == NULL) { throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno))); } @@ -4006,7 +4006,7 @@ static void llm_load_vocab( } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) { vocab.linefeed_id = vocab.special_pad_id; } else { - const std::vector ids = llama_tokenize_internal(vocab, "\u010A", false); + const std::vector ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); vocab.linefeed_id = ids[0]; } From d03224ac9840351023ff8abcf4aa0542258a53df Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Sun, 24 Mar 2024 09:44:01 +0800 Subject: [PATCH 351/401] Support build win release for SYCL (#6241) * support release win * fix value * fix value * fix value * fix error * fix error * fix format --- .github/workflows/build.yml | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index bf42df8fe58d4..0e7643bbaa6a0 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -800,6 +800,7 @@ jobs: windows-latest-cmake-sycl: runs-on: windows-latest + defaults: run: shell: bash @@ -808,7 +809,6 @@ jobs: WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel - steps: - name: Clone id: checkout @@ -823,6 +823,32 @@ jobs: id: cmake_build run: examples/sycl/win-build-sycl.bat + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + 7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip .\build\bin\* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v3 + with: + path: | + llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip + ios-xcode-build: runs-on: macos-latest From ddf65685105a39a57b1e7f80c3aa502a6313af24 Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Sun, 24 Mar 2024 12:04:25 +0800 Subject: [PATCH 352/401] [SYCL] offload op (#6217) * remove no USM methods * leave the schedule to ggml_backend_sched entirely --- ggml-sycl.cpp | 293 ++++---------------------------------------------- ggml-sycl.h | 16 ++- ggml.c | 10 -- llama.cpp | 36 +++---- 4 files changed, 51 insertions(+), 304 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index cc9ee0762b903..fc4d2964ccac9 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -740,11 +740,7 @@ namespace dpct sycl::queue &default_queue() { -#ifdef DPCT_USM_LEVEL_NONE - return out_of_order_queue(); -#else return in_order_queue(); -#endif // DPCT_USM_LEVEL_NONE } void queues_wait_and_throw() @@ -763,11 +759,7 @@ namespace dpct sycl::queue *create_queue(bool enable_exception_handler = false) { -#ifdef DPCT_USM_LEVEL_NONE - return create_out_of_order_queue(enable_exception_handler); -#else return create_in_order_queue(enable_exception_handler); -#endif // DPCT_USM_LEVEL_NONE } sycl::queue *create_queue(sycl::context context, sycl::device device, @@ -1075,11 +1067,6 @@ namespace dpct static pointer_access_attribute get_pointer_attribute(sycl::queue &q, const void *ptr) { -#ifdef DPCT_USM_LEVEL_NONE - return mem_mgr::instance().is_device_ptr(ptr) - ? pointer_access_attribute::device_only - : pointer_access_attribute::host_only; -#else switch (sycl::get_pointer_type(ptr, q.get_context())) { case sycl::usm::alloc::unknown: @@ -1090,7 +1077,6 @@ namespace dpct case sycl::usm::alloc::host: return pointer_access_attribute::host_device; } -#endif } template @@ -1273,11 +1259,7 @@ namespace dpct static inline void *dpct_malloc(size_t size, sycl::queue &q) { -#ifdef DPCT_USM_LEVEL_NONE - return mem_mgr::instance().mem_alloc(size * sizeof(byte_t)); -#else return sycl::malloc_device(size, q.get_device(), q.get_context()); -#endif // DPCT_USM_LEVEL_NONE } #define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F)) @@ -1301,25 +1283,7 @@ namespace dpct static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr, valueT value, size_t size) { -#ifdef DPCT_USM_LEVEL_NONE - auto &mm = mem_mgr::instance(); - assert(mm.is_device_ptr(dev_ptr)); - auto alloc = mm.translate_ptr(dev_ptr); - size_t offset = (valueT *)dev_ptr - (valueT *)alloc.alloc_ptr; - - return q.submit([&](sycl::handler &cgh) - { - auto r = sycl::range<1>(size); - auto o = sycl::id<1>(offset); - auto new_buffer = alloc.buffer.reinterpret( - sycl::range<1>(alloc.size / sizeof(valueT))); - sycl::accessor - acc(new_buffer, cgh, r, o); - cgh.fill(acc, value); }); -#else return q.fill(dev_ptr, value, size); -#endif // DPCT_USM_LEVEL_NONE } /** @@ -1413,72 +1377,8 @@ namespace dpct { if (!size) return sycl::event{}; -#ifdef DPCT_USM_LEVEL_NONE - auto &mm = mem_mgr::instance(); - auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); - - switch (real_direction) - { - case host_to_host: - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); }); - case host_to_device: - { - auto alloc = mm.translate_ptr(to_ptr); - size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto o = sycl::id<1>(offset); - sycl::accessor - acc(alloc.buffer, cgh, r, o); - cgh.copy(from_ptr, acc); }); - } - case device_to_host: - { - auto alloc = mm.translate_ptr(from_ptr); - size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto o = sycl::id<1>(offset); - sycl::accessor - acc(alloc.buffer, cgh, r, o); - cgh.copy(acc, to_ptr); }); - } - case device_to_device: - { - auto to_alloc = mm.translate_ptr(to_ptr); - auto from_alloc = mm.translate_ptr(from_ptr); - size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr; - size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto to_o = sycl::id<1>(to_offset); - auto from_o = sycl::id<1>(from_offset); - sycl::accessor - to_acc(to_alloc.buffer, cgh, r, to_o); - sycl::accessor - from_acc(from_alloc.buffer, cgh, r, from_o); - cgh.copy(from_acc, to_acc); }); - } - default: - throw std::runtime_error("dpct_memcpy: invalid direction value"); - } -#else return q.memcpy(to_ptr, from_ptr, size, dep_events); GGML_UNUSED(direction); -#endif // DPCT_USM_LEVEL_NONE } // Get actual copy range and make sure it will not exceed range. @@ -1618,45 +1518,15 @@ namespace dpct break; } case device_to_device: -#ifdef DPCT_USM_LEVEL_NONE - { - auto &mm = mem_mgr::instance(); - auto to_alloc = mm.translate_ptr(to_surface); - auto from_alloc = mm.translate_ptr(from_surface); - size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr; - size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr; - event_list.push_back(q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto to_o = sycl::id<1>(to_offset); - auto from_o = sycl::id<1>(from_offset); - sycl::accessor - to_acc(to_alloc.buffer, cgh, - get_copy_range(size, to_slice, to_range.get(0)), to_o); - sycl::accessor - from_acc(from_alloc.buffer, cgh, - get_copy_range(size, from_slice, from_range.get(0)), from_o); - cgh.parallel_for( - size, - [=](sycl::id<3> id) { - to_acc[get_offset(id, to_slice, to_range.get(0))] = - from_acc[get_offset(id, from_slice, from_range.get(0))]; - }); })); - } -#else - event_list.push_back(q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - cgh.parallel_for( - size, - [=](sycl::id<3> id) { - to_surface[get_offset(id, to_slice, to_range.get(0))] = - from_surface[get_offset(id, from_slice, from_range.get(0))]; - }); })); -#endif - break; + event_list.push_back(q.submit([&](sycl::handler &cgh){ + cgh.depends_on(dep_events); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_surface[get_offset(id, to_slice, to_range.get(0))] = + from_surface[get_offset(id, from_slice, from_range.get(0))]; + }); })); + break; default: throw std::runtime_error("dpct_memcpy: invalid direction value"); } @@ -1754,11 +1624,7 @@ namespace dpct { if (ptr) { -#ifdef DPCT_USM_LEVEL_NONE - detail::mem_mgr::instance().mem_free(ptr); -#else sycl::free(ptr, q.get_context()); -#endif // DPCT_USM_LEVEL_NONE } } @@ -1766,11 +1632,7 @@ namespace dpct inline auto get_memory(const void *x) { T *new_x = reinterpret_cast(const_cast(x)); -#ifdef DPCT_USM_LEVEL_NONE - return dpct::get_buffer>(new_x); -#else return new_x; -#endif } template @@ -2222,72 +2084,8 @@ namespace dpct { if (!size) return sycl::event{}; -#ifdef DPCT_USM_LEVEL_NONE - auto &mm = mem_mgr::instance(); - auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); - - switch (real_direction) - { - case host_to_host: - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); }); - case host_to_device: - { - auto alloc = mm.translate_ptr(to_ptr); - size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto o = sycl::id<1>(offset); - sycl::accessor - acc(alloc.buffer, cgh, r, o); - cgh.copy(from_ptr, acc); }); - } - case device_to_host: - { - auto alloc = mm.translate_ptr(from_ptr); - size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto o = sycl::id<1>(offset); - sycl::accessor - acc(alloc.buffer, cgh, r, o); - cgh.copy(acc, to_ptr); }); - } - case device_to_device: - { - auto to_alloc = mm.translate_ptr(to_ptr); - auto from_alloc = mm.translate_ptr(from_ptr); - size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr; - size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr; - return q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto r = sycl::range<1>(size); - auto to_o = sycl::id<1>(to_offset); - auto from_o = sycl::id<1>(from_offset); - sycl::accessor - to_acc(to_alloc.buffer, cgh, r, to_o); - sycl::accessor - from_acc(from_alloc.buffer, cgh, r, from_o); - cgh.copy(from_acc, to_acc); }); - } - default: - throw std::runtime_error("dpct_memcpy: invalid direction value"); - } -#else return q.memcpy(to_ptr, from_ptr, size, dep_events); GGML_UNUSED(direction); -#endif // DPCT_USM_LEVEL_NONE } // Get actual copy range and make sure it will not exceed range. @@ -2427,34 +2225,6 @@ namespace dpct break; } case device_to_device: -#ifdef DPCT_USM_LEVEL_NONE - { - auto &mm = mem_mgr::instance(); - auto to_alloc = mm.translate_ptr(to_surface); - auto from_alloc = mm.translate_ptr(from_surface); - size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr; - size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr; - event_list.push_back(q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - auto to_o = sycl::id<1>(to_offset); - auto from_o = sycl::id<1>(from_offset); - sycl::accessor - to_acc(to_alloc.buffer, cgh, - get_copy_range(size, to_slice, to_range.get(0)), to_o); - sycl::accessor - from_acc(from_alloc.buffer, cgh, - get_copy_range(size, from_slice, from_range.get(0)), from_o); - cgh.parallel_for( - size, - [=](sycl::id<3> id) { - to_acc[get_offset(id, to_slice, to_range.get(0))] = - from_acc[get_offset(id, from_slice, from_range.get(0))]; - }); })); - } -#else event_list.push_back(q.submit([&](sycl::handler &cgh) { cgh.depends_on(dep_events); @@ -2464,7 +2234,6 @@ namespace dpct to_surface[get_offset(id, to_slice, to_range.get(0))] = from_surface[get_offset(id, from_slice, from_range.get(0))]; }); })); -#endif break; default: throw std::runtime_error("dpct_memcpy: invalid direction value"); @@ -2655,9 +2424,6 @@ namespace dpct void *c[], library_data_t c_type, int ldc, int batch_size, library_data_t scaling_type) { -#ifdef DPCT_USM_LEVEL_NONE - throw std::runtime_error("this API is unsupported when USM level is none"); -#else if (scaling_type == library_data_t::real_float && c_type == library_data_t::complex_float) { @@ -2792,7 +2558,6 @@ namespace dpct default: throw std::runtime_error("the combination of data type is unsupported"); } -#endif } /// Computes a batch of matrix-matrix product with general matrices. @@ -3131,24 +2896,9 @@ namespace dpct template typename std::enable_if::type &operator[](size_t index) { init(); - #ifdef DPCT_USM_LEVEL_NONE - return dpct::get_buffer::type>( - _device_ptr) - .template get_access()[index]; - #else return _device_ptr[index]; - #endif // DPCT_USM_LEVEL_NONE } - #ifdef DPCT_USM_LEVEL_NONE - /// Get sycl::accessor for the device memory object when usm is not used. - accessor_t get_access(sycl::handler &cgh) { - return get_buffer(_device_ptr) - .template reinterpret(_range) - .template get_access::mode, - detail::memory_traits::target>(cgh); - } - #else /// Get dpct::accessor with dimension info for the device memory object /// when usm is used and dimension is greater than 1. template @@ -3156,7 +2906,6 @@ namespace dpct get_access(sycl::handler &cgh) { return dpct_accessor_t((T *)_device_ptr, _range); } - #endif // DPCT_USM_LEVEL_NONE private: device_memory(value_t *memory_ptr, size_t size) @@ -3201,15 +2950,6 @@ namespace dpct /// Default constructor device_memory() : base(1) {} - - #ifdef DPCT_USM_LEVEL_NONE - /// Get sycl::accessor for the device memory object when usm is not used. - accessor_t get_access(sycl::handler &cgh) { - auto buf = get_buffer(base::get_ptr()) - .template reinterpret(sycl::range<1>(1)); - return accessor_t(buf, cgh); - } - #endif // DPCT_USM_LEVEL_NONE }; } // namespace detail @@ -13181,7 +12921,7 @@ int get_work_group_size(int user_device_id) { return prop.get_max_work_group_size(); } -void ggml_init_sycl() try { +static void ggml_init_sycl() try { static bool initialized = false; if (!initialized) { @@ -16677,6 +16417,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { }; ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { + ggml_init_sycl(); if (device_index>=g_device_count or device_index<0) { printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", device_index, g_device_count-1); @@ -17046,6 +16787,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + ggml_init_sycl(); // FIXME: this is not thread safe static std::map, struct ggml_backend_buffer_type> buft_map; @@ -17379,6 +17121,13 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons UNUSED(backend); } +GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) { + const int min_batch_size = 32; + return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; + GGML_UNUSED(backend); +} + + static ggml_backend_i ggml_backend_sycl_interface = { /* .get_name = */ ggml_backend_sycl_name, /* .free = */ ggml_backend_sycl_free, @@ -17392,7 +17141,7 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, - /* .offload_op = */ NULL, + /* .offload_op = */ ggml_backend_sycl_offload_op, /* .event_new = */ NULL, /* .event_free = */ NULL, /* .event_record = */ NULL, @@ -17406,7 +17155,7 @@ static ggml_guid_t ggml_backend_sycl_guid() { } GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) { - ggml_init_sycl(); // TODO: remove from ggml.c + ggml_init_sycl(); check_allow_gpu_index(device); diff --git a/ggml-sycl.h b/ggml-sycl.h index 1c9d52115222b..a9f776fc1dd59 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -16,16 +16,22 @@ extern "C" { #define GGML_SYCL_MAX_DEVICES 48 #define GGML_SYCL_NAME "SYCL" -GGML_API void ggml_init_sycl(void); -GGML_API bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +// backend API GGML_API ggml_backend_t ggml_backend_sycl_init(int device); + +// devide buffer GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); + +// split tensor buffer that splits matrices by rows across multiple devices +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); + +// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); + GGML_API void ggml_backend_sycl_print_sycl_devices(void); GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len); GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size); GGML_API GGML_CALL int ggml_backend_sycl_get_device_count(); -GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); @@ -34,6 +40,10 @@ GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index); GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id); GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode(); + +// SYCL doesn't support registering host memory, keep here for reference +// GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); +// GGML_API GGML_CALL void ggml_backend_sycl_unregister_host_buffer(void * buffer); #ifdef __cplusplus } #endif diff --git a/ggml.c b/ggml.c index 54365b7ae26c6..18f10a3dc2f75 100644 --- a/ggml.c +++ b/ggml.c @@ -291,8 +291,6 @@ inline static void * ggml_calloc(size_t num, size_t size) { #include "ggml-opencl.h" #elif defined(GGML_USE_VULKAN) #include "ggml-vulkan.h" -#elif defined(GGML_USE_SYCL) -#include "ggml-sycl.h" #endif // floating point type used to accumulate sums @@ -2698,8 +2696,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { ggml_cl_init(); #elif defined(GGML_USE_VULKAN) ggml_vk_init_cpu_assist(); -#elif defined(GGML_USE_SYCL) - ggml_init_sycl(); #endif ggml_setup_op_has_task_pass(); @@ -16115,12 +16111,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU); #endif // GGML_USE_VULKAN -#ifdef GGML_USE_SYCL - bool skip_cpu = ggml_sycl_compute_forward(params, tensor); - if (skip_cpu) { - return; - } -#endif // GGML_USE_SYCL switch (tensor->op) { case GGML_OP_DUP: { diff --git a/llama.cpp b/llama.cpp index 9614cdb17400f..61587cb7abf5a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13632,30 +13632,28 @@ struct llama_context * llama_new_context_with_model( } } #elif defined(GGML_USE_SYCL) - if (model->n_gpu_layers > 0) { - // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); + // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { + ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); + if (backend == nullptr) { + int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_LAYER requires a backend for each GPU + for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { + ggml_backend_t backend = ggml_backend_sycl_init(i); if (backend == nullptr) { - int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu); - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu); + int id_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i); llama_free(ctx); return nullptr; } ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_LAYER requires a backend for each GPU - for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - ggml_backend_t backend = ggml_backend_sycl_init(i); - if (backend == nullptr) { - int id_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES); - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } } } #elif defined(GGML_USE_KOMPUTE) From 586e7bc561be88e929a9afca7e67d8ead00c53bd Mon Sep 17 00:00:00 2001 From: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> Date: Sun, 24 Mar 2024 17:54:07 +0900 Subject: [PATCH 353/401] sampling : deduplicated code for probability distribution access (#6240) * sampling: remove duplicated code for probability distribution access * free original_logits * fix original_logits allocation * fixes based on review @cebtenzzre * change function name to `llama_sampling_prepare` --- common/sampling.cpp | 93 ++++++--------------------- common/sampling.h | 8 ++- examples/speculative/speculative.cpp | 3 +- retrieval | Bin 0 -> 1637080 bytes 4 files changed, 28 insertions(+), 76 deletions(-) create mode 100755 retrieval diff --git a/common/sampling.cpp b/common/sampling.cpp index 5a54509827cbf..45d68b26c2b93 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -168,76 +168,19 @@ static llama_token llama_sampling_sample_impl( bool is_resampling) { // Add a parameter to indicate if we are resampling const llama_sampling_params & params = ctx_sampling->params; - const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); - const float temp = params.temp; - const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; - const float penalty_repeat = params.penalty_repeat; - const float penalty_freq = params.penalty_freq; - const float penalty_present = params.penalty_present; const int mirostat = params.mirostat; const float mirostat_tau = params.mirostat_tau; const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - auto & prev = ctx_sampling->prev; - auto & cur = ctx_sampling->cur; - - llama_token id = 0; - - // Get a pointer to the logits - float * logits = llama_get_logits_ith(ctx_main, idx); - - // Declare original_logits at the beginning of the function scope std::vector original_logits; - + auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits); if (!is_resampling) { - // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this. - original_logits = std::vector(logits, logits + llama_n_vocab(llama_get_model(ctx_main))); - } - - // apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - if (ctx_cfg) { - float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); - llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); - } - - cur.clear(); - - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array cur_p = { cur.data(), cur.size(), false }; - - // apply penalties - const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; - const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); - if (penalty_tokens_used_size) { - const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; - - llama_sample_repetition_penalties(ctx_main, &cur_p, - penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, - penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); - - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { - cur_p.data[idx].logit = nl_logit; - break; - } - } - } - } - - // If we are in the resampling phase, apply grammar checks before sampling logic - if (is_resampling && ctx_sampling->grammar != NULL) { - llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); + GGML_ASSERT(!original_logits.empty()); } + llama_token id = 0; + // Get a pointer to the logits + float * logits = llama_get_logits_ith(ctx_main, idx); if (temp < 0.0) { // greedy sampling, with probs @@ -302,11 +245,13 @@ static llama_token llama_sampling_sample_impl( return id; } -static llama_token_data_array llama_sample_probability_distribution_impl( +static llama_token_data_array llama_sampling_prepare_impl( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, - const int idx) { + const int idx, + bool apply_grammar, + std::vector * original_logits) { const llama_sampling_params & params = ctx_sampling->params; const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); @@ -315,6 +260,7 @@ static llama_token_data_array llama_sample_probability_distribution_impl( const float penalty_repeat = params.penalty_repeat; const float penalty_freq = params.penalty_freq; const float penalty_present = params.penalty_present; + const bool penalize_nl = params.penalize_nl; auto & prev = ctx_sampling->prev; @@ -323,8 +269,10 @@ static llama_token_data_array llama_sample_probability_distribution_impl( // Get a pointer to the logits float * logits = llama_get_logits_ith(ctx_main, idx); - // Declare original_logits at the beginning of the function scope - std::vector original_logits; + if (apply_grammar && original_logits != NULL) { + // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this. + *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))}; + } // apply params.logit_bias map for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { @@ -364,12 +312,11 @@ static llama_token_data_array llama_sample_probability_distribution_impl( } } - // apply grammar checks - if (ctx_sampling->grammar != NULL) { + // apply grammar checks before sampling logic + if (apply_grammar && ctx_sampling->grammar != NULL) { llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); } - llama_sample_softmax(ctx_main, &cur_p); return cur_p; } @@ -382,12 +329,14 @@ llama_token llama_sampling_sample( return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); } -llama_token_data_array llama_sampling_probability_distribution( +llama_token_data_array llama_sampling_prepare( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, - const int idx) { - return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx); + const int idx, + bool apply_grammar, + std::vector * original_logits) { + return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits); } void llama_sampling_accept( diff --git a/common/sampling.h b/common/sampling.h index 79a998be8e408..56ed991b8478a 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -131,12 +131,14 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, int idx = 0); -// returns the probability that token of given id will be sampled -llama_token_data_array llama_sampling_probability_distribution( +// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters. +llama_token_data_array llama_sampling_prepare( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, - int idx = 0); + int idx = 0, + bool apply_grammar = true, + std::vector * original_logits = nullptr); void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index e991b88460713..8b31b678a6849 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -219,7 +219,8 @@ int main(int argc, char ** argv) { if (params.sparams.temp > 0) { // stochastic verification - llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + llama_token_data_array dist_tgt = llama_sampling_prepare(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft], true, NULL); + llama_sample_softmax(ctx_tgt, &dist_tgt); float p_tgt = 0, p_dft = 0; // GGML_ASSERT(dist_tgt.size() == dist_dft.size()); diff --git a/retrieval b/retrieval new file mode 100755 index 0000000000000000000000000000000000000000..dd31789f89c3a41fb84b88c08444eb8e4a9dbfbe GIT binary patch literal 1637080 zcmeFad3==B_4t3EnFOB62AHs8l3~%AAfm+$4P_<)4U1@zR&8xVKl4ZBAB3<-}^jE^2}tCptYahKi|Aw 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