From 65c64dc36f9bca5b3f100614cdd02bf12d6b3e49 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 10 Apr 2024 15:23:12 +0200 Subject: [PATCH 01/48] convert.py : add consolidated.safetensors for mixtral 8x22b (#6587) --- convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert.py b/convert.py index e860ac89fc277..24df0a4d84dc3 100755 --- a/convert.py +++ b/convert.py @@ -1350,7 +1350,7 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - globs = ["model-00001-of-*.safetensors", "model.safetensors"] + globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority From 4f407a0a353dae4726c74cc33250b623a4911dd7 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 10 Apr 2024 17:24:14 +0200 Subject: [PATCH 02/48] llama : add model types for mixtral (#6589) --- llama.cpp | 30 +++++++++++++++++++++--------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index 8dbf474865038..9ad9b10cb72cb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1705,6 +1705,8 @@ enum e_model { MODEL_MEDIUM, MODEL_LARGE, MODEL_XL, + MODEL_8x7B, + MODEL_8x22B, }; static const size_t kiB = 1024; @@ -3558,6 +3560,8 @@ static const char * llama_model_type_name(e_model type) { case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; case MODEL_XL: return "1.5B"; + case MODEL_8x7B: return "8x7B"; + case MODEL_8x22B: return "8x22B"; default: return "?B"; } } @@ -3672,15 +3676,23 @@ static void llm_load_hparams( { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - switch (hparams.n_layer) { - case 22: model.type = e_model::MODEL_1B; break; - case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = e_model::MODEL_7B; break; - case 40: model.type = e_model::MODEL_13B; break; - case 48: model.type = e_model::MODEL_34B; break; - case 60: model.type = e_model::MODEL_30B; break; - case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; - default: model.type = e_model::MODEL_UNKNOWN; + if (hparams.n_expert == 8) { + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_8x7B; break; + case 56: model.type = e_model::MODEL_8x22B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + switch (hparams.n_layer) { + case 22: model.type = e_model::MODEL_1B; break; + case 26: model.type = e_model::MODEL_3B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_13B; break; + case 48: model.type = e_model::MODEL_34B; break; + case 60: model.type = e_model::MODEL_30B; break; + case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } } } break; case LLM_ARCH_MINICPM: From b3a96f27f065a828f08c5d89ff60aab5361188fe Mon Sep 17 00:00:00 2001 From: Ralph Soika Date: Wed, 10 Apr 2024 19:18:25 +0200 Subject: [PATCH 03/48] minor layout improvements (#6572) * minor layout improvements * added missing file, run deps.sh locally --- examples/server/index.html.hpp | 5349 +++++++++++++++-------------- examples/server/public/index.html | 22 +- 2 files changed, 2714 insertions(+), 2657 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 122d3ff999f04..14f0e8f1951f3 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -82,2802 +82,2839 @@ unsigned char index_html[] = { 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 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0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, + 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, + 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, + 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, + 0x0a, 0x0a }; -unsigned int index_html_len = 34552; +unsigned int index_html_len = 34994; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 9fe61eb1b1b13..68661ee4d004b 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -51,6 +51,26 @@ margin-bottom: 0.5em; } + button, input, textarea, .button, a.button, select { + color: #666; + border: 1px solid #ddd; + border-radius: 4px; + line-height: 1.5em; + padding: 0.25em 0.25em; + text-decoration: none; + font-size: 1.1rem; + } + + button { + border: 1px solid #2a8aad; + background: #3584e4; + font-weight: normal; + color: #fff; + } + button:disabled { + background: #9cbce5; + } + #write form { margin: 1em 0 0 0; display: flex; @@ -567,7 +587,7 @@ runCompletion(); } return html` -
+
From 8228b66dbc16290c5cbd70e80ab47c068e2569d8 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 10 Apr 2024 20:16:48 +0200 Subject: [PATCH 04/48] gguf : add option to not check tensor data (#6582) This commit adds an option to the gguf example to not check the tensor data. The motivation for this is that it can be nice to use the gguf tool to read other .gguf files that were not created by the gguf tool. Signed-off-by: Daniel Bevenius --- examples/gguf/gguf.cpp | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index 5444503a5d218..57514377107d7 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -142,7 +142,7 @@ static bool gguf_ex_read_0(const std::string & fname) { } // read and create ggml_context containing the tensors and their data -static bool gguf_ex_read_1(const std::string & fname) { +static bool gguf_ex_read_1(const std::string & fname, bool check_data) { struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { @@ -206,7 +206,7 @@ static bool gguf_ex_read_1(const std::string & fname) { printf("\n\n"); // check data - { + if (check_data) { const float * data = (const float *) cur->data; for (int j = 0; j < ggml_nelements(cur); ++j) { if (data[j] != 100 + i) { @@ -229,9 +229,16 @@ static bool gguf_ex_read_1(const std::string & fname) { int main(int argc, char ** argv) { if (argc < 3) { - printf("usage: %s data.gguf r|w\n", argv[0]); + printf("usage: %s data.gguf r|w [n]\n", argv[0]); + printf("r: read data.gguf file\n"); + printf("w: write data.gguf file\n"); + printf("n: no check of tensor data\n"); return -1; } + bool check_data = true; + if (argc == 4) { + check_data = false; + } const std::string fname(argv[1]); const std::string mode (argv[2]); @@ -242,7 +249,7 @@ int main(int argc, char ** argv) { GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file"); } else if (mode == "r") { GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file"); - GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file"); + GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file"); } return 0; From b804b1ef77351d2a11be945462c6c251710476cb Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Thu, 11 Apr 2024 14:51:07 +0200 Subject: [PATCH 05/48] eval-callback: Example how to use eval callback for debugging (#6576) * gguf-debug: Example how to use ggml callback for debugging * gguf-debug: no mutex, verify type, fix stride. * llama: cv eval: move cb eval field in common gpt_params * ggml_debug: use common gpt_params to pass cb eval. Fix get tensor SIGV random. * ggml_debug: ci: add tests * ggml_debug: EOL in CMakeLists.txt * ggml_debug: Remove unused param n_batch, no batching here * ggml_debug: fix trailing spaces * ggml_debug: fix trailing spaces * common: fix cb_eval and user data not initialized * ci: build revert label * ggml_debug: add main test label * doc: add a model: add a link to ggml-debug * ggml-debug: add to make toolchain * ggml-debug: tests add the main label * ggml-debug: ci add test curl label * common: allow the warmup to be disabled in llama_init_from_gpt_params * ci: add curl test * ggml-debug: better tensor type support * gitignore : ggml-debug * ggml-debug: printing also the sum of each tensor * ggml-debug: remove block size * eval-callback: renamed from ggml-debug * eval-callback: fix make toolchain --------- Co-authored-by: slaren Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 8 +- .gitignore | 1 + Makefile | 6 +- common/common.cpp | 4 +- common/common.h | 4 + docs/HOWTO-add-model.md | 2 + examples/CMakeLists.txt | 1 + examples/eval-callback/CMakeLists.txt | 9 ++ examples/eval-callback/README.md | 95 ++++++++++++ examples/eval-callback/eval-callback.cpp | 185 +++++++++++++++++++++++ examples/imatrix/imatrix.cpp | 26 ++-- llama.cpp | 2 +- 12 files changed, 320 insertions(+), 23 deletions(-) create mode 100644 examples/eval-callback/CMakeLists.txt create mode 100644 examples/eval-callback/README.md create mode 100644 examples/eval-callback/eval-callback.cpp diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ff7238abad76c..f10ed416122d4 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -52,7 +52,7 @@ jobs: id: cmake_test run: | cd build - ctest -L main --verbose --timeout 900 + ctest -L 'main|curl' --verbose --timeout 900 - name: Determine tag name id: tag @@ -209,21 +209,21 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install build-essential + sudo apt-get install build-essential libcurl4-openssl-dev - name: Build id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON cmake --build . --config Release -j $(nproc) - name: Test id: cmake_test run: | cd build - ctest -L main --verbose --timeout 900 + ctest -L 'main|curl' --verbose --timeout 900 - name: Test llama2c conversion id: llama2c_test diff --git a/.gitignore b/.gitignore index 9fb5b80c38e74..fdc5184a1e89b 100644 --- a/.gitignore +++ b/.gitignore @@ -48,6 +48,7 @@ models-mnt /convert-llama2c-to-ggml /embd-input-test /embedding +/eval-callback /gguf /gguf-llama-simple /gguf-split diff --git a/Makefile b/Makefile index 11b31c5c84182..2fd805a97501f 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 gguf-split llama-bench libllava.a llava-cli baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search \ retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o # Binaries only useful for tests @@ -800,6 +800,10 @@ gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(O $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +eval-callback: examples/eval-callback/eval-callback.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/common/common.cpp b/common/common.cpp index 98fc8388cf3e0..dda514785171b 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1745,6 +1745,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_orig_ctx = params.yarn_orig_ctx; cparams.pooling_type = params.pooling_type; cparams.defrag_thold = params.defrag_thold; + cparams.cb_eval = params.cb_eval; + cparams.cb_eval_user_data = params.cb_eval_user_data; cparams.offload_kqv = !params.no_kv_offload; cparams.type_k = kv_cache_type_from_str(params.cache_type_k); @@ -2192,7 +2194,7 @@ std::tuple llama_init_from_gpt_par params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } - { + if (params.warmup) { LOG("warming up the model with an empty run\n"); std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; diff --git a/common/common.h b/common/common.h index a7f476c1bf0cb..65272b0baaa41 100644 --- a/common/common.h +++ b/common/common.h @@ -80,6 +80,9 @@ struct gpt_params { int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold + ggml_backend_sched_eval_callback cb_eval = nullptr; + void * cb_eval_user_data = nullptr; + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; @@ -156,6 +159,7 @@ struct gpt_params { bool infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading + bool warmup = true; // warmup run std::string cache_type_k = "f16"; // KV cache data type for the K std::string cache_type_v = "f16"; // KV cache data type for the V diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md index 3581f3e658bbe..a56b78344e313 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/HOWTO-add-model.md @@ -100,6 +100,8 @@ Have a look to existing implementation like `build_llama`, `build_dbrx` or `buil When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR. +Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback). + ## GGUF specification https://github.com/ggerganov/ggml/blob/master/docs/gguf.md diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 76496bf06d135..f421769cc2f0a 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -19,6 +19,7 @@ else() add_subdirectory(benchmark) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) + add_subdirectory(eval-callback) add_subdirectory(finetune) add_subdirectory(gritlm) add_subdirectory(gguf-split) diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt new file mode 100644 index 0000000000000..d53f374226b9a --- /dev/null +++ b/examples/eval-callback/CMakeLists.txt @@ -0,0 +1,9 @@ +set(TARGET eval-callback) +add_executable(${TARGET} eval-callback.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(TEST_TARGET test-eval-callback) +add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42) +set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md new file mode 100644 index 0000000000000..66a37e8783a9b --- /dev/null +++ b/examples/eval-callback/README.md @@ -0,0 +1,95 @@ +# llama.cpp/examples/eval-callback + +A simple example which demonstrates how to use callback during the inference. +It simply prints to the console all operations and tensor data. + +Usage: + +```shell +eval-callback \ + --hf-repo ggml-org/models \ + --hf-file phi-2/ggml-model-q4_0.gguf \ + --model phi-2-q4_0.gguf \ + --prompt hello \ + --seed 42 \ + -ngl 33 +``` + +Will print: + +```shell +llm_load_tensors: offloaded 33/33 layers to GPU +... +llama_new_context_with_model: n_ctx = 512 +... +llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB +llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB +llama_new_context_with_model: graph nodes = 1225 +llama_new_context_with_model: graph splits = 2 +ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.0181, 0.0272, 0.0272, ...], + ], + ] +ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -0.6989, 1.0636, 1.0636, ...], + ], + ] +ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1800, 0.2817, 0.2632, ...], + ], + ] +ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1863, 0.2970, 0.2604, ...], + ], + ] +ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1238, 1.2876, -1.8086, ...], + ], + ] +ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +``` diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp new file mode 100644 index 0000000000000..f70d62128a8c8 --- /dev/null +++ b/examples/eval-callback/eval-callback.cpp @@ -0,0 +1,185 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include +#include +#include + +/** + * This the arbitrary data which will be passed to each callback. + * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor. + */ +struct callback_data { + std::vector data; +}; + +static std::string ggml_ne_string(const ggml_tensor * t) { + std::string str; + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + str += std::to_string(t->ne[i]); + if (i + 1 < GGML_MAX_DIMS) { + str += ", "; + } + } + return str; +} + +static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { + float sum = 0; + for (int64_t i3 = 0; i3 < ne[3]; i3++) { + printf(" [\n"); + for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) { + printf(" [\n"); + for (int64_t i1 = 0; i1 < ne[1] && i1 < n; i1++) { + printf(" ["); + for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) { + size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; + float v; + if (type == GGML_TYPE_F16) { + v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i); + } else if (type == GGML_TYPE_F32) { + v = *(float *) data + i; + } else if (type == GGML_TYPE_I32) { + v = (float) *(int32_t *) data + i; + } else if (type == GGML_TYPE_I16) { + v = (float) *(int16_t *) data + i; + } else if (type == GGML_TYPE_I8) { + v = (float) *(int8_t *) data + i; + } else { + GGML_ASSERT(false); + } + printf("%8.4f", v); + sum += v; + if (i0 < ne[0] - 1 && i0 < n - 1) printf(", "); + } + if (ne[0] > n) printf(", ..."); + printf("],\n"); + } + if (ne[1] > n) printf(" ...\n"); + printf(" ],\n"); + } + if (ne[2] > n) printf(" ...\n"); + printf(" ]\n"); + printf(" sum = %f\n", sum); + } +} + +/** + * GGML operations callback during the graph execution. + * + * @param t current tensor + * @param ask 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. + * see ggml_backend_sched_eval_callback + * @param user_data user data to pass at each call back + * @return true to receive data or continue the graph, false otherwise + */ +static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + + const struct ggml_tensor * src0 = t->src[0]; + const struct ggml_tensor * src1 = t->src[1]; + + if (ask) { + return true; // Always retrieve data + } + + char src1_str[128] = {0}; + if (src1) { + sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str()); + } + + printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, + t->name, ggml_type_name(t->type), ggml_op_name(t->op), + src0->name, ggml_ne_string(src0).c_str(), + src1 ? src1_str : "", + ggml_ne_string(t).c_str()); + + + // copy the data from the GPU memory if needed + const bool is_host = ggml_backend_buffer_is_host(t->buffer); + + if (!is_host) { + auto n_bytes = ggml_nbytes(t); + cb_data->data.resize(n_bytes); + ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes); + } + + if (!ggml_is_quantized(t->type)) { + uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data(); + ggml_print_tensor(data, t->type, t->ne, t->nb, 3); + } + + return true; +} + +static bool run(llama_context * ctx, const gpt_params & params) { + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + + std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + + return true; +} + +int main(int argc, char ** argv) { + + callback_data cb_data; + + gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + + print_build_info(); + + std::mt19937 rng(params.seed); + if (params.random_prompt) { + params.prompt = gpt_random_prompt(rng); + } + + llama_backend_init(); + llama_numa_init(params.numa); + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = ggml_debug; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + // init + llama_model * model; + llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); + return 1; + } + + // print system information + { + fprintf(stderr, "\n"); + fprintf(stderr, "%s\n", get_system_info(params).c_str()); + } + + bool OK = run(ctx, params); + if (!OK) { + return 1; + } + + llama_print_timings(ctx); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + return 0; +} diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 1bf55f90c0f49..ff624c539b395 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -597,24 +597,18 @@ int main(int argc, char ** argv) { llama_backend_init(); llama_numa_init(params.numa); - llama_model_params mparams = llama_model_params_from_gpt_params(params); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); - if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); - return 1; - } - - llama_context_params cparams = llama_context_params_from_gpt_params(params); - // pass the callback to the backend scheduler // it will be executed for each node during the graph computation - cparams.cb_eval = ik_collect_imatrix; - cparams.cb_eval_user_data = NULL; - - llama_context * ctx = llama_new_context_with_model(model, cparams); - if (ctx == NULL) { - fprintf(stderr, "%s: error: unable to create context\n", __func__); + params.cb_eval = ik_collect_imatrix; + params.cb_eval_user_data = NULL; + params.warmup = false; + + // init + llama_model * model; + llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); return 1; } diff --git a/llama.cpp b/llama.cpp index 9ad9b10cb72cb..b6e2ade9134d9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11121,7 +11121,7 @@ struct llm_tokenizer_bpe { add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol } - // add the fnished tokens to the final list keeping correct order for next and prev + // add the finished tokens to the final list keeping correct order for next and prev for (auto & sym : symbols) { if (sym.n > 0) { sym.prev = final_prev_index; From f4183afe6a22f356ee222a710686ae7f83dbd949 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 11 Apr 2024 15:22:47 +0200 Subject: [PATCH 06/48] scripts : add --outdir option to hf.sh (#6600) * scripts : add --outdir option to hf.sh This commit adds an option to the hf.sh script that allows the user to specify an output directory for the downloaded file. The motivation for this changes is that examples that use the hf.sh script to download models from huggingface can now specify the output directory, perhaps to the `models` directory to keep them in one place and not clutter the root directory. Signed-off-by: Daniel Bevenius * squash! scripts : add --outdir option to hf.sh Fix format of the --outdir option in the usage message. Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- scripts/hf.sh | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/scripts/hf.sh b/scripts/hf.sh index 1e9e5a6eac168..58f83d6fe92a5 100755 --- a/scripts/hf.sh +++ b/scripts/hf.sh @@ -3,9 +3,9 @@ # Shortcut for downloading HF models # # Usage: -# ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) -# ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) -# ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf) # # all logs go to stderr @@ -14,7 +14,7 @@ function log { } function usage { - log "Usage: $0 [[--url] ] [--repo ] [--file ] [-h|--help]" + log "Usage: $0 [[--url] ] [--repo ] [--file ] [--outdir [-h|--help]" exit 1 } @@ -26,9 +26,9 @@ function has_cmd { } if has_cmd wget; then - cmd="wget -q --show-progress -c -O %s %s" + cmd="wget -q --show-progress -c -O %s/%s %s" elif has_cmd curl; then - cmd="curl -C - -f -o %s -L %s" + cmd="curl -C - -f --output-dir %s -o %s -L %s" else log "[E] curl or wget not found" exit 1 @@ -37,6 +37,7 @@ fi url="" repo="" file="" +outdir="." # parse args while [[ $# -gt 0 ]]; do @@ -53,6 +54,10 @@ while [[ $# -gt 0 ]]; do file="$2" shift 2 ;; + --outdir) + outdir="$2" + shift 2 + ;; -h|--help) usage ;; @@ -94,10 +99,10 @@ basename=$(basename $url) log "[+] attempting to download $basename" if [ -n "$cmd" ]; then - cmd=$(printf "$cmd" "$basename" "$url") + cmd=$(printf "$cmd" "$outdir" "$basename" "$url") log "[+] $cmd" if $cmd; then - echo $basename + echo $outdir/$basename exit 0 fi fi From 1bbdaf6ecda6f0a360dfb307b256fcb6838c560b Mon Sep 17 00:00:00 2001 From: Hugo Roussel Date: Thu, 11 Apr 2024 19:52:21 +0200 Subject: [PATCH 07/48] ci: download artifacts to release directory (#6612) When action download-artifact was updated to v4, the default download path changed. This fix binaries not being uploaded to releases. --- .github/workflows/build.yml | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f10ed416122d4..0527899a1120d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -938,6 +938,12 @@ jobs: - name: Download artifacts id: download-artifact uses: actions/download-artifact@v4 + with: + path: ./artifact + + - name: Move artifacts + id: move_artifacts + run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release - name: Create release id: create_release @@ -956,7 +962,7 @@ jobs: const path = require('path'); const fs = require('fs'); const release_id = '${{ steps.create_release.outputs.id }}'; - for (let file of await fs.readdirSync('./artifact')) { + for (let file of await fs.readdirSync('./artifact/release')) { if (path.extname(file) === '.zip') { console.log('uploadReleaseAsset', file); await github.repos.uploadReleaseAsset({ @@ -964,7 +970,7 @@ jobs: repo: context.repo.repo, release_id: release_id, name: file, - data: await fs.readFileSync(`./artifact/${file}`) + data: await fs.readFileSync(`./artifact/release/${file}`) }); } } From cbaadc92942c50aab599a9e4c163afc1f44f7c26 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Thu, 11 Apr 2024 19:47:34 +0100 Subject: [PATCH 08/48] grammars: 1.5x faster inference w/ complex grammars (vector reserves / reuses) (#6609) * grammars: reserve rejects & next candidates * grammars: reuse new_stacks * grammars: fix missing sig change in llama.h * grammars: fix test (api changed) * grammars: update gbnf-validator.cpp * grammars: simpler syntax (no swap) --- examples/gbnf-validator/gbnf-validator.cpp | 2 +- llama.cpp | 16 ++++++++++------ llama.h | 5 +++-- tests/test-grammar-integration.cpp | 6 +++--- 4 files changed, 17 insertions(+), 12 deletions(-) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index e4c0c1689c7a4..091069ffa699c 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -17,7 +17,7 @@ static bool llama_sample_grammar_string(struct llama_grammar * grammar, const st size_t pos = 0; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); if (grammar->stacks.empty()) { error_pos = pos; error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'"; diff --git a/llama.cpp b/llama.cpp index b6e2ade9134d9..ad07059c4533a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11912,12 +11912,13 @@ static void llama_grammar_advance_stack( // be positioned at a character range (see `llama_grammar_advance_stack`), and // produces the N possible stacks if the given char is accepted at those // positions -std::vector> llama_grammar_accept( +void llama_grammar_accept( const std::vector> & rules, const std::vector> & stacks, - const uint32_t chr) { + const uint32_t chr, + std::vector> & new_stacks) { - std::vector> new_stacks; + new_stacks.clear(); for (const auto & stack : stacks) { if (stack.empty()) { @@ -11936,8 +11937,6 @@ std::vector> llama_grammar_accept( llama_grammar_advance_stack(rules, new_stack, new_stacks); } } - - return new_stacks; } static std::vector llama_grammar_reject_candidates( @@ -11951,6 +11950,7 @@ static std::vector llama_grammar_reject_candidates_for_ const std::vector & candidates) { std::vector rejects; + rejects.reserve(candidates.size()); if (stack.empty()) { for (const auto & tok : candidates) { @@ -11964,6 +11964,8 @@ static std::vector llama_grammar_reject_candidates_for_ const llama_grammar_element * stack_pos = stack.back(); std::vector next_candidates; + next_candidates.reserve(candidates.size()); + for (const auto & tok : candidates) { if (*tok.code_points == 0) { // reached end of full codepoints in token, reject iff it ended in a partial sequence @@ -12771,8 +12773,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece, grammar->partial_utf8); const auto & code_points = decoded.first; + std::vector> tmp_new_stacks; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks); + grammar->stacks = tmp_new_stacks; } grammar->partial_utf8 = decoded.second; GGML_ASSERT(!grammar->stacks.empty()); diff --git a/llama.h b/llama.h index b770a275ff02f..b5da686f7b7e5 100644 --- a/llama.h +++ b/llama.h @@ -1097,10 +1097,11 @@ const std::vector> & llama_internal struct llama_context * ctx ); -std::vector> llama_grammar_accept( +void llama_grammar_accept( const std::vector> & rules, const std::vector> & stacks, - const uint32_t chr); + const uint32_t chr, + std::vector> & new_stacks); std::pair, llama_partial_utf8> decode_utf8( const std::string & src, diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 0a9c3b6f5f7c3..2d8f228e3769d 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -38,7 +38,7 @@ number ::= [0-9]+)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); assert(!grammar->stacks.empty()); } @@ -138,7 +138,7 @@ ws ::= [ \t\n\r]?)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { ++pos; auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); // Expect that each code point will not cause the grammar to fail if (grammar->stacks.empty()) { @@ -173,7 +173,7 @@ ws ::= [ \t\n\r]?)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); if (grammar->stacks.empty()) { parse_failed = true; break; From a474f50ebb3e10be3371562f75f3f573f1a86b5f Mon Sep 17 00:00:00 2001 From: Nikolas <127742645+nneubacher@users.noreply.github.com> Date: Thu, 11 Apr 2024 21:56:29 +0200 Subject: [PATCH 09/48] Refactor Error Handling for CUDA (#6575) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Refactor Error Handling for CUDA Add guidance for setting CUDA_DOCKER_ARCH to match GPU compute capability for CUDA versions < 11.7. Include link to NVIDIA's CUDA GPUs documentation for compute capability reference. * Update Makefile Improved wording Co-authored-by: Johannes Gäßler --------- Co-authored-by: Johannes Gäßler --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 2fd805a97501f..7a69ad1b3c14f 100644 --- a/Makefile +++ b/Makefile @@ -646,7 +646,7 @@ CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])' ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) ifndef CUDA_DOCKER_ARCH ifndef CUDA_POWER_ARCH -$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH) +$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus ) endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) From f7001ccc5aa359fcf41bba19d1c99c3d25c9bcc7 Mon Sep 17 00:00:00 2001 From: Clint Herron Date: Thu, 11 Apr 2024 17:44:48 -0400 Subject: [PATCH 10/48] As suggested by @slaren, disabling Metal for test to fix CI build on OSX from #6576 (#6619) --- examples/eval-callback/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt index d53f374226b9a..c56ba780b215f 100644 --- a/examples/eval-callback/CMakeLists.txt +++ b/examples/eval-callback/CMakeLists.txt @@ -5,5 +5,5 @@ target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) set(TEST_TARGET test-eval-callback) -add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42) +add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) From 04a5ac211ef40936295980b7cdf0ba6e97093146 Mon Sep 17 00:00:00 2001 From: Clint Herron Date: Thu, 11 Apr 2024 21:44:50 -0400 Subject: [PATCH 11/48] Optimization: eliminate addition of redundant stacks when advancing grammar. (#6616) --- llama.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index ad07059c4533a..73ff607062884 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11861,7 +11861,9 @@ static void llama_grammar_advance_stack( std::vector> & new_stacks) { if (stack.empty()) { - new_stacks.emplace_back(stack); + if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) { + new_stacks.emplace_back(stack); + } return; } @@ -11898,7 +11900,10 @@ static void llama_grammar_advance_stack( } case LLAMA_GRETYPE_CHAR: case LLAMA_GRETYPE_CHAR_NOT: - new_stacks.emplace_back(stack); + if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) { + // only add the stack if it's not a duplicate of one we already have + new_stacks.emplace_back(stack); + } break; default: // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range From 9ed2737acc233716374860e6b2ea7399c4aae29e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Apr 2024 11:15:05 +0300 Subject: [PATCH 12/48] ci : disable Metal for macOS-latest-cmake-x64 (#6628) --- .github/workflows/build.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0527899a1120d..63143bc94dd68 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -101,7 +101,9 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON .. + # Metal is disabled due to intermittent failures with Github runners not having a GPU: + # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test From 81da18e71ccfc196d4516fbea5dc3a6a1f92dccb Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 12 Apr 2024 10:26:47 +0200 Subject: [PATCH 13/48] eval-callback: use ggml_op_desc to pretty print unary operator name (#6631) --- examples/eval-callback/eval-callback.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index f70d62128a8c8..05f7d6ab1a2d3 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -93,7 +93,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { } printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, - t->name, ggml_type_name(t->type), ggml_op_name(t->op), + t->name, ggml_type_name(t->type), ggml_op_desc(t), src0->name, ggml_ne_string(src0).c_str(), src1 ? src1_str : "", ggml_ne_string(t).c_str()); From dee7f8d6928cc680cc969f7d93f98c3e24dcad41 Mon Sep 17 00:00:00 2001 From: MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com> Date: Fri, 12 Apr 2024 16:28:12 +0800 Subject: [PATCH 14/48] Correct free memory and total memory. (#6630) Co-authored-by: MasterYi --- llama.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 73ff607062884..dad2c4fbffb16 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1638,17 +1638,17 @@ static size_t llama_get_device_memory(int device) { #if defined(GGML_USE_CUDA) size_t total; size_t free; - ggml_backend_cuda_get_device_memory(device, &total, &free); + ggml_backend_cuda_get_device_memory(device, &free, &total); return free; #elif defined(GGML_USE_SYCL) size_t total; size_t free; - ggml_backend_sycl_get_device_memory(device, &total, &free); + ggml_backend_sycl_get_device_memory(device, &free, &total); return free; #elif defined(GGML_USE_VULKAN) size_t total; size_t free; - ggml_backend_vk_get_device_memory(device, &total, &free); + ggml_backend_vk_get_device_memory(device, &free, &total); return free; #else return 1; From ef21ce4ccb41164cb52997bd2210d92bc6a6c5d1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Apr 2024 11:49:58 +0300 Subject: [PATCH 15/48] imatrix : remove invalid assert (#6632) --- examples/imatrix/imatrix.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index ff624c539b395..73609d3e6ae00 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -107,9 +107,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * // the top-k selected expert ids are stored in the ids tensor // for simplicity, always copy ids to host, because it is small - // take into account that ids is not contiguous! GGML_ASSERT(ids->ne[1] == src1->ne[1]); - GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int))); m_ids.resize(ggml_nbytes(ids)/sizeof(int)); ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids)); From 5c4d767ac028c0f9c31cba3fceaf765c6097abfc Mon Sep 17 00:00:00 2001 From: Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> Date: Fri, 12 Apr 2024 10:52:36 +0200 Subject: [PATCH 16/48] chore: Fix markdown warnings (#6625) --- README-sycl.md | 94 +++++++++++++++--------------- README.md | 38 ++++++------ SECURITY.md | 6 +- examples/llava/MobileVLM-README.md | 2 +- examples/llava/README.md | 2 +- examples/main/README.md | 2 +- examples/perplexity/README.md | 31 +++++----- examples/quantize/README.md | 22 +++---- 8 files changed, 98 insertions(+), 99 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 4372a32e32b1e..169d2ca0b60e0 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -8,9 +8,9 @@ - [Linux](#linux) - [Windows](#windows) - [Environment Variable](#environment-variable) -- [Known Issue](#known-issue) -- [Q&A](#q&a) -- [Todo](#todo) +- [Known Issue](#known-issues) +- [Q&A](#qa) +- [TODO](#todo) ## Background @@ -54,10 +54,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, ## OS -|OS|Status|Verified| -|-|-|-| -|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| -|Windows|Support|Windows 11| +| OS | Status | Verified | +|---------|---------|------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 | +| Windows | Support | Windows 11 | ## Hardware @@ -66,13 +66,13 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, **Verified devices** -|Intel GPU| Status | Verified Model| -|-|-|-| -|Intel Data Center Max Series| Support| Max 1550| -|Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770, 730M| -|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| +| Intel GPU | Status | Verified Model | +|-------------------------------|---------|---------------------------------------| +| Intel Data Center Max Series | Support | Max 1550 | +| Intel Data Center Flex Series | Support | Flex 170 | +| Intel Arc Series | Support | Arc 770, 730M | +| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | +| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | *Notes:* @@ -89,10 +89,10 @@ The BLAS acceleration on Nvidia GPU through oneAPI can be obtained using the Nvi **Verified devices** -|Nvidia GPU| Status | Verified Model| -|-|-|-| -|Ampere Series| Support| A100, A4000| -|Ampere Series *(Mobile)*| Support| RTX 40 Series| +| Nvidia GPU | Status | Verified Model | +|--------------------------|---------|----------------| +| Ampere Series | Support | A100, A4000 | +| Ampere Series *(Mobile)* | Support | RTX 40 Series | *Notes:* - Support for Nvidia targets through oneAPI is currently limited to Linux platforms. @@ -167,7 +167,7 @@ Platform #0: Intel(R) OpenCL HD Graphics - **Nvidia GPU** -In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cublas)-* are installed. +In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. Installation can be verified by running the following: ```sh nvidia-smi @@ -313,10 +313,10 @@ found 6 SYCL devices: | 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616| ``` -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero driver/runtime, recommended | -|compute capability 3.0|OpenCL driver/runtime, slower than level-zero in most cases| +| Attribute | Note | +|------------------------|-------------------------------------------------------------| +| compute capability 1.3 | Level-zero driver/runtime, recommended | +| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases | 4. Launch inference @@ -325,10 +325,10 @@ There are two device selection modes: - Single device: Use one device target specified by the user. - Multiple devices: Automatically select the devices with the same largest Max compute-units. -|Device selection|Parameter| -|-|-| -|Single device|--split-mode none --main-gpu DEVICE_ID | -|Multiple devices|--split-mode layer (default)| +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | Examples: @@ -486,10 +486,10 @@ found 6 SYCL devices: ``` -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| +| Attribute | Note | +|------------------------|-----------------------------------------------------------| +| compute capability 1.3 | Level-zero running time, recommended | +| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases | 4. Launch inference @@ -499,10 +499,10 @@ 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)| +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | Examples: @@ -540,20 +540,20 @@ use 1 SYCL GPUs: [0] with Max compute units:512 #### Build -|Name|Value|Function| -|-|-|-| -|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.| -|LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA|Set the SYCL target device type.| -|LLAMA_SYCL_F16|OFF *(default)* \|ON *(optional)*|Enable FP16 build with SYCL code path.| -|CMAKE_C_COMPILER|icx|Set *icx* compiler for SYCL code path.| -|CMAKE_CXX_COMPILER|icpx *(Linux)*, icx *(Windows)*|Set `icpx/icx` compiler for SYCL code path.| +| Name | Value | Function | +|--------------------|-----------------------------------|---------------------------------------------| +| LLAMA_SYCL | ON (mandatory) | Enable build with SYCL code path. | +| LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. | +| LLAMA_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. | +| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. | +| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | #### Runtime -|Name|Value|Function| -|-|-|-| -|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| +| Name | Value | Function | +|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| +| 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 Issues @@ -591,6 +591,6 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ### **GitHub contribution**: Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay. -## Todo +## TODO - Support row layer split for multiple card runs. diff --git a/README.md b/README.md index 462b1b180c09c..00a487fc6868e 100644 --- a/README.md +++ b/README.md @@ -485,14 +485,14 @@ Building the program with BLAS support may lead to some performance improvements The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: - | Option | Legal values | Default | Description | - |--------------------------------|------------------------|---------|-------------| - | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | - | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | - | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | - | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | + | Option | Legal values | Default | Description | + |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | + | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | + | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | - #### hipBLAS @@ -534,11 +534,11 @@ Building the program with BLAS support may lead to some performance improvements If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): - | Option | Legal values | Default | Description | - |-------------------------|------------------------|---------|-------------| - | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | - | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | + | Option | Legal values | Default | Description | + |-------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - #### CLBlast @@ -746,11 +746,11 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. | Model | Original size | Quantized size (Q4_0) | -|------:|--------------:|-----------------------:| -| 7B | 13 GB | 3.9 GB | -| 13B | 24 GB | 7.8 GB | -| 30B | 60 GB | 19.5 GB | -| 65B | 120 GB | 38.5 GB | +|------:|--------------:|----------------------:| +| 7B | 13 GB | 3.9 GB | +| 13B | 24 GB | 7.8 GB | +| 30B | 60 GB | 19.5 GB | +| 65B | 120 GB | 38.5 GB | ### Quantization @@ -758,7 +758,7 @@ Several quantization methods are supported. They differ in the resulting model d *(outdated)* -| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | +| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| | 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | | 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | diff --git a/SECURITY.md b/SECURITY.md index 14504b1bfff39..f4322c6ee4d18 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -49,11 +49,11 @@ If you intend to run multiple models in parallel with shared memory, it is your 1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity. -1. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. +2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. -1. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. +3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. -1. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. +4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. ## Reporting a vulnerability diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 96b048525239f..413e433dd9c07 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -22,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## Model conversion -- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: +1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: ```sh git clone https://huggingface.co/mtgv/MobileVLM-1.7B diff --git a/examples/llava/README.md b/examples/llava/README.md index 67cb0f22b7d95..d4810d42e097c 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## LLaVA 1.5 -- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: +1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b diff --git a/examples/main/README.md b/examples/main/README.md index bb696b5620198..10a589cebb345 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -310,7 +310,7 @@ These options help improve the performance and memory usage of the LLaMA models. ### Quantization -For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run). +For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). ## Additional Options diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index 50e1af0111dd6..1a8c0dd6436e2 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -3,19 +3,18 @@ TODO ## Llama 2 70B Scorechart -Quantization | Model size (GiB) | Perplexity | Delta to fp16 --- | -- | -- | -- -Q4_0 | 36.20 | 3.5550 | 3.61% -Q4_1 | 40.20 | 3.5125 | 2.37% -Q5_0 | 44.20 | 3.4744 | 1.26% -Q2_K | 27.27 | 3.7339 | 8.82% -Q3_K_S | 27.86 | 3.7019 | 7.89% -Q3_K_M | 30.83 | 3.5932 | 4.72% -Q3_K_L | 33.67 | 3.5617 | 3.80% -Q4_K_S | 36.39 | 3.4852 | 1.57% -Q4_K_M | 38.54 | 3.4725 | 1.20% -Q5_K_S | 44.20 | 3.4483 | 0.50% -Q5_K_M | 45.41 | 3.4451 | 0.40% -Q6_K | 52.70 | 3.4367 | 0.16% -fp16 | 128.5 | 3.4313 | - - +| Quantization | Model size (GiB) | Perplexity | Delta to fp16 | +|--------------|------------------|------------|---------------| +| Q4_0 | 36.20 | 3.5550 | 3.61% | +| Q4_1 | 40.20 | 3.5125 | 2.37% | +| Q5_0 | 44.20 | 3.4744 | 1.26% | +| Q2_K | 27.27 | 3.7339 | 8.82% | +| Q3_K_S | 27.86 | 3.7019 | 7.89% | +| Q3_K_M | 30.83 | 3.5932 | 4.72% | +| Q3_K_L | 33.67 | 3.5617 | 3.80% | +| Q4_K_S | 36.39 | 3.4852 | 1.57% | +| Q4_K_M | 38.54 | 3.4725 | 1.20% | +| Q5_K_S | 44.20 | 3.4483 | 0.50% | +| Q5_K_M | 45.41 | 3.4451 | 0.40% | +| Q6_K | 52.70 | 3.4367 | 0.16% | +| fp16 | 128.5 | 3.4313 | - | diff --git a/examples/quantize/README.md b/examples/quantize/README.md index c8b9a27a0b04e..8a10365c07a60 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -4,17 +4,17 @@ TODO ## Llama 2 7B -Quantization | Bits per Weight (BPW) --- | -- -Q2_K | 3.35 -Q3_K_S | 3.50 -Q3_K_M | 3.91 -Q3_K_L | 4.27 -Q4_K_S | 4.58 -Q4_K_M | 4.84 -Q5_K_S | 5.52 -Q5_K_M | 5.68 -Q6_K | 6.56 +| Quantization | Bits per Weight (BPW) | +|--------------|-----------------------| +| Q2_K | 3.35 | +| Q3_K_S | 3.50 | +| Q3_K_M | 3.91 | +| Q3_K_L | 4.27 | +| Q4_K_S | 4.58 | +| Q4_K_M | 4.84 | +| Q5_K_S | 5.52 | +| Q5_K_M | 5.68 | +| Q6_K | 6.56 | ## Llama 2 13B Quantization | Bits per Weight (BPW) From 91c736015b66ba1d0b82cbae6313b6d5eaa61b68 Mon Sep 17 00:00:00 2001 From: jiez <373447296@qq.com> Date: Fri, 12 Apr 2024 18:45:06 +0800 Subject: [PATCH 17/48] llama : add gguf_remove_key + remove split meta during quantize (#6591) * Remove split metadata when quantize model shards * Find metadata key by enum * Correct loop range for gguf_remove_key and code format * Free kv memory --------- Co-authored-by: z5269887 --- ggml.c | 65 ++++++++++++++++++++++++++++++++++--------------------- ggml.h | 3 +++ llama.cpp | 4 ++++ 3 files changed, 47 insertions(+), 25 deletions(-) diff --git a/ggml.c b/ggml.c index 793b67f4c7020..90af4342da691 100644 --- a/ggml.c +++ b/ggml.c @@ -20550,6 +20550,32 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { return ok; } +static void gguf_free_kv(struct gguf_kv * kv) { + if (kv->key.data) { + GGML_FREE(kv->key.data); + } + + if (kv->type == GGUF_TYPE_STRING) { + if (kv->value.str.data) { + GGML_FREE(kv->value.str.data); + } + } + + if (kv->type == GGUF_TYPE_ARRAY) { + if (kv->value.arr.data) { + if (kv->value.arr.type == GGUF_TYPE_STRING) { + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { + struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; + if (str->data) { + GGML_FREE(str->data); + } + } + } + GGML_FREE(kv->value.arr.data); + } + } +} + struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); @@ -20899,31 +20925,7 @@ void gguf_free(struct gguf_context * ctx) { if (ctx->kv) { // free string memory - not great.. for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { - struct gguf_kv * kv = &ctx->kv[i]; - - if (kv->key.data) { - GGML_FREE(kv->key.data); - } - - if (kv->type == GGUF_TYPE_STRING) { - if (kv->value.str.data) { - GGML_FREE(kv->value.str.data); - } - } - - if (kv->type == GGUF_TYPE_ARRAY) { - if (kv->value.arr.data) { - if (kv->value.arr.type == GGUF_TYPE_STRING) { - for (uint64_t j = 0; j < kv->value.arr.n; ++j) { - struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; - if (str->data) { - GGML_FREE(str->data); - } - } - } - GGML_FREE(kv->value.arr.data); - } - } + gguf_free_kv(&ctx->kv[i]); } GGML_FREE(ctx->kv); @@ -21148,6 +21150,19 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) { return n_kv; } +void gguf_remove_key(struct gguf_context * ctx, const char * key) { + const int idx = gguf_find_key(ctx, key); + if (idx >= 0) { + const int n_kv = gguf_get_n_kv(ctx); + gguf_free_kv(&ctx->kv[idx]); + for (int i = idx; i < n_kv-1; ++i) { + ctx->kv[i] = ctx->kv[i+1]; + } + ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv)); + ctx->header.n_kv--; + } +} + void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) { const int idx = gguf_get_or_add_key(ctx, key); diff --git a/ggml.h b/ggml.h index abe3767f22418..e9ed8eeee7919 100644 --- a/ggml.h +++ b/ggml.h @@ -2289,6 +2289,9 @@ extern "C" { GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i); GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i); + // removes key if it exists + GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key); + // overrides existing values or adds a new one GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val); GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val); diff --git a/llama.cpp b/llama.cpp index dad2c4fbffb16..83dd55efa99e6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13535,6 +13535,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s 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); + // Remove split metadata + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); if (params->kv_overrides) { const std::vector & overrides = *(const std::vector *)params->kv_overrides; From 24ee66ed0d908d156bd0d1747b63a636a495cd7a Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 12 Apr 2024 13:49:21 +0200 Subject: [PATCH 18/48] server : coherent log output for KV cache full (#6637) --- examples/server/server.cpp | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2e791190b740a..b08a09a57bf37 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1083,7 +1083,7 @@ struct server_context { }; if (llama_decode(ctx, batch_view) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERROR("llama_decode() failed", {}); return; } } @@ -1281,7 +1281,11 @@ struct server_context { } 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()); + LOG_ERROR("task error", { + {"id_multi", id_multi}, + {"id_task", id_task}, + {"error", error}, + }); server_task_result res; res.id = id_task; @@ -2186,7 +2190,11 @@ struct server_context { 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); + LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", { + {"i", i}, + {"n_batch", ret}, + {"ret", ret}, + }); for (auto & slot : slots) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; @@ -2196,12 +2204,16 @@ struct server_context { 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); - // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; + LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", { + {"i", i}, + {"n_batch", n_batch}, + {"ret", ret}, + }); + continue; // continue loop of n_batch } From 4cc120c7443cf9dab898736f3c3b45dc8f14672b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 12 Apr 2024 14:11:46 +0200 Subject: [PATCH 19/48] infill : add download instructions for model (#6626) * infill : add download instructions for model This commit adds instructions on how to download a CodeLlama model using the `hf.sh` script. This will download the model and place it in the `models` directory which is the same model use later by the infill example. Signed-off-by: Daniel Bevenius * squash! infill : add download instructions for model Clarify the reason for using CodeLlama. Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/infill/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/examples/infill/README.md b/examples/infill/README.md index 8c97f719b00a0..6b076c8390abf 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -36,6 +36,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi ### Example +Download a model that supports infill, for example CodeLlama: +```console +scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models +``` + ```bash ./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " ``` From fbbc030ba93561fac842af994c5c6c4c1147f13b Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 12 Apr 2024 18:13:20 +0200 Subject: [PATCH 20/48] metal : unify mul_mv_id kernels (#6556) --- ggml-metal.m | 5 + ggml-metal.metal | 1189 ++++-------------------------------- ggml.c | 1 - tests/test-backend-ops.cpp | 1 + 4 files changed, 141 insertions(+), 1055 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 419d8b9e56878..38da384b18c58 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1926,7 +1926,12 @@ static enum ggml_status ggml_metal_graph_compute( { nth0 = 4; nth1 = 16; + #if QK_K == 64 + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; + #else pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; + #endif + } break; default: { diff --git a/ggml-metal.metal b/ggml-metal.metal index 9a29f57a38c6b..3a823e65be996 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -857,15 +857,16 @@ void mul_vec_q_n_f32_impl( device const void * src0, device const float * src1, device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, + 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, uint3 tgpig, uint tiisg, uint sgitg) { const int nb = ne00/QK4_0; @@ -942,7 +943,7 @@ kernel void kernel_mul_mv_q4_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q4_1_f32( @@ -968,7 +969,7 @@ kernel void kernel_mul_mv_q4_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_0_f32( @@ -994,7 +995,7 @@ kernel void kernel_mul_mv_q5_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_1_f32( @@ -1020,7 +1021,7 @@ kernel void kernel_mul_mv_q5_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } @@ -1039,6 +1040,7 @@ void kernel_mul_mv_q8_0_f32_impl( 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]]) { @@ -1119,7 +1121,7 @@ kernel void kernel_mul_mv_q8_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } #define N_F32_F32 4 @@ -2709,6 +2711,7 @@ void kernel_mul_mv_q2_K_f32_impl( 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]]) { @@ -2871,7 +2874,7 @@ kernel void kernel_mul_mv_q2_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } #if QK_K == 256 @@ -2888,6 +2891,7 @@ void kernel_mul_mv_q3_K_f32_impl( 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]]) { @@ -3046,6 +3050,7 @@ void kernel_mul_mv_q3_K_f32_impl( 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]]) { @@ -3135,7 +3140,7 @@ kernel void kernel_mul_mv_q3_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } #if QK_K == 256 @@ -3152,6 +3157,7 @@ void kernel_mul_mv_q4_K_f32_impl( 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]]) { @@ -3265,6 +3271,7 @@ void kernel_mul_mv_q4_K_f32_impl( 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]]) { @@ -3373,7 +3380,7 @@ kernel void kernel_mul_mv_q4_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q5_K_f32_impl( @@ -3389,6 +3396,7 @@ void kernel_mul_mv_q5_K_f32_impl( 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]]) { @@ -3579,7 +3587,7 @@ kernel void kernel_mul_mv_q5_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q6_K_f32_impl( @@ -3595,6 +3603,7 @@ void kernel_mul_mv_q6_K_f32_impl( 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]]) { @@ -3713,7 +3722,7 @@ kernel void kernel_mul_mv_q6_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } // ======================= "True" 2-bit @@ -4396,6 +4405,7 @@ void kernel_mul_mv_iq1_s_f32_impl( 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]]) { @@ -4485,6 +4495,7 @@ void kernel_mul_mv_iq1_m_f32_impl( 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]]) { @@ -4593,11 +4604,12 @@ void kernel_mul_mv_iq4_nl_f32_impl( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + threadgroup int8_t * shared_values_i8 [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { + threadgroup float * shared_values = (threadgroup float *)shared_values_i8; const int nb = ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -4687,11 +4699,11 @@ void kernel_mul_mv_iq4_xs_f32_impl( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + threadgroup int8_t * shared_values_i8 [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - + threadgroup float * shared_values = (threadgroup float *)shared_values_i8; const int nb = ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -4794,7 +4806,7 @@ kernel void kernel_mul_mv_iq1_s_f32( 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); + kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq1_m_f32")]] @@ -4822,7 +4834,7 @@ kernel void kernel_mul_mv_iq1_m_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_nl_f32")]] @@ -4846,7 +4858,7 @@ kernel void kernel_mul_mv_iq4_nl_f32( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + 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]]) { @@ -4875,7 +4887,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + 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]]) { @@ -6022,135 +6034,52 @@ template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel // matrix-vector multiplication // -[[host_name("kernel_mul_mv_id_f32_f32")]] -kernel void kernel_mul_mv_id_f32_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_f32_f32_impl( - src0, - src1 + bid*nb11, - dst + bid*ne0, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg); -} - -[[host_name("kernel_mul_mv_id_f16_f32")]] -kernel void kernel_mul_mv_id_f16_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; +typedef void (kernel_mul_mv_impl_t)( + device const char * src0, + device const char * 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]]); - kernel_mul_mv_f16_f32_impl( - src0, - src1 + bid*nb11, - dst + bid*ne0, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg); -} +typedef void (kernel_mul_mv2_impl_t)( + 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]]); -[[host_name("kernel_mul_mv_id_q8_0_f32")]] -kernel void kernel_mul_mv_id_q8_0_f32( - device const char * src0s, +template +void mmv_fn( + device const char * src0, device const char * src1, device float * dst, - device const char * ids, - constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -6169,43 +6098,19 @@ kernel void kernel_mul_mv_id_q8_0_f32( constant uint64_t & nb1, constant uint & r2, constant uint & r3, - constant int & idx, + 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q8_0_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); + impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,ne10,ne11,ne12,nb10,nb11,nb12,ne0,ne1,r2,r3,tgpig,tiisg); } -[[host_name("kernel_mul_mv_id_q4_0_f32")]] -kernel void kernel_mul_mv_id_q4_0_f32( - device const char * src0s, +template +void mmv_fn( + device const char * src0, device const char * src1, device float * dst, - device const char * ids, - constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -6224,43 +6129,18 @@ kernel void kernel_mul_mv_id_q4_0_f32( constant uint64_t & nb1, constant uint & r2, constant uint & r3, - constant int & idx, + 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); + impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); } -[[host_name("kernel_mul_mv_id_q4_1_f32")]] -kernel void kernel_mul_mv_id_q4_1_f32( - device const char * src0s, +typedef void (mul_mv_impl_fn_t)( + device const char * src0, device const char * src1, device float * dst, - device const char * ids, - constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -6279,38 +6159,14 @@ kernel void kernel_mul_mv_id_q4_1_f32( constant uint64_t & nb1, constant uint & r2, constant uint & r3, - constant int & idx, + 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} + uint sgitg[[simdgroup_index_in_threadgroup]]); -[[host_name("kernel_mul_mv_id_q5_0_f32")]] -kernel void kernel_mul_mv_id_q5_0_f32( +template +kernel void kernel_mul_mv_id( device const char * src0s, device const char * src1, device float * dst, @@ -6335,6 +6191,7 @@ kernel void kernel_mul_mv_id_q5_0_f32( constant uint & r2, constant uint & r3, constant int & idx, + 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]], @@ -6346,26 +6203,36 @@ kernel void kernel_mul_mv_id_q5_0_f32( const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; device const char * src0 = src0s + id*nb02; - mul_vec_q_n_f32_impl( + impl_fn( src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, + src1 + bid*nb11, + dst + bid*ne0, ne00, ne01, ne02, + nb00, + nb01, + nb02, ne10, + ne11, ne12, + ne13, + nb10, + nb11, + nb12, ne0, ne1, + nb1, r2, r3, + shared_values, tgpig, + tiitg, tiisg, sgitg); } -[[host_name("kernel_mul_mv_id_q5_1_f32")]] -kernel void kernel_mul_mv_id_q5_1_f32( +typedef void (kernel_mul_mv_id_t)( device const char * src0s, device const char * src1, device float * dst, @@ -6390,819 +6257,33 @@ kernel void kernel_mul_mv_id_q5_1_f32( constant uint & r2, constant uint & r3, constant int & idx, + 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]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); + uint sgitg[[simdgroup_index_in_threadgroup]]); + +template [[host_name("kernel_mul_mv_id_f32_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq1_m_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +#if QK_K != 64 +template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +#endif - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q2_K_f32")]] -kernel void kernel_mul_mv_id_q2_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q2_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q3_K_f32")]] -kernel void kernel_mul_mv_id_q3_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q3_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q4_K_f32")]] -kernel void kernel_mul_mv_id_q4_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q4_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q5_K_f32")]] -kernel void kernel_mul_mv_id_q5_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q5_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q6_K_f32")]] -kernel void kernel_mul_mv_id_q6_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q6_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] -kernel void kernel_mul_mv_id_iq2_xxs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq2_xxs_f32_impl( - src0, - (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_iq2_xs_f32")]] -kernel void kernel_mul_mv_id_iq2_xs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq2_xs_f32_impl( - src0, - (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_iq3_xxs_f32")]] -kernel void kernel_mul_mv_id_iq3_xxs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq3_xxs_f32_impl( - src0, - (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_iq3_s_f32")]] -kernel void kernel_mul_mv_id_iq3_s_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq3_s_f32_impl( - src0, - (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_iq2_s_f32")]] -kernel void kernel_mul_mv_id_iq2_s_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq2_s_f32_impl( - src0, - (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 * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq1_s_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq1_m_f32")]] -kernel void kernel_mul_mv_id_iq1_m_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq1_m_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq4_nl_f32")]] -kernel void kernel_mul_mv_id_iq4_nl_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq4_nl_f32_impl( - src0, - (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_iq4_xs_f32")]] -kernel void kernel_mul_mv_id_iq4_xs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - 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, - 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]]) { - 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]; - device const char * src0 = src0s + id*nb02; - -#if QK_K == 64 - kernel_mul_mv_iq4_nl_f32_impl( -#else - kernel_mul_mv_iq4_xs_f32_impl( -#endif - src0, - (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.c b/ggml.c index 90af4342da691..14288d29ded6b 100644 --- a/ggml.c +++ b/ggml.c @@ -11012,7 +11012,6 @@ static void ggml_compute_forward_mul_mat_id( } // initialize matrix_row_counts - GGML_ASSERT(wdata == wdata_src1_end); memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); // group rows by src0 matrix diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 51b3487b2a948..b50675952b51d 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2014,6 +2014,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op for (int n_mats : {2, 4, 8}) { for (int id = 0; id < n_mats; id++) { for (bool v : {false, true}) { + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 1, 256, v)); test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v)); } } From ab9a3240a9da941fdef5cd4a25f2b97c2f5a67aa Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Fri, 12 Apr 2024 19:43:38 +0100 Subject: [PATCH 21/48] =?UTF-8?q?JSON=20schema=20conversion:=20=E2=9A=A1?= =?UTF-8?q?=EF=B8=8F=20faster=20repetitions,=20min/maxLength=20for=20strin?= =?UTF-8?q?gs,=20cap=20number=20length=20(#6555)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * json: rename python schema converter to make import easier * server: skip null json_schema / grammar fields * json: deps management for primitive rules (+ allow null values) * json: optimize repetitions for minItems/maxItems and regexps: `a{,3}` goes from `"a"? "a"? "a"?` (explosive combos) to `(a (a (a)?)?)?` * grammars: add troubleshooting section to readme * json: cap length of numbers to 15 digits before/after decimal point (avoids infinite gen, e.g. "one third" -> `0.333333333333...`) * json: unify all repetition code (w/ or w/o sep) * json: support string minLength/maxLength * server+json: update server/README w/ result_format * nits * json: fix type error w/ python 3.8 * json: fix server/README (json_schema in /completion vs. result_format in /v1/chat/completions) * json: simplify DOT `{"type": "string", "pattern": "^.$"}` * json: remove recursion in opt_repetitions (avoids Python stack overflow) * json: rm dead code * json: rm useless assert & ggml.h import --- common/json-schema-to-grammar.cpp | 233 +- ...o-grammar.py => json_schema_to_grammar.py} | 212 +- examples/regex-to-grammar.py | 2 +- examples/server/README.md | 5 + .../server/json-schema-to-grammar.mjs.hpp | 3322 +++++++++-------- .../server/public/json-schema-to-grammar.mjs | 200 +- examples/server/server.cpp | 2 +- examples/ts-type-to-grammar.sh | 4 +- grammars/README.md | 10 + tests/test-json-schema-to-grammar.cpp | 243 +- 10 files changed, 2326 insertions(+), 1907 deletions(-) rename examples/{json-schema-to-grammar.py => json_schema_to_grammar.py} (73%) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 0e468034642f7..0f8f1b1d41bdc 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -11,35 +11,101 @@ using json = nlohmann::ordered_json; +template +static std::string join(Iterator begin, Iterator end, const std::string & separator); + +static std::string repeat(const std::string & str, size_t n); + +static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) { + if (separator_rule.empty()) { + if (min_items == 0 && max_items == 1) { + return item_rule + "?"; + } else if (min_items == 1 && max_items == std::numeric_limits::max()) { + return item_rule + "+"; + } + } + + std::string result; + if (min_items > 0) { + if (item_rule_is_literal && separator_rule.empty()) { + result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\""; + } else { + std::vector items(min_items, item_rule); + result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " "); + } + } + + std::function opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string { + auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule; + + if (up_to_n == 0) { + return ""; + } else if (up_to_n == 1) { + return "(" + content + ")?"; + } else if (!separator_rule.empty() && !prefix_with_sep) { + return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?"; + } else { + std::string res = repeat("(" + content + " ", up_to_n); + // strip trailing space + res = res.substr(0, res.length() - 1); + res += repeat(")?", up_to_n); + return res; + } + }; + + if (min_items > 0 && max_items != min_items) { + result += " "; + } + + if (max_items != std::numeric_limits::max()) { + result += opt_repetitions(max_items - min_items, min_items > 0); + } else { + std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")"; + if (min_items == 0 && !separator_rule.empty()) { + result = "(" + item_rule + " " + item_operator + "*)?"; + } else { + result += item_operator + "*"; + } + } + + return result; +} + 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] " +struct BuiltinRule { + std::string content; + std::vector deps; +}; + +const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15); + +std::unordered_map PRIMITIVE_RULES = { + {"boolean", {"(\"true\" | \"false\") space", {}}}, + {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}}, + {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}}, + {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}}, + {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}}, + {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}}, + {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, + {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, + {"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"} + "\"-\" [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", {}}}, + {"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}}, + {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, + {"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"} + +std::unordered_map STRING_FORMAT_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", "time"}}}, + {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}}, + {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}}, + {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}} }; static bool is_reserved_name(const std::string & name) { @@ -47,7 +113,7 @@ static bool is_reserved_name(const std::string & name) { 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); + for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first); } return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); } @@ -192,7 +258,7 @@ class SchemaConverter { if (_dotall) { rule = "[\\U00000000-\\U0010FFFF]"; } else { - rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]"; + rule = "[^\\x0A\\x0D]"; } return _add_rule("dot", rule); }; @@ -308,47 +374,21 @@ class SchemaConverter { 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 (!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); } - 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; + sub = sub_id; } + seq.back().first = build_repetition( + sub_is_literal ? "\"" + sub + "\"" : sub, + min_times, + max_times, + "", + sub_is_literal + ); + seq.back().second = false; } else { std::string literal; auto is_non_literal = [&](char c) { @@ -424,7 +464,7 @@ class SchemaConverter { 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); + std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule); prop_kv_rule_names["*"] = kv_rule; optional_props.push_back("*"); } @@ -486,6 +526,25 @@ class SchemaConverter { return rule; } + std::string _add_primitive(const std::string & name, const BuiltinRule & rule) { + auto n = _add_rule(name, rule.content); + for (const auto & dep : rule.deps) { + BuiltinRule dep_rule; + auto it = PRIMITIVE_RULES.find(dep); + if (it == PRIMITIVE_RULES.end()) { + it = STRING_FORMAT_RULES.find(dep); + if (it == STRING_FORMAT_RULES.end()) { + _errors.push_back("Rule " + dep + " not known"); + continue; + } + } + if (_rules.find(dep) == _rules.end()) { + _add_primitive(dep, it->second); + } + } + return n; + } + public: SchemaConverter( const std::function & fetch_json, @@ -647,49 +706,33 @@ class SchemaConverter { 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); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); + + return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); } } 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"; + return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); + } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { + auto prim_name = schema_format + "-string"; + return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name))); + } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + int min_len = schema.contains("minLength") ? schema["minLength"].get() : 0; + int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); + return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); } 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"); + return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("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())); + return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); } } diff --git a/examples/json-schema-to-grammar.py b/examples/json_schema_to_grammar.py similarity index 73% rename from examples/json-schema-to-grammar.py rename to examples/json_schema_to_grammar.py index 91dd734cc26ff..826cd3f7271d1 100755 --- a/examples/json-schema-to-grammar.py +++ b/examples/json_schema_to_grammar.py @@ -6,37 +6,94 @@ import sys from typing import Any, Dict, List, Set, Tuple, Union +def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False): + if not separator_rule: + if min_items == 0 and max_items == 1: + return f'{item_rule}?' + elif min_items == 1 and max_items is None: + return f'{item_rule}+' + + result = '' + + if min_items > 0: + if item_rule_is_literal and separator_rule is None: + result = '"' + (item_rule[1:-1] * min_items) + '"' + else: + result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items) + + def opt_repetitions(up_to_n, prefix_with_sep=False): + ''' + - n=4, no sep: '(a (a (a (a)?)?)?)?' + - n=4, sep=',', prefix: '("," a ("," a ("," a ("," a)?)?)?)?' + - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?' + ''' + + content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule + if up_to_n == 0: + return '' + elif up_to_n == 1: + return f'({content})?' + elif separator_rule and not prefix_with_sep: + return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?' + else: + return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n) + + if min_items > 0 and max_items != min_items: + result += ' ' + + if max_items is not None: + result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) + else: + item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})' + + if min_items == 0 and separator_rule: + result = f'({item_rule} {item_operator}*)?' + else: + result += f'{item_operator}*' + + return result + + +class BuiltinRule: + def __init__(self, content: str, deps: list = None): + self.content = content + self.deps = deps or [] + +_up_to_15_digits = _build_repetition('[0-9]', 0, 15) + # whitespace is constrained to a single space char to prevent model "running away" in # whitespace. Also maybe improves generation quality? SPACE_RULE = '" "?' 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' : '"\\"" ' + ' "-" '.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''', - 'null': '"null" space', + 'boolean' : BuiltinRule('("true" | "false") space', []), + 'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []), + 'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []), + 'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + 'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']), + 'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + 'uuid' : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []), + 'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []), + 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), + 'null' : BuiltinRule('"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', +STRING_FORMAT_RULES = { + 'date' : BuiltinRule('[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' : BuiltinRule('([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' : BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), } -RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()]) +DOTALL = '[\\U00000000-\\U0010FFFF]' +DOT = '[^\\x0A\\x0D]' + +RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()]) INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') @@ -46,8 +103,6 @@ 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, allow_fetch, dotall, raw_pattern): @@ -55,7 +110,9 @@ def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): self._allow_fetch = allow_fetch self._dotall = dotall self._raw_pattern = raw_pattern - self._rules = {'space': SPACE_RULE} + self._rules = { + 'space': SPACE_RULE, + } self._refs = {} self._refs_being_resolved = set() @@ -65,6 +122,29 @@ def _format_literal(self, literal): ) return f'"{escaped}"' + def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str: + ''' + not_literal('a') -> '[^a]' + not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?' + ''' + assert len(literal) > 0, 'Empty literal not supported' + def recurse(i: int): + c = literal[i] + if maybe_escaped_underscores and c == '_': + yield f'[^{c}\\\\]' + yield ' | ' + yield f'"\\\\"? "{c}"' + else: + yield f'[^{c}]' + if i < len(literal) - 1: + yield ' | ' + yield self._format_literal(c) + yield ' (' + yield from recurse(i + 1) + yield ')?' + + return ''.join(('(', *recurse(0), ')')) + def _add_rule(self, name, rule): esc_name = INVALID_RULE_CHARS_RE.sub('-', name) if esc_name not in self._rules or self._rules[esc_name] == rule: @@ -169,10 +249,10 @@ def transform() -> Tuple[str, bool]: def get_dot(): if self._dotall: - rule = '[\\U00000000-\\U0010FFFF]' + rule = DOTALL else: # Accept any character... except \n and \r line break chars (\x0A and \xOD) - rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]' + rule = DOT return self._add_rule(f'dot', rule) def join_seq(): @@ -246,26 +326,14 @@ def join_seq(): (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 - ) + 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] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False) else: literal = '' while i < length: @@ -373,49 +441,47 @@ def add_component(comp_schema, is_required): ' "]" space') else: 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) + return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space') 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( + return self._add_primitive( '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 in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES: + prim_name = f'{schema_format}-string' + return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name])) + + elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema): + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + min_len = schema.get('minLength', 0) + max_len = schema.get('maxLength') + + return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space') 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') + return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['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] - ) + return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type]) + + def _add_primitive(self, name: str, rule: BuiltinRule): + n = self._add_rule(name, rule.content) + + for dep in rule.deps: + dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep) + assert dep_rule, f'Rule {dep} not known' + if dep not in self._rules: + self._add_primitive(dep, dep_rule) + return n 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 @@ -437,7 +503,7 @@ def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[st 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}' + self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}' ) optional_props.append("*") diff --git a/examples/regex-to-grammar.py b/examples/regex-to-grammar.py index c98118e95a858..5cd9210a4dfc6 100644 --- a/examples/regex-to-grammar.py +++ b/examples/regex-to-grammar.py @@ -8,7 +8,7 @@ "python", os.path.join( os.path.dirname(os.path.realpath(__file__)), - "json-schema-to-grammar.py"), + "json_schema_to_grammar.py"), *rest, "-", "--raw-pattern", diff --git a/examples/server/README.md b/examples/server/README.md index a6fc92eaa9b29..918ac1295d660 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -11,6 +11,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp. * Continuous batching * Multimodal (wip) * Monitoring endpoints + * Schema-constrained JSON response format The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). @@ -250,6 +251,8 @@ node index.js `grammar`: Set grammar for grammar-based sampling. Default: no grammar + `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. + `seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. `ignore_eos`: Ignore end of stream token and continue generating. Default: `false` @@ -365,6 +368,8 @@ Notice that each `probs` is an array of length `n_probs`. See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. + The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}`), similar to other OpenAI-inspired API providers. + *Examples:* You can use either Python `openai` library with appropriate checkpoints: diff --git a/examples/server/json-schema-to-grammar.mjs.hpp b/examples/server/json-schema-to-grammar.mjs.hpp index ef1d5dcf3a2eb..da5035bf89f42 100644 --- a/examples/server/json-schema-to-grammar.mjs.hpp +++ b/examples/server/json-schema-to-grammar.mjs.hpp @@ -2,1643 +2,1825 @@ unsigned char json_schema_to_grammar_mjs[] = { 0x2f, 0x2f, 0x20, 0x57, 0x41, 0x52, 0x4e, 0x49, 0x4e, 0x47, 0x3a, 0x20, 0x54, 0x68, 0x69, 0x73, 0x20, 0x66, 0x69, 0x6c, 0x65, 0x20, 0x77, 0x61, 0x73, 0x20, 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json_schema_to_grammar_mjs_len = 19690; +unsigned int json_schema_to_grammar_mjs_len = 21866; diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index 17bd60fdedb8b..8e0be1b405e3c 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -1,33 +1,95 @@ -// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first. +// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first. const SPACE_RULE = '" "?'; +function _buildRepetition(itemRule, minItems, maxItems, opts={}) { + const separatorRule = opts.separatorRule ?? ''; + const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false + + if (separatorRule === '') { + if (minItems === 0 && maxItems === 1) { + return `${itemRule}?`; + } else if (minItems === 1 && maxItems === undefined) { + return `${itemRule}+`; + } + } + + let result = ''; + if (minItems > 0) { + if (itemRuleIsLiteral && separatorRule === '') { + result = `"${itemRule.slice(1, -1).repeat(minItems)}"`; + } else { + result = Array.from({ length: minItems }, () => itemRule) + .join(separatorRule !== '' ? ` ${separatorRule} ` : ' '); + } + } + + const optRepetitions = (upToN, prefixWithSep=false) => { + const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule; + if (upToN === 0) { + return ''; + } else if (upToN === 1) { + return `(${content})?`; + } else if (separatorRule !== '' && !prefixWithSep) { + return `(${content} ${optRepetitions(upToN - 1, true)})?`; + } else { + return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join(''); + } + }; + + if (minItems > 0 && maxItems !== minItems) { + result += ' '; + } + + if (maxItems !== undefined) { + result += optRepetitions(maxItems - minItems, minItems > 0); + } else { + const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`; + + if (minItems === 0 && separatorRule !== '') { + result = `(${itemRule} ${itemOperator}*)?`; + } else { + result += `${itemOperator}*`; + } + } + + return result; +} + +class BuiltinRule { + constructor(content, deps) { + this.content = content; + this.deps = deps || []; + } +} + +const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15); + 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', + boolean : new BuiltinRule('("true" | "false") space', []), + 'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []), + 'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []), + number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']), + value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + uuid : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []), + char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []), + string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']), + null : new BuiltinRule('"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 STRING_FORMAT_RULES = { + 'date' : new BuiltinRule('[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' : new BuiltinRule('([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' : new BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), +} -const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES}; +const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES}; const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; @@ -158,7 +220,7 @@ export class SchemaConverter { rule = '[\\U00000000-\\U0010FFFF]'; } else { // Accept any character... except \n and \r line break chars (\x0A and \xOD) - rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'; + rule = '[^\\x0A\\x0D]'; } return this._addRule('dot', rule); }; @@ -259,26 +321,19 @@ export class SchemaConverter { 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}-${Object.keys(subRuleIds).length + 1}`, sub); - subRuleIds[sub] = id; - } - sub = id; + if (!subIsLiteral) { + let id = subRuleIds[sub]; + if (id === undefined) { + id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub); + subRuleIds[sub] = id; } - - const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${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]; + sub = id; } + + seq[seq.length - 1] = [ + _buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}), + false + ]; } else { let literal = ''; while (i < length) { @@ -394,47 +449,48 @@ export class SchemaConverter { ); } else { const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`); - const listItemOperator = `( "," space ${itemRuleName} )`; - let successiveItems = ''; - let minItems = schema.minItems || 0; + const minItems = schema.minItems || 0; const maxItems = schema.maxItems; - if (minItems > 0) { - successiveItems = listItemOperator.repeat(minItems - 1); - minItems--; - } - if (maxItems !== undefined && maxItems > minItems) { - successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1); - } else { - successiveItems += `${listItemOperator}*`; - } - const rule = minItems === 0 - ? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space` - : `"[" space ${itemRuleName} ${successiveItems} "]" space`; - return this._addRule(ruleName, rule); + return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space'); } } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) { return this._visitPattern(schema.pattern, ruleName); } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.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'; + return this._addPrimitive( + ruleName === 'root' ? 'root' : schemaFormat, + PRIMITIVE_RULES['uuid'] + ); + } else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) { + const primName = `${schema.format}-string` + return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName])); + } else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) { + const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']); + const minLen = schema.minLength || 0; + const maxLen = schema.maxLength; + return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space'); } 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'); + return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object'])); } else { if (!(schemaType in PRIMITIVE_RULES)) { throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); } // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero - return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); + return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); + } + } + + _addPrimitive(name, rule) { + let n = this._addRule(name, rule.content); + for (const dep of rule.deps) { + const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep]; + if (!depRule) { + throw new Error(`Rule ${dep} not known`); + } + if (!(dep in this._rules)) { + this._addPrimitive(dep, depRule); + } } + return n; } _buildObjectRule(properties, required, name, additionalProperties) { @@ -462,7 +518,7 @@ export class SchemaConverter { const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`); propKvRuleNames['*'] = this._addRule( `${subName}-kv`, - `${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`); + `${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`); optionalProps.push('*'); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b08a09a57bf37..634e653ada284 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -859,7 +859,7 @@ struct server_context { slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); // process "json_schema" and "grammar" - if (data.contains("json_schema") && data.contains("grammar")) { + if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) { send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST); return false; } else if (data.contains("json_schema") && !data.contains("grammar")) { diff --git a/examples/ts-type-to-grammar.sh b/examples/ts-type-to-grammar.sh index a7ad001737c25..9abba2a3daa7d 100755 --- a/examples/ts-type-to-grammar.sh +++ b/examples/ts-type-to-grammar.sh @@ -1,7 +1,7 @@ #!/bin/bash # # ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}" -# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json +# python examples/json_schema_to_grammar.py https://json.schemastore.org/tsconfig.json # set -euo pipefail @@ -25,4 +25,4 @@ npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type M # 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 "$SCHEMA_FILE" +./examples/json_schema_to_grammar.py "$SCHEMA_FILE" diff --git a/grammars/README.md b/grammars/README.md index e1383fa5c6a58..c924e8d46e5cb 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -89,3 +89,13 @@ This guide provides a brief overview. Check out the GBNF files in this directory ``` ./main -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' ``` + +## Troubleshooting + +Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218). + +### Efficient optional repetitions + +A common pattern is to allow repetitions of a pattern `x` up to N times. + +While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) will result in extremely slow inference. Instead, you can write `(x (x (x ... (x)?...)?)?)?` (w/ N-deep nesting) diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 4d110cf5aab86..b2ce4d260a5a4 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -104,16 +104,16 @@ static void test_all(const std::string & lang, std::function test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + "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 { From 4bd0f93e4ab4fe6682e7d0241c1bdec1397e954a Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Sat, 13 Apr 2024 11:33:52 +0200 Subject: [PATCH 22/48] model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com> --- README.md | 1 + convert-hf-to-gguf.py | 96 ++++++ examples/eval-callback/eval-callback.cpp | 26 +- gguf-py/gguf/constants.py | 15 + gguf-py/gguf/tensor_mapping.py | 58 ++-- llama.cpp | 375 ++++++++++++++++------- scripts/get-wikitext-2.sh | 3 +- 7 files changed, 427 insertions(+), 147 deletions(-) diff --git a/README.md b/README.md index 00a487fc6868e..fa2a4db834df2 100644 --- a/README.md +++ b/README.md @@ -94,6 +94,7 @@ Typically finetunes of the base models below are supported as well. - [x] LLaMA 2 🦙🦙 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) +- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct) - [X] Falcon - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 63710676bad1c..e1ac09e024b11 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1427,6 +1427,102 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("DbrxForCausalLM") +class DbrxModel(Model): + model_arch = gguf.MODEL_ARCH.DBRX + + def set_gguf_parameters(self): + ffn_config = self.hparams["ffn_config"] + attn_config = self.hparams["attn_config"] + self.gguf_writer.add_name(self.hparams["model_type"]) + self.gguf_writer.add_block_count(self.hparams["n_layers"]) + + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"]) + + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"]) + + self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"]) + + self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"]) + self.gguf_writer.add_file_type(self.ftype) + + self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"]) + self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"]) + + self.gguf_writer.add_layer_norm_eps(1e-5) + + 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") + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + n_expert = self.hparams["ffn_config"]["moe_num_experts"] + n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] + n_embd = self.hparams["d_model"] + + # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose + # original implementation expects (n_expert, n_ff, n_embd) for all experts weights + # But llama.cpp moe graph works differently + # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions + # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor + exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} + "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + experts = False + for exp_tensor_name in exp_tensor_names.keys(): + if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: + experts = True + data_torch = data_torch.view(n_expert, n_ff, n_embd) + if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: + data_torch = data_torch.permute(*permute_tensor) + break + + 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 + # In MoE models the ffn tensors are typically most of the model weights, + # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. + # Every other model has the weight names ending in .weight, + # let's assume that is the convention which is not the case for dbrx: + # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 + new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # Most of the codebase that takes in 1D tensors only handles F32 tensors + # and most of the outputs tensors are F32. + if data_dtype != np.float32 and n_dims == 1: + print(f"Can not map tensor {name!r}: all 1D tensors must be F32") + sys.exit() + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + 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 n_dims > 1: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + @Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 05f7d6ab1a2d3..29b5f3b3c12c8 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -28,14 +28,27 @@ static std::string ggml_ne_string(const ggml_tensor * t) { } static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { + GGML_ASSERT(n > 0); float sum = 0; for (int64_t i3 = 0; i3 < ne[3]; i3++) { printf(" [\n"); - for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) { + for (int64_t i2 = 0; i2 < ne[2]; i2++) { + if (i2 == n && ne[2] > 2*n) { + printf(" ..., \n"); + i2 = ne[2] - n; + } printf(" [\n"); - for (int64_t i1 = 0; i1 < ne[1] && i1 < n; i1++) { + for (int64_t i1 = 0; i1 < ne[1]; i1++) { + if (i1 == n && ne[1] > 2*n) { + printf(" ..., \n"); + i1 = ne[1] - n; + } printf(" ["); - for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) { + for (int64_t i0 = 0; i0 < ne[0]; i0++) { + if (i0 == n && ne[0] > 2*n) { + printf("..., "); + i0 = ne[0] - n; + } size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; float v; if (type == GGML_TYPE_F16) { @@ -51,17 +64,14 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne } else { GGML_ASSERT(false); } - printf("%8.4f", v); + printf("%12.4f", v); sum += v; - if (i0 < ne[0] - 1 && i0 < n - 1) printf(", "); + if (i0 < ne[0] - 1) printf(", "); } - if (ne[0] > n) printf(", ..."); printf("],\n"); } - if (ne[1] > n) printf(" ...\n"); printf(" ],\n"); } - if (ne[2] > n) printf(" ...\n"); printf(" ]\n"); printf(" sum = %f\n", sum); } diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a6454a10e20b9..2566b2fb8f296 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -126,6 +126,7 @@ class MODEL_ARCH(IntEnum): MAMBA = auto() XVERSE = auto() COMMAND_R = auto() + DBRX = auto() class MODEL_TENSOR(IntEnum): @@ -195,6 +196,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.MAMBA: "mamba", MODEL_ARCH.XVERSE: "xverse", MODEL_ARCH.COMMAND_R: "command-r", + MODEL_ARCH.DBRX: "dbrx", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -642,6 +644,19 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_Q_NORM, ], + MODEL_ARCH.DBRX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], # TODO } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 4f02d298e13de..ec6fcbb838425 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -10,7 +10,7 @@ class TensorNameMap: # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact qwen + "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx "transformer.word_embeddings", # falcon "word_embeddings", # bloom "model.embed_tokens", # llama-hf @@ -48,7 +48,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -60,7 +60,7 @@ class TensorNameMap: "transformer.ln_f", # gpt2 gpt-j falcon "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth - "transformer.norm_f", # mpt + "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon @@ -96,6 +96,7 @@ class TensorNameMap: "model.layers.{bid}.norm", # mamba-qbert "backbone.layers.{bid}.norm", # mamba "transformer.decoder_layer.{bid}.rms_norm", # Grok + "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx ), # Attention norm 2 @@ -108,6 +109,7 @@ class TensorNameMap: "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox "transformer.h.{bid}.attn.c_attn", # gpt2 qwen "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon @@ -152,23 +154,24 @@ class TensorNameMap: # Attention output MODEL_TENSOR.ATTN_OUT: ( - "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen - "transformer.blocks.{bid}.attn.out_proj", # mpt - "transformer.h.{bid}.self_attention.dense", # falcon - "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf - "layers.{bid}.attention.wo", # llama-pth - "encoder.layer.{bid}.attention.output.dense", # bert - "transformer.h.{bid}.attn.out_proj", # gpt-j - "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon - "model.layers.{bid}.self_attn.dense", # persimmon - "h.{bid}.attn.c_proj", # gpt2 - "transformer.h.{bid}.mixer.out_proj", # phi2 - "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 + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "model.layers.{bid}.self_attn.dense", # persimmon + "h.{bid}.attn.c_proj", # gpt2 + "transformer.h.{bid}.mixer.out_proj", # phi2 + "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 + "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx ), # Attention output norm @@ -176,6 +179,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.LayerNorm", # bert "encoder.layers.{bid}.norm1", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_1", # Grok + "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx ), # Rotary embeddings @@ -202,9 +206,10 @@ class TensorNameMap: ), 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 + "layers.{bid}.feed_forward.gate", # mixtral + "model.layers.{bid}.block_sparse_moe.gate", # mixtral + "transformer.decoder_layer.{bid}.router", # Grok + "transformer.blocks.{bid}.ffn.router.layer", # dbrx ), # Feed-forward up @@ -233,6 +238,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_UP_EXP: ( "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx ), # AWQ-activation gate @@ -251,8 +257,9 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_GATE_EXP: ( - "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear" # Grok (merged) + "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx ), # Feed-forward down @@ -280,6 +287,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_DOWN_EXP: ( "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx ), MODEL_TENSOR.ATTN_Q_NORM: ( diff --git a/llama.cpp b/llama.cpp index 83dd55efa99e6..b93c1abcd85d6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -105,7 +105,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 8 +#define LLAMA_MAX_EXPERTS 16 // @@ -220,6 +220,7 @@ enum llm_arch { LLM_ARCH_MAMBA, LLM_ARCH_XVERSE, LLM_ARCH_COMMAND_R, + LLM_ARCH_DBRX, LLM_ARCH_UNKNOWN, }; @@ -252,6 +253,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_XVERSE, "xverse" }, { LLM_ARCH_COMMAND_R, "command-r" }, + { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -934,6 +936,22 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, + { + LLM_ARCH_DBRX, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1707,6 +1725,7 @@ enum e_model { MODEL_XL, MODEL_8x7B, MODEL_8x22B, + MODEL_16x12B, }; static const size_t kiB = 1024; @@ -3562,6 +3581,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_XL: return "1.5B"; case MODEL_8x7B: return "8x7B"; case MODEL_8x22B: return "8x22B"; + case MODEL_16x12B: return "16x12B"; default: return "?B"; } } @@ -3983,6 +4003,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_DBRX: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv); + + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_16x12B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4671,6 +4701,39 @@ static bool llm_load_tensors( layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); } } break; + case LLM_ARCH_DBRX: + { + if (n_expert == 0) { + throw std::runtime_error("DBRX model cannot have zero experts"); + } + + 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}); + } + + 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.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}); + + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + } + } break; case LLM_ARCH_BAICHUAN: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -6433,62 +6496,7 @@ struct llm_build_context { 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_exps, 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_exps, selected_experts, i, cur); - cb(cur_gate, "ffn_moe_gate", il); - - cur_gate = ggml_silu(ctx0, cur_gate); - cb(cur_gate, "ffn_moe_silu", il); - - cur_expert = ggml_mul(ctx0, cur_up, cur_gate); - cb(cur_expert, "ffn_moe_gate_par", il); - - cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, 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; + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il); } cur = ggml_add(ctx0, cur, ffn_inp); @@ -6520,6 +6528,78 @@ struct llm_build_context { return gf; } + // REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505 + ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, int 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_exps, selected_experts, i, cur); + cb(cur_up, "ffn_moe_up", il); + + ggml_tensor * gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur); + cb(gate, "ffn_moe_gate", il); + + switch (type_op) { + case LLM_FFN_SILU: + { + gate = ggml_silu(ctx0, gate); + cb(gate, "ffn_moe_silu", il); + } break; + case LLM_FFN_GELU: + { + gate = ggml_gelu(ctx0, gate); + cb(gate, "ffn_moe_gelu", il); + } break; + default: + GGML_ASSERT(false); + } + + cur_expert = ggml_mul(ctx0, cur_up, gate); + cb(cur_expert, "ffn_moe_gate_par", il); + + cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, 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); + } + } + + return moe_out; + } + struct ggml_cgraph * build_baichuan() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -6967,74 +7047,143 @@ struct llm_build_context { 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); + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il); + + // 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; - ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts] - cb(probs, "ffn_moe_probs", il); + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); - // 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); + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); - 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); + // Grok + // multiply logits by output_multiplier_scale of 0.5773502691896257 - weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok] + cur = ggml_scale(ctx0, cur, 0.5773502691896257f); - ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); - cb(weights_sum, "ffn_moe_weights_sum", il); + cb(cur, "result_output", -1); - weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok] - cb(weights, "ffn_moe_weights_norm", il); + ggml_build_forward_expand(gf, cur); - // compute expert outputs - ggml_tensor * moe_out = nullptr; + return gf; + } - for (int i = 0; i < n_expert_used; ++i) { - ggml_tensor * cur_expert; + struct ggml_cgraph * build_dbrx() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur); - cb(cur_up, "ffn_moe_up", il); + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; - ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur); - cb(cur_gate, "ffn_moe_gate", il); + 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); + GGML_ASSERT(n_embd_head == hparams.n_rot); - //GeLU - cur_gate = ggml_gelu(ctx0, cur_gate); - cb(cur_gate, "ffn_moe_gelu", il); + struct ggml_tensor * cur; + struct ggml_tensor * inpL; - cur_expert = ggml_mul(ctx0, cur_up, cur_gate); - cb(cur_expert, "ffn_moe_gate_par", il); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd] - cb(cur_expert, "ffn_moe_down", il); + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); - 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); + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - if (i == 0) { - moe_out = cur_expert; - } else { - moe_out = ggml_add(ctx0, moe_out, cur_expert); - cb(moe_out, "ffn_moe_out", il); - } - } + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; - cur = moe_out; + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); - // 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); + // self-attention + { + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; + + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(cur, "wqkv_clamped", il); + + 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); + 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, NULL, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + 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].attn_out_norm, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_out_norm", il); + + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il); cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -7052,18 +7201,13 @@ struct llm_build_context { cur = inpL; cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, NULL, - LLM_NORM_RMS, cb, -1); + model.output_norm, NULL, + LLM_NORM, 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); @@ -9785,6 +9929,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_command_r(); } break; + case LLM_ARCH_DBRX: + { + result = llm.build_dbrx(); + } break; default: GGML_ASSERT(false); } @@ -14638,6 +14786,7 @@ 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_DBRX: case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh index 7ca760fa61304..b01476a46015a 100755 --- a/scripts/get-wikitext-2.sh +++ b/scripts/get-wikitext-2.sh @@ -1,10 +1,11 @@ #!/bin/bash wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip +unzip wikitext-2-raw-v1.zip echo "Usage:" echo "" -echo " ./perplexity -m model.gguf -f wiki.test.raw [other params]" +echo " ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]" echo "" exit 0 From b5e7285baffb0da8a6619567b52d8e67de41291d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 14 Apr 2024 00:21:55 +0200 Subject: [PATCH 23/48] CUDA: fix matrix multiplication logic for tests (#6667) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bff8ad9d96e88..e62f45b6160e5 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1946,7 +1946,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } 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) { + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || fp16_performance_good) && !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) { From de17e3f7455dc7fd298cc61d86798533b9ca7a29 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Sun, 14 Apr 2024 10:42:29 +0800 Subject: [PATCH 24/48] fix memcpy() crash, add missed cmd in guide, fix softmax (#6622) * disable mmap to fix memcpy crash, add missed cmd in guide, fix softmax * refactor to disable mmap for SYCL backend * fix compile error in other os * refactor the solution, use host buf to fix it, instead of disable mmap * keep to support mmap() * use host buff to reduce malloc times * revert to malloc/free solution, for threaad safe --- README-sycl.md | 58 ++++++++++--------------------------- examples/sycl/build.sh | 2 +- examples/sycl/run-llama2.sh | 1 + ggml-sycl.cpp | 19 ++++++++---- 4 files changed, 32 insertions(+), 48 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 169d2ca0b60e0..f6dbfd87864e4 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -68,7 +68,7 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, | Intel GPU | Status | Verified Model | |-------------------------------|---------|---------------------------------------| -| Intel Data Center Max Series | Support | Max 1550 | +| Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | | Intel Arc Series | Support | Arc 770, 730M | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | @@ -84,8 +84,7 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, - **Execution Unit (EU)** - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use. -### Nvidia GPU -The BLAS acceleration on Nvidia GPU through oneAPI can be obtained using the Nvidia plugins for oneAPI and the cuBLAS backend of the upstream oneMKL library. Details and instructions on how to setup the runtime and library can be found in [this section](#i-setup-environment) +### Other Vendor GPU **Verified devices** @@ -94,14 +93,9 @@ The BLAS acceleration on Nvidia GPU through oneAPI can be obtained using the Nvi | Ampere Series | Support | A100, A4000 | | Ampere Series *(Mobile)* | Support | RTX 40 Series | -*Notes:* - - Support for Nvidia targets through oneAPI is currently limited to Linux platforms. - - - Please make sure the native oneAPI MKL *(dedicated to intel CPUs and GPUs)* is not "visible" at this stage to properly setup and use the built-from-source oneMKL with cuBLAS backend in llama.cpp for Nvidia GPUs. - - ## Docker The docker build option is currently limited to *intel GPU* targets. + ### Build image ```sh # Using FP16 @@ -168,29 +162,10 @@ Platform #0: Intel(R) OpenCL HD Graphics - **Nvidia GPU** In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. -Installation can be verified by running the following: -```sh -nvidia-smi -``` -Please make sure at least one CUDA device is available, which can be displayed like this *(here an A100-40GB Nvidia GPU)*: -``` -+---------------------------------------------------------------------------------------+ -| NVIDIA-SMI 535.54.03 Driver Version: 535.54.03 CUDA Version: 12.2 | -|-----------------------------------------+----------------------+----------------------+ -| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | -| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | -| | | MIG M. | -|=========================================+======================+======================| -| 0 NVIDIA A100-PCIE-40GB On | 00000000:8D:00.0 Off | 0 | -| N/A 36C P0 57W / 250W | 4MiB / 40960MiB | 0% Default | -| | | Disabled | -+-----------------------------------------+----------------------+----------------------+ -``` - 2. **Install Intel® oneAPI Base toolkit** -- **Base installation** +- **For Intel GPU** The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. @@ -202,10 +177,10 @@ Upon a successful installation, SYCL is enabled for the available intel devices, - **Adding support to Nvidia GPUs** -**oneAPI**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. +**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. -**oneMKL**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs. +**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs. ```sh git clone https://github.com/oneapi-src/oneMKL @@ -237,7 +212,7 @@ When targeting an intel GPU, the user should expect one or more level-zero devic - **Nvidia GPU** -Similarly, user targetting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow: +Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow: ``` [opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix] [opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix] @@ -260,6 +235,9 @@ cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icp # Option 2: Use FP32 by default cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +#build all binary +cmake --build . --config Release -j -v ``` #### Nvidia GPU @@ -278,6 +256,10 @@ cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=i # Option 2: Use FP32 by default cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +#build all binary +cmake --build . --config Release -j -v + ``` ### III. Run the inference @@ -357,7 +339,6 @@ Otherwise, you can run the script: *Notes:* -- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `/bin/main` if faced with the issue. - Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: ```sh @@ -438,7 +419,7 @@ cd build cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON -make +make -j ``` Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions: @@ -525,7 +506,6 @@ Otherwise, run the following wrapper script: Note: -- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `main.exe` if faced with the issue. - Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: ```sh @@ -557,12 +537,6 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ## Known Issues -- Hanging during startup - - llama.cpp uses *mmap* as the default mode for reading the model file and copying it to the GPU. In some systems, `memcpy` might behave abnormally and therefore hang. - - - **Solution**: add `--no-mmap` or `--mmap 0` flag to the `main` executable. - - `Split-mode:[row]` is not supported. ## Q&A @@ -574,7 +548,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 - General compiler error: - - Remove build folder or try a clean-build. + - Remove **build** folder or try a clean-build. - I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux. diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh index f20391d7a0000..db46d57cabe0b 100755 --- a/examples/sycl/build.sh +++ b/examples/sycl/build.sh @@ -20,4 +20,4 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx #cmake --build . --config Release --target llama-bench #build all binary -cmake --build . --config Release -v +cmake --build . --config Release -j -v diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index c979a52f6f5a0..7b39a18c0681d 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -12,6 +12,7 @@ if [ $# -gt 0 ]; then GGML_SYCL_SINGLE_GPU=1 else GGML_SYCL_DEVICE=0 + GGML_SYCL_SINGLE_GPU=0 fi #export GGML_SYCL_DEBUG=1 diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 55a1eedb553b1..86091cfbfbde5 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3154,7 +3154,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define SYCL_SCALE_BLOCK_SIZE 256 #define SYCL_CLAMP_BLOCK_SIZE 256 #define SYCL_ROPE_BLOCK_SIZE 256 -#define SYCL_SOFT_MAX_BLOCK_SIZE 1024 #define SYCL_ALIBI_BLOCK_SIZE 32 #define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 #define SYCL_QUANTIZE_BLOCK_SIZE 256 @@ -13080,11 +13079,13 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * 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; + int max_block_size = g_work_group_size; + while (nth < ncols_x && nth < max_block_size) nth *= 2; + if (nth>max_block_size) nth = max_block_size; + 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)); @@ -13094,6 +13095,12 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * const size_t local_mem_size = stream->get_device().get_info(); if (n_local_scratch*sizeof(float) < local_mem_size) { + if (ncols_x > max_block_size) { + 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); + return; + } switch (ncols_x) { case 32: soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, @@ -16814,11 +16821,13 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - + char* host_buf = (char*)malloc(size); + memcpy(host_buf, data, size); SYCL_CHECK( CHECK_TRY_ERROR((*stream) - .memcpy((char *)tensor->data + offset, data, size) + .memcpy((char *)tensor->data + offset, host_buf, size) .wait())); + free(host_buf); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ From a4ec34e1cd68560e7ff16e21ddc6962b6abd3d1d Mon Sep 17 00:00:00 2001 From: James A Capozzoli <157492257+jac-jim@users.noreply.github.com> Date: Sun, 14 Apr 2024 04:40:18 -0400 Subject: [PATCH 25/48] convert : enable the `--use-temp-file` cli flag (#6645) --- convert-hf-to-gguf.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index e1ac09e024b11..b51d683075316 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -43,17 +43,18 @@ class SentencePieceTokenTypes(IntEnum): class Model(ABC): _model_classes: dict[str, type[Model]] = {} - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool): self.dir_model = dir_model self.ftype = ftype self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.use_temp_file = use_temp_file self.is_safetensors = self._is_model_safetensors() 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.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) @property @@ -2459,6 +2460,7 @@ def parse_args() -> argparse.Namespace: "model", type=Path, help="directory containing model file", ) + parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)") return parser.parse_args() @@ -2502,7 +2504,7 @@ def main() -> None: with torch.inference_mode(): model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file) print("Set model parameters") model_instance.set_gguf_parameters() From e689fc4e912feb19085be6894f475a873759cbfe Mon Sep 17 00:00:00 2001 From: Jaemin Son Date: Sun, 14 Apr 2024 20:12:36 +0900 Subject: [PATCH 26/48] [bug fix] convert github repository_owner to lowercase (#6673) --- .github/workflows/docker.yml | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index eefd87878cbc1..9b03d19bc77c6 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -91,6 +91,12 @@ jobs: echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT fi + - name: Downcase github.repository_owner + run: | + echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV + env: + GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' + - name: Build and push Docker image (versioned) if: github.event_name == 'push' uses: docker/build-push-action@v4 @@ -98,7 +104,7 @@ jobs: context: . push: true platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" + tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" file: ${{ matrix.config.dockerfile }} - name: Build and push Docker image (tagged) @@ -107,5 +113,5 @@ jobs: context: . push: ${{ github.event_name == 'push' }} platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" + tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" file: ${{ matrix.config.dockerfile }} From 8800226d65d5c98cd34eede6a6c05c78405c52da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 14 Apr 2024 13:12:59 +0200 Subject: [PATCH 27/48] Fix --split-max-size (#6655) * Fix --split-max-size Byte size calculation was done on int and overflowed. * add tests.sh * add examples test scripts to ci run Will autodiscover examples/*/tests.sh scripts and run them. * move WORK_PATH to a subdirectory * clean up before and after test * explicitly define which scripts to run * add --split-max-size to readme --- ci/run.sh | 49 ++++++++++++++++ examples/gguf-split/README.md | 1 + examples/gguf-split/gguf-split.cpp | 4 +- examples/gguf-split/tests.sh | 89 ++++++++++++++++++++++++++++++ 4 files changed, 141 insertions(+), 2 deletions(-) create mode 100644 examples/gguf-split/tests.sh diff --git a/ci/run.sh b/ci/run.sh index 19776b5f7c6ff..085dfd42faf32 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -153,6 +153,52 @@ function gg_sum_ctest_release { gg_printf '```\n' } +# test_scripts_debug + +function gg_run_test_scripts_debug { + cd ${SRC} + + set -e + + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + +# test_scripts_release + +function gg_run_test_scripts_release { + cd ${SRC} + + set -e + + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + function gg_get_model { local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf" local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" @@ -642,6 +688,9 @@ test $ret -eq 0 && gg_run ctest_release if [ -z ${GG_BUILD_LOW_PERF} ]; then test $ret -eq 0 && gg_run embd_bge_small + test $ret -eq 0 && gg_run test_scripts_debug + test $ret -eq 0 && gg_run test_scripts_release + if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then test $ret -eq 0 && gg_run open_llama_3b_v2 diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md index ddb1f76497aed..ad1d86651b46d 100644 --- a/examples/gguf-split/README.md +++ b/examples/gguf-split/README.md @@ -5,5 +5,6 @@ CLI to split / merge GGUF files. **Command line options:** - `--split`: split GGUF to multiple GGUF, default operation. +- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`. - `--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 index 24acbf02a4eed..39c75e0a7a802 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -59,10 +59,10 @@ static size_t split_str_to_n_bytes(std::string str) { int n; if (str.back() == 'M') { sscanf(str.c_str(), "%d", &n); - n_bytes = n * 1024 * 1024; // megabytes + n_bytes = (size_t)n * 1024 * 1024; // megabytes } else if (str.back() == 'G') { sscanf(str.c_str(), "%d", &n); - n_bytes = n * 1024 * 1024 * 1024; // gigabytes + n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes } else { throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back())); } diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh new file mode 100644 index 0000000000000..879522f7e2512 --- /dev/null +++ b/examples/gguf-split/tests.sh @@ -0,0 +1,89 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + echo "usage: $0 path_to_build_binary [path_to_temp_folder]" + echo "example: $0 ../../build/bin ../../tmp" + exit 1 +fi + +if [ $# -gt 1 ] +then + TMP_DIR=$2 +else + TMP_DIR=/tmp +fi + +set -x + +SPLIT=$1/gguf-split +MAIN=$1/main +WORK_PATH=$TMP_DIR/gguf-split +CUR_DIR=$(pwd) + +mkdir -p "$WORK_PATH" + +# Clean up in case of previously failed test +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf + +# 1. Get a model +( + cd $WORK_PATH + "$CUR_DIR"/../../scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf +) +echo PASS + +# 2. Split with max tensors strategy +$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split +echo PASS +echo + +# 2b. Test the sharded model is loading properly +$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# 3. Merge +$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf +echo PASS +echo + +# 3b. Test the merged model is loading properly +$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# 4. Split with no tensor in metadata +#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors +#echo PASS +#echo + +# 4b. Test the sharded model is loading properly +#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32 +#echo PASS +#echo + +# 5. Merge +#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf +#echo PASS +#echo + +# 5b. Test the merged model is loading properly +#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32 +#echo PASS +#echo + +# 6. Split with size strategy +$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G +echo PASS +echo + +# 6b. Test the sharded model is loading properly +$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# Clean up +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf From 422c2aff1c9735853c9d8f5162104e41a364adc4 Mon Sep 17 00:00:00 2001 From: Dave Date: Sun, 14 Apr 2024 07:14:19 -0400 Subject: [PATCH 28/48] Added support for GGML_OP_CLAMP in Metal (#6662) * Added support for GGML_OP_CLAMP in Metal * Corrected size --------- Co-authored-by: dave-fl --- ggml-metal.m | 22 ++++++++++++++++++++++ ggml-metal.metal | 9 +++++++++ 2 files changed, 31 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 38da384b18c58..0207b787a0255 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -37,6 +37,7 @@ GGML_METAL_KERNEL_TYPE_DIV_ROW, GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, + GGML_METAL_KERNEL_TYPE_CLAMP, GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, GGML_METAL_KERNEL_TYPE_GELU, @@ -468,6 +469,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); @@ -713,6 +715,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_SCALE: + case GGML_OP_CLAMP: case GGML_OP_SQR: case GGML_OP_SUM_ROWS: return true; @@ -1154,6 +1157,25 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_OP_CLAMP: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; + + float min; + float max; + memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { case GGML_UNARY_OP_TANH: diff --git a/ggml-metal.metal b/ggml-metal.metal index 3a823e65be996..56748166ca4ca 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -213,6 +213,15 @@ kernel void kernel_scale_4( dst[tpig] = src0[tpig] * scale; } +kernel void kernel_clamp( + device const float * src0, + device float * dst, + constant float & min, + constant float & max, + uint tpig[[thread_position_in_grid]]) { + dst[tpig] = src0[tpig] < min ? min : (src0[tpig] > max ? max : src0[tpig]); +} + kernel void kernel_relu( device const float * src0, device float * dst, From f184dd920852d6d372b754f871ee06cfe6f977ad Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Apr 2024 16:55:30 +0300 Subject: [PATCH 29/48] flake.lock: Update (#6669) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index ed48dd8da7aca..2e2ab493289af 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1712163089, - "narHash": "sha256-Um+8kTIrC19vD4/lUCN9/cU9kcOsD1O1m+axJqQPyMM=", + "lastModified": 1712791164, + "narHash": "sha256-3sbWO1mbpWsLepZGbWaMovSO7ndZeFqDSdX0hZ9nVyw=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "fd281bd6b7d3e32ddfa399853946f782553163b5", + "rev": "1042fd8b148a9105f3c0aca3a6177fd1d9360ba5", "type": "github" }, "original": { From 04fbc5f23e9708136ff03ebe194c7a7e965a7ca4 Mon Sep 17 00:00:00 2001 From: Chao Jiang Date: Mon, 15 Apr 2024 00:16:34 +0800 Subject: [PATCH 30/48] Add Command R chat template (#6650) * Add chat template for command-r model series * Fix indentation * Add chat template test for command-r models and update the implementation to trim whitespaces * Remove debug print --- llama.cpp | 15 +++++++++++++++ tests/test-chat-template.cpp | 4 ++++ 2 files changed, 19 insertions(+) diff --git a/llama.cpp b/llama.cpp index b93c1abcd85d6..e3205e58cd64c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -16625,6 +16625,21 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "### Response:\n"; } + } else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) { + // CohereForAI/c4ai-command-r-plus + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } else if (role == "user") { + ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } else if (role == "assistant") { + ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } + } + if (add_ass) { + ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"; + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 73c3536fdb878..522cc7d0d9e84 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -45,6 +45,8 @@ int main(void) { // Orca-Vicuna // No template included in tokenizer_config.json, so this template likely needs to be manually set. "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}", + // CohereForAI/c4ai-command-r-plus + "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% 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 %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}" }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -69,6 +71,8 @@ int main(void) { "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there\nUSER: Who are you\nASSISTANT: I am an assistant \nUSER: Another question\nASSISTANT:", // Orca-Vicuna "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there\nUSER: Who are you\nASSISTANT: I am an assistant \nUSER: Another question\nASSISTANT:", + // CohereForAI/c4ai-command-r-plus + "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", }; std::vector formatted_chat(1024); int32_t res; From 1958f7e06ca2d2e3ab5698cc67513ba359144d8e Mon Sep 17 00:00:00 2001 From: David Renshaw Date: Sun, 14 Apr 2024 15:24:15 -0400 Subject: [PATCH 31/48] llama : add missing kv clear in llama_beam_search (#6664) --- llama.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/llama.cpp b/llama.cpp index e3205e58cd64c..cf95cea148568 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13063,6 +13063,11 @@ struct llama_beam_search_data { } llama_logit_info logit_info(ctx); std::vector next_tokens = logit_info.top_k(n_beams); + + // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode() + // call in loop() will conclusively fill in the kv slot once the beams converge at this position. + llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + size_t i=0; if (next_beams.size() < n_beams) { for (; next_beams.size() < n_beams ; ++i) { From 17e98d4c96a583d420f12046bc92102381dbd28e Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Mon, 15 Apr 2024 17:12:26 +0800 Subject: [PATCH 32/48] fix mul_mat_id() for new input, make the ut pass (#6682) --- ggml-sycl.cpp | 96 +++++++++++++++++++++++++++------------------------ 1 file changed, 50 insertions(+), 46 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 86091cfbfbde5..f5bb7da86988c 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15996,73 +15996,76 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { -#if 0 - ggml_sycl_mul_mat_id_sycl(dst); - // TODO: mmq/mmv support -#endif + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && + "mul_mat_id does not support split buffers"); + const ggml_tensor *ids = dst->src[2]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - const int64_t nb11 = src1->nb[1]; - const int64_t nb1 = dst->nb[1]; + const size_t nb11 = src1->nb[1]; + const size_t nb1 = dst->nb[1]; - 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 int32_t id = ((int32_t *)dst->op_params)[0]; + const int32_t n_as = src0->ne[2]; std::vector ids_host(ggml_nbytes(ids)); + const char *ids_dev = (const char *)ids->data; - 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]; - SYCL_CHECK(CHECK_TRY_ERROR( - 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)); - } + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); + SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - 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 ggml_tensor_extra_gpu *src0_extra = + (const ggml_tensor_extra_gpu *)src0->extra; + 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; + ggml_tensor_extra_gpu src0_row_extra; ggml_tensor_extra_gpu src1_row_extra; ggml_tensor_extra_gpu dst_row_extra; + ggml_tensor src0_row = *src0; ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; src1_row.backend = GGML_BACKEND_TYPE_GPU; dst_row.backend = GGML_BACKEND_TYPE_GPU; + src0_row.extra = &src0_row_extra; 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 *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU + ? (char *)src0->data + : (char *)src0_extra->data_device[g_main_device]; + 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]; - if (src1->ne[1] == 1) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); + src0_row.ne[2] = 1; + src0_row.ne[3] = 1; + src0_row.nb[3] = src0->nb[2]; + if (src1->ne[1] == 1) { for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - //int32_t row_id; - //SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0])); - //SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0])); - - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[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); - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + src0_row_extra.data_device[g_main_device] = + src0_original + row_id * src0->nb[2]; + src1_row_extra.data_device[g_main_device] = + src1_original + i01 * src1->nb[1]; + dst_row_extra.data_device[g_main_device] = + dst_original + i01 * dst->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] = 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); + ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); } } else { sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); @@ -16072,8 +16075,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, 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]; - 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]); @@ -16086,7 +16087,7 @@ 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).wait())); + src1_original + i01 * nb11, nb11))); num_src1_rows++; } @@ -16094,6 +16095,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, continue; } + src0_row_extra.data_device[g_main_device] = + src0_original + row_id * src0->nb[2]; + src1_row.ne[1] = num_src1_rows; dst_row.ne[1] = num_src1_rows; @@ -16105,7 +16109,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1; - ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); + ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); num_src1_rows = 0; for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { @@ -16119,7 +16123,7 @@ 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).wait())); + dst_contiguous.get() + num_src1_rows * nb1, nb1))); num_src1_rows++; } } From 7fc16a2c32650d46e79b382ecc6720f58c82f31b Mon Sep 17 00:00:00 2001 From: Steven Prichard Date: Mon, 15 Apr 2024 05:14:46 -0500 Subject: [PATCH 33/48] swift : linux support (#6590) - Package.swift now supports conditional compilation based on OS - Allows for package to be used by SPM on Non-Apple platforms Co-authored-by: Steven Prichard --- Package.swift | 69 +++++++++++++++++++++++++++++++-------------------- 1 file changed, 42 insertions(+), 27 deletions(-) diff --git a/Package.swift b/Package.swift index 8b7195869b787..fbd0973be7f62 100644 --- a/Package.swift +++ b/Package.swift @@ -2,6 +2,44 @@ import PackageDescription +var sources = [ + "ggml.c", + "llama.cpp", + "unicode.cpp", + "unicode-data.cpp", + "ggml-alloc.c", + "ggml-backend.c", + "ggml-quants.c", +] + +var resources: [Resource] = [] +var linkerSettings: [LinkerSetting] = [] +var cSettings: [CSetting] = [ + .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), + .unsafeFlags(["-fno-objc-arc"]), + // NOTE: NEW_LAPACK will required iOS version 16.4+ + // We should consider add this in the future when we drop support for iOS 14 + // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) + // .define("ACCELERATE_NEW_LAPACK"), + // .define("ACCELERATE_LAPACK_ILP64") +] + +#if canImport(Darwin) +sources.append("ggml-metal.m") +resources.append(.process("ggml-metal.metal")) +linkerSettings.append(.linkedFramework("Accelerate")) +cSettings.append( + contentsOf: [ + .define("GGML_USE_ACCELERATE"), + .define("GGML_USE_METAL") + ] +) +#endif + +#if os(Linux) + cSettings.append(.define("_GNU_SOURCE")) +#endif + let package = Package( name: "llama", platforms: [ @@ -28,34 +66,11 @@ let package = Package( "ggml-cuda.h", "Makefile" ], - sources: [ - "ggml.c", - "llama.cpp", - "unicode.cpp", - "unicode-data.cpp", - "ggml-alloc.c", - "ggml-backend.c", - "ggml-quants.c", - "ggml-metal.m", - ], - resources: [ - .process("ggml-metal.metal") - ], + sources: sources, + resources: resources, publicHeadersPath: "spm-headers", - cSettings: [ - .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), - .define("GGML_USE_ACCELERATE"), - .unsafeFlags(["-fno-objc-arc"]), - .define("GGML_USE_METAL"), - // NOTE: NEW_LAPACK will required iOS version 16.4+ - // We should consider add this in the future when we drop support for iOS 14 - // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) - // .define("ACCELERATE_NEW_LAPACK"), - // .define("ACCELERATE_LAPACK_ILP64") - ], - linkerSettings: [ - .linkedFramework("Accelerate") - ] + cSettings: cSettings, + linkerSettings: linkerSettings ) ], cxxLanguageStandard: .cxx11 From 3272896d79465fd2befef3425dac8e83933b7ea4 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Mon, 15 Apr 2024 14:18:47 +0200 Subject: [PATCH 34/48] server : revert "minor layout improvements" (#6684) This reverts commit b3a96f27f065a828f08c5d89ff60aab5361188fe. --- examples/server/index.html.hpp | 5351 ++++++++++++++--------------- examples/server/public/index.html | 22 +- 2 files changed, 2658 insertions(+), 2715 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 14f0e8f1951f3..122d3ff999f04 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -82,2839 +82,2802 @@ unsigned char index_html[] = { 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x62, - 0x75, 0x74, 0x74, 0x6f, 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- } - #write form { margin: 1em 0 0 0; display: flex; @@ -587,7 +567,7 @@ runCompletion(); } return html` -
+
From 132f55795e51094954f1b1f647f97648be724a3a Mon Sep 17 00:00:00 2001 From: compilade Date: Mon, 15 Apr 2024 08:56:55 -0400 Subject: [PATCH 35/48] llama : fix restoring the number of outputs from state files (#6687) --- llama.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/llama.cpp b/llama.cpp index cf95cea148568..a5ef2fd8fa575 100644 --- a/llama.cpp +++ b/llama.cpp @@ -15478,6 +15478,8 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch); ctx->output_ids[id] = i; } + + ctx->n_outputs = n_outputs; } } From 7593639ce335e8d7f89aa9a54d616951f273af60 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Mon, 15 Apr 2024 18:35:21 +0100 Subject: [PATCH 36/48] `main`: add --json-schema / -j flag (#6659) * main: add --json-schema / -j * json: move json-schema-to-grammar to common lib * json: fix zig build --- Makefile | 6 +++--- build.zig | 14 +++++++------- common/CMakeLists.txt | 4 +--- common/common.cpp | 15 +++++++++++++++ examples/main/README.md | 4 +++- examples/server/CMakeLists.txt | 2 +- tests/CMakeLists.txt | 2 +- 7 files changed, 31 insertions(+), 16 deletions(-) diff --git a/Makefile b/Makefile index 7a69ad1b3c14f..8f3e17da4c57b 100644 --- a/Makefile +++ b/Makefile @@ -688,7 +688,7 @@ llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml $(CXX) $(CXXFLAGS) -c $< -o $@ COMMON_H_DEPS = common/common.h common/sampling.h common/log.h -COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ @@ -756,7 +756,7 @@ batched: examples/batched/batched.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) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) +batched-bench: examples/batched-bench/batched-bench.cpp build-info.o 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) @@ -788,7 +788,7 @@ 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 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) +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 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) diff --git a/build.zig b/build.zig index 7f36e596888da..e05ca2120ba4c 100644 --- a/build.zig +++ b/build.zig @@ -128,14 +128,14 @@ 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, unicode, unicode_data, common, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, 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, unicode_data, common, buildinfo, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, 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, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 1d840e5f73877..0ec8d6d8d03b5 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -47,9 +47,6 @@ if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() -set(TARGET json-schema-to-grammar) -add_library(${TARGET} OBJECT json-schema-to-grammar.cpp json-schema-to-grammar.h) - set(TARGET common) add_library(${TARGET} STATIC @@ -63,6 +60,7 @@ add_library(${TARGET} STATIC grammar-parser.h grammar-parser.cpp json.hpp + json-schema-to-grammar.cpp train.h train.cpp ngram-cache.h diff --git a/common/common.cpp b/common/common.cpp index dda514785171b..52576cba37bdd 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,4 +1,6 @@ #include "common.h" +#include "json.hpp" +#include "json-schema-to-grammar.h" #include "llama.h" #include @@ -68,6 +70,8 @@ #define LLAMA_CURL_MAX_HEADER_LENGTH 256 #endif // LLAMA_USE_CURL +using json = nlohmann::ordered_json; + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -1148,6 +1152,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa ); return true; } + if (arg == "-j" || arg == "--json-schema") { + if (++i >= argc) { + invalid_param = true; + return true; + } + sparams.grammar = json_schema_to_grammar(json::parse(argv[i])); + return true; + } if (arg == "--override-kv") { if (++i >= argc) { invalid_param = true; @@ -1353,6 +1365,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); printf(" --grammar-file FNAME file to read grammar from\n"); + printf(" -j SCHEMA, --json-schema SCHEMA\n"); + printf(" JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n"); + printf(" For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n"); printf(" --cfg-negative-prompt PROMPT\n"); printf(" negative prompt to use for guidance. (default: empty)\n"); printf(" --cfg-negative-prompt-file FNAME\n"); diff --git a/examples/main/README.md b/examples/main/README.md index 10a589cebb345..649f4e0f35820 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -304,10 +304,12 @@ These options help improve the performance and memory usage of the LLaMA models. - `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation. -### Grammars +### Grammars & JSON schemas - `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax. +- `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead. + ### Quantization For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index d2ee47d01d767..61f58417449a8 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -11,7 +11,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common json-schema-to-grammar ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) find_package(OpenSSL REQUIRED) target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index b5d7bb59c60db..89f23ca2de998 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -25,7 +25,7 @@ function(llama_test source) add_executable(${TEST_TARGET} ${source} get-model.cpp) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE common json-schema-to-grammar) + target_link_libraries(${TEST_TARGET} PRIVATE common) add_test( NAME ${TEST_TARGET} WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY} From 4fbd8098e63670c6ae11a8adc350f5ba191cfda3 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 16 Apr 2024 08:13:13 +0200 Subject: [PATCH 37/48] gguf : add special tokens metadata for FIM/Infill (#6689) This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius --- convert-hf-to-gguf.py | 15 ++++++++++ gguf-py/gguf/constants.py | 9 ++++++ gguf-py/gguf/gguf_writer.py | 12 ++++++++ llama.cpp | 58 ++++++++++++++++++++++++++++++------- 4 files changed, 83 insertions(+), 11 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index b51d683075316..6d28ab5e4919c 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1221,6 +1221,14 @@ def set_vocab(self): except FileNotFoundError: self._set_vocab_llama_hf() + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot']) + special_vocab._set_special_token("prefix", 32007) + special_vocab._set_special_token("suffix", 32008) + special_vocab._set_special_token("middle", 32009) + special_vocab._set_special_token("eot", 32010) + special_vocab.add_to_gguf(self.gguf_writer) + def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams @@ -2240,6 +2248,13 @@ class GemmaModel(Model): def set_vocab(self): self._set_vocab_sentencepiece() + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot']) + special_vocab._set_special_token("prefix", 67) + special_vocab._set_special_token("suffix", 69) + special_vocab._set_special_token("middle", 68) + special_vocab._set_special_token("eot", 70) + special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): hparams = self.hparams diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2566b2fb8f296..1358206a3e569 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -90,6 +90,11 @@ class Tokenizer: HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" + # FIM/Infill special tokens constants + PREFIX_ID = "tokenizer.ggml.prefix_token_id" + SUFFIX_ID = "tokenizer.ggml.suffix_token_id" + MIDDLE_ID = "tokenizer.ggml.middle_token_id" + EOT_ID = "tokenizer.ggml.eot_token_id" # @@ -885,3 +890,7 @@ def get_type(val: Any) -> GGUFValueType: KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV +KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID +KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID +KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID +KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index f4c4407667bda..ff9326d59c717 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -469,6 +469,18 @@ def add_add_space_prefix(self, value: bool) -> None: def add_chat_template(self, value: str) -> None: self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) + def add_prefix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PREFIX_ID, id) + + def add_suffix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id) + + def add_middle_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id) + + def add_eot_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOT_ID, id) + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: diff --git a/llama.cpp b/llama.cpp index a5ef2fd8fa575..38e5936254e4a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -327,6 +327,10 @@ enum llm_kv { LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, + LLM_KV_TOKENIZER_PREFIX_ID, + LLM_KV_TOKENIZER_SUFFIX_ID, + LLM_KV_TOKENIZER_MIDDLE_ID, + LLM_KV_TOKENIZER_EOT_ID, }; static const std::map LLM_KV_NAMES = { @@ -399,6 +403,10 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, + { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" }, + { LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" }, + { LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" }, + { LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" }, }; struct LLM_KV { @@ -2055,10 +2063,10 @@ struct llama_vocab { int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add. id linefeed_id = 13; - id special_prefix_id = 32007; - id special_middle_id = 32009; - id special_suffix_id = 32008; - id special_eot_id = 32010; + id special_prefix_id = -1; + id special_suffix_id = -1; + id special_middle_id = -1; + id special_eot_id = -1; bool add_space_prefix = true; @@ -4072,6 +4080,30 @@ static void llm_load_vocab( vocab.special_cls_id = -1; vocab.special_mask_id = -1; + // For Fill-In-the-Middle (FIM)/infill models which where converted + // prior to support of FIM special tokens in GGUF, the following + // will allow those models to continue to work. The general names + // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and + // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once + // new versions of these models have been published. + std::string gen_name; + ml.get_key(LLM_KV_GENERAL_NAME, gen_name); + std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(), + [](unsigned char c){ return std::tolower(c); }); + if (gen_name.find("code") != std::string::npos) { + if (model.arch == LLM_ARCH_LLAMA) { + vocab.special_prefix_id = 32007; + vocab.special_suffix_id = 32008; + vocab.special_middle_id = 32009; + vocab.special_eot_id = 32010; + } else if (model.arch == LLM_ARCH_GEMMA) { + vocab.special_prefix_id = 67; + vocab.special_suffix_id = 69; + vocab.special_middle_id = 68; + vocab.special_eot_id = 70; + } + } + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); if (add_space_prefix_keyidx != -1) { vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); @@ -4185,13 +4217,17 @@ static void llm_load_vocab( // special tokens { const std::vector> special_token_types = { - { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, - { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, - { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, - { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, - { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, - { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, - { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, + { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, + { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, + { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, + { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, + { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, + { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, + { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, + { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id }, + { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id }, + { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id }, + { LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id }, }; for (const auto & it : special_token_types) { const std::string & key = kv(std::get<0>(it)); From 58227ffdeb4fb89cacb0cffaadf76b1914324ad3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 16 Apr 2024 09:28:33 +0300 Subject: [PATCH 38/48] perplexity : require positive --ctx-size arg (#6695) --- examples/perplexity/perplexity.cpp | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bab79aaea89ca..9a3374fdc13f2 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1852,12 +1852,20 @@ int main(int argc, char ** argv) { const int32_t n_ctx = params.n_ctx; + if (n_ctx <= 0) { + fprintf(stderr, "%s: perplexity tool requires '--ctx-size' > 0\n", __func__); + return 1; + } + 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; + const int32_t n_seq = std::max(1, params.n_batch / n_ctx); + const int32_t n_kv = n_seq * n_ctx; + params.n_parallel = n_seq; - params.n_ctx = n_kv; + 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); From 8a56075b07a8b571bf95a912ffdce4c928c2b414 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 16 Apr 2024 08:34:06 +0200 Subject: [PATCH 39/48] gritlm : add --outdir option to hf.sh script (#6699) This commit updates the hf.sh script usage to include the --outdir option and specifies the models directory as the output directory. The motivation for this is to avoid cluttering the root directory with model files. Signed-off-by: Daniel Bevenius --- examples/gritlm/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md index 64cc192040c43..a3a3c1389ca27 100644 --- a/examples/gritlm/README.md +++ b/examples/gritlm/README.md @@ -21,12 +21,12 @@ 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 +$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models ``` Run the example using the downloaded model: ```console -$ ./gritlm -m gritlm-7b_q4_1.gguf +$ ./gritlm -m models/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 From f4dea7da1841a92d2788b0535063abf2f0e28461 Mon Sep 17 00:00:00 2001 From: Shijie <821898965@qq.com> Date: Tue, 16 Apr 2024 23:40:48 +0800 Subject: [PATCH 40/48] llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 99 +++++++++++++ ggml-metal.m | 57 ++++++-- ggml-metal.metal | 26 ++++ gguf-py/gguf/constants.py | 169 +++++++++++++--------- gguf-py/gguf/tensor_mapping.py | 34 ++++- llama.cpp | 252 +++++++++++++++++++++++++++++++-- tests/test-backend-ops.cpp | 1 + 7 files changed, 537 insertions(+), 101 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6d28ab5e4919c..a93b0666c6b86 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1700,6 +1700,105 @@ class Qwen2Model(Model): model_arch = gguf.MODEL_ARCH.QWEN2 +@Model.register("Qwen2MoeForCausalLM") +class Qwen2MoeModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN2MOE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + + 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) + n_experts = self.hparams.get("num_experts") + experts = dict() + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + 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() + + # process the experts separately + if name.find("experts") != -1: + experts[name] = data + if len(experts) >= n_experts * 3: + # merge the experts into a single 3d tensor + for bid in range(block_count): + for w_name in ["down_proj", "gate_proj", "up_proj"]: + full = True + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + if ename not in experts: + full = False + break + if not full: + continue + + datas = [] + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(experts[ename]) + del experts[ename] + + data = np.stack(datas, axis=0) + data_dtype = data.dtype + + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + if self.ftype == 1 and data_dtype == np.float32: + data = data.astype(np.float16) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + continue + + # 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 + + # 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 or new_name.endswith("_norm.weight")): + 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}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts.keys()}") + + @Model.register("GPT2LMHeadModel") class GPT2Model(Model): model_arch = gguf.MODEL_ARCH.GPT2 diff --git a/ggml-metal.m b/ggml-metal.m index 0207b787a0255..ae6ddeacd347f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -41,8 +41,11 @@ GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, GGML_METAL_KERNEL_TYPE_GELU, + GGML_METAL_KERNEL_TYPE_GELU_4, GGML_METAL_KERNEL_TYPE_GELU_QUICK, + GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, GGML_METAL_KERNEL_TYPE_SILU, + GGML_METAL_KERNEL_TYPE_SILU_4, GGML_METAL_KERNEL_TYPE_SOFT_MAX, GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, @@ -473,8 +476,11 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); @@ -1178,6 +1184,9 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { + // we are not taking into account the strides, so for now require contiguous tensors + GGML_ASSERT(ggml_is_contiguous(src0)); + case GGML_UNARY_OP_TANH: { id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; @@ -1204,42 +1213,60 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_GELU_QUICK: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_SILU: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; default: { diff --git a/ggml-metal.metal b/ggml-metal.metal index 56748166ca4ca..82a8cad93c8bc 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -242,6 +242,15 @@ constant float GELU_QUICK_COEF = -1.702f; constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; kernel void kernel_gelu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + + dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + +kernel void kernel_gelu_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { @@ -255,6 +264,15 @@ kernel void kernel_gelu( } kernel void kernel_gelu_quick( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + + dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x))); +} + +kernel void kernel_gelu_quick_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { @@ -264,6 +282,14 @@ kernel void kernel_gelu_quick( } kernel void kernel_silu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + dst[tpig] = x / (1.0f + exp(-x)); +} + +kernel void kernel_silu_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 1358206a3e569..df861164fe2f1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -120,6 +120,7 @@ class MODEL_ARCH(IntEnum): STABLELM = auto() QWEN = auto() QWEN2 = auto() + QWEN2MOE = auto() PHI2 = auto() PLAMO = auto() CODESHELL = auto() @@ -135,41 +136,45 @@ class MODEL_ARCH(IntEnum): class MODEL_TENSOR(IntEnum): - TOKEN_EMBD = auto() - TOKEN_EMBD_NORM = auto() - TOKEN_TYPES = auto() - POS_EMBD = auto() - OUTPUT = auto() - OUTPUT_NORM = auto() - ROPE_FREQS = auto() - ATTN_Q = auto() - ATTN_K = auto() - ATTN_V = auto() - ATTN_QKV = auto() - ATTN_OUT = auto() - ATTN_NORM = auto() - ATTN_NORM_2 = auto() - ATTN_OUT_NORM = auto() - ATTN_ROT_EMBD = auto() - FFN_GATE_INP = auto() - FFN_NORM = auto() - FFN_GATE = auto() - FFN_DOWN = auto() - FFN_UP = auto() - FFN_ACT = auto() - FFN_GATE_EXP = auto() - FFN_DOWN_EXP = auto() - FFN_UP_EXP = auto() - 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() + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_OUT_NORM = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE_INP = auto() + FFN_GATE_INP_SHEXP = auto() + FFN_NORM = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_ACT = auto() + FFN_GATE_EXP = auto() + FFN_DOWN_EXP = auto() + FFN_UP_EXP = auto() + FFN_GATE_SHEXP = auto() + FFN_DOWN_SHEXP = auto() + FFN_UP_SHEXP = auto() + 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] = { @@ -190,6 +195,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", MODEL_ARCH.QWEN2: "qwen2", + MODEL_ARCH.QWEN2MOE: "qwen2moe", MODEL_ARCH.PHI2: "phi2", MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", @@ -205,41 +211,45 @@ class MODEL_TENSOR(IntEnum): } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { - MODEL_TENSOR.TOKEN_EMBD: "token_embd", - MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", - MODEL_TENSOR.TOKEN_TYPES: "token_types", - MODEL_TENSOR.POS_EMBD: "position_embd", - MODEL_TENSOR.OUTPUT_NORM: "output_norm", - MODEL_TENSOR.OUTPUT: "output", - MODEL_TENSOR.ROPE_FREQS: "rope_freqs", - MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", - MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", - MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", - MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", - MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", - MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", - MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", - MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", - MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", - MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", - MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", - MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", - MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", - MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", - MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", - MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", - MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", - MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", - MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", - MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", - 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_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", + MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", + MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp", + MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", + MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", + MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", + MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + 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]] = { @@ -474,6 +484,25 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.QWEN2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], MODEL_ARCH.PLAMO: [ 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 ec6fcbb838425..10de36fa883ee 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -208,10 +208,15 @@ class TensorNameMap: MODEL_TENSOR.FFN_GATE_INP: ( "layers.{bid}.feed_forward.gate", # mixtral "model.layers.{bid}.block_sparse_moe.gate", # mixtral + "model.layers.{bid}.mlp.gate", # qwen2moe "transformer.decoder_layer.{bid}.router", # Grok "transformer.blocks.{bid}.ffn.router.layer", # dbrx ), + MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe + ), + # Feed-forward up MODEL_TENSOR.FFN_UP: ( "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox @@ -236,9 +241,14 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_UP_EXP: ( - "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) - "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx + "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx + "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_UP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe ), # AWQ-activation gate @@ -260,6 +270,11 @@ class TensorNameMap: "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx + "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_GATE_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe ), # Feed-forward down @@ -285,9 +300,14 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_DOWN_EXP: ( - "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) - "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx + "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx + "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_DOWN_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -366,7 +386,7 @@ def __init__(self, arch: MODEL_ARCH, n_blocks: int): if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 8 + n_experts = 60 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/llama.cpp b/llama.cpp index 38e5936254e4a..340e68fde3ac9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -105,7 +105,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 16 +#define LLAMA_MAX_EXPERTS 60 // @@ -209,6 +209,7 @@ enum llm_arch { LLM_ARCH_STABLELM, LLM_ARCH_QWEN, LLM_ARCH_QWEN2, + LLM_ARCH_QWEN2MOE, LLM_ARCH_PHI2, LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, @@ -242,6 +243,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_QWEN2MOE, "qwen2moe" }, { LLM_ARCH_PHI2, "phi2" }, { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, @@ -437,6 +439,7 @@ enum llm_tensor { LLM_TENSOR_ATTN_OUT_NORM, LLM_TENSOR_ATTN_ROT_EMBD, LLM_TENSOR_FFN_GATE_INP, + LLM_TENSOR_FFN_GATE_INP_SHEXP, LLM_TENSOR_FFN_NORM, LLM_TENSOR_FFN_GATE, LLM_TENSOR_FFN_DOWN, @@ -448,6 +451,9 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_DOWN_SHEXP, + LLM_TENSOR_FFN_GATE_SHEXP, + LLM_TENSOR_FFN_UP_SHEXP, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, @@ -745,6 +751,28 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_QWEN2MOE, + { + { 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_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_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_PHI2, { @@ -1731,6 +1759,7 @@ enum e_model { MODEL_MEDIUM, MODEL_LARGE, MODEL_XL, + MODEL_A2_7B, MODEL_8x7B, MODEL_8x22B, MODEL_16x12B, @@ -1917,6 +1946,12 @@ struct llama_layer { struct ggml_tensor * ffn_down_exps; struct ggml_tensor * ffn_up_exps ; + // ff shared expert (shexp) + struct ggml_tensor * ffn_gate_inp_shexp; + struct ggml_tensor * ffn_gate_shexp; + struct ggml_tensor * ffn_down_shexp; + struct ggml_tensor * ffn_up_shexp; + // ff bias struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 @@ -3587,6 +3622,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; case MODEL_XL: return "1.5B"; + case MODEL_A2_7B: return "A2.7B"; case MODEL_8x7B: return "8x7B"; case MODEL_8x22B: return "8x22B"; case MODEL_16x12B: return "16x12B"; @@ -3886,6 +3922,14 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_QWEN2MOE: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 24: model.type = e_model::MODEL_A2_7B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_PHI2: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -5156,6 +5200,54 @@ 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_QWEN2MOE: + { + 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}); + } + + 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}); + + // 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.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, n_expert}); + + GGML_ASSERT(hparams.n_expert > 0); + GGML_ASSERT(hparams.n_expert_used > 0); + + // MoE branch + auto n_ff_exp = n_ff / hparams.n_expert_used; + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + + // Shared expert branch + layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff}); + } + } break; case LLM_ARCH_PHI2: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -6532,7 +6624,7 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "ffn_norm", il); - cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il); + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, il); } cur = ggml_add(ctx0, cur, ffn_inp); @@ -6565,7 +6657,7 @@ struct llm_build_context { } // REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505 - ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, int il) { + ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, bool norm_w, int 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); @@ -6582,11 +6674,13 @@ struct llm_build_context { 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); + if (norm_w) { + 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); + 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; @@ -7083,7 +7177,7 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "ffn_norm", il); - cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il); + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, true, il); // Grok // if layer_out_norm is present then apply it before adding the input @@ -7219,7 +7313,7 @@ struct llm_build_context { LLM_NORM, cb, il); cb(cur, "attn_out_norm", il); - cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il); + cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, il); cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -8434,6 +8528,141 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_qwen2moe() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + 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); + + // 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); + 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); + 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); + 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); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // 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 * moe_out = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, false, il); + + // FFN shared expert + { + ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur); + cb(cur_gate_inp, "ffn_shexp_gate_inp", il); + + // sigmoid + ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp); + cb(cur_gate, "ffn_shexp_gate", il); + + ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up_shexp, NULL, + model.layers[il].ffn_gate_shexp, NULL, + model.layers[il].ffn_down_shexp, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur_ffn, "ffn_shexp", il); + + ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate); + cb(ffn_shexp_out, "ffn_shexp_out", il); + + moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out); + cb(moe_out, "ffn_out", il); + + cur = moe_out; + } + + 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, 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; + } + struct ggml_cgraph * build_phi2() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -9917,6 +10146,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_qwen2(); } break; + case LLM_ARCH_QWEN2MOE: + { + result = llm.build_qwen2moe(); + } break; case LLM_ARCH_PHI2: { result = llm.build_phi2(); @@ -14834,6 +15067,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_STABLELM: case LLM_ARCH_QWEN: case LLM_ARCH_QWEN2: + case LLM_ARCH_QWEN2MOE: case LLM_ARCH_PHI2: case LLM_ARCH_GEMMA: case LLM_ARCH_STARCODER2: diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b50675952b51d..21adba42e3148 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1878,6 +1878,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op // unary ops for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) { test_cases.emplace_back(new test_unary((ggml_unary_op) op)); + test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 })); } test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false)); From dbceec87c0221ec952e69448df6a71f1372a7487 Mon Sep 17 00:00:00 2001 From: Ashish <1856117+ashishdatta@users.noreply.github.com> Date: Tue, 16 Apr 2024 08:48:35 -0700 Subject: [PATCH 41/48] llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade --- convert-hf-to-gguf.py | 82 +++++++++++++++++++++++++++++++++++++++ gguf-py/gguf/constants.py | 2 + llama.cpp | 62 +++++++++++++++++++++++------ 3 files changed, 134 insertions(+), 12 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a93b0666c6b86..f321d77de11f8 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1207,9 +1207,91 @@ def set_gguf_parameters(self): 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_head_count_kv(hparams["num_key_value_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(self.find_hparam(["layer_norm_eps", "norm_eps"])) + 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) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + q_norms = dict() + k_norms = dict() + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + 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() + n_dims = len(data.shape) + if name.find("q_layernorm.norms") != -1: + q_norms[name] = data + if len(q_norms) >= (block_count * n_head): + self._stack_qk_norm(block_count, name, tensor_map, n_head, q_norms, n_dims, layer_name="q_layernorm") + continue + if name.find("k_layernorm.norms") != -1: + k_norms[name] = data + if len(k_norms) >= (block_count * n_kv_head): + self._stack_qk_norm(block_count, name, tensor_map, n_kv_head, k_norms, n_dims, layer_name="k_layernorm") + continue + + # 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 + + # 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 or new_name.endswith("_norm.weight")): + 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 not new_name.endswith("_norm.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) + + def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"): + for bid in range(block_count): + datas = [] + for xid in range(n_head): + ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" + datas.append(norms[ename]) + del norms[ename] + data = np.stack(datas, axis=0) + data_dtype = data.dtype + merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" + new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): + 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 not new_name.endswith("_norm.weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + @Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") class LlamaModel(Model): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index df861164fe2f1..4b0b6c4c6d008 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -455,6 +455,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/llama.cpp b/llama.cpp index 340e68fde3ac9..579986d1a5e2e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -716,6 +716,8 @@ static const std::map> LLM_TENSOR_NA { 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_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, { @@ -1744,6 +1746,7 @@ enum e_model { MODEL_4B, MODEL_7B, MODEL_8B, + MODEL_12B, MODEL_13B, MODEL_14B, MODEL_15B, @@ -3607,6 +3610,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; + case MODEL_12B: return "12B"; case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; @@ -3898,6 +3902,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_12B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -5128,8 +5133,13 @@ static bool llm_load_tensors( layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - 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}); + // optional q and k layernorms, present in StableLM 2 12B + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + + // optional FFN norm, not present in StableLM 2 12B which uses parallel residual + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); 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}); @@ -8197,7 +8207,7 @@ struct llm_build_context { 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, @@ -8206,6 +8216,8 @@ struct llm_build_context { LLM_NORM, cb, il); cb(cur, "attn_norm", il); + struct ggml_tensor * inpSA = cur; + // self-attention { // compute Q and K and RoPE them @@ -8230,15 +8242,36 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cb(Qcur, "Qcur", il); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + cb(Kcur, "Kcur", il); + + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + NULL, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); + } + if (model.layers[il].attn_k_norm) { + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + NULL, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", il); + } + + Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + 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, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8253,20 +8286,25 @@ struct llm_build_context { // skip computing output for unused tokens struct ggml_tensor * inp_out_ids = build_inp_out_ids(); cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); 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); - + if (model.layers[il].ffn_norm) { + 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); + } else { + // parallel residual + cur = inpSA; + } cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, From 8cc91dc63c0df397d644a581b2cbeea74eb51ae0 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Tue, 16 Apr 2024 14:55:30 -0400 Subject: [PATCH 42/48] ggml : add llamafile sgemm (#6414) This change upstreams llamafile's cpu matrix multiplication kernels which improve image and prompt evaluation speed. For starters, Q4_0 and Q8_0 weights should go ~40% faster on CPU. The biggest benefits are with data types like f16 / f32, which process prompts 2x faster thus making them faster than quantized data types for prompt evals. This change also introduces bona fide AVX512 support since tinyBLAS is able to exploit the larger register file. For example, on my CPU llama.cpp llava-cli processes an image prompt at 305 tokens/second, using the Q4_K and Q4_0 types, which has always been faster than if we used f16 LLaVA weights, which at HEAD go 188 tokens/second. With this change, f16 LLaVA performance leap frogs to 464 tokens/second. On Intel Core i9-14900K this change improves F16 prompt perf by 5x. For example, using llama.cpp at HEAD with Mistral 7b f16 to process a 215 token prompt will go 13 tok/sec. This change has fixes making it go 52 tok/sec. It's mostly thanks to my vectorized outer product kernels but also because I added support for correctly counting the number of cores on Alderlake, so the default thread count discounts Intel's new efficiency cores. Only Linux right now can count cores. This work was sponsored by Mozilla who's given permission to change the license of this code from Apache 2.0 to MIT. To read more about what's improved, and how it works, see: https://justine.lol/matmul/ --- CMakeLists.txt | 2 + Makefile | 10 +- Package.swift | 1 + build.zig | 15 +- common/common.cpp | 73 ++ common/common.h | 3 +- examples/llama-bench/llama-bench.cpp | 2 +- ggml-impl.h | 2 +- ggml-quants.c | 2 +- ggml.c | 54 ++ sgemm.cpp | 1148 ++++++++++++++++++++++++++ sgemm.h | 12 + 12 files changed, 1312 insertions(+), 12 deletions(-) create mode 100644 sgemm.cpp create mode 100644 sgemm.h diff --git a/CMakeLists.txt b/CMakeLists.txt index 19fdfa46ca4f1..158174c2094d2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1151,6 +1151,8 @@ add_library(ggml OBJECT ggml-backend.h ggml-quants.c ggml-quants.h + sgemm.cpp + sgemm.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} diff --git a/Makefile b/Makefile index 8f3e17da4c57b..928fb14cad628 100644 --- a/Makefile +++ b/Makefile @@ -219,6 +219,11 @@ ifdef LLAMA_DISABLE_LOGS MK_CPPFLAGS += -DLOG_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS +# disable ggml.c's use of sgemm.cpp +ifdef LLAMA_NO_LLAMAFILE + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=0 +endif + # warnings WARN_FLAGS = -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int \ @@ -676,13 +681,16 @@ 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 $@ +sgemm.o: sgemm.cpp sgemm.h ggml.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + unicode.o: unicode.cpp unicode.h $(CXX) $(CXXFLAGS) -c $< -o $@ unicode-data.o: unicode-data.cpp unicode-data.h $(CXX) $(CXXFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o sgemm.o 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 $@ diff --git a/Package.swift b/Package.swift index fbd0973be7f62..183e647575b42 100644 --- a/Package.swift +++ b/Package.swift @@ -4,6 +4,7 @@ import PackageDescription var sources = [ "ggml.c", + "sgemm.cpp", "llama.cpp", "unicode.cpp", "unicode-data.cpp", diff --git a/build.zig b/build.zig index e05ca2120ba4c..c35e801f88129 100644 --- a/build.zig +++ b/build.zig @@ -112,6 +112,7 @@ pub fn build(b: *std.build.Builder) !void { make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; const ggml = make.obj("ggml", "ggml.c"); + const sgemm = make.obj("sgemm", "sgemm.cpp"); 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"); @@ -128,14 +129,14 @@ 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, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, 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, unicode_data, common, json_schema_to_grammar, buildinfo, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); + _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/common.cpp b/common/common.cpp index 52576cba37bdd..cf69535e2d1f5 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -108,6 +108,79 @@ int32_t get_num_physical_cores() { return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; } +#if defined(__x86_64__) && defined(__linux__) +#include + +static void cpuid(unsigned leaf, unsigned subleaf, + unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) { + __asm__("movq\t%%rbx,%%rsi\n\t" + "cpuid\n\t" + "xchgq\t%%rbx,%%rsi" + : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx) + : "0"(leaf), "2"(subleaf)); +} + +static int pin_cpu(int cpu) { + cpu_set_t mask; + CPU_ZERO(&mask); + CPU_SET(cpu, &mask); + return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask); +} + +static bool is_hybrid_cpu(void) { + unsigned eax, ebx, ecx, edx; + cpuid(7, 0, &eax, &ebx, &ecx, &edx); + return !!(edx & (1u << 15)); +} + +static bool is_running_on_efficiency_core(void) { + unsigned eax, ebx, ecx, edx; + cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx); + int intel_atom = 0x20; + int core_type = (eax & 0xff000000u) >> 24; + return core_type == intel_atom; +} + +static int count_math_cpus(int cpu_count) { + int result = 0; + for (int cpu = 0; cpu < cpu_count; ++cpu) { + if (pin_cpu(cpu)) { + return -1; + } + if (is_running_on_efficiency_core()) { + continue; // efficiency cores harm lockstep threading + } + ++cpu; // hyperthreading isn't useful for linear algebra + ++result; + } + return result; +} + +#endif // __x86_64__ && __linux__ + +/** + * Returns number of CPUs on system that are useful for math. + */ +int get_math_cpu_count() { +#if defined(__x86_64__) && defined(__linux__) + int cpu_count = sysconf(_SC_NPROCESSORS_ONLN); + if (cpu_count < 1) { + return get_num_physical_cores(); + } + if (is_hybrid_cpu()) { + cpu_set_t affinity; + if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { + int result = count_math_cpus(cpu_count); + pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); + if (result > 0) { + return result; + } + } + } +#endif + return get_num_physical_cores(); +} + void process_escapes(std::string & input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; diff --git a/common/common.h b/common/common.h index 65272b0baaa41..cca44268e6df5 100644 --- a/common/common.h +++ b/common/common.h @@ -39,6 +39,7 @@ extern char const *LLAMA_BUILD_TARGET; struct llama_control_vector_load_info; +int get_math_cpu_count(); int32_t get_num_physical_cores(); // @@ -48,7 +49,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed - int32_t n_threads = get_num_physical_cores(); + int32_t n_threads = get_math_cpu_count(); int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_threads_batch_draft = -1; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 27e113203f1ea..8b532c8b6a98a 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -190,7 +190,7 @@ static const cmd_params cmd_params_defaults = { /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_num_physical_cores()}, + /* n_threads */ {get_math_cpu_count()}, /* n_gpu_layers */ {99}, /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, diff --git a/ggml-impl.h b/ggml-impl.h index e68b728775c41..0c997d3ed521f 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -88,7 +88,7 @@ typedef uint16_t ggml_fp16_internal_t; #if defined(_MSC_VER) || defined(__MINGW32__) #include #else -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__) #if !defined(__riscv) #include #endif diff --git a/ggml-quants.c b/ggml-quants.c index 32e84434a8c1b..32360a1f1be50 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -132,7 +132,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) { } static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || defined(__AVX512VNNI__) +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) const __m256i zero = _mm256_setzero_si256(); const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); return _mm256_cvtepi32_ps(summed_pairs); diff --git a/ggml.c b/ggml.c index 14288d29ded6b..119686be68d9c 100644 --- a/ggml.c +++ b/ggml.c @@ -4,6 +4,7 @@ #include "ggml-impl.h" #include "ggml-quants.h" #include "ggml.h" +#include "sgemm.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -32,6 +33,14 @@ #include #endif +#ifndef GGML_USE_LLAMAFILE +#ifdef __ARM_FEATURE_MATMUL_INT8 +#define GGML_USE_LLAMAFILE 0 +#else +#define GGML_USE_LLAMAFILE 1 +#endif +#endif + #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) @@ -10810,6 +10819,28 @@ static void ggml_compute_forward_mul_mat( } #endif +#if GGML_USE_LLAMAFILE + if (nb10 == ggml_type_size(src1->type)) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(src0->type), + (const char *)src1->data + i12*nb12 + i13*nb13, + nb11/ggml_type_size(src1->type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + params->type, + src0->type, + src1->type, + dst->type)) + goto UseGgmlGemm1; + return; + } +UseGgmlGemm1:; +#endif + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; @@ -10841,6 +10872,29 @@ static void ggml_compute_forward_mul_mat( const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); +#if GGML_USE_LLAMAFILE + if (nb10 == ggml_type_size(src1->type) || src1->type != vec_dot_type) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(src0->type), + (const char *)wdata + (nb12/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i12 + + nb13/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i13), + row_size/ggml_type_size(vec_dot_type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + params->type, + src0->type, + vec_dot_type, + dst->type)) + goto UseGgmlGemm2; + return; + } +UseGgmlGemm2:; +#endif + const int64_t nr0 = ne01; // src0 rows const int64_t nr1 = ne1*ne12*ne13; // src1 rows diff --git a/sgemm.cpp b/sgemm.cpp new file mode 100644 index 0000000000000..6900f04cfb242 --- /dev/null +++ b/sgemm.cpp @@ -0,0 +1,1148 @@ +// -*- mode:c++;indent-tabs-mode:nil;c-basic-offset:4;coding:utf-8 -*- +// vi: set et ft=c++ ts=4 sts=4 sw=4 fenc=utf-8 :vi +// +// Copyright 2024 Mozilla Foundation +// +// Permission is hereby granted, free of charge, to any person obtaining +// a copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to +// permit persons to whom the Software is furnished to do so, subject to +// the following conditions: +// +// The above copyright notice and this permission notice shall be +// included in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS +// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +// +// _ _ ___ _ _ ___ +// | |_(_)_ _ _ _| _ ) | /_\ / __| +// | _| | ' \ || | _ \ |__ / _ \\__ \. +// \__|_|_||_\_, |___/____/_/ \_\___/ +// |__/ +// +// BASIC LINEAR ALGEBRA SUBPROGRAMS +// +// +// This file implements multithreaded CPU matrix multiplication for the +// common contiguous use case C = Aᵀ * B. These kernels are designed to +// have excellent performance[1] for matrices that fit in the CPU cache +// without imposing any overhead such as cache filling or malloc calls. +// +// This implementation does not guarantee any upper bound with rounding +// errors, which grow along with k. Our goal's to maximally exploit the +// hardware for performance, and then use whatever resources remain for +// improving numerical accuracy. +// +// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online]. +// Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024]. + +#pragma GCC diagnostic ignored "-Wpedantic" +#pragma GCC diagnostic ignored "-Wignored-attributes" + +#include "sgemm.h" +#include "ggml-impl.h" +#include "ggml-quants.h" + +#ifdef _MSC_VER +#define NOINLINE __declspec(noinline) +#else +#define NOINLINE __attribute__((__noinline__)) +#endif + +#if defined(__ARM_NEON) || defined(__AVX512F__) +#define VECTOR_REGISTERS 32 +#else +#define VECTOR_REGISTERS 16 +#endif + +// there will be blocks +#define BEGIN_KERNEL(RM, RN) \ + int ytiles = (m - m0) / RM; \ + int xtiles = (n - n0) / RN; \ + int tiles = ytiles * xtiles; \ + int duty = (tiles + nth - 1) / nth; \ + int start = duty * ith; \ + int end = start + duty; \ + if (end > tiles) \ + end = tiles; \ + for (int job = start; job < end; ++job) { \ + int i = m0 + job / xtiles * RM; \ + int j = n0 + job % xtiles * RN; + +#define END_KERNEL() } + +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) + +namespace { + +inline float unhalf(ggml_fp16_t d) { + return GGML_FP16_TO_FP32(d); +} + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED ARITHMETIC OPERATIONS + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline __m128 add(__m128 x, __m128 y) { return _mm_add_ps(x, y); } +inline __m128 sub(__m128 x, __m128 y) { return _mm_sub_ps(x, y); } +inline __m128 mul(__m128 x, __m128 y) { return _mm_mul_ps(x, y); } +#endif // __SSE__ + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline __m256 add(__m256 x, __m256 y) { return _mm256_add_ps(x, y); } +inline __m256 sub(__m256 x, __m256 y) { return _mm256_sub_ps(x, y); } +inline __m256 mul(__m256 x, __m256 y) { return _mm256_mul_ps(x, y); } +#endif // __AVX__ + +#if defined(__AVX512F__) +inline __m512 add(__m512 x, __m512 y) { return _mm512_add_ps(x, y); } +inline __m512 sub(__m512 x, __m512 y) { return _mm512_sub_ps(x, y); } +inline __m512 mul(__m512 x, __m512 y) { return _mm512_mul_ps(x, y); } +#endif // __AVX512F__ + +#if defined(__ARM_NEON) +inline float32x4_t add(float32x4_t x, float32x4_t y) { return vaddq_f32(x, y); } +inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vsubq_f32(x, y); } +inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vmulq_f32(x, y); } +#endif // __ARM_NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) +inline float16x8_t add(float16x8_t x, float16x8_t y) { return vaddq_f16(x, y); } +inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } +inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED HORIZONTAL SUM + +#if defined(__ARM_NEON) +inline float hsum(float32x4_t x) { + return vaddvq_f32(x); +} +#endif // __ARM_NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) +inline float hsum(float16x8_t x) { + return vaddvq_f32(vaddq_f32(vcvt_f32_f16(vget_low_f16(x)), + vcvt_f32_f16(vget_high_f16(x)))); +} +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline float hsum(__m128 x) { +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + x = _mm_add_ps(x, _mm_movehl_ps(x, x)); + x = _mm_add_ss(x, _mm_movehdup_ps(x)); +#else + __m128 t; + t = _mm_shuffle_ps(x, x, _MM_SHUFFLE(2, 3, 0, 1)); + x = _mm_add_ps(x, t); + t = _mm_movehl_ps(t, x); + x = _mm_add_ss(x, t); +#endif + return _mm_cvtss_f32(x); +} +#endif + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline float hsum(__m256 x) { + return hsum(_mm_add_ps(_mm256_extractf128_ps(x, 1), + _mm256_castps256_ps128(x))); +} +#endif // __AVX__ + +#if defined(__AVX512F__) +inline float hsum(__m512 x) { + return _mm512_reduce_add_ps(x); +} +#endif // __AVX512F__ + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED MEMORY LOADING + +template T load(const U *); + +#if defined(__ARM_NEON) +template <> inline float32x4_t load(const float *p) { + return vld1q_f32(p); +} +#if !defined(_MSC_VER) +template <> inline float16x8_t load(const ggml_fp16_t *p) { + return vld1q_f16((const float16_t *)p); +} +template <> inline float32x4_t load(const ggml_fp16_t *p) { + return vcvt_f32_f16(vld1_f16((const float16_t *)p)); +} +#endif // _MSC_VER +#endif // __ARM_NEON + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +template <> inline __m128 load(const float *p) { + return _mm_loadu_ps(p); +} +#endif // __SSE__ + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +template <> inline __m256 load(const float *p) { + return _mm256_loadu_ps(p); +} +#endif // __AVX__ + +#if defined(__F16C__) +template <> inline __m256 load(const ggml_fp16_t *p) { + return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p)); +} +#endif // __F16C__ + +#if defined(__AVX512F__) +template <> inline __m512 load(const float *p) { + return _mm512_loadu_ps(p); +} +template <> inline __m512 load(const ggml_fp16_t *p) { + return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p)); +} +#endif // __AVX512F__ + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// ABSTRACTIONS + +/** + * Computes a * b + c. + * + * This operation will become fused into a single arithmetic instruction + * if the hardware has support for this feature, e.g. Intel Haswell+ (c. + * 2013), AMD Bulldozer+ (c. 2011), etc. + */ +template +inline U madd(T a, T b, U c) { + return add(mul(a, b), c); +} + +/** + * Computes a * b + c with error correction. + * + * @see W. Kahan, "Further remarks on reducing truncation errors," + * Communications of the ACM, vol. 8, no. 1, p. 40, Jan. 1965, + * doi: 10.1145/363707.363723. + */ +template +inline U madder(T a, T b, U c, U *e) { + U y = sub(mul(a, b), *e); + U t = add(c, y); + *e = sub(sub(t, c), y); + return t; +} + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// FLOATING POINT MATRIX MULTIPLICATION + +template +class tinyBLAS { + public: + tinyBLAS(int k, + const TA *A, int lda, + const TB *B, int ldb, + TC *C, int ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int m, int n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + NOINLINE void mnpack(int m0, int m, int n0, int n) { + int mc, nc, mp, np; + if (m - m0 <= 0 || n - n0 <= 0) + return; + if (VECTOR_REGISTERS >= 32 && n - n0 >= 5 && m - m0 >= 5) { + mc = 5; + nc = 5; + gemm5x5(m0, m, n0, n); + } else if (n - n0 >= 4 && m - m0 >= 3) { + mc = 3; + nc = 4; + gemm3x4(m0, m, n0, n); + } else if (n - n0 >= 4) { + mc = 1; + nc = 4; + gemm1x4(m0, m, n0, n); + } else if (m - m0 >= 4) { + mc = 4; + nc = 1; + gemm4x1(m0, m, n0, n); + } else { + mc = 1; + nc = 1; + gemm1x1(m0, m, n0, n); + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, mp, np, n); + mnpack(mp, m, np, n); + } + + NOINLINE void gemm5x5(int m0, int m, int n0, int n) { + BEGIN_KERNEL(5, 5) + D c00 = {0}; + D c01 = {0}; + D c02 = {0}; + D c03 = {0}; + D c04 = {0}; + D c10 = {0}; + D c11 = {0}; + D c12 = {0}; + D c13 = {0}; + D c14 = {0}; + D c20 = {0}; + D c21 = {0}; + D c22 = {0}; + D c23 = {0}; + D c24 = {0}; + D c30 = {0}; + D c31 = {0}; + D c32 = {0}; + D c33 = {0}; + D c34 = {0}; + D c40 = {0}; + D c41 = {0}; + D c42 = {0}; + D c43 = {0}; + D c44 = {0}; + for (int l = 0; l < k; l += KN) { + V k0 = load(B + ldb * (j + 0) + l); + V k1 = load(B + ldb * (j + 1) + l); + V k2 = load(B + ldb * (j + 2) + l); + V k3 = load(B + ldb * (j + 3) + l); + V k4 = load(B + ldb * (j + 4) + l); + V a0 = load(A + lda * (i + 0) + l); + c00 = madd(a0, k0, c00); + c01 = madd(a0, k1, c01); + c02 = madd(a0, k2, c02); + c03 = madd(a0, k3, c03); + c04 = madd(a0, k4, c04); + V a1 = load(A + lda * (i + 1) + l); + c10 = madd(a1, k0, c10); + c11 = madd(a1, k1, c11); + c12 = madd(a1, k2, c12); + c13 = madd(a1, k3, c13); + c14 = madd(a1, k4, c14); + V a2 = load(A + lda * (i + 2) + l); + c20 = madd(a2, k0, c20); + c21 = madd(a2, k1, c21); + c22 = madd(a2, k2, c22); + c23 = madd(a2, k3, c23); + c24 = madd(a2, k4, c24); + V a3 = load(A + lda * (i + 3) + l); + c30 = madd(a3, k0, c30); + c31 = madd(a3, k1, c31); + c32 = madd(a3, k2, c32); + c33 = madd(a3, k3, c33); + c34 = madd(a3, k4, c34); + V a4 = load(A + lda * (i + 4) + l); + c40 = madd(a4, k0, c40); + c41 = madd(a4, k1, c41); + c42 = madd(a4, k2, c42); + c43 = madd(a4, k3, c43); + c44 = madd(a4, k4, c44); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 0) + (i + 1)] = hsum(c10); + C[ldc * (j + 0) + (i + 2)] = hsum(c20); + C[ldc * (j + 0) + (i + 3)] = hsum(c30); + C[ldc * (j + 0) + (i + 4)] = hsum(c40); + C[ldc * (j + 1) + (i + 0)] = hsum(c01); + C[ldc * (j + 1) + (i + 1)] = hsum(c11); + C[ldc * (j + 1) + (i + 2)] = hsum(c21); + C[ldc * (j + 1) + (i + 3)] = hsum(c31); + C[ldc * (j + 1) + (i + 4)] = hsum(c41); + C[ldc * (j + 2) + (i + 0)] = hsum(c02); + C[ldc * (j + 2) + (i + 1)] = hsum(c12); + C[ldc * (j + 2) + (i + 2)] = hsum(c22); + C[ldc * (j + 2) + (i + 3)] = hsum(c32); + C[ldc * (j + 2) + (i + 4)] = hsum(c42); + C[ldc * (j + 3) + (i + 0)] = hsum(c03); + C[ldc * (j + 3) + (i + 1)] = hsum(c13); + C[ldc * (j + 3) + (i + 2)] = hsum(c23); + C[ldc * (j + 3) + (i + 3)] = hsum(c33); + C[ldc * (j + 3) + (i + 4)] = hsum(c43); + C[ldc * (j + 4) + (i + 0)] = hsum(c04); + C[ldc * (j + 4) + (i + 1)] = hsum(c14); + C[ldc * (j + 4) + (i + 2)] = hsum(c24); + C[ldc * (j + 4) + (i + 3)] = hsum(c34); + C[ldc * (j + 4) + (i + 4)] = hsum(c44); + END_KERNEL() + } + + NOINLINE void gemm3x4(int m0, int m, int n0, int n) { + BEGIN_KERNEL(3, 4) + D c00 = {0}; + D c01 = {0}; + D c02 = {0}; + D c03 = {0}; + D c10 = {0}; + D c11 = {0}; + D c12 = {0}; + D c13 = {0}; + D c20 = {0}; + D c21 = {0}; + D c22 = {0}; + D c23 = {0}; + for (int l = 0; l < k; l += KN) { + V k0 = load(B + ldb * (j + 0) + l); + V k1 = load(B + ldb * (j + 1) + l); + V k2 = load(B + ldb * (j + 2) + l); + V k3 = load(B + ldb * (j + 3) + l); + V a0 = load(A + lda * (i + 0) + l); + c00 = madd(a0, k0, c00); + c01 = madd(a0, k1, c01); + c02 = madd(a0, k2, c02); + c03 = madd(a0, k3, c03); + V a1 = load(A + lda * (i + 1) + l); + c10 = madd(a1, k0, c10); + c11 = madd(a1, k1, c11); + c12 = madd(a1, k2, c12); + c13 = madd(a1, k3, c13); + V a2 = load(A + lda * (i + 2) + l); + c20 = madd(a2, k0, c20); + c21 = madd(a2, k1, c21); + c22 = madd(a2, k2, c22); + c23 = madd(a2, k3, c23); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 0) + (i + 1)] = hsum(c10); + C[ldc * (j + 0) + (i + 2)] = hsum(c20); + C[ldc * (j + 1) + (i + 0)] = hsum(c01); + C[ldc * (j + 1) + (i + 1)] = hsum(c11); + C[ldc * (j + 1) + (i + 2)] = hsum(c21); + C[ldc * (j + 2) + (i + 0)] = hsum(c02); + C[ldc * (j + 2) + (i + 1)] = hsum(c12); + C[ldc * (j + 2) + (i + 2)] = hsum(c22); + C[ldc * (j + 3) + (i + 0)] = hsum(c03); + C[ldc * (j + 3) + (i + 1)] = hsum(c13); + C[ldc * (j + 3) + (i + 2)] = hsum(c23); + END_KERNEL() + } + + NOINLINE void gemm1x4(int m0, int m, int n0, int n) { + BEGIN_KERNEL(1, 4) + D c00 = {0}, e00 = {0}; + D c01 = {0}, e01 = {0}; + D c02 = {0}, e02 = {0}; + D c03 = {0}, e03 = {0}; + for (int l = 0; l < k; l += KN) { + V a = load(A + lda * (i + 0) + l); + c00 = madder(a, load(B + ldb * (j + 0) + l), c00, &e00); + c01 = madder(a, load(B + ldb * (j + 1) + l), c01, &e01); + c02 = madder(a, load(B + ldb * (j + 2) + l), c02, &e02); + c03 = madder(a, load(B + ldb * (j + 3) + l), c03, &e03); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 1) + (i + 0)] = hsum(c01); + C[ldc * (j + 2) + (i + 0)] = hsum(c02); + C[ldc * (j + 3) + (i + 0)] = hsum(c03); + END_KERNEL() + } + + NOINLINE void gemm4x1(int m0, int m, int n0, int n) { + BEGIN_KERNEL(4, 1) + D c00 = {0}, e00 = {0}; + D c10 = {0}, e10 = {0}; + D c20 = {0}, e20 = {0}; + D c30 = {0}, e30 = {0}; + for (int l = 0; l < k; l += KN) { + V b = load(B + ldb * (j + 0) + l); + c00 = madder(load(A + lda * (i + 0) + l), b, c00, &e00); + c10 = madder(load(A + lda * (i + 1) + l), b, c10, &e10); + c20 = madder(load(A + lda * (i + 2) + l), b, c20, &e20); + c30 = madder(load(A + lda * (i + 3) + l), b, c30, &e30); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 0) + (i + 1)] = hsum(c10); + C[ldc * (j + 0) + (i + 2)] = hsum(c20); + C[ldc * (j + 0) + (i + 3)] = hsum(c30); + END_KERNEL() + } + + NOINLINE void gemm1x1(int m0, int m, int n0, int n) { + BEGIN_KERNEL(1, 1) + D c = {0}, e = {0}; + for (int l = 0; l < k; l += KN) + c = madder(load(A + lda * i + l), + load(B + ldb * j + l), c, &e); + C[ldc * j + i] = hsum(c); + END_KERNEL() + } + + const TA *const A; + const TB *const B; + TC *const C; + const int k; + const int lda; + const int ldb; + const int ldc; + const int ith; + const int nth; +}; + +////////////////////////////////////////////////////////////////////////////////////////// +// QUANT ZERO MATRIX MULTIPLICATION + +#if defined(__ARM_FEATURE_DOTPROD) +template +class tinyBLAS_Q0_ARM { + public: + tinyBLAS_Q0_ARM(int k, + const TA *A, int lda, + const block_q8_0 *B, int ldb, + float *C, int ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int m, int n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + NOINLINE void mnpack(int m0, int m, int n0, int n) { + int mc, nc, mp, np; + if (m - m0 <= 0 || n - n0 <= 0) + return; + if (m - m0 >= 3 && n - n0 >= 3) { + mc = 3; + nc = 3; + gemm3x3(m0, m, n0, n); + } else { + mc = 1; + nc = 1; + gemm1x1(m0, m, n0, n); + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, mp, np, n); + mnpack(mp, m, np, n); + } + + NOINLINE void gemm3x3(int m0, int m, int n0, int n) { + BEGIN_KERNEL(3, 3) + int32x4_t zero = vdupq_n_s32(0); + float32x4_t c00 = vdupq_n_f32(0.f); + float32x4_t c01 = vdupq_n_f32(0.f); + float32x4_t c02 = vdupq_n_f32(0.f); + float32x4_t c10 = vdupq_n_f32(0.f); + float32x4_t c11 = vdupq_n_f32(0.f); + float32x4_t c12 = vdupq_n_f32(0.f); + float32x4_t c20 = vdupq_n_f32(0.f); + float32x4_t c21 = vdupq_n_f32(0.f); + float32x4_t c22 = vdupq_n_f32(0.f); + const TA *Ap0 = A + lda * (i + 0); + const TA *Ap1 = A + lda * (i + 1); + const TA *Ap2 = A + lda * (i + 2); + const block_q8_0 *Bp0 = B + ldb * (j + 0); + const block_q8_0 *Bp1 = B + ldb * (j + 1); + const block_q8_0 *Bp2 = B + ldb * (j + 2); + for (int l = 0; l < k; ++l) { + c00 = vmlaq_n_f32( + c00, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp0 + l)), + load_hi(Ap0 + l), load_hi(Bp0 + l))), + unhalf(Ap0[l].d) * unhalf(Bp0[l].d)); + c01 = vmlaq_n_f32( + c01, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp1 + l)), + load_hi(Ap0 + l), load_hi(Bp1 + l))), + unhalf(Ap0[l].d) * unhalf(Bp1[l].d)); + c02 = vmlaq_n_f32( + c02, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp2 + l)), + load_hi(Ap0 + l), load_hi(Bp2 + l))), + unhalf(Ap0[l].d) * unhalf(Bp2[l].d)); + c10 = vmlaq_n_f32( + c10, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp0 + l)), + load_hi(Ap1 + l), load_hi(Bp0 + l))), + unhalf(Ap1[l].d) * unhalf(Bp0[l].d)); + c11 = vmlaq_n_f32( + c11, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp1 + l)), + load_hi(Ap1 + l), load_hi(Bp1 + l))), + unhalf(Ap1[l].d) * unhalf(Bp1[l].d)); + c12 = vmlaq_n_f32( + c12, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp2 + l)), + load_hi(Ap1 + l), load_hi(Bp2 + l))), + unhalf(Ap1[l].d) * unhalf(Bp2[l].d)); + c20 = vmlaq_n_f32( + c20, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp0 + l)), + load_hi(Ap2 + l), load_hi(Bp0 + l))), + unhalf(Ap2[l].d) * unhalf(Bp0[l].d)); + c21 = vmlaq_n_f32( + c21, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp1 + l)), + load_hi(Ap2 + l), load_hi(Bp1 + l))), + unhalf(Ap2[l].d) * unhalf(Bp1[l].d)); + c22 = vmlaq_n_f32( + c22, + vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp2 + l)), + load_hi(Ap2 + l), load_hi(Bp2 + l))), + unhalf(Ap2[l].d) * unhalf(Bp2[l].d)); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 0) + (i + 1)] = hsum(c10); + C[ldc * (j + 0) + (i + 2)] = hsum(c20); + C[ldc * (j + 1) + (i + 0)] = hsum(c01); + C[ldc * (j + 1) + (i + 1)] = hsum(c11); + C[ldc * (j + 1) + (i + 2)] = hsum(c21); + C[ldc * (j + 2) + (i + 0)] = hsum(c02); + C[ldc * (j + 2) + (i + 1)] = hsum(c12); + C[ldc * (j + 2) + (i + 2)] = hsum(c22); + END_KERNEL() + } + + NOINLINE void gemm1x1(int m0, int m, int n0, int n) { + BEGIN_KERNEL(1, 1) + float32x4_t acc = vdupq_n_f32(0.f); + const TA *Ap = A + lda * i; + const block_q8_0 *Bp = B + ldb * j; + for (int l = 0; l < k; ++l) { + acc = vmlaq_n_f32(acc, + vcvtq_f32_s32(vdotq_s32( + vdotq_s32(vdupq_n_s32(0), load_lo(Ap + l), load_lo(Bp + l)), + load_hi(Ap + l), load_hi(Bp + l))), + unhalf(Ap[l].d) * unhalf(Bp[l].d)); + } + C[ldc * j + i] = hsum(acc); + END_KERNEL() + } + + inline int8x16_t load_lo(const block_q8_0 *b) { + return vld1q_s8(b->qs); + } + inline int8x16_t load_hi(const block_q8_0 *b) { + return vld1q_s8(b->qs + 16); + } + + inline int8x16_t load_lo(const block_q4_0 *b) { + return vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vld1q_u8(b->qs), + vdupq_n_u8(0x0f))), + vdupq_n_s8(0x8)); + } + inline int8x16_t load_hi(const block_q4_0 *b) { + return vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(vld1q_u8(b->qs), 4)), + vdupq_n_s8(0x8)); + } + + const TA *const A; + const block_q8_0 *const B; + float *const C; + const int k; + const int lda; + const int ldb; + const int ldc; + const int ith; + const int nth; +}; +#endif // __ARM_FEATURE_DOTPROD + +#if defined(__AVX2__) || defined(__AVX512F__) +template +class tinyBLAS_Q0_AVX2 { + public: + tinyBLAS_Q0_AVX2(int k, + const TA *A, int lda, + const TB *B, int ldb, + TC *C, int ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int m, int n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + NOINLINE void mnpack(int m0, int m, int n0, int n) { + int mc, nc, mp, np; + if (m - m0 <= 0 || n - n0 <= 0) + return; + if (m - m0 >= 4 && n - n0 >= 3) { + mc = 4; + nc = 3; + gemm4x3(m0, m, n0, n); + } else if (m - m0 >= 4 && n - n0 >= 1) { + mc = 4; + nc = 1; + gemm4x1(m0, m, n0, n); + } else if (m - m0 >= 1 && n - n0 >= 4) { + mc = 1; + nc = 4; + gemm1x4(m0, m, n0, n); + } else { + mc = 1; + nc = 1; + gemm1x1(m0, m, n0, n); + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, mp, np, n); + mnpack(mp, m, np, n); + } + + NOINLINE void gemm4x3(int m0, int m, int n0, int n) { + BEGIN_KERNEL(4, 3) + __m256 c00 = _mm256_setzero_ps(); + __m256 c10 = _mm256_setzero_ps(); + __m256 c20 = _mm256_setzero_ps(); + __m256 c30 = _mm256_setzero_ps(); + __m256 c01 = _mm256_setzero_ps(); + __m256 c11 = _mm256_setzero_ps(); + __m256 c21 = _mm256_setzero_ps(); + __m256 c31 = _mm256_setzero_ps(); + __m256 c02 = _mm256_setzero_ps(); + __m256 c12 = _mm256_setzero_ps(); + __m256 c22 = _mm256_setzero_ps(); + __m256 c32 = _mm256_setzero_ps(); + const TA *Ap0 = A + lda * (i + 0); + const TA *Ap1 = A + lda * (i + 1); + const TA *Ap2 = A + lda * (i + 2); + const TA *Ap3 = A + lda * (i + 3); + const TB *Bp0 = B + ldb * (j + 0); + const TB *Bp1 = B + ldb * (j + 1); + const TB *Bp2 = B + ldb * (j + 2); + for (int l = 0; l < k; ++l) { + float da0 = unhalf(Ap0[l].d); + float da1 = unhalf(Ap1[l].d); + float da2 = unhalf(Ap2[l].d); + float da3 = unhalf(Ap3[l].d); + __m256i e0 = load(Ap0 + l); + __m256i e1 = load(Ap1 + l); + __m256i e2 = load(Ap2 + l); + __m256i e3 = load(Ap3 + l); + float db0 = unhalf(Bp0[l].d); + __m256 d00 = _mm256_set1_ps(da0 * db0); + __m256 d10 = _mm256_set1_ps(da1 * db0); + __m256 d20 = _mm256_set1_ps(da2 * db0); + __m256 d30 = _mm256_set1_ps(da3 * db0); + __m256i f0 = load(Bp0 + l); + __m256i u0 = _mm256_sign_epi8(f0, f0); + __m256i s00 = _mm256_sign_epi8(e0, f0); + __m256i s10 = _mm256_sign_epi8(e1, f0); + __m256i s20 = _mm256_sign_epi8(e2, f0); + __m256i s30 = _mm256_sign_epi8(e3, f0); + c00 = madd(d00, updot(u0, s00), c00); + c10 = madd(d10, updot(u0, s10), c10); + c20 = madd(d20, updot(u0, s20), c20); + c30 = madd(d30, updot(u0, s30), c30); + float db1 = unhalf(Bp1[l].d); + __m256 d01 = _mm256_set1_ps(da0 * db1); + __m256 d11 = _mm256_set1_ps(da1 * db1); + __m256 d21 = _mm256_set1_ps(da2 * db1); + __m256 d31 = _mm256_set1_ps(da3 * db1); + __m256i f1 = load(Bp1 + l); + __m256i u1 = _mm256_sign_epi8(f1, f1); + __m256i s01 = _mm256_sign_epi8(e0, f1); + __m256i s11 = _mm256_sign_epi8(e1, f1); + __m256i s21 = _mm256_sign_epi8(e2, f1); + __m256i s31 = _mm256_sign_epi8(e3, f1); + c01 = madd(d01, updot(u1, s01), c01); + c11 = madd(d11, updot(u1, s11), c11); + c21 = madd(d21, updot(u1, s21), c21); + c31 = madd(d31, updot(u1, s31), c31); + float db2 = unhalf(Bp2[l].d); + __m256 d02 = _mm256_set1_ps(da0 * db2); + __m256 d12 = _mm256_set1_ps(da1 * db2); + __m256 d22 = _mm256_set1_ps(da2 * db2); + __m256 d32 = _mm256_set1_ps(da3 * db2); + __m256i f2 = load(Bp2 + l); + __m256i u2 = _mm256_sign_epi8(f2, f2); + __m256i s02 = _mm256_sign_epi8(e0, f2); + __m256i s12 = _mm256_sign_epi8(e1, f2); + __m256i s22 = _mm256_sign_epi8(e2, f2); + __m256i s32 = _mm256_sign_epi8(e3, f2); + c02 = madd(d02, updot(u2, s02), c02); + c12 = madd(d12, updot(u2, s12), c12); + c22 = madd(d22, updot(u2, s22), c22); + c32 = madd(d32, updot(u2, s32), c32); + } + C[ldc * (j + 0) + (i + 0)] = hsum(c00); + C[ldc * (j + 0) + (i + 1)] = hsum(c10); + C[ldc * (j + 0) + (i + 2)] = hsum(c20); + C[ldc * (j + 0) + (i + 3)] = hsum(c30); + C[ldc * (j + 1) + (i + 0)] = hsum(c01); + C[ldc * (j + 1) + (i + 1)] = hsum(c11); + C[ldc * (j + 1) + (i + 2)] = hsum(c21); + C[ldc * (j + 1) + (i + 3)] = hsum(c31); + C[ldc * (j + 2) + (i + 0)] = hsum(c02); + C[ldc * (j + 2) + (i + 1)] = hsum(c12); + C[ldc * (j + 2) + (i + 2)] = hsum(c22); + C[ldc * (j + 2) + (i + 3)] = hsum(c32); + END_KERNEL() + } + + NOINLINE void gemm4x1(int m0, int m, int n0, int n) { + BEGIN_KERNEL(4, 1) + __m256 c0 = _mm256_setzero_ps(); + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + const TA *Ap0 = A + lda * (i + 0); + const TA *Ap1 = A + lda * (i + 1); + const TA *Ap2 = A + lda * (i + 2); + const TA *Ap3 = A + lda * (i + 3); + const TB *Bp = B + ldb * j; + for (int l = 0; l < k; ++l) { + float db0 = unhalf(Bp[l].d); + __m256i f = load(Bp + l); + __m256i u = _mm256_sign_epi8(f, f); + __m256 d0 = _mm256_set1_ps(unhalf(Ap0[l].d) * db0); + __m256 d1 = _mm256_set1_ps(unhalf(Ap1[l].d) * db0); + __m256 d2 = _mm256_set1_ps(unhalf(Ap2[l].d) * db0); + __m256 d3 = _mm256_set1_ps(unhalf(Ap3[l].d) * db0); + __m256i e0 = load(Ap0 + l); + __m256i e1 = load(Ap1 + l); + __m256i e2 = load(Ap2 + l); + __m256i e3 = load(Ap3 + l); + __m256i s0 = _mm256_sign_epi8(e0, f); + __m256i s1 = _mm256_sign_epi8(e1, f); + __m256i s2 = _mm256_sign_epi8(e2, f); + __m256i s3 = _mm256_sign_epi8(e3, f); + __m256 g0 = updot(u, s0); + __m256 g1 = updot(u, s1); + __m256 g2 = updot(u, s2); + __m256 g3 = updot(u, s3); + c0 = madd(d0, g0, c0); + c1 = madd(d1, g1, c1); + c2 = madd(d2, g2, c2); + c3 = madd(d3, g3, c3); + } + C[ldc * j + (i + 0)] = hsum(c0); + C[ldc * j + (i + 1)] = hsum(c1); + C[ldc * j + (i + 2)] = hsum(c2); + C[ldc * j + (i + 3)] = hsum(c3); + END_KERNEL() + } + + NOINLINE void gemm1x4(int m0, int m, int n0, int n) { + BEGIN_KERNEL(1, 4) + __m256 c0 = _mm256_setzero_ps(); + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + const TB *Bp0 = B + ldb * (j + 0); + const TB *Bp1 = B + ldb * (j + 1); + const TB *Bp2 = B + ldb * (j + 2); + const TB *Bp3 = B + ldb * (j + 3); + const TA *Ap = A + lda * i; + for (int l = 0; l < k; ++l) { + float da0 = unhalf(Ap[l].d); + __m256i f = load(Ap + l); + __m256i u = _mm256_sign_epi8(f, f); + __m256 d0 = _mm256_set1_ps(unhalf(Bp0[l].d) * da0); + __m256 d1 = _mm256_set1_ps(unhalf(Bp1[l].d) * da0); + __m256 d2 = _mm256_set1_ps(unhalf(Bp2[l].d) * da0); + __m256 d3 = _mm256_set1_ps(unhalf(Bp3[l].d) * da0); + __m256 g0 = updot(u, _mm256_sign_epi8(load(Bp0 + l), f)); + __m256 g1 = updot(u, _mm256_sign_epi8(load(Bp1 + l), f)); + __m256 g2 = updot(u, _mm256_sign_epi8(load(Bp2 + l), f)); + __m256 g3 = updot(u, _mm256_sign_epi8(load(Bp3 + l), f)); + c0 = madd(d0, g0, c0); + c1 = madd(d1, g1, c1); + c2 = madd(d2, g2, c2); + c3 = madd(d3, g3, c3); + } + C[ldc * (j + 0) + i] = hsum(c0); + C[ldc * (j + 1) + i] = hsum(c1); + C[ldc * (j + 2) + i] = hsum(c2); + C[ldc * (j + 3) + i] = hsum(c3); + END_KERNEL() + } + + NOINLINE void gemm1x1(int m0, int m, int n0, int n) { + BEGIN_KERNEL(1, 1) + __m256 c = _mm256_setzero_ps(); + const TA *Ap = A + lda * i; + const TB *Bp = B + ldb * j; + for (int l = 0; l < k; ++l) { + __m256 d = _mm256_set1_ps(unhalf(Ap[l].d) * unhalf(Bp[l].d)); + __m256i e = load(Ap + l); + __m256i f = load(Bp + l); + __m256 g = updot(_mm256_sign_epi8(e, e), _mm256_sign_epi8(f, e)); + c = madd(d, g, c); + } + C[ldc * j + i] = hsum(c); + END_KERNEL() + } + + inline __m256i load(const block_q8_0 *b) { + return _mm256_loadu_si256((const __m256i *)b->qs); + } + + inline __m256i load(const block_q4_0 *b) { + return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8)); + } + + inline __m256 updot(__m256i u, __m256i s) { + __m256i res; +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s); +#else + res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s)); +#endif + return _mm256_cvtepi32_ps(res); + } + + static inline __m256i denibble(const uint8_t *p) { + const __m128i tmp = _mm_loadu_si128((const __m128i *)p); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8(15); + return _mm256_and_si256(lowMask, bytes); + } + + const TA *const A; + const TB *const B; + TC *const C; + const int k; + const int lda; + const int ldb; + const int ldc; + const int ith; + const int nth; +}; +#endif // __AVX2__ + +} // namespace + +/** + * Performs optimized matrix multiplication on CPU. + * + * This subroutine may compute C = Aᵀ * B with column major ordering. + * Despite its name, this isn't a generalized implementation. Work is + * only performed when a handwritten kernel is written and available. + * Otherwise the caller should fall back to a general matmul routine. + * + * For example, for single-threaded single-precision GEMM you can say + * + * llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc, + * 0, 1, GGML_TASK_TYPE_COMPUTE, + * GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32); + * + * @param m is rows in `A` and `C` + * @param n is cols in `B` and `C` + * @param k is cols in `A` and rows in `B` + * @param A is first input matrix (always transposed) + * @param lda is row stride of `A` + * @param B is second input matrix (never transposed) + * @param ldb is row stride of `B` + * @param C is input/output array of output matrices + * @param ldc is row stride of `C` + * @param ith is thread id (must be less than `nth`) + * @param nth is number of threads (must be greater than zero) + * @param task is GGML task type + * @param Atype is GGML data type of `A` + * @param Btype is GGML data type of `B` + * @param Ctype is GGML data type of `C` + * @return true if this function was able to service the matmul request + */ +bool llamafile_sgemm(int m, int n, int k, const void *A, int lda, const void *B, int ldb, void *C, + int ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) { + + assert(m >= 0); + assert(n >= 0); + assert(k >= 0); + assert(lda >= k); + assert(ldb >= k); + assert(ldc >= m); + assert(nth > 0); + assert(ith < nth); + assert(1ll * lda * m <= 0x7fffffff); + assert(1ll * ldb * n <= 0x7fffffff); + assert(1ll * ldc * n <= 0x7fffffff); + + if (Ctype != GGML_TYPE_F32) + return false; + + switch (Atype) { + + case GGML_TYPE_F32: { + if (Btype != GGML_TYPE_F32) + return false; +#if defined(__AVX512F__) + if (k % 16) + return false; + tinyBLAS<16, __m512, __m512, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__AVX__) || defined(__AVX2__) + if (k % 8) + return false; + tinyBLAS<8, __m256, __m256, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_NEON) + if (n < 4) + return false; + if (k % 4) + return false; + tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_F16: { +#if defined(__AVX512F__) + if (k % 16) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__) + if (k % 8) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) + if (n < 8) + return false; + if (k % 8) + return false; + if (Btype != GGML_TYPE_F16) + return false; + tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_NEON) && !defined(_MSC_VER) + if (k % 4) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_Q8_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) + tinyBLAS_Q0_AVX2 tb{ + k, (const block_q8_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_DOTPROD) + tinyBLAS_Q0_ARM tb{ + k, (const block_q8_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_Q4_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) + tinyBLAS_Q0_AVX2 tb{ + k, (const block_q4_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_DOTPROD) + tinyBLAS_Q0_ARM tb{ + k, (const block_q4_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + default: + return false; + } + + (void)m; + (void)n; + (void)k; + (void)A; + (void)lda; + (void)B; + (void)ldb; + (void)C; + (void)ldc; + (void)ith; + (void)nth; + (void)task; + (void)Atype; + (void)Btype; + (void)Ctype; +} diff --git a/sgemm.h b/sgemm.h new file mode 100644 index 0000000000000..da23b209c4dd5 --- /dev/null +++ b/sgemm.h @@ -0,0 +1,12 @@ +#pragma once +#include +#ifdef __cplusplus +extern "C" { +#endif + +bool llamafile_sgemm(int, int, int, const void *, int, const void *, int, + void *, int, int, int, int, int, int, int); + +#ifdef __cplusplus +} +#endif From 666867b799ddd9da7dfdc905ece291ecf286effa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 16 Apr 2024 23:50:22 +0300 Subject: [PATCH 43/48] ggml : fix llamafile sgemm wdata offsets (#6710) ggml-ci --- CMakeLists.txt | 6 ++++++ Makefile | 2 ++ ggml.c | 11 ++++------- 3 files changed, 12 insertions(+), 7 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 158174c2094d2..2cc0df3fb2e67 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -88,6 +88,7 @@ endif() # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) +option(LLAMA_LLAMAFILE "llama: use llamafile SGEMM" ON) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUDA "llama: use CUDA" OFF) option(LLAMA_CUBLAS "llama: use CUDA (deprecated, use LLAMA_CUDA)" OFF) @@ -286,6 +287,7 @@ if (LLAMA_METAL) ${METALKIT_FRAMEWORK} ) endif() + if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) @@ -368,6 +370,10 @@ if (LLAMA_BLAS) endif() endif() +if (LLAMA_LLAMAFILE) + add_compile_definitions(GGML_USE_LLAMAFILE) +endif() + if (LLAMA_QKK_64) add_compile_definitions(GGML_QKK_64) endif() diff --git a/Makefile b/Makefile index 928fb14cad628..9a711743ba94b 100644 --- a/Makefile +++ b/Makefile @@ -222,6 +222,8 @@ endif # LLAMA_DISABLE_LOGS # disable ggml.c's use of sgemm.cpp ifdef LLAMA_NO_LLAMAFILE MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=0 +else + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=1 endif # warnings diff --git a/ggml.c b/ggml.c index 119686be68d9c..593c603f493be 100644 --- a/ggml.c +++ b/ggml.c @@ -33,12 +33,8 @@ #include #endif -#ifndef GGML_USE_LLAMAFILE #ifdef __ARM_FEATURE_MATMUL_INT8 -#define GGML_USE_LLAMAFILE 0 -#else -#define GGML_USE_LLAMAFILE 1 -#endif +#undef GGML_USE_LLAMAFILE #endif #if defined(_MSC_VER) @@ -10879,8 +10875,9 @@ UseGgmlGemm1:; if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01/ggml_type_size(src0->type), - (const char *)wdata + (nb12/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i12 + - nb13/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i13), + (const char *)wdata + ggml_row_size(vec_dot_type, + nb12/ggml_type_size(src1->type)*i12 + + nb13/ggml_type_size(src1->type)*i13), row_size/ggml_type_size(vec_dot_type), (char *)dst->data + i12*nb2 + i13*nb3, nb1/ggml_type_size(dst->type), From 532c1737a14bb4b99747e6f460874947df37e450 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 16 Apr 2024 23:50:38 +0300 Subject: [PATCH 44/48] llama : make general.name optional (#6709) --- llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 579986d1a5e2e..f4f4063cf6062 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4136,9 +4136,11 @@ static void llm_load_vocab( // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once // new versions of these models have been published. std::string gen_name; - ml.get_key(LLM_KV_GENERAL_NAME, gen_name); + ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false); + std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(), [](unsigned char c){ return std::tolower(c); }); + if (gen_name.find("code") != std::string::npos) { if (model.arch == LLM_ARCH_LLAMA) { vocab.special_prefix_id = 32007; From facb8b56f8fd3bb10a693bf0943ae9d69d0828ef Mon Sep 17 00:00:00 2001 From: "Zheng.Deng" <32841220+dengzheng-cloud@users.noreply.github.com> Date: Wed, 17 Apr 2024 04:51:07 +0800 Subject: [PATCH 45/48] convert : fix autoawq gemma (#6704) * fix autoawq quantized gemma model convert error using autoawq to quantize gemma model will include a lm_head.weight tensor in model-00001-of-00002.safetensors. it result in this situation that convert-hf-to-gguf.py can't map lm_head.weight. skip loading this tensor could prevent this error. * change code to full string match and print necessary message change code to full string match and print a short message to inform users that lm_head.weight has been skipped. --------- Co-authored-by: Zheng.Deng <32841220+CUGfred@users.noreply.github.com> --- convert-hf-to-gguf.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f321d77de11f8..c14186abbc2a6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2458,6 +2458,12 @@ def write_tensors(self): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + print(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + continue + old_dtype = data_torch.dtype # convert any unsupported data types to float32 From 8dd1ec8b3ffbfa2d26e82e672cea89f5eeb2f141 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Wed, 17 Apr 2024 14:47:50 +0200 Subject: [PATCH 46/48] readme : add UI (#6724) * Update README.md * Update README.md --------- Co-authored-by: Georgi Gerganov --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index fa2a4db834df2..e3eae60bb015b 100644 --- a/README.md +++ b/README.md @@ -189,6 +189,8 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [MindMac](https://mindmac.app) (proprietary) - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) + *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* --- From 3b8f1ec4b18770531d0b1d792f3edf08254e4f0c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 17 Apr 2024 23:58:26 +0300 Subject: [PATCH 47/48] llamafile : tmp disable + build sgemm.o when needed (#6716) * build : sgemm.o only when needed ggml-ci * llamafile : tmp disable due to MoE bug ggml-ci --- CMakeLists.txt | 38 ++++++++++++++++++++++++++------------ Makefile | 28 +++++++++++++++------------- 2 files changed, 41 insertions(+), 25 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 2cc0df3fb2e67..f134a153bb4ff 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -43,6 +43,18 @@ else() set(LLAMA_METAL_DEFAULT OFF) endif() +# TODO: fix this for Android CI +# https://github.com/ggerganov/llama.cpp/pull/6716#issuecomment-2061509191 +#if (CMAKE_SYSTEM_NAME MATCHES "ANDROID") +# set(LLAMA_LLAMAFILE_DEFAULT OFF) +#else() +# set(LLAMA_LLAMAFILE_DEFAULT ON) +#endif() + +# TODO: temporary disable until MoE is fixed +# https://github.com/ggerganov/llama.cpp/pull/6716 +set(LLAMA_LLAMAFILE_DEFAULT OFF) + # general option(BUILD_SHARED_LIBS "build shared libraries" OFF) option(LLAMA_STATIC "llama: static link libraries" OFF) @@ -88,7 +100,7 @@ endif() # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) -option(LLAMA_LLAMAFILE "llama: use llamafile SGEMM" ON) +option(LLAMA_LLAMAFILE "llama: use llamafile SGEMM" ${LLAMA_LLAMAFILE_DEFAULT}) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUDA "llama: use CUDA" OFF) option(LLAMA_CUBLAS "llama: use CUDA (deprecated, use LLAMA_CUDA)" OFF) @@ -372,6 +384,9 @@ endif() if (LLAMA_LLAMAFILE) add_compile_definitions(GGML_USE_LLAMAFILE) + + set(GGML_HEADERS_LLAMAFILE sgemm.h) + set(GGML_SOURCES_LLAMAFILE sgemm.cpp) endif() if (LLAMA_QKK_64) @@ -1157,17 +1172,16 @@ add_library(ggml OBJECT ggml-backend.h ggml-quants.c ggml-quants.h - sgemm.cpp - sgemm.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} - ${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} + ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} + ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} + ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} + ${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} + ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) diff --git a/Makefile b/Makefile index 9a711743ba94b..760015f294729 100644 --- a/Makefile +++ b/Makefile @@ -219,13 +219,6 @@ ifdef LLAMA_DISABLE_LOGS MK_CPPFLAGS += -DLOG_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS -# disable ggml.c's use of sgemm.cpp -ifdef LLAMA_NO_LLAMAFILE - MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=0 -else - MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=1 -endif - # warnings WARN_FLAGS = -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int \ @@ -391,6 +384,15 @@ ifdef LLAMA_OPENBLAS MK_LDFLAGS += $(shell pkg-config --libs openblas) endif # LLAMA_OPENBLAS +# TODO: temporary disable until MoE is fixed +# https://github.com/ggerganov/llama.cpp/pull/6716 +LLAMA_NO_LLAMAFILE := 1 + +ifndef LLAMA_NO_LLAMAFILE + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE + OBJS += sgemm.o +endif + ifdef LLAMA_BLIS MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis MK_LDFLAGS += -lblis -L/usr/local/lib @@ -487,11 +489,9 @@ ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/com ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(NVCC_COMPILE) - endif # LLAMA_CUDA ifdef LLAMA_CLBLAST - MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) @@ -610,6 +610,11 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI +ifndef LLAMA_NO_LLAMAFILE +sgemm.o: sgemm.cpp sgemm.h ggml.h + $(CXX) $(CXXFLAGS) -c $< -o $@ +endif + GF_CC := $(CC) include scripts/get-flags.mk @@ -683,16 +688,13 @@ 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 $@ -sgemm.o: sgemm.cpp sgemm.h ggml.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - unicode.o: unicode.cpp unicode.h $(CXX) $(CXXFLAGS) -c $< -o $@ unicode-data.o: unicode-data.cpp unicode-data.h $(CXX) $(CXXFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o sgemm.o +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o 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 $@ From c71bfd736ee99a56e697697b39240f2ee06ed26d Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 18 Apr 2024 09:04:47 +0200 Subject: [PATCH 48/48] llama : fix compatibility with old 2 expert models (#6735) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index f4f4063cf6062..8c1446296fe35 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4592,7 +4592,7 @@ static bool llm_load_tensors( size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output // for moe merged tensors - ctx_size += ggml_tensor_overhead()*hparams.n_expert*n_layer; + ctx_size += ggml_tensor_overhead()*n_layer*3; std::map ctx_map; for (auto & it : buft_layer_count) {