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[Draft] Tensor Parallel support to llama.cpp #9648

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[Draft] Tensor Parallel support to llama.cpp #9648

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cb8507b
add tensor parallelism support to SYCL
Aug 22, 2024
c9ae191
add tensor parallel support
Sep 26, 2024
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2 changes: 2 additions & 0 deletions ggml/include/ggml-sycl.h
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Expand Up @@ -29,6 +29,8 @@ GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_typ
GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);

GGML_API void ggml_backend_sycl_print_sycl_devices(void);
GGML_API int ggml_backend_sycl_rank(void);
GGML_API int ggml_backend_sycl_world_size(void);
GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len);
GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size);
GGML_API GGML_CALL int ggml_backend_sycl_get_device_count();
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25 changes: 24 additions & 1 deletion ggml/include/ggml.h
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Expand Up @@ -581,6 +581,27 @@ extern "C" {
GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
};

// ggml object
struct ggml_object {
size_t offs;
size_t size;

struct ggml_object * next;

enum ggml_object_type type;

char padding[4];
};

static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);

enum tensor_parallel_mode {
TENSOR_NO_CHANGE,
TENSOR_SPLIT_BY_ROW,
TENSOR_SPLIT_BY_COLUMN,
TENSOR_KEEPED_ON_MASTER
};
Comment on lines +598 to +603
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Changes to the common ggml code should not be made unless absolutely necessary, which is not likely to be the case here. We already have a way to handle this with custom buffer types like the existing CUDA and SYCL split buffer types. You can extend this model instead by creating a different buffer type for tensors split by column. The "tensors kept on master" is just the default buffer type.


// n-dimensional tensor
struct ggml_tensor {
enum ggml_type type;
Expand Down Expand Up @@ -616,7 +637,9 @@ extern "C" {

void * extra; // extra things e.g. for ggml-cuda.cu

// char padding[4];
enum tensor_parallel_mode split_mode; // {tensor_parallel_mode::TENSOR_NO_CHANGE};

char padding[12];
};

static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
Expand Down
11 changes: 11 additions & 0 deletions ggml/src/CMakeLists.txt
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Expand Up @@ -566,6 +566,17 @@ if (GGML_SYCL)
list(APPEND GGML_EXTRA_LIBS_PRIVATE DNNL::dnnl)
endif()

set(oneCCL_DIR "/opt/intel/oneapi/ccl/latest/lib/cmake/oneCCL")
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@NeoZhangJianyu NeoZhangJianyu Sep 26, 2024 •
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The real oneapi path is not always in /opt/intel/oneapi/.
Please use ENV{ONEAPI_ROOT} which is mandatory env variable in cmakefile.

Same for following script

set(MPI_INCLUDE_PATH "/opt/intel/oneapi/mpi/latest/include")
set(MPI_LIBRARY_PATH "/opt/intel/oneapi/mpi/latest/lib/")
set(ONECCL_INCLUDE_PATH "/opt/intel/oneapi/ccl/latest/include")
set(ONECCL_LIBRARY_PATH "/opt/intel/oneapi/ccl/latest/lib/")
include_directories(${MPI_INCLUDE_PATH} ${ONECCL_INCLUDE_PATH})
find_library(MPI_LIBRARY mpi HINTS ${MPI_LIBRARY_PATH})
find_library(ONECCL_LIBRARY ccl HINTS ${ONECCL_LIBRARY_PATH})
# find_package(oneCCL REQUIRED)
message("-- oneCCL found")
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Add script for not found oneCCL.

oneCCL is not included in oneAPI base toolkit, please print the message to guide user how to install it.

set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} ${MPI_LIBRARY_PATH} ${ONECCL_LIBRARY_PATH})
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GGML_EXTRA_LIBS was recently split into GGML_EXTRA_LIBS_PUBLIC and GGML_EXTRA_LIBS_PRIVATE, so I think the line above won't work anymore
Also why there are paths to the lib directories inside this variable instead of found mpi/ccl libraries?

if (WIN32)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
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113 changes: 105 additions & 8 deletions ggml/src/ggml-sycl.cpp
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Expand Up @@ -1239,6 +1239,15 @@ static void relu_f32_sycl(const float *x, float *dst, const int k,
});
}

static void allreduce_f32_sycl(const float *x, float *dst, const int k,
queue_ptr stream) {
auto dev = ccl::create_device(stream->get_device());
auto ctx = ccl::create_context(stream->get_context());
auto comm = dpct::dev_mgr::instance().create_ccl_communicator(dev, ctx);
auto ccl_stream = ccl::create_stream(*stream);
ccl::allreduce(x, dst, k, ccl::reduction::sum, comm, ccl_stream).wait();
}

static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
Expand Down Expand Up @@ -1736,6 +1745,16 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
}

int ggml_backend_sycl_rank() {
// use ccl rank as main gpu
return dpct::dev_mgr::instance().get_rank();
}

int ggml_backend_sycl_world_size() {
// use ccl rank as main gpu
return dpct::dev_mgr::instance().get_world_size();
}

void ggml_backend_sycl_print_sycl_devices() {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
int device_count = dpct::dev_mgr::instance().device_count();
Expand Down Expand Up @@ -2270,6 +2289,21 @@ inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor
(void) src1_dd;
}

inline void ggml_sycl_op_allreduce(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd,
const queue_ptr &main_stream) {

GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);

allreduce_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);

(void) src1;
(void) dst;
(void) src1_dd;
}

static void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
Expand Down Expand Up @@ -3179,6 +3213,13 @@ static void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *
GGML_SYCL_DEBUG("call %s done\n", __func__);
}

static void ggml_sycl_allreduce(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_SYCL_DEBUG("call %s\n", __func__);
ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_allreduce);
GGML_SYCL_DEBUG("call %s done\n", __func__);
}


static void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_SYCL_DEBUG("call %s\n", __func__);
ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid);
Expand Down Expand Up @@ -3530,6 +3571,9 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
} else {
ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
}
if (src0->split_mode == tensor_parallel_mode::TENSOR_SPLIT_BY_COLUMN) {
ggml_sycl_allreduce(ctx, dst, src1, dst);
}
}


Expand Down Expand Up @@ -4193,6 +4237,41 @@ catch (sycl::exception const &exc) {
std::exit(1);
}

static bool split_tensor(const struct ggml_tensor * src, void* dst, const void* data, enum tensor_parallel_mode split_mode) {
int rank = ggml_backend_sycl_rank();
int world_size = ggml_backend_sycl_world_size();
auto type_traits = ggml_internal_get_type_traits(src->type);
size_t element_size = type_traits.type_size / type_traits.blck_size;
const int64_t dst_size = ggml_nelements(src) * element_size / world_size;
switch (split_mode) {
case tensor_parallel_mode::TENSOR_SPLIT_BY_COLUMN: {
const int64_t nr = ggml_nrows(src);
const int64_t nc = src->ne[0];
const int64_t ndr = nr;
const int64_t ndc = nc / world_size;
for (size_t i = 0; i < nr; ++i) {
memcpy(((char*)dst) + i * ndc * element_size,
((char*)data) + i * nc * element_size + ndc * rank * element_size, ndc * element_size);
}
} break;
case tensor_parallel_mode::TENSOR_SPLIT_BY_ROW: {
memcpy(((char*)dst), ((char*)data) + dst_size * rank, dst_size);
} break;
case tensor_parallel_mode::TENSOR_KEEPED_ON_MASTER: {
if (rank == 0) {
memcpy(((char*)dst), ((char*)data), dst_size);
} else {
memset(((char*)dst), 0, dst_size);
}
} break;
default: {
return false;
} break;
}
return true;
}


static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
ggml_tensor *tensor,
const void *data, size_t offset,
Expand All @@ -4205,7 +4284,14 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
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);

if (tensor->split_mode == tensor_parallel_mode::TENSOR_NO_CHANGE) {
memcpy(host_buf, data, size);
} else {
if (!split_tensor(tensor, ((void*)host_buf), data, tensor->split_mode)) {
std::cerr << "split tensor failed!" << std::endl;
}
}
SYCL_CHECK(
CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size)
.wait()));
Expand Down Expand Up @@ -4419,14 +4505,25 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) {
static bool ggml_backend_sycl_buffer_type_initialized = false;

if (!ggml_backend_sycl_buffer_type_initialized) {
for (int i = 0; i < ggml_sycl_info().device_count; i++) {
auto & device_i = dpct::dev_mgr::instance().get_device(i);
queue_ptr stream = &(device_i.default_queue());
ggml_backend_sycl_buffer_types[i] = {
if (ggml_backend_sycl_world_size() > 1) {
auto rank = ggml_backend_sycl_rank();
auto & device_tp = dpct::dev_mgr::instance().get_device(rank);
queue_ptr stream = &(device_tp.default_queue());
// TODO(xi): buffer_types always use 0 to avoid changes on public code
ggml_backend_sycl_buffer_types[0] = {
/* .iface = */ ggml_backend_sycl_buffer_type_interface,
/* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i), stream},
};
}
/* .context = */ new ggml_backend_sycl_buffer_type_context{rank, GGML_SYCL_NAME + std::to_string(rank), stream},
};
} else {
for (int i = 0; i < ggml_sycl_info().device_count; i++) {
auto & device_i = dpct::dev_mgr::instance().get_device(i);
queue_ptr stream = &(device_i.default_queue());
ggml_backend_sycl_buffer_types[i] = {
/* .iface = */ ggml_backend_sycl_buffer_type_interface,
/* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i), stream},
};
}
}
ggml_backend_sycl_buffer_type_initialized = true;
}
return &ggml_backend_sycl_buffer_types[device];
Expand Down
40 changes: 39 additions & 1 deletion ggml/src/ggml-sycl/dpct/helper.hpp
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Expand Up @@ -13,10 +13,13 @@
#ifndef GGML_SYCL_DPCT_HELPER_HPP
#define GGML_SYCL_DPCT_HELPER_HPP

#include <stdlib.h>
#include <sycl/sycl.hpp>
#include <sycl/half_type.hpp>
#include <oneapi/ccl.hpp>
#include <oneapi/mkl.hpp>
#include <map>
#include <mpi.h>

#include "ggml.h"

Expand Down Expand Up @@ -870,7 +873,12 @@ namespace dpct
}
return -1;
}
inline int get_rank() { return _rank; }
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These new functions have no relationship with DPCT.
It's better to move the ggml-sycl/src.
Recommend to reduce the dependence on DPCT code.

inline int get_world_size() { return _world_size; }
inline ccl::communicator create_ccl_communicator(ccl::device dev, ccl::context ctx) {
return ccl::create_communicator(_world_size, _rank, dev, ctx, _kvs);
}
inline std::string get_preferred_gpu_platform_name() {
std::string result;
Expand Down Expand Up @@ -993,6 +1001,31 @@ namespace dpct
static bool compare_backend(std::string &backend1, std::string &backend2) {
return convert_backend_index(backend1) < convert_backend_index(backend2);
}
static void mpi_finalize() {
static int is_finalized = 0;
MPI_Finalized(&is_finalized);
if (!is_finalized) MPI_Finalize();
}
void init_ccl() {
ccl::init();
MPI_Init(NULL, NULL);
MPI_Comm_size(MPI_COMM_WORLD, &_world_size);
MPI_Comm_rank(MPI_COMM_WORLD, &_rank);
atexit(mpi_finalize);
ccl::kvs::address_type main_addr;
if (_rank == 0) {
_kvs = ccl::create_main_kvs();
main_addr = _kvs->get_address();
MPI_Bcast((void *)main_addr.data(), main_addr.size(), MPI_BYTE, 0, MPI_COMM_WORLD);
}
else {
MPI_Bcast((void *)main_addr.data(), main_addr.size(), MPI_BYTE, 0, MPI_COMM_WORLD);
_kvs = ccl::create_kvs(main_addr);
}
}
dev_mgr()
{
sycl::device default_device =
Expand Down Expand Up @@ -1050,6 +1083,7 @@ namespace dpct
_cpu_device = _devs.size() - 1;
}
}
init_ccl();
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@NeoZhangJianyu NeoZhangJianyu Sep 26, 2024 •
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mv this init() function to ggml-sycl/src.

}
void check_id(unsigned int id) const
{
Expand All @@ -1066,6 +1100,10 @@ namespace dpct
/// thread-id to device-id map.
std::map<unsigned int, unsigned int> _thread2dev_map;
int _cpu_device = -1;
// For tensor parallelsim
int _rank = 0;
int _world_size = 1;
ccl::shared_ptr_class<ccl::kvs> _kvs;
};
static inline sycl::queue &get_default_queue()
Expand Down
1 change: 1 addition & 0 deletions include/llama.h
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Expand Up @@ -204,6 +204,7 @@ extern "C" {
LLAMA_SPLIT_MODE_NONE = 0, // single GPU
LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
LLAMA_SPLIT_MODE_TENSOR = 3, // split tensors across GPUs
};

// TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979)
Expand Down
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