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llama.cpp/ggml/src/ggml-blas/ggml-blas.cpp

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#include "ggml-impl.h"
#include "ggml-blas.h"
#include "ggml-backend-impl.h"
#include "ggml.h"
#include "ggml-backend.h"
#include <future>
#include <thread>
#include <vector>
#include <cstring>
#include <cstdint>
#if defined(GGML_BLAS_USE_ACCELERATE)
# include <Accelerate/Accelerate.h>
#elif defined(GGML_BLAS_USE_MKL)
# include <mkl.h>
#elif defined(GGML_BLAS_USE_BLIS)
# include <blis.h>
#elif defined(GGML_BLAS_USE_NVPL)
# include <nvpl_blas.h>
#else
# include <cblas.h>
#endif
struct ggml_backend_blas_buffer {
void * data; // dequantized data
size_t size; // ggml_nelements * sizeof(float)
};
struct ggml_backend_blas_buffer_type_context {
int n_threads;
#ifndef GGML_USE_OPENMP
std::vector<std::future<void>> tasks;
#endif
};
// BLAS backend - buffer
static void ggml_backend_blas_buffer_free_buffer(ggml_backend_buffer_t buffer) {
GGML_ASSERT(buffer);
ggml_backend_blas_buffer * buf_ctx = (ggml_backend_blas_buffer *)buffer->context;
ggml_aligned_free(buf_ctx->data, buf_ctx->size);
ggml_aligned_free(buffer->context, buffer->size);
}
static void * ggml_backend_blas_buffer_get_base(ggml_backend_buffer_t buffer) {
GGML_ASSERT(buffer);
uintptr_t data = (uintptr_t)buffer->context;
// align the buffer
if (data % TENSOR_ALIGNMENT != 0) {
data = GGML_PAD(data, TENSOR_ALIGNMENT);
}
return (void *)data;
}
static enum ggml_status ggml_backend_blas_buffer_init_tensor(
ggml_backend_buffer_t buffer,
ggml_tensor * tensor) {
if (tensor->view_src != NULL) {
assert(tensor->view_src->buffer->buft == buffer->buft);
return GGML_STATUS_SUCCESS;
}
if (tensor->type != GGML_TYPE_F32) {
ggml_backend_blas_buffer * extra = new ggml_backend_blas_buffer;
extra->data = ggml_aligned_malloc(ggml_nelements(tensor) * sizeof(float)); // sizeof(float) because dequantized
extra->size = ggml_nelements(tensor) * sizeof(float);
tensor->extra = extra;
}
return GGML_STATUS_SUCCESS;
}
static void ggml_backend_blas_buffer_memset_tensor(
ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
uint8_t value,
size_t offset,
size_t size) {
GGML_ASSERT(tensor);
memset((char *)tensor->data + offset, value, size);
GGML_UNUSED(buffer);
}
static void ggml_backend_blas_buffer_set_tensor(
ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
GGML_ASSERT(tensor);
memcpy((char *)tensor->data + offset, data, size);
ggml_backend_blas_buffer_type_context * buft_ctx = (ggml_backend_blas_buffer_type_context *)buffer->buft->context;
ggml_backend_blas_buffer * extra = (ggml_backend_blas_buffer *)tensor->extra;
const int64_t ne00 = tensor->ne[0];
const int64_t ne01 = tensor->ne[1];
const int64_t ne02 = tensor->ne[2];
const int64_t ne03 = tensor->ne[3];
const int64_t nb00 = tensor->nb[0];
const int64_t nb01 = tensor->nb[1];
const int64_t nb02 = tensor->nb[2];
const int64_t nb03 = tensor->nb[3];
const int64_t ne_plane = ne01*ne00;
if (tensor->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS
&& tensor->type != GGML_TYPE_F32
&& ggml_get_type_traits(tensor->type)->to_float != NULL) {
const auto * type_traits = ggml_get_type_traits(tensor->type);
ggml_to_float_t const to_float = type_traits->to_float;
GGML_ASSERT(to_float != nullptr);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const void * x = (char *)tensor->data + i02*nb02 + i03*nb03;
float * const wplane = (float *)extra->data + i02*ne_plane + i03*ne02*ne_plane;
const int min_cols_per_thread = 4096;
const int min_rows_per_thread = std::max((int)(min_cols_per_thread / ne00), 1);
const int n_threads = std::max(std::min(buft_ctx->n_threads, (int)(ne01 / min_rows_per_thread)), 1);
#ifdef GGML_USE_OPENMP
#pragma omp parallel for num_threads(n_threads)
for (int64_t i01 = 0; i01 < ne01; i01++) {
to_float((const char *)x + i01*nb01, wplane + i01*ne00, ne00);
}
#else
for (int i = 1; i < n_threads; i++) {
const int64_t start = (i + 0) * ne01/n_threads;
const int64_t end = (i + 1) * ne01/n_threads;
if (start < end) {
buft_ctx->tasks.push_back(std::async(std::launch::async, [=]() {
for (int64_t i01 = start; i01 < end; i01++) {
to_float((const char *)x + i01*nb01, wplane + i01*ne00, ne00);
}
}));
}
}
{
// reuse the current thread for the first task
const int64_t start = 0;
const int64_t end = ne01/n_threads;
for (int64_t i01 = start; i01 < end; i01++) {
to_float((const char *)x + i01*nb01, wplane + i01*ne00, ne00);
}
}
#endif
}
}
#ifndef GGML_USE_OPENMP
// wait for all tasks to finish
for (auto & task : buft_ctx->tasks) {
task.get();
}
buft_ctx->tasks.clear();
#endif
}
}
static void ggml_backend_blas_buffer_get_tensor(
ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
GGML_ASSERT(tensor);
memcpy(data, (const char *)tensor->data + offset, size);
GGML_UNUSED(buffer);
}
static void ggml_backend_blas_buffer_clear(
ggml_backend_buffer_t buffer,
uint8_t value) {
GGML_ASSERT(buffer);
memset(buffer->context, value, buffer->size);
}
static const ggml_backend_buffer_i ggml_backend_blas_buffer_i = {
/* .free_buffer = */ ggml_backend_blas_buffer_free_buffer,
/* .get_base = */ ggml_backend_blas_buffer_get_base,
/* .init_tensor = */ ggml_backend_blas_buffer_init_tensor,
/* .memset_tensor = */ ggml_backend_blas_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_blas_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_blas_buffer_get_tensor,
/* .cpy_tensor = */ NULL,
/* .clear = */ ggml_backend_blas_buffer_clear,
/* .reset = */ NULL,
};
// BLAS backend buffer type
static const char * ggml_backend_blas_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "BLAS";
GGML_UNUSED(buft);
}
static ggml_backend_buffer_t ggml_backend_blas_buffer_type_alloc_buffer(
ggml_backend_buffer_type_t buft,
size_t size) {
void * data = ggml_aligned_malloc(size);
if (data == nullptr) {
GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_blas_buffer_i, data, size);
}
static size_t ggml_backend_blas_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return TENSOR_ALIGNMENT;
GGML_UNUSED(buft);
}
static bool ggml_backend_blas_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return true;
GGML_UNUSED(buft);
}
static ggml_backend_buffer_type_t ggml_backend_blas_buffer_type(void) {
static ggml_backend_blas_buffer_type_context buft_ctx = {
/* .n_threads = */ (int)std::thread::hardware_concurrency(),
#ifndef GGML_USE_OPENMP
/* .tasks = */ std::vector<std::future<void>>(),
#endif
};
static ggml_backend_buffer_type ggml_backend_blas_buffer_type = {
/* .iface = */ {
/* .get_name = */ ggml_backend_blas_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_blas_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_blas_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_blas_buffer_type_is_host,
},
/* .device = */ NULL,
/* .context = */ &buft_ctx,
};
return &ggml_backend_blas_buffer_type;
}
struct ggml_backend_blas_context {
int n_threads;
};
static void ggml_backend_blas_mul_mat(
ggml_backend_blas_context * ctx,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
const ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const int64_t ne_plane = ne01*ne00;
const ggml_backend_blas_buffer * extra = (ggml_backend_blas_buffer *)src0->extra;
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
// switch to dequantized F32 data
if (type != GGML_TYPE_F32) {
x = (float *)extra->data + i02*ne_plane + i03*ne02*ne_plane;
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
}
}
GGML_UNUSED(ctx);
}
static const char * ggml_backend_blas_get_name(ggml_backend_t backend) {
return "BLAS";
GGML_UNUSED(backend);
}
static void ggml_backend_blas_free(ggml_backend_t backend) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
delete ctx;
delete backend;
}
static ggml_status ggml_backend_blas_graph_compute(
ggml_backend_t backend,
ggml_cgraph * cgraph) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (ggml_op_is_empty(node->op)) {
continue;
}
switch (node->op) {
case GGML_OP_MUL_MAT:
{
ggml_backend_blas_mul_mat(ctx, node);
} break;
default:
GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
}
}
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}
static const ggml_backend_i ggml_backend_blas_i = {
/* .get_name = */ ggml_backend_blas_get_name,
/* .free = */ ggml_backend_blas_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_blas_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .graph_optimize = */ NULL,
};
static ggml_guid_t ggml_backend_blas_guid(void) {
static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d };
return &guid;
}
ggml_backend_t ggml_backend_blas_init(void) {
ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
if (ctx == NULL) {
return NULL;
}
ctx->n_threads = GGML_DEFAULT_N_THREADS;
ggml_backend_t blas_backend = new ggml_backend {
/* .guid = */ ggml_backend_blas_guid(),
/* .iface = */ ggml_backend_blas_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
/* .context = */ ctx,
};
if (blas_backend == NULL) {
delete ctx;
return NULL;
}
return blas_backend;
}
bool ggml_backend_is_blas(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid());
}
void ggml_backend_blas_set_n_threads(ggml_backend_t backend, int n_threads) {
GGML_ASSERT(ggml_backend_is_blas(backend));
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
ctx->n_threads = n_threads;
#if defined(OPENBLAS_VERSION)
openblas_set_num_threads(ctx->n_threads);
#endif
#if defined(GGML_BLAS_USE_BLIS)
bli_thread_set_num_threads(ctx->n_threads);
#endif
#if defined(GGML_BLAS_USE_NVPL)
nvpl_blas_set_num_threads(ctx->n_threads);
#endif
}
struct ggml_backend_blas_device_context {};
static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) {
return "BLAS";
GGML_UNUSED(dev);
}
static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) {
#if defined(GGML_BLAS_USE_ACCELERATE)
return "Accelerate";
#elif defined(GGML_BLAS_USE_MKL)
return "MKL";
#elif defined(GGML_BLAS_USE_BLIS)
return "BLIS";
#elif defined(GGML_BLAS_USE_NVPL)
return "NVPL";
#elif defined(OPENBLAS_VERSION)
return "OpenBLAS";
#else
return "BLAS";
#endif
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
props->name = ggml_backend_blas_device_get_name(dev);
props->description = ggml_backend_blas_device_get_description(dev);
props->type = ggml_backend_blas_device_get_type(dev);
ggml_backend_blas_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_blas_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_blas_buffer_type();
GGML_UNUSED(dev);
}
static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
if (ggml_op_is_empty(dst->op)) {
return true;
}
switch (dst->op) {
case GGML_OP_MUL_MAT:
{
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal value
const int64_t min_batch = 32;
return ggml_is_contiguous(src0)
&& ggml_is_contiguous(src1)
&& src1->type == GGML_TYPE_F32
// NOTE: llama-bench creates views that somehow does not go through init_tensor
// this prevents the uninitialized views from being used in BLAS
&& src0->view_src == nullptr && src1->view_src == nullptr
&& (ne0 >= min_batch && ne1 >= min_batch && ne10 >= min_batch)
&& (src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
}
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_blas_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(dev);
}
static const ggml_backend_device_i ggml_backend_blas_device_i = {
/* .get_name = */ ggml_backend_blas_device_get_name,
/* .get_description = */ ggml_backend_blas_device_get_description,
/* .get_memory = */ ggml_backend_blas_device_get_memory,
/* .get_type = */ ggml_backend_blas_device_get_type,
/* .get_props = */ ggml_backend_blas_device_get_props,
/* .init_backend = */ ggml_backend_blas_device_init_backend,
/* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ NULL,
/* .supports_op = */ ggml_backend_blas_device_supports_op,
/* .supports_buft = */ ggml_backend_blas_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// BLAS backend - backend (reg)
static const char * ggml_backend_blas_reg_get_name(ggml_backend_reg_t reg) {
return "BLAS";
GGML_UNUSED(reg);
}
static size_t ggml_backend_blas_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_blas_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_blas_device_context ctx;
static ggml_backend_device ggml_backend_blas_device = {
/* .iface = */ ggml_backend_blas_device_i,
/* .reg = */ reg,
/* .context = */ &ctx,
};
return &ggml_backend_blas_device;
}
static void * ggml_backend_blas_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_blas_set_n_threads;
}
return nullptr;
GGML_UNUSED(reg);
}
static const ggml_backend_reg_i ggml_backend_blas_reg_i = {
/* .get_name = */ ggml_backend_blas_reg_get_name,
/* .get_device_count = */ ggml_backend_blas_reg_get_device_count,
/* .get_device = */ ggml_backend_blas_reg_get_device,
/* .get_proc_address = */ ggml_backend_blas_get_proc_address,
};
ggml_backend_reg_t ggml_backend_blas_reg(void) {
static ggml_backend_reg ggml_backend_blas_reg = {
/* .api_version = */ GGML_BACKEND_API_VERSION,
/* .iface = */ ggml_backend_blas_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_blas_reg;
}
GGML_BACKEND_DL_IMPL(ggml_backend_blas_reg)
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