ggml : add CLBlast support (#1164)

* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing

* Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers

* Finish merge of ClBlast support

* Move CLBlast implementation to separate file

Add buffer reuse code (adapted from slaren's cuda implementation)

* Add q4_2 and q4_3 CLBlast support, improve code

* Double CLBlast speed by disabling OpenBLAS thread workaround

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>

* Fix device selection env variable names

* Fix cast in opencl kernels

* Add CLBlast to CMakeLists.txt

* Replace buffer pool with static buffers a, b, qb, c

Fix compile warnings

* Fix typos, use GGML_TYPE defines, improve code

* Improve btype dequant kernel selection code, add error if type is unsupported

* Improve code quality

* Move internal stuff out of header
* Use internal enums instead of CLBlast enums
* Remove leftover C++ includes and defines
* Make event use easier to read

Co-authored-by: Henri Vasserman <henv@hot.ee>

* Use c compiler for opencl files

* Simplify code, fix include

* First check error, then release event

* Make globals static, fix indentation

* Rename dequant kernels file to conform with other file names

* Fix import cl file name

---------

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
pull/1015/merge master-7296c96
0cc4m 1 year ago committed by GitHub
parent 78ec543733
commit 7296c961d9
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@ -67,6 +67,7 @@ endif()
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
@ -168,6 +169,21 @@ if (LLAMA_CUBLAS)
endif()
endif()
if (LLAMA_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
message(STATUS "CLBlast found")
set(GGML_OPENCL_SOURCES ggml-opencl.c ggml-opencl.h)
add_compile_definitions(GGML_USE_CLBLAST)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
else()
message(WARNING "CLBlast not found")
endif()
endif()
if (LLAMA_ALL_WARNINGS)
if (NOT MSVC)
set(c_flags
@ -307,7 +323,8 @@ endif()
add_library(ggml OBJECT
ggml.c
ggml.h
${GGML_CUDA_SOURCES})
${GGML_CUDA_SOURCES}
${GGML_OPENCL_SOURCES})
target_include_directories(ggml PUBLIC .)
target_compile_features(ggml PUBLIC c_std_11) # don't bump

@ -105,14 +105,21 @@ ifdef LLAMA_OPENBLAS
LDFLAGS += -lopenblas
endif
ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
OBJS += ggml-cuda.o
NVCC = nvcc
NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
endif
ifdef LLAMA_CLBLAST
CFLAGS += -DGGML_USE_CLBLAST
LDFLAGS += -lclblast -lOpenCL
OBJS += ggml-opencl.o
ggml-opencl.o: ggml-opencl.c ggml-opencl.h
$(CC) $(CFLAGS) -c $< -o $@
endif
ifdef LLAMA_GPROF
CFLAGS += -pg
CXXFLAGS += -pg

@ -0,0 +1,84 @@
#define MULTILINE_QUOTE(...) #__VA_ARGS__
const char * clblast_dequant = MULTILINE_QUOTE(
struct block_q4_0
{
float d;
uchar qs[16];
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q4_1
{
float d;
float m;
uchar qs[16];
};
__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const float m = blocks[i].m;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = (vi & 0xf) * d + m;
result[index + 1] = (vi >> 4) * d + m;
}
struct block_q4_2
{
ushort d;
uchar qs[8];
};
__kernel void dequantize_row_q4_2(__global struct block_q4_2* blocks, __global float* result) {
const uint i = get_global_id(0) / 16;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);;
const uchar vi = blocks[i].qs[l];
const uint index = i*16 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q4_3
{
ushort d;
ushort m;
uchar qs[8];
};
__kernel void dequantize_row_q4_3(__global struct block_q4_3* blocks, __global float* result) {
const uint i = get_global_id(0) / 16;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &(blocks[i].d));
const float m = vload_half(0, (__global half*) &(blocks[i].m));
const uchar vi = blocks[i].qs[l];
const uint index = i*16 + l*2;
result[index + 0] = (vi & 0xf) * d + m;
result[index + 1] = (vi >> 4) * d + m;
}
);

@ -0,0 +1,216 @@
#include "ggml-opencl.h"
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast_c.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#include "ggml-opencl-dequant.cl"
#define CL_CHECK(err, name) \
do { \
cl_int err_ = (err); \
if (err_ != CL_SUCCESS) { \
fprintf(stderr, "OpenCL %s error %d at %s:%d\n", name, err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
static cl_platform_id platform;
static cl_device_id device;
static cl_context context;
static cl_command_queue queue;
static cl_program program;
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q4_2, kernel_q4_3;
static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
cl_program p;
char *program_log;
size_t program_size, log_size;
int err;
program_size = strlen(program_buffer);
p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
if(err < 0) {
fprintf(stderr, "OpenCL error creating program");
exit(1);
}
err = clBuildProgram(p, 0, NULL, NULL, NULL, NULL);
if(err < 0) {
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
program_log = (char*) malloc(log_size + 1);
program_log[log_size] = '\0';
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
printf("%s\n", program_log);
free(program_log);
exit(1);
}
return p;
}
void ggml_cl_init(void) {
cl_int err = 0;
char * GGML_CLBLAST_PLATFORM = getenv("GGML_CLBLAST_PLATFORM");
char * GGML_CLBLAST_DEVICE = getenv("GGML_CLBLAST_DEVICE");
int plat_num = (GGML_CLBLAST_PLATFORM == NULL ? 0 : atoi(GGML_CLBLAST_PLATFORM));
int dev_num = (GGML_CLBLAST_DEVICE == NULL ? 0 : atoi(GGML_CLBLAST_DEVICE));
printf("\nInitializing CLBlast (First Run)...");
printf("\nAttempting to use: Platform=%d, Device=%d (If invalid, program will crash)\n",plat_num,dev_num);
cl_uint num_platforms;
clGetPlatformIDs(0, NULL, &num_platforms);
cl_platform_id* platforms = (cl_platform_id*)malloc(num_platforms*sizeof(cl_platform_id));
clGetPlatformIDs(num_platforms, platforms, NULL);
platform = platforms[plat_num];
char platform_buffer[1024];
clGetPlatformInfo(platform, CL_PLATFORM_NAME, sizeof(platform_buffer), &platform_buffer, NULL);
cl_uint num_devices;
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, NULL, &num_devices);
cl_device_id* devices = (cl_device_id*)malloc(num_devices*sizeof(cl_device_id));
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, num_devices, devices, NULL);
device = devices[dev_num];
char device_buffer[1024];
clGetDeviceInfo(device, CL_DEVICE_NAME, sizeof(device_buffer), &device_buffer, NULL);
printf("Using Platform: %s Device: %s\n", platform_buffer, device_buffer);
context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
CL_CHECK(err, "clCreateContext");
queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
CL_CHECK(err, "clCreateCommandQueue");
free(platforms);
free(devices);
program = build_program_from_source(context, device, clblast_dequant);
// Prepare dequantize kernels
kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_2 = clCreateKernel(program, "dequantize_row_q4_2", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_3 = clCreateKernel(program, "dequantize_row_q4_3", &err);
CL_CHECK(err, "clCreateKernel");
}
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
if (req_size <= *cur_size) {
return;
}
// Reallocate buffer with enough space
if (*cur_size > 0) {
clReleaseMemObject(*buf);
}
cl_int err;
*buf = clCreateBuffer(context, flags, req_size, NULL, &err);
*cur_size = req_size;
CL_CHECK(err, "clCreateBuffer");
}
void ggml_cl_sgemm_wrapper(
const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b,
const int m, const int n, const int k,
const float alpha, const void *host_a, const int lda,
const float *host_b, const int ldb, const float beta,
float *host_c, const int ldc, const int btype) {
cl_int err = 0;
cl_kernel kernel;
size_t global = n * k, local, size_qb;
bool dequant;
switch (btype) {
case GGML_TYPE_F32:
dequant = false;
break;
case GGML_TYPE_Q4_0:
dequant = true;
kernel = kernel_q4_0;
local = 16;
size_qb = global * (sizeof(float) + local) / 32;
break;
case GGML_TYPE_Q4_1:
dequant = true;
kernel = kernel_q4_1;
local = 16;
size_qb = global * (sizeof(float) * 2 + local) / 32;
break;
case GGML_TYPE_Q4_2:
dequant = true;
kernel = kernel_q4_2;
local = 8;
size_qb = global * (sizeof(short) + local) / 16;
break;
case GGML_TYPE_Q4_3:
dequant = true;
kernel = kernel_q4_3;
local = 8;
size_qb = global * (sizeof(short) * 2 + local) / 16;
break;
default:
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
abort();
}
const size_t size_a = m * k * sizeof(float);
const size_t size_b = n * k * sizeof(float);
const size_t size_c = m * n * sizeof(float);
// Prepare buffers
ggml_cl_malloc(size_a, &cl_size_a, CL_MEM_READ_ONLY, &cl_buffer_a);
if (dequant) {
ggml_cl_malloc(size_qb, &cl_size_qb, CL_MEM_READ_ONLY, &cl_buffer_qb);
}
ggml_cl_malloc(size_b, &cl_size_b, CL_MEM_READ_WRITE, &cl_buffer_b);
ggml_cl_malloc(size_c, &cl_size_c, CL_MEM_WRITE_ONLY, &cl_buffer_c);
cl_event ev_a, ev_qb, ev_b;
if (dequant) {
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
CL_CHECK(err, "clSetKernelArg");
clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
} else {
clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
}
clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
if (dequant) {
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
CL_CHECK(err, "clEnqueueNDRangeKernel");
clReleaseEvent(ev_qb);
}
clWaitForEvents(1, &ev_a);
clWaitForEvents(1, &ev_b);
clReleaseEvent(ev_a);
clReleaseEvent(ev_b);
cl_event ev_sgemm;
CLBlastSgemm((CLBlastLayout)order,
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
m, n, k,
alpha,
cl_buffer_a, 0, lda,
cl_buffer_b, 0, ldb,
beta,
cl_buffer_c, 0, ldc,
&queue, &ev_sgemm);
cl_event ev_c;
clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c);
// Wait for completion
clWaitForEvents(1, &ev_c);
clReleaseEvent(ev_sgemm);
clReleaseEvent(ev_c);
}

@ -0,0 +1,24 @@
#pragma once
#ifdef __cplusplus
extern "C" {
#endif
void ggml_cl_init(void);
enum ggml_blas_order {
GGML_BLAS_ORDER_ROW_MAJOR = 101,
GGML_BLAS_ORDER_COLUMN_MAJOR = 102,
};
enum ggml_blas_op {
GGML_BLAS_OP_N = 111,
GGML_BLAS_OP_T = 112,
GGML_BLAS_OP_C = 113,
};
void ggml_cl_sgemm_wrapper(const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b, const int m, const int n, const int k, const float alpha, const void *host_a, const int lda, const float *host_b, const int ldb, const float beta, float *host_c, const int ldc, const int btype);
#ifdef __cplusplus
}
#endif

@ -149,6 +149,8 @@ inline static void* ggml_aligned_malloc(size_t size) {
#include <cblas.h>
#elif defined(GGML_USE_CUBLAS)
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
#undef MIN
@ -4363,6 +4365,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
// initialize cuBLAS
#if defined(GGML_USE_CUBLAS)
ggml_init_cublas();
#elif defined(GGML_USE_CLBLAST)
ggml_cl_init();
#endif
is_first_call = false;
@ -8104,7 +8108,7 @@ static void ggml_compute_forward_rms_norm(
// ggml_compute_forward_mul_mat
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_compute_forward_mul_mat_use_blas(
@ -8129,6 +8133,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
return false;
}
#endif
static void ggml_compute_forward_mul_mat_f32(
@ -8144,7 +8149,7 @@ static void ggml_compute_forward_mul_mat_f32(
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int64_t ne10 = src1->ne[0];
#endif
const int64_t ne11 = src1->ne[1];
@ -8201,7 +8206,7 @@ static void ggml_compute_forward_mul_mat_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8250,8 +8255,15 @@ static void ggml_compute_forward_mul_mat_f32(
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#else
#elif defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01,
GGML_TYPE_F32);
#else
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
@ -8395,7 +8407,7 @@ static void ggml_compute_forward_mul_mat_f16_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
GGML_ASSERT(nb10 == sizeof(float));
@ -8472,6 +8484,19 @@ static void ggml_compute_forward_mul_mat_f16_f32(
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01,
GGML_TYPE_F32);
#else
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
@ -8646,7 +8671,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8698,7 +8723,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
else {
GGML_ASSERT(false);
}
#else
#elif !defined(GGML_USE_CLBLAST)
float * const wdata = params->wdata;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
#endif
@ -8717,6 +8742,8 @@ static void ggml_compute_forward_mul_mat_q_f32(
dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, g_cudaStream);
CUDA_CHECK(cudaGetLastError());
#elif defined(GGML_USE_CLBLAST)
const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
#else
{
size_t id = 0;
@ -8743,8 +8770,15 @@ static void ggml_compute_forward_mul_mat_q_f32(
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#else
#elif defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01,
type);
#else
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
@ -11583,7 +11617,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
size_t cur = 0;
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
@ -11600,7 +11634,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
@ -13100,7 +13134,7 @@ int ggml_cpu_has_wasm_simd(void) {
}
int ggml_cpu_has_blas(void) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
return 1;
#else
return 0;
@ -13115,6 +13149,18 @@ int ggml_cpu_has_cublas(void) {
#endif
}
int ggml_cpu_has_clblast(void) {
#if defined(GGML_USE_CLBLAST)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_gpublas(void) {
return ggml_cpu_has_cublas() || ggml_cpu_has_clblast();
}
int ggml_cpu_has_sse3(void) {
#if defined(__SSE3__)
return 1;

@ -858,10 +858,11 @@ extern "C" {
GGML_API int ggml_cpu_has_wasm_simd (void);
GGML_API int ggml_cpu_has_blas (void);
GGML_API int ggml_cpu_has_cublas (void);
GGML_API int ggml_cpu_has_clblast (void);
GGML_API int ggml_cpu_has_gpublas (void);
GGML_API int ggml_cpu_has_sse3 (void);
GGML_API int ggml_cpu_has_vsx (void);
//
// Internal types and functions exposed for tests and benchmarks
//

@ -1085,7 +1085,7 @@ static bool llama_eval_internal(
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
ggml_cgraph gf = {};
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_cublas() ? 1 : n_threads;
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, tokens, N*ggml_element_size(embd));

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