mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 15:29:43 +00:00
OpenCL Token Generation Acceleration (#1459)
* Move back to C++ for OpenCL * Refactor OpenCL code to work more like the CUDA code, add missing functions * Deduplicate dequant kernels * Add OpenCL compile options * Use compile args for preprocessing constants * Restore default platform + device selection by id behavior --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Henri Vasserman <henv@hot.ee>
This commit is contained in:
0cc4m
GitHub
parent
7e4ea5beff
commit
2e6cd4b025
8 changed files with 1113 additions and 536 deletions
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@ -201,7 +201,7 @@ if (LLAMA_CLBLAST)
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if (CLBlast_FOUND)
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message(STATUS "CLBlast found")
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set(GGML_OPENCL_SOURCES ggml-opencl.c ggml-opencl.h)
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set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
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add_compile_definitions(GGML_USE_CLBLAST)
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5
Makefile
5
Makefile
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@ -138,6 +138,7 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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endif
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ifdef LLAMA_CLBLAST
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CFLAGS += -DGGML_USE_CLBLAST
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CXXFLAGS += -DGGML_USE_CLBLAST
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# Mac provides OpenCL as a framework
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ifeq ($(UNAME_S),Darwin)
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LDFLAGS += -lclblast -framework OpenCL
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@ -145,8 +146,8 @@ ifdef LLAMA_CLBLAST
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LDFLAGS += -lclblast -lOpenCL
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endif
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OBJS += ggml-opencl.o
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ggml-opencl.o: ggml-opencl.c ggml-opencl.h
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$(CC) $(CFLAGS) -c $< -o $@
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ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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endif
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ifneq ($(filter aarch64%,$(UNAME_M)),)
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# Apple M1, M2, etc.
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474
ggml-opencl.c
474
ggml-opencl.c
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@ -1,474 +0,0 @@
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#include "ggml-opencl.h"
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#define CL_TARGET_OPENCL_VERSION 110
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#include <clblast_c.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include "ggml.h"
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#define MULTILINE_QUOTE(...) #__VA_ARGS__
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static const char * program_source = MULTILINE_QUOTE(
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typedef char int8_t;
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typedef uchar uint8_t;
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typedef int int32_t;
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typedef uint uint32_t;
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struct __attribute__ ((packed)) block_q4_0
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{
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half d;
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uint8_t qs[16]; /* QK4_0 / 2 */
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};
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struct __attribute__ ((packed)) block_q4_1
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{
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half d;
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half m;
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uint8_t qs[16]; /* QK4_1 / 2 */
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};
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struct __attribute__ ((packed)) block_q5_0
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{
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half d;
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uint32_t qh;
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uint8_t qs[16]; /* QK5_0 / 2 */
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};
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struct __attribute__ ((packed)) block_q5_1
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{
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half d;
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half m;
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uint32_t qh;
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uint8_t qs[16]; /* QK5_1 / 2 */
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};
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struct __attribute__ ((packed)) block_q8_0
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{
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half d;
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int8_t qs[32]; /* QK8_0 */
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};
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__kernel void dequantize_row_q4_0(__global struct block_q4_0* x, __global float* y) {
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const uint i = get_global_id(0) / 32; /* QK4_0 */
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const uint j = get_local_id(0);
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const float d = vload_half(0, (__global half*) &x[i].d);
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const int x0 = (x[i].qs[j] & 0xf) - 8;
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const int x1 = (x[i].qs[j] >> 4) - 8;
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y[i*32 + j + 0 ] = x0*d;
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y[i*32 + j + 16] = x1*d;
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}
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__kernel void dequantize_row_q4_1(__global struct block_q4_1* x, __global float* y) {
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const uint i = get_global_id(0) / 32; /* QK4_1 */
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const uint j = get_local_id(0);
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const float d = vload_half(0, (__global half*) &x[i].d);
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const float m = vload_half(0, (__global half*) &x[i].m);
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const int x0 = (x[i].qs[j] & 0xf);
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const int x1 = (x[i].qs[j] >> 4);
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y[i*32 + j + 0 ] = x0*d + m;
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y[i*32 + j + 16] = x1*d + m;
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}
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__kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float* y) {
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const uint i = get_global_id(0) / 32; /* QK5_0 */
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const uint j = get_local_id(0);
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const float d = vload_half(0, (__global half*) &x[i].d);
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uint32_t qh = x[i].qh;
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const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
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const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
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const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
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const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;
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y[i*32 + j + 0 ] = x0*d;
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y[i*32 + j + 16] = x1*d;
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}
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__kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float* y) {
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const uint i = get_global_id(0) / 32; /* QK5_1 */
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const uint j = get_local_id(0);
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const float d = vload_half(0, (__global half*) &x[i].d);
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const float m = vload_half(0, (__global half*) &x[i].m);
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uint32_t qh = x[i].qh;
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const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
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const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
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const int x0 = (x[i].qs[j] & 0xf) | xh_0;
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const int x1 = (x[i].qs[j] >> 4) | xh_1;
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y[i*32 + j + 0 ] = x0*d + m;
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y[i*32 + j + 16] = x1*d + m;
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}
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__kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float* y) {
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const uint i = get_global_id(0) / 32; /* QK8_0 */
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const uint j = get_local_id(0);
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const float d = vload_half(0, (__global half*) &x[i].d);
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y[i*32 + j] = x[i].qs[j]*d;
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}
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);
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#define CL_CHECK(err) \
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do { \
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cl_int err_ = (err); \
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if (err_ != CL_SUCCESS) { \
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fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
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#err, err_, __FILE__, __LINE__); \
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exit(1); \
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} \
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} while (0)
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#define CLBLAST_CHECK(err) \
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do { \
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CLBlastStatusCode err_ = (err); \
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if (err_ != CLBlastSuccess) { \
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fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
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#err, err_, __FILE__, __LINE__); \
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exit(1); \
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} \
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} while (0)
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static cl_platform_id platform;
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static cl_device_id device;
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static cl_context context;
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static cl_command_queue queue;
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static cl_program program;
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static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q5_0, kernel_q5_1, kernel_q8_0;
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static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
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static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
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static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
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cl_program p;
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char *program_log;
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size_t program_size, log_size;
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int err;
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program_size = strlen(program_buffer);
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p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
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if(err < 0) {
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fprintf(stderr, "OpenCL error creating program");
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exit(1);
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}
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err = clBuildProgram(p, 0, NULL, NULL, NULL, NULL);
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if(err < 0) {
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clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
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program_log = (char*) malloc(log_size + 1);
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program_log[log_size] = '\0';
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clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
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printf("%s\n", program_log);
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free(program_log);
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exit(1);
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}
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return p;
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}
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void ggml_cl_init(void) {
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cl_int err = 0;
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struct cl_device;
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struct cl_platform {
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cl_platform_id id;
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unsigned number;
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char name[128];
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char vendor[128];
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struct cl_device * devices;
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unsigned n_devices;
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struct cl_device * default_device;
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};
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struct cl_device {
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struct cl_platform * platform;
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cl_device_id id;
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unsigned number;
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cl_device_type type;
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char name[128];
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};
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enum { NPLAT = 16, NDEV = 16 };
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struct cl_platform platforms[NPLAT];
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unsigned n_platforms = 0;
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struct cl_device devices[NDEV];
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unsigned n_devices = 0;
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struct cl_device * default_device = NULL;
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platform = NULL;
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device = NULL;
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cl_platform_id platform_ids[NPLAT];
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CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
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for (unsigned i = 0; i < n_platforms; i++) {
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struct cl_platform * p = &platforms[i];
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p->number = i;
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p->id = platform_ids[i];
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CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
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CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
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cl_device_id device_ids[NDEV];
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cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
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if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
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p->n_devices = 0;
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} else {
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CL_CHECK(clGetDeviceIDsError);
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}
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p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
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p->default_device = NULL;
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for (unsigned j = 0; j < p->n_devices; j++) {
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struct cl_device * d = &devices[n_devices];
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d->number = n_devices++;
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d->id = device_ids[j];
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d->platform = p;
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CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
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CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
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if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
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p->default_device = d;
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}
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}
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if (default_device == NULL && p->default_device != NULL) {
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default_device = p->default_device;
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}
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}
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if (n_devices == 0) {
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fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
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exit(1);
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}
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char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
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char * user_device_string = getenv("GGML_OPENCL_DEVICE");
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int user_platform_number = -1;
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int user_device_number = -1;
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unsigned n;
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if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
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user_platform_number = (int)n;
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}
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if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
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user_device_number = (int)n;
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}
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struct cl_device * selected_devices = devices;
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unsigned n_selected_devices = n_devices;
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if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
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for (unsigned i = 0; i < n_platforms; i++) {
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struct cl_platform * p = &platforms[i];
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if (strstr(p->name, user_platform_string) != NULL ||
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strstr(p->vendor, user_platform_string) != NULL) {
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user_platform_number = (int)i;
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break;
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}
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}
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if (user_platform_number == -1) {
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fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
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exit(1);
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}
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}
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if (user_platform_number != -1) {
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struct cl_platform * p = &platforms[user_platform_number];
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selected_devices = p->devices;
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n_selected_devices = p->n_devices;
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default_device = p->default_device;
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if (n_selected_devices == 0) {
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fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
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exit(1);
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}
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}
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if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
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for (unsigned i = 0; i < n_selected_devices; i++) {
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struct cl_device * d = &selected_devices[i];
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if (strstr(d->name, user_device_string) != NULL) {
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user_device_number = d->number;
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break;
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}
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}
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if (user_device_number == -1) {
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fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
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exit(1);
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}
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}
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if (user_device_number != -1) {
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selected_devices = &devices[user_device_number];
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n_selected_devices = 1;
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default_device = &selected_devices[0];
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}
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GGML_ASSERT(n_selected_devices > 0);
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if (default_device == NULL) {
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default_device = &selected_devices[0];
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}
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fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
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fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
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if (default_device->type != CL_DEVICE_TYPE_GPU) {
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fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
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}
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platform = default_device->platform->id;
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device = default_device->id;
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cl_context_properties properties[] = {
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(intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
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};
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CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
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CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
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(err != CL_INVALID_PROPERTY && err != CL_INVALID_VALUE ? err :
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(queue = clCreateCommandQueue(context, device, 0, &err), err)
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)));
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program = build_program_from_source(context, device, program_source);
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// Prepare dequantize kernels
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CL_CHECK((kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
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CL_CHECK((kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
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CL_CHECK((kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
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CL_CHECK((kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
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CL_CHECK((kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
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}
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static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
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if (req_size <= *cur_size) {
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return;
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}
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// Reallocate buffer with enough space
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if (*cur_size > 0) {
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clReleaseMemObject(*buf);
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}
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cl_int err;
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CL_CHECK((*buf = clCreateBuffer(context, flags, req_size, NULL, &err), err));
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*cur_size = req_size;
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}
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void ggml_cl_sgemm_wrapper(
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const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b,
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const int m, const int n, const int k,
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const float alpha, const void *host_a, const int lda,
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const float *host_b, const int ldb, const float beta,
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float *host_c, const int ldc, const int btype) {
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cl_kernel kernel;
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size_t global = n * k, local, size_qb;
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bool dequant;
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switch (btype) {
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case GGML_TYPE_F32:
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dequant = false;
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break;
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case GGML_TYPE_Q4_0:
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||||
dequant = true;
|
||||
kernel = kernel_q4_0;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_1;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) * 2 + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q5_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q5_0;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + sizeof(uint32_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q5_1:
|
||||
dequant = true;
|
||||
kernel = kernel_q5_1;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) * 2 + sizeof(uint32_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q8_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q8_0;
|
||||
local = 32;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
|
||||
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) {
|
||||
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb));
|
||||
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b));
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb));
|
||||
} else {
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b));
|
||||
}
|
||||
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a));
|
||||
if (dequant) {
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b));
|
||||
CL_CHECK(clReleaseEvent(ev_qb));
|
||||
}
|
||||
CL_CHECK(clWaitForEvents(1, &ev_a));
|
||||
CL_CHECK(clWaitForEvents(1, &ev_b));
|
||||
CL_CHECK(clReleaseEvent(ev_a));
|
||||
CL_CHECK(clReleaseEvent(ev_b));
|
||||
|
||||
cl_event ev_sgemm;
|
||||
CLBLAST_CHECK(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;
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c));
|
||||
|
||||
// Wait for completion
|
||||
CL_CHECK(clWaitForEvents(1, &ev_c));
|
||||
CL_CHECK(clReleaseEvent(ev_sgemm));
|
||||
CL_CHECK(clReleaseEvent(ev_c));
|
||||
}
|
||||
1034
ggml-opencl.cpp
Normal file
1034
ggml-opencl.cpp
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,23 +1,21 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#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,
|
||||
};
|
||||
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
|
||||
|
||||
enum ggml_blas_op {
|
||||
GGML_BLAS_OP_N = 111,
|
||||
GGML_BLAS_OP_T = 112,
|
||||
GGML_BLAS_OP_C = 113,
|
||||
};
|
||||
void * ggml_cl_host_malloc(size_t size);
|
||||
void ggml_cl_host_free(void * ptr);
|
||||
|
||||
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);
|
||||
void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
83
ggml.c
83
ggml.c
|
|
@ -9431,7 +9431,7 @@ static void ggml_compute_forward_rms_norm_back(
|
|||
|
||||
// ggml_compute_forward_mul_mat
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
// 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(
|
||||
|
|
@ -9472,7 +9472,7 @@ 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_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
#endif
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
|
@ -9536,9 +9536,16 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
|
|
@ -9558,21 +9565,11 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
#if 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,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
|
||||
|
|
@ -9711,9 +9708,16 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
GGML_ASSERT(nb10 == sizeof(float));
|
||||
|
||||
|
|
@ -9743,20 +9747,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
assert(id*sizeof(float) <= params->wsize);
|
||||
}
|
||||
|
||||
#if 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);
|
||||
|
||||
|
|
@ -9768,7 +9758,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -9931,9 +9920,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
|
|
@ -9956,9 +9952,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
|
||||
#else
|
||||
{
|
||||
size_t id = 0;
|
||||
for (int64_t i01 = 0; i01 < ne01; ++i01) {
|
||||
|
|
@ -9970,23 +9963,12 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
}
|
||||
|
||||
const float * x = wdata;
|
||||
#endif
|
||||
|
||||
#if 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,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -14165,9 +14147,16 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
cur = ggml_cl_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#endif
|
||||
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_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
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
|
||||
|
|
@ -14181,13 +14170,13 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
#endif
|
||||
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
|
||||
cur = 0;
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1;
|
||||
}
|
||||
#endif
|
||||
} 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_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
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]);
|
||||
|
|
|
|||
1
ggml.h
1
ggml.h
|
|
@ -249,6 +249,7 @@ extern "C" {
|
|||
enum ggml_backend {
|
||||
GGML_BACKEND_CPU = 0,
|
||||
GGML_BACKEND_CUDA = 1,
|
||||
GGML_BACKEND_CL = 2,
|
||||
};
|
||||
|
||||
// model file types
|
||||
|
|
|
|||
32
llama.cpp
32
llama.cpp
|
|
@ -12,6 +12,8 @@
|
|||
#include "ggml.h"
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#include "ggml-opencl.h"
|
||||
#endif
|
||||
|
||||
#include <array>
|
||||
|
|
@ -1092,7 +1094,7 @@ static void llama_model_load_internal(
|
|||
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
|
||||
}
|
||||
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
#else
|
||||
#elif !defined(GGML_USE_CLBLAST)
|
||||
(void) n_gpu_layers;
|
||||
#endif
|
||||
}
|
||||
|
|
@ -1125,7 +1127,33 @@ static void llama_model_load_internal(
|
|||
done_size += lt.size;
|
||||
}
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
{
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
fprintf(stderr, "ggml_opencl: offloading %d layers to GPU\n", n_gpu);
|
||||
|
||||
size_t vram_total = 0;
|
||||
|
||||
for (int i = 0; i < n_gpu; ++i) {
|
||||
const auto & layer = model.layers[i];
|
||||
|
||||
ggml_cl_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
|
||||
ggml_cl_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
|
||||
ggml_cl_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
|
||||
ggml_cl_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
|
||||
ggml_cl_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
|
||||
ggml_cl_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
|
||||
ggml_cl_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);
|
||||
}
|
||||
if (n_gpu_layers > (int) hparams.n_layer) {
|
||||
fprintf(stderr, "ggml_opencl: offloading output layer to GPU\n");
|
||||
ggml_cl_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
|
||||
}
|
||||
|
||||
fprintf(stderr, "ggml_opencl: total VRAM used: %zu MB\n", vram_total / 1024 / 1024);
|
||||
}
|
||||
#endif
|
||||
|
||||
if (progress_callback) {
|
||||
progress_callback(1.0f, progress_callback_user_data);
|
||||
|
|
|
|||
Loading…
Reference in a new issue