haraldwolff/llama.cpp
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OpenCL Token Generation Acceleration (#1459)

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* 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 2023-05-22 23:33:24 +02:00 committed by GitHub
parent 7e4ea5beff
commit 2e6cd4b025
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
8 changed files with 1113 additions and 536 deletions
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2
CMakeLists.txt
View file

@ -201,7 +201,7 @@ if (LLAMA_CLBLAST)
if (CLBlast_FOUND)
message(STATUS "CLBlast found")
set(GGML_OPENCL_SOURCES ggml-opencl.c ggml-opencl.h)
set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
add_compile_definitions(GGML_USE_CLBLAST)

5
Makefile
View file

@ -138,6 +138,7 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
endif
ifdef LLAMA_CLBLAST
CFLAGS += -DGGML_USE_CLBLAST
CXXFLAGS += -DGGML_USE_CLBLAST
# Mac provides OpenCL as a framework
ifeq ($(UNAME_S),Darwin)
LDFLAGS += -lclblast -framework OpenCL
@ -145,8 +146,8 @@ ifdef LLAMA_CLBLAST
LDFLAGS += -lclblast -lOpenCL
endif
OBJS += ggml-opencl.o
ggml-opencl.o: ggml-opencl.c ggml-opencl.h
$(CC) $(CFLAGS) -c $< -o $@
ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.

474
ggml-opencl.c
View file

@ -1,474 +0,0 @@
#include "ggml-opencl.h"
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast_c.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#define MULTILINE_QUOTE(...) #__VA_ARGS__
static const char * program_source = MULTILINE_QUOTE(
typedef char int8_t;
typedef uchar uint8_t;
typedef int int32_t;
typedef uint uint32_t;
struct __attribute__ ((packed)) block_q4_0
{
half d;
uint8_t qs[16]; /* QK4_0 / 2 */
};
struct __attribute__ ((packed)) block_q4_1
{
half d;
half m;
uint8_t qs[16]; /* QK4_1 / 2 */
};
struct __attribute__ ((packed)) block_q5_0
{
half d;
uint32_t qh;
uint8_t qs[16]; /* QK5_0 / 2 */
};
struct __attribute__ ((packed)) block_q5_1
{
half d;
half m;
uint32_t qh;
uint8_t qs[16]; /* QK5_1 / 2 */
};
struct __attribute__ ((packed)) block_q8_0
{
half d;
int8_t qs[32]; /* QK8_0 */
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* x, __global float* y) {
const uint i = get_global_id(0) / 32; /* QK4_0 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
const int x0 = (x[i].qs[j] & 0xf) - 8;
const int x1 = (x[i].qs[j] >> 4) - 8;
y[i*32 + j + 0 ] = x0*d;
y[i*32 + j + 16] = x1*d;
}
__kernel void dequantize_row_q4_1(__global struct block_q4_1* x, __global float* y) {
const uint i = get_global_id(0) / 32; /* QK4_1 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
const float m = vload_half(0, (__global half*) &x[i].m);
const int x0 = (x[i].qs[j] & 0xf);
const int x1 = (x[i].qs[j] >> 4);
y[i*32 + j + 0 ] = x0*d + m;
y[i*32 + j + 16] = x1*d + m;
}
__kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float* y) {
const uint i = get_global_id(0) / 32; /* QK5_0 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
uint32_t qh = x[i].qh;
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;
y[i*32 + j + 0 ] = x0*d;
y[i*32 + j + 16] = x1*d;
}
__kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float* y) {
const uint i = get_global_id(0) / 32; /* QK5_1 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
const float m = vload_half(0, (__global half*) &x[i].m);
uint32_t qh = x[i].qh;
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
const int x0 = (x[i].qs[j] & 0xf) | xh_0;
const int x1 = (x[i].qs[j] >> 4) | xh_1;
y[i*32 + j + 0 ] = x0*d + m;
y[i*32 + j + 16] = x1*d + m;
}
__kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float* y) {
const uint i = get_global_id(0) / 32; /* QK8_0 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
y[i*32 + j] = x[i].qs[j]*d;
}
);
#define CL_CHECK(err) \
do { \
cl_int err_ = (err); \
if (err_ != CL_SUCCESS) { \
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
#err, err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
#define CLBLAST_CHECK(err) \
do { \
CLBlastStatusCode err_ = (err); \
if (err_ != CLBlastSuccess) { \
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
#err, 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_q5_0, kernel_q5_1, kernel_q8_0;
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;
struct cl_device;
struct cl_platform {
cl_platform_id id;
unsigned number;
char name[128];
char vendor[128];
struct cl_device * devices;
unsigned n_devices;
struct cl_device * default_device;
};
struct cl_device {
struct cl_platform * platform;
cl_device_id id;
unsigned number;
cl_device_type type;
char name[128];
};
enum { NPLAT = 16, NDEV = 16 };
struct cl_platform platforms[NPLAT];
unsigned n_platforms = 0;
struct cl_device devices[NDEV];
unsigned n_devices = 0;
struct cl_device * default_device = NULL;
platform = NULL;
device = NULL;
cl_platform_id platform_ids[NPLAT];
CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
for (unsigned i = 0; i < n_platforms; i++) {
struct cl_platform * p = &platforms[i];
p->number = i;
p->id = platform_ids[i];
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
cl_device_id device_ids[NDEV];
cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
p->n_devices = 0;
} else {
CL_CHECK(clGetDeviceIDsError);
}
p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
p->default_device = NULL;
for (unsigned j = 0; j < p->n_devices; j++) {
struct cl_device * d = &devices[n_devices];
d->number = n_devices++;
d->id = device_ids[j];
d->platform = p;
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
p->default_device = d;
}
}
if (default_device == NULL && p->default_device != NULL) {
default_device = p->default_device;
}
}
if (n_devices == 0) {
fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
exit(1);
}
char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
char * user_device_string = getenv("GGML_OPENCL_DEVICE");
int user_platform_number = -1;
int user_device_number = -1;
unsigned n;
if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
user_platform_number = (int)n;
}
if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
user_device_number = (int)n;
}
struct cl_device * selected_devices = devices;
unsigned n_selected_devices = n_devices;
if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
for (unsigned i = 0; i < n_platforms; i++) {
struct cl_platform * p = &platforms[i];
if (strstr(p->name, user_platform_string) != NULL ||
strstr(p->vendor, user_platform_string) != NULL) {
user_platform_number = (int)i;
break;
}
}
if (user_platform_number == -1) {
fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
exit(1);
}
}
if (user_platform_number != -1) {
struct cl_platform * p = &platforms[user_platform_number];
selected_devices = p->devices;
n_selected_devices = p->n_devices;
default_device = p->default_device;
if (n_selected_devices == 0) {
fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
exit(1);
}
}
if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
for (unsigned i = 0; i < n_selected_devices; i++) {
struct cl_device * d = &selected_devices[i];
if (strstr(d->name, user_device_string) != NULL) {
user_device_number = d->number;
break;
}
}
if (user_device_number == -1) {
fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
exit(1);
}
}
if (user_device_number != -1) {
selected_devices = &devices[user_device_number];
n_selected_devices = 1;
default_device = &selected_devices[0];
}
GGML_ASSERT(n_selected_devices > 0);
if (default_device == NULL) {
default_device = &selected_devices[0];
}
fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
if (default_device->type != CL_DEVICE_TYPE_GPU) {
fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
}
platform = default_device->platform->id;
device = default_device->id;
cl_context_properties properties[] = {
(intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
};
CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
(err != CL_INVALID_PROPERTY && err != CL_INVALID_VALUE ? err :
(queue = clCreateCommandQueue(context, device, 0, &err), err)
)));
program = build_program_from_source(context, device, program_source);
// Prepare dequantize kernels
CL_CHECK((kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
CL_CHECK((kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
CL_CHECK((kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
CL_CHECK((kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
CL_CHECK((kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
}
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;
CL_CHECK((*buf = clCreateBuffer(context, flags, req_size, NULL, &err), err));
*cur_size = req_size;
}
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_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(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
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File diff suppressed because it is too large Load diff

18
ggml-opencl.h
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@ -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
View file

@ -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
View file

@ -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
View file

@ -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);