Refactor OpenCL code to work more like the CUDA code, add missing functions
This commit is contained in:
0cc4m
parent
82bc517b9a
commit
c77966524a
6 changed files with 658 additions and 188 deletions
5
Makefile
5
Makefile
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@ -134,6 +134,7 @@ 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 ($(UNAME_S),Darwin)
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LDFLAGS += -lclblast -framework OpenCL
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@ -141,8 +142,8 @@ ifdef LLAMA_CLBLAST
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LDFLAGS += -lclblast -lOpenCL
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endif
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OBJS += ggml-opencl.o
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ggml-opencl.o: ggml-opencl.c ggml-opencl.h
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$(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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709
ggml-opencl.cpp
709
ggml-opencl.cpp
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@ -1,7 +1,9 @@
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#include "ggml-opencl.h"
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#include <atomic>
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#define CL_TARGET_OPENCL_VERSION 110
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#include <clblast_c.h>
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#include <clblast.h>
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#include <stdlib.h>
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#include <stdio.h>
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@ -9,14 +11,18 @@
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#include "ggml.h"
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#define CL_DMMV_BLOCK_SIZE 32;
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#define MULTILINE_QUOTE(...) #__VA_ARGS__
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const char * clblast_dequant = 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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constant uint QK4_0 = 32;
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constant uint QR4_0 = 2;
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struct block_q4_0
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{
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float d;
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@ -24,6 +30,7 @@ struct block_q4_0
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};
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constant uint QK4_1 = 32;
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constant uint QR4_1 = 2;
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struct block_q4_1
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{
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float d;
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@ -32,6 +39,7 @@ struct block_q4_1
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};
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constant uint QK5_0 = 32;
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constant uint QR5_0 = 2;
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struct __attribute__ ((packed)) block_q5_0
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{
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half d;
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@ -40,6 +48,7 @@ struct __attribute__ ((packed)) block_q5_0
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};
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constant uint QK5_1 = 32;
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constant uint QR5_1 = 2;
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struct block_q5_1
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{
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half d;
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@ -49,6 +58,7 @@ struct block_q5_1
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};
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constant uint QK8_0 = 32;
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constant uint QR8_0 = 1;
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struct block_q8_0
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{
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float d;
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@ -56,6 +66,13 @@ struct block_q8_0
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};
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__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
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const uint i = get_global_id(0);
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y[i] = vload_half(0, &x[i]);
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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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constant uint qk = QK4_0;
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@ -137,6 +154,53 @@ __kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float*
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y[i*qk + j] = x[i].qs[j]*d;
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}
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__kernel void dequantize_mul_mat_vec(__global struct block_q4_0* x, __local float* tmp, __global float* y, __global float* dst, int ncols) {
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const int row = get_global_id(0);
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const int tid = get_local_id(0);
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const int block_size = get_local_size(0);
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const uint qk = QK4_0;
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const uint qr = QR4_0;
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const int y_offset = qr == 1 ? 1 : qk/2;
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tmp[tid] = 0;
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for (int i = 0; i < ncols/block_size; i += 2) {
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const int col = i*block_size + 2*tid;
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const int ib = (row*ncols + col)/qk; // block index
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const int iqs = (col%qk)/qr; // quant index
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const int iybs = col - col%qk; // y block start index
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// dequantize
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float v0, v1;
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const float d = x[ib].d;
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const uint8_t vui = x[ib].qs[iqs];
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const int8_t vi0 = vui & 0xF;
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const int8_t vi1 = vui >> 4;
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v0 = (vi0 - 8)*d;
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v1 = (vi1 - 8)*d;
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// matrix multiplication
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tmp[tid] += v0 * y[iybs + iqs + 0];
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tmp[tid] += v1 * y[iybs + iqs + y_offset];
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}
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// sum up partial sums and write back result
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barrier(CLK_LOCAL_MEM_FENCE);
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for (int s=block_size/2; s>0; s>>=1) {
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if (tid < s) {
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tmp[tid] += tmp[tid + s];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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if (tid == 0) {
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dst[row] = tmp[0];
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}
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}
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);
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#define CL_CHECK(err, name) \
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@ -153,9 +217,10 @@ 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_kernel convert_fp16_to_fp32_cl;
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static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
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static cl_kernel dequantize_mul_mat_vec_cl;
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static bool fp16_support;
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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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@ -208,7 +273,19 @@ void ggml_cl_init(void) {
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device = devices[dev_num];
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char device_buffer[1024];
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clGetDeviceInfo(device, CL_DEVICE_NAME, sizeof(device_buffer), &device_buffer, NULL);
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printf("Using Platform: %s Device: %s\n", platform_buffer, device_buffer);
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size_t ext_str_size;
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clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
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char* ext_buffer = (char*) malloc(sizeof(char) * ext_str_size);
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clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
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// Check if ext_buffer contains cl_khr_fp16
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for (size_t i = 0; i < ext_str_size - 12; i++) {
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if (memcmp(ext_buffer + i, "cl_khr_fp16", 11) == 0) {
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fp16_support = true;
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break;
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}
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}
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free(ext_buffer);
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printf("Using Platform: %s Device: %s FP16: %d\n", platform_buffer, device_buffer, fp16_support);
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context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
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CL_CHECK(err, "clCreateContext");
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queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
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@ -219,143 +296,519 @@ void ggml_cl_init(void) {
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program = build_program_from_source(context, device, clblast_dequant);
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// Prepare dequantize kernels
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kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err);
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// FP16 to FP32 kernel
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convert_fp16_to_fp32_cl = clCreateKernel(program, "convert_fp16_to_fp32", &err);
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CL_CHECK(err, "clCreateKernel");
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kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err);
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// Dequantize kernels
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dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err);
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CL_CHECK(err, "clCreateKernel");
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kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err);
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dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err);
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CL_CHECK(err, "clCreateKernel");
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kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err);
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dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err);
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CL_CHECK(err, "clCreateKernel");
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kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err);
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dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err);
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CL_CHECK(err, "clCreateKernel");
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dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err);
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CL_CHECK(err, "clCreateKernel");
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// dequant mul mat kernel
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dequantize_mul_mat_vec_cl = clCreateKernel(program, "dequantize_mul_mat_vec", &err);
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CL_CHECK(err, "clCreateKernel");
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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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static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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return &dequantize_row_q4_0_cl;
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case GGML_TYPE_Q4_1:
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return &dequantize_row_q4_1_cl;
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case GGML_TYPE_Q5_0:
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return &dequantize_row_q5_0_cl;
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case GGML_TYPE_Q5_1:
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return &dequantize_row_q5_1_cl;
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case GGML_TYPE_Q8_0:
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return &dequantize_row_q8_0_cl;
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case GGML_TYPE_F16:
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return &convert_fp16_to_fp32_cl;
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default:
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return nullptr;
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}
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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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static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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return &dequantize_mul_mat_vec_cl;
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// case GGML_TYPE_Q4_1:
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// return dequantize_mul_mat_vec_q4_1_cl;
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// case GGML_TYPE_Q5_0:
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// return dequantize_mul_mat_vec_q5_0_cl;
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// case GGML_TYPE_Q5_1:
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// return dequantize_mul_mat_vec_q5_1_cl;
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// case GGML_TYPE_Q8_0:
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// return dequantize_mul_mat_vec_q8_0_cl;
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// case GGML_TYPE_F16:
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// return convert_mul_mat_vec_f16_cl;
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default:
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return nullptr;
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}
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}
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// buffer pool for cl
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#define MAX_CL_BUFFERS 256
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struct scoped_spin_lock {
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std::atomic_flag& lock;
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scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
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while (lock.test_and_set(std::memory_order_acquire)) {
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; // spin
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}
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}
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~scoped_spin_lock() {
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lock.clear(std::memory_order_release);
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}
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scoped_spin_lock(const scoped_spin_lock&) = delete;
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scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
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};
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struct cl_buffer {
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cl_mem mem;
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size_t size = 0;
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};
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static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
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static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
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static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size, cl_mem_flags flags) {
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scoped_spin_lock lock(g_cl_pool_lock);
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cl_int err;
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*buf = clCreateBuffer(context, flags, req_size, NULL, &err);
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*cur_size = req_size;
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for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
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cl_buffer& b = g_cl_buffer_pool[i];
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if (b.size > 0 && b.size >= size) {
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cl_mem mem = b.mem;
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*actual_size = b.size;
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b.size = 0;
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return mem;
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}
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}
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cl_mem mem = clCreateBuffer(context, flags, size, NULL, &err);
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CL_CHECK(err, "clCreateBuffer");
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*actual_size = size;
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return mem;
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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_int err = 0;
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static void ggml_cl_pool_free(cl_mem mem, size_t size) {
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scoped_spin_lock lock(g_cl_pool_lock);
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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;
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kernel = kernel_q4_0;
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local = 16;
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size_qb = global * (sizeof(float) + local) / 32;
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break;
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case GGML_TYPE_Q4_1:
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dequant = true;
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kernel = kernel_q4_1;
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local = 16;
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size_qb = global * (sizeof(float) * 2 + local) / 32;
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break;
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case GGML_TYPE_Q5_0:
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dequant = true;
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kernel = kernel_q5_0;
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local = 16;
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size_qb = global * (sizeof(ggml_fp16_t) + sizeof(uint32_t) + local) / 32;
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break;
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case GGML_TYPE_Q5_1:
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dequant = true;
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kernel = kernel_q5_1;
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local = 16;
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size_qb = global * (sizeof(ggml_fp16_t) * 2 + sizeof(uint32_t) + local) / 32;
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break;
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case GGML_TYPE_Q8_0:
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dequant = true;
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kernel = kernel_q8_0;
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local = 32;
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size_qb = global * (sizeof(float) + local) / 32;
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break;
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default:
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fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
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abort();
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for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
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cl_buffer& b = g_cl_buffer_pool[i];
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if (b.size == 0) {
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b.mem = mem;
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b.size = size;
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return;
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}
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}
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const size_t size_a = m * k * sizeof(float);
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const size_t size_b = n * k * sizeof(float);
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const size_t size_c = m * n * sizeof(float);
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// Prepare buffers
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ggml_cl_malloc(size_a, &cl_size_a, CL_MEM_READ_ONLY, &cl_buffer_a);
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if (dequant) {
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ggml_cl_malloc(size_qb, &cl_size_qb, CL_MEM_READ_ONLY, &cl_buffer_qb);
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}
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ggml_cl_malloc(size_b, &cl_size_b, CL_MEM_READ_WRITE, &cl_buffer_b);
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ggml_cl_malloc(size_c, &cl_size_c, CL_MEM_WRITE_ONLY, &cl_buffer_c);
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cl_event ev_a, ev_qb, ev_b;
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if (dequant) {
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err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
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err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
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CL_CHECK(err, "clSetKernelArg");
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err = clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
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CL_CHECK(err, "clEnqueueWriteBuffer qb");
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} else {
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err = clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
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CL_CHECK(err, "clEnqueueWriteBuffer b");
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}
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err = clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
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CL_CHECK(err, "clEnqueueWriteBuffer a");
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if (dequant) {
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err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
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CL_CHECK(err, "clEnqueueNDRangeKernel");
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clReleaseEvent(ev_qb);
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}
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clWaitForEvents(1, &ev_a);
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clWaitForEvents(1, &ev_b);
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clReleaseEvent(ev_a);
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clReleaseEvent(ev_b);
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cl_event ev_sgemm;
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CLBlastStatusCode status = 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);
|
||||
|
||||
if (status != CLBlastSuccess) {
|
||||
fprintf(stderr, "Error: CLBlast SGEMM %d\n", status);
|
||||
abort();
|
||||
}
|
||||
|
||||
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);
|
||||
fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n");
|
||||
clReleaseMemObject(mem);
|
||||
}
|
||||
|
||||
static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) {
|
||||
cl_int err;
|
||||
const uint64_t ne0 = src->ne[0];
|
||||
const uint64_t ne1 = src->ne[1];
|
||||
const uint64_t nb0 = src->nb[0];
|
||||
const uint64_t nb1 = src->nb[1];
|
||||
const uint64_t nb2 = src->nb[2];
|
||||
const uint64_t nb3 = src->nb[3];
|
||||
const enum ggml_type type = src->type;
|
||||
const size_t ts = ggml_type_size(type);
|
||||
const size_t bs = ggml_blck_size(type);
|
||||
|
||||
const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
|
||||
if (nb0 == ts && nb1 == ts*ne0/bs) {
|
||||
err = clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*nb1, x, 0, NULL, ev);
|
||||
return err;
|
||||
}
|
||||
if (nb0 == ts) {
|
||||
const size_t buffer_origin[3] = { offset, 0, 0 };
|
||||
const size_t host_origin[3] = { 0, 0, 0 };
|
||||
const size_t region[3] = { ts*ne0/bs, ne1, 1 };
|
||||
err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts*ne0/bs, 0, nb1, 0, x, 0, NULL, ev);
|
||||
return err;
|
||||
}
|
||||
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
||||
// pretend the row is a matrix with cols=1
|
||||
const size_t buffer_origin[3] = { offset, i1, 0 };
|
||||
const size_t host_origin[3] = { 0, 0, 0 };
|
||||
const size_t region[3] = { ts/bs, ne0, 1 };
|
||||
err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, 0, 0, nb0, 0, ((const char *)x) + i1*nb0, 0, NULL, ev);
|
||||
if (err != CL_SUCCESS) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return err;
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[3];
|
||||
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
||||
const int nb2 = dst->nb[2];
|
||||
const int nb3 = dst->nb[3];
|
||||
|
||||
const float alpha = 1.0f;
|
||||
const float beta = 0.0f;
|
||||
const int x_ne = ne01 * ne00;
|
||||
const int y_ne = ne11 * ne10;
|
||||
const int d_ne = ne11 * ne01;
|
||||
|
||||
size_t x_size, y_size, d_size;
|
||||
cl_mem d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
|
||||
cl_int err;
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy data to device
|
||||
err = ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL);
|
||||
err |= ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL);
|
||||
CL_CHECK(err, "ggml_cl_h2d_tensor_2d");
|
||||
|
||||
CL_CHECK(clFinish(queue), "clFinish");
|
||||
|
||||
// compute
|
||||
cl_event ev_sgemm;
|
||||
clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
|
||||
clblast::Transpose::kYes, clblast::Transpose::kNo,
|
||||
ne01, ne11, ne10,
|
||||
alpha,
|
||||
d_X, 0, ne00,
|
||||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
&queue, &ev_sgemm);
|
||||
|
||||
if (status != clblast::StatusCode::kSuccess) {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
// copy dst to host
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
err = clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL);
|
||||
CL_CHECK(err, "clEnqueueReadBuffer");
|
||||
}
|
||||
}
|
||||
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
ggml_cl_pool_free(d_D, d_size);
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t /* wsize */) {
|
||||
GGML_ASSERT(fp16_support);
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[3];
|
||||
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
||||
const int nb10 = src1->nb[0];
|
||||
const int nb11 = src1->nb[1];
|
||||
const int nb12 = src1->nb[2];
|
||||
const int nb13 = src1->nb[3];
|
||||
|
||||
const int nb2 = dst->nb[2];
|
||||
const int nb3 = dst->nb[3];
|
||||
|
||||
const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f);
|
||||
const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f);
|
||||
const int x_ne = ne01 * ne00;
|
||||
const int y_ne = ne11 * ne10;
|
||||
const int d_ne = ne11 * ne01;
|
||||
|
||||
size_t x_size, y_size, d_size;
|
||||
cl_mem d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
|
||||
cl_int err;
|
||||
|
||||
bool src1_cont_rows = nb10 == sizeof(float);
|
||||
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy src0 to device
|
||||
err = ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL);
|
||||
CL_CHECK(err, "ggml_cl_h2d_tensor_2d");
|
||||
|
||||
// convert src1 to fp16
|
||||
// TODO: use multiple threads
|
||||
ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
|
||||
char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
|
||||
if (src1_cont_rows) {
|
||||
if (src1_cont_cols) {
|
||||
ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
|
||||
}
|
||||
else {
|
||||
for (int64_t i01 = 0; i01 < ne11; i01++) {
|
||||
ggml_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
for (int64_t i01 = 0; i01 < ne11; i01++) {
|
||||
for (int64_t i00 = 0; i00 < ne10; i00++) {
|
||||
// very slow due to no inlining
|
||||
tmp[i01*ne10 + i00] = ggml_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// copy src1 to device
|
||||
err |= clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL);
|
||||
CL_CHECK(err, "ggml_cl_h2d_tensor_2d");
|
||||
|
||||
CL_CHECK(clFinish(queue), "clFinish");
|
||||
|
||||
// compute
|
||||
cl_event ev_sgemm;
|
||||
clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
|
||||
clblast::Transpose::kYes, clblast::Transpose::kNo,
|
||||
ne01, ne11, ne10,
|
||||
alpha,
|
||||
d_X, 0, ne00,
|
||||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
&queue, &ev_sgemm);
|
||||
|
||||
if (status != clblast::StatusCode::kSuccess) {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
// copy dst to host, then convert to float
|
||||
err = clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL);
|
||||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
ggml_fp16_to_fp32_row(tmp, d, d_ne);
|
||||
}
|
||||
}
|
||||
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
ggml_cl_pool_free(d_D, d_size);
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[3];
|
||||
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
||||
const int nb2 = dst->nb[2];
|
||||
const int nb3 = dst->nb[3];
|
||||
const ggml_type type = src0->type;
|
||||
const bool mul_mat_vec = ne11 == 1;
|
||||
|
||||
const float alpha = 1.0f;
|
||||
const float beta = 0.0f;
|
||||
const int x_ne = ne01 * ne00;
|
||||
const int y_ne = ne11 * ne10;
|
||||
const int d_ne = ne11 * ne01;
|
||||
const size_t q_sz = ggml_type_size(type) * x_ne / ggml_blck_size(type);
|
||||
|
||||
size_t x_size, y_size, d_size, q_size;
|
||||
cl_mem d_X;
|
||||
if (!mul_mat_vec) {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size, CL_MEM_READ_WRITE);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Q;
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
d_Q = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
}
|
||||
|
||||
cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
|
||||
GGML_ASSERT(to_fp32_cl != nullptr);
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
cl_event ev_Q, ev_sgemm;
|
||||
|
||||
// copy src0 to device if necessary
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, &ev_Q), "ggml_cl_h2d_tensor_2d");
|
||||
} else if (src0->backend == GGML_BACKEND_CL) {
|
||||
d_Q = * (cl_mem *) src0->data;
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
|
||||
printf("Gogogo\n");
|
||||
// copy src1 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL), "ggml_cl_h2d_tensor_2d");
|
||||
|
||||
// compute
|
||||
// dequantize_mul_mat_vec(__global void * vx, __local float* tmp, __global float * y, __global float * dst, __global int ncols, __global int vx_type) {
|
||||
const size_t global = ne00;
|
||||
const size_t local = CL_DMMV_BLOCK_SIZE;
|
||||
const cl_int ncols = ne01;
|
||||
const cl_int qtype = src0->type;
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 0, sizeof(cl_mem), &d_Q), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 1, sizeof(float) * local, NULL), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 2, sizeof(cl_mem), &d_Y), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 3, sizeof(cl_mem), &d_D), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 4, sizeof(cl_int), &ncols), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(dequantize_mul_mat_vec_cl, 5, sizeof(cl_int), &qtype), "clSetKernelArg");
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, dequantize_mul_mat_vec_cl, 1, NULL, &global, &local, 1, &ev_Q, &ev_sgemm), "clEnqueueNDRangeKernel");
|
||||
} else { // general dequantization kernel + CLBlast matrix matrix multiplication
|
||||
// convert src0 to fp32 on device
|
||||
const size_t global = x_ne;
|
||||
const size_t local = 16;
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q), "clSetKernelArg");
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X), "clSetKernelArg");
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, &local, 1, &ev_Q, NULL), "clEnqueueNDRangeKernel");
|
||||
|
||||
// copy src1 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL), "ggml_cl_h2d_tensor_2d");
|
||||
|
||||
// wait for conversion
|
||||
CL_CHECK(clFinish(queue), "clFinish");
|
||||
|
||||
// compute
|
||||
clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
|
||||
clblast::Transpose::kYes, clblast::Transpose::kNo,
|
||||
ne01, ne11, ne10,
|
||||
alpha,
|
||||
d_X, 0, ne00,
|
||||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
&queue, &ev_sgemm);
|
||||
|
||||
if (status != clblast::StatusCode::kSuccess) {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
// copy dst to host
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL), "clEnqueueReadBuffer");
|
||||
}
|
||||
}
|
||||
|
||||
if (!mul_mat_vec) {
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
}
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
ggml_cl_pool_free(d_D, d_size);
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
ggml_cl_pool_free(d_Q, q_size);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
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 values for these
|
||||
if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
|
||||
src1->type == GGML_TYPE_F32 &&
|
||||
dst->type == GGML_TYPE_F32 &&
|
||||
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_CL)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
|
||||
// If device doesn't support FP16
|
||||
if (!fp16_support) {
|
||||
return false;
|
||||
}
|
||||
|
||||
size_t src0_sz = ggml_nbytes(src0);
|
||||
size_t src1_sz = ggml_nbytes(src1);
|
||||
|
||||
// mul_mat_q: src0 is converted to fp32 on device
|
||||
size_t mul_mat_q_transfer = src0_sz + src1_sz;
|
||||
|
||||
// mul_mat_f16: src1 is converted to fp16 on cpu
|
||||
size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1);
|
||||
|
||||
// choose the smaller one to transfer to the device
|
||||
// TODO: this is not always the best choice due to the overhead of converting to fp16
|
||||
return mul_mat_f16_transfer < mul_mat_q_transfer;
|
||||
}
|
||||
|
||||
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) {
|
||||
GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst));
|
||||
|
||||
if (src0->type == GGML_TYPE_F32) {
|
||||
ggml_cl_mul_mat_f32(src0, src1, dst);
|
||||
}
|
||||
else if (src0->type == GGML_TYPE_F16) {
|
||||
if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
|
||||
ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize);
|
||||
}
|
||||
else {
|
||||
ggml_cl_mul_mat_q_f32(src0, src1, dst);
|
||||
}
|
||||
}
|
||||
else if (ggml_is_quantized(src0->type)) {
|
||||
ggml_cl_mul_mat_q_f32(src0, src1, dst);
|
||||
}
|
||||
else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
|
||||
return ggml_nelements(src1) * sizeof(ggml_fp16_t);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
void ggml_cl_transform_tensor(ggml_tensor * tensor) {
|
||||
const int64_t ne0 = tensor->ne[0];
|
||||
const int64_t ne1 = tensor->ne[1];
|
||||
const int64_t ne2 = tensor->ne[2];
|
||||
const int64_t ne3 = tensor->ne[3];
|
||||
|
||||
const ggml_type type = tensor->type;
|
||||
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
|
||||
|
||||
size_t q_size;
|
||||
cl_mem* d_Q = (cl_mem*) malloc(sizeof(cl_mem));
|
||||
*d_Q = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
|
||||
// copy tensor to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, *d_Q, 0, tensor, 0, 0, NULL), "ggml_cl_h2d_tensor_2d");
|
||||
CL_CHECK(clFinish(queue), "clFinish");
|
||||
|
||||
tensor->data = d_Q;
|
||||
tensor->backend = GGML_BACKEND_CL;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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
|
|
@ -9094,7 +9094,7 @@ 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(
|
||||
|
|
@ -9135,7 +9135,7 @@ 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];
|
||||
|
|
@ -9199,9 +9199,16 @@ 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;
|
||||
|
|
@ -9221,21 +9228,11 @@ 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);
|
||||
|
|
@ -9374,9 +9371,16 @@ 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));
|
||||
|
||||
|
|
@ -9406,20 +9410,6 @@ 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);
|
||||
|
||||
|
|
@ -9431,7 +9421,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -9594,9 +9583,16 @@ 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;
|
||||
|
|
@ -9619,9 +9615,6 @@ 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) {
|
||||
|
|
@ -9633,23 +9626,12 @@ 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
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -13658,9 +13640,16 @@ 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
|
||||
|
|
@ -13674,13 +13663,13 @@ 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
|
||||
|
|
|
|||
30
llama.cpp
30
llama.cpp
|
|
@ -11,6 +11,8 @@
|
|||
#include "ggml.h"
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#include "ggml-opencl.h"
|
||||
#endif
|
||||
|
||||
#include <array>
|
||||
|
|
@ -1027,7 +1029,7 @@ static void llama_model_load_internal(
|
|||
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
|
||||
|
||||
model.mapping = std::move(ml->mapping);
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
{
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
|
|
@ -1053,6 +1055,32 @@ static void llama_model_load_internal(
|
|||
|
||||
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
{
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, 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, "%s: [cublas] offloading output layer to GPU\n", __func__);
|
||||
ggml_cl_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
}
|
||||
#else
|
||||
(void) n_gpu_layers;
|
||||
#endif
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue