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OpenCL: Fix duplication of layers in VRAM and RAM, add GPU mul kernel #1653

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Merged
merged 7 commits into from
Jun 4, 2023
Merged

OpenCL: Fix duplication of layers in VRAM and RAM, add GPU mul kernel #1653

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ebc5d06
Use events instead of clFinish, where possible
0cc4m May 27, 2023
97c5cca
OpenCL: Don't load gpu layers into RAM, add mul_f32 kernel
0cc4m May 27, 2023
ac6b49e
Reduce queueing overhead for contiguous tensors by using single mul k…
0cc4m May 30, 2023
49aaf08
Merge remote-tracking branch 'origin/master' into opencl-dev
0cc4m May 31, 2023
5e1eecf
Adapt to #1612 cl_mem malloc changes
0cc4m May 31, 2023
457aaf5
Reduce code duplication between cuda and opencl branches
0cc4m Jun 1, 2023
24239f0
Improve implementation
0cc4m Jun 1, 2023
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179 changes: 169 additions & 10 deletions ggml-opencl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
#include <array>
#include <atomic>
#include <sstream>
#include <vector>

#define CL_TARGET_OPENCL_VERSION 110
#include <clblast.h>
Expand Down Expand Up @@ -197,6 +198,18 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
}
);

std::string mul_template = MULTILINE_QUOTE(
__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);

if (i >= get_global_size(0)) {
return;
}

dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky];
}
);

#define CL_CHECK(err) \
do { \
cl_int err_ = (err); \
Expand Down Expand Up @@ -239,6 +252,13 @@ std::array<std::string, 30> dequant_mul_mat_vec_str_values = {
"convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16"
};

std::array<std::string, 2> mul_str_keys = {
"KERNEL_NAME", "TYPE"
};
std::array<std::string, 2> mul_str_values = {
"mul_f32", "float"
};

std::string& replace(std::string& s, const std::string& from, const std::string& to) {
size_t pos = 0;
while ((pos = s.find(from, pos)) != std::string::npos) {
Expand All @@ -261,6 +281,13 @@ std::string generate_kernels() {
src << dequant_kernel << '\n';
src << dmmv_kernel << '\n';
}
for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) {
std::string mul_kernel = mul_template;
for (size_t j = 0; j < mul_str_keys.size(); j++) {
replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]);
}
src << mul_kernel << '\n';
}
return src.str();
}

Expand All @@ -272,6 +299,7 @@ static cl_program program;
static cl_kernel convert_row_f16_cl;
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;
static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
static cl_kernel mul_f32_cl;
static bool fp16_support;

static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
Expand Down Expand Up @@ -513,6 +541,9 @@ void ggml_cl_init(void) {
CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));

// mul kernel
CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
}

static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
Expand Down Expand Up @@ -649,6 +680,98 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
return err;
}

static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src1->backend == GGML_BACKEND_CL);
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[2];
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int64_t nb10 = src1->nb[0];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
size_t x_size;
size_t d_size;

cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size, CL_MEM_READ_ONLY); // src0
cl_mem d_Y = *(cl_mem*) src1->data; // src1 is already on device, broadcasted.
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size, CL_MEM_WRITE_ONLY); // dst

for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const int i0 = i03*ne02 + i02;

cl_event ev;

// copy src0 to device
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, i0, src0, i03, i02, &ev));

if (nb10 == sizeof(float)) {
// Contiguous, avoid overhead from queueing many kernel runs
const int64_t i13 = i03%ne13;
const int64_t i12 = i02%ne12;
const int i1 = i13*ne12*ne11 + i12*ne11;

cl_int x_offset = 0;
cl_int y_offset = i1*ne10;
cl_int d_offset = 0;

size_t global = ne00 * ne01;
cl_int ky = ne10;
CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
} else {
for (int64_t i01 = 0; i01 < ne01; i01++) {
const int64_t i13 = i03%ne13;
const int64_t i12 = i02%ne12;
const int64_t i11 = i01%ne11;
const int i1 = i13*ne12*ne11 + i12*ne11 + i11;

cl_int x_offset = i01*ne00;
cl_int y_offset = i1*ne10;
cl_int d_offset = i01*ne00;

// compute
size_t global = ne00;
cl_int ky = ne10;
CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
}
}

CL_CHECK(clReleaseEvent(ev));
CL_CHECK(clFinish(queue));

// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
}
}
ggml_cl_pool_free(d_X, x_size);
ggml_cl_pool_free(d_D, d_size);
}

void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cl_mul_f32(src0, src1, dst);
}

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];
Expand Down Expand Up @@ -867,42 +990,46 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *

for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
cl_event ev_sgemm;
size_t ev_idx = 0;
std::vector<cl_event> events;
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suggest move this out of loop and give a reasonable initial size. You can use clear in the inner-loop


// 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, NULL));
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
} 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
// copy src1 to device
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, events.data() + ev_idx++));

// compute
const size_t global = ne01 * CL_DMMV_BLOCK_SIZE;
const size_t local = CL_DMMV_BLOCK_SIZE;
const cl_int ncols = ne00;
events.emplace_back();
CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
CL_CHECK(clFinish(queue));
CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, 0, NULL, &ev_sgemm));
CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
} else { // general dequantization kernel + CLBlast matrix matrix multiplication
// convert src0 to fp32 on device
const size_t global = x_ne;
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
CL_CHECK(clFinish(queue));
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, 0, NULL, NULL));
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));

// copy src1 to device
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));

events.emplace_back();

// wait for conversion
CL_CHECK(clFinish(queue));

Expand All @@ -915,7 +1042,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
d_Y, 0, ne10,
beta,
d_D, 0, ne01,
&queue, &ev_sgemm);
&queue, events.data() + ev_idx++);

if (status != clblast::StatusCode::kSuccess) {
GGML_ASSERT(false);
Expand All @@ -924,8 +1051,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *

// 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));
clReleaseEvent(ev_sgemm);
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
for (auto *event : events) {
clReleaseEvent(event);
}
}
}

Expand Down Expand Up @@ -1032,3 +1161,33 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
tensor->data = dst;
tensor->backend = GGML_BACKEND_CL;
}

void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
cl_int err;
FILE * fp = fopen(fname, "rb");

const size_t size = ggml_nbytes(tensor);

cl_mem* dst = (cl_mem*) malloc(sizeof(cl_mem));
CL_CHECK((*dst = clCreateBuffer(context, CL_MEM_READ_ONLY, size, nullptr, &err), err));
void * buf_host = malloc(size);

#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same

size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}

clEnqueueWriteBuffer(queue, *dst, CL_TRUE, 0, size, buf_host, 0, nullptr, nullptr);

tensor->data = dst;
free(buf_host);
fclose(fp);
}
2 changes: 2 additions & 0 deletions ggml-opencl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@ extern "C" {

void ggml_cl_init(void);

void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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);
Expand All @@ -16,6 +17,7 @@ void * ggml_cl_host_malloc(size_t size);
void ggml_cl_host_free(void * ptr);

void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset);
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warning: parameter 'offset' is const-qualified in the function declaration; const-qualification of parameters only has an effect in function definitions [readability-avoid-const-params-in-decls]

Suggested change
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset);
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, size_t offset);

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the comment is valid


#ifdef __cplusplus
}
Expand Down
7 changes: 7 additions & 0 deletions ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -8095,6 +8095,13 @@ static void ggml_compute_forward_mul_f32(
}
return;
}
#elif defined(GGML_USE_CLBLAST)
if (src1->backend == GGML_BACKEND_CL) {
if (ith == 0) {
ggml_cl_mul(src0, src1, dst);
}
return;
}
#endif

const int64_t nr = ggml_nrows(src0);
Expand Down
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