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opencl: tiled mul_mat with local memory for f16 and f32 #14809

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2 changes: 2 additions & 0 deletions ggml/src/ggml-opencl/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,8 @@ set(GGML_OPENCL_KERNELS
mul_mv_q4_0_f32_1d_16x_flat
mul_mv_q6_k
mul_mv_id_q4_0_f32_8x_flat
mul_mm_f32_f32_l4_lm
mul_mm_f16_f32_l4_lm
mul
norm
relu
Expand Down
144 changes: 132 additions & 12 deletions ggml/src/ggml-opencl/ggml-opencl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))

#define UNUSED(x) (void)(x)

Expand Down Expand Up @@ -395,6 +396,8 @@ struct ggml_backend_opencl_context {
cl_program program_conv_2d_f16_f32;
cl_program program_tsembd;
cl_program program_mul_mv_id_q4_0_f32_8x_flat;
cl_program program_mul_mm_f32_f32_l4_lm;
cl_program program_mul_mm_f16_f32_l4_lm;

cl_kernel kernel_add, kernel_add_row;
cl_kernel kernel_mul, kernel_mul_row;
Expand Down Expand Up @@ -449,6 +452,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_conv_2d_f16_f32;
cl_kernel kernel_timestep_embedding;
cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
cl_kernel kernel_mul_mm_f32_f32_l4_lm;
cl_kernel kernel_mul_mm_f16_f32_l4_lm;

std::vector<ProfilingInfo> profiling_info;

Expand Down Expand Up @@ -1039,6 +1044,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}

// mul_mm_f32_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_f32_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_f32_f32_l4_lm.cl");
#endif
backend_ctx->program_mul_mm_f32_f32_l4_lm =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

CL_CHECK((backend_ctx->kernel_mul_mm_f32_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f32_f32_l4_lm, "kernel_mul_mm_f32_f32_l4_lm", &err), err));
GGML_LOG_CONT(".");
}

// mul_mm_f16_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_f16_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_f16_f32_l4_lm.cl");
#endif
backend_ctx->program_mul_mm_f16_f32_l4_lm =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_l4_lm, "kernel_mul_mm_f16_f32_l4_lm", &err), err));
GGML_LOG_CONT(".");
}

// mul
{
#ifdef GGML_OPENCL_EMBED_KERNELS
Expand Down Expand Up @@ -5139,18 +5176,6 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co

ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;

if (src0t == GGML_TYPE_F16 && src1t == GGML_TYPE_F32 &&
src0->ne[1] > 32 && // M > 32
src1->ne[1] > 32 && // N > 32
src0->ne[0] > 32 && // K > 32
src0->ne[2] == 1 && src0->ne[3] == 1 &&
src1->ne[2] == 1 && src1->ne[3] == 1 &&
ggml_is_contiguous(src0) && ggml_is_contiguous(src1) &&
backend_ctx->kernel_mul_mat_f16_f32_tiled != NULL) {
ggml_cl_mul_mat_f16_f32_tiled(backend, src0, src1, dst);
return;
}

ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
Expand Down Expand Up @@ -5497,6 +5522,101 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
} // if (ne01 && ne1)
#endif // GGML_OPENCL_USE_ADRENO_KERNELS

// GEMM using local memory
// Current BK = 16, so ne00 % 16 == 0
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1t == GGML_TYPE_F32 &&
ne00 % 16 == 0 &&
ne11 > 1) {
switch(src0t) {
case GGML_TYPE_F32: {
kernel = backend_ctx->kernel_mul_mm_f32_f32_l4_lm;
nth0 = 128; // calculated as (BM*BN)/(TM*TN)

int batch_stride_a = ne00*ne01;
int batch_stride_b = ne10*ne11;
int batch_stride_d = ne0*ne1;

CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));

// 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
size_t local_work_size[] = {(size_t)nth0, 1, 1};

backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_F16: {
kernel = backend_ctx->kernel_mul_mm_f16_f32_l4_lm;
nth0 = 128; // calculated as (BM*BN)/(TM*TN)

int batch_stride_a = ne00*ne01;
int batch_stride_b = ne10*ne11;
int batch_stride_d = ne0*ne1;

CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));

// 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
size_t local_work_size[] = {(size_t)nth0, 1, 1};

backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
default:
break;
}
}

if (src0t == GGML_TYPE_F16 && src1t == GGML_TYPE_F32 &&
src0->ne[1] > 32 && // M > 32
src1->ne[1] > 32 && // N > 32
src0->ne[0] > 32 && // K > 32
src0->ne[2] == 1 && src0->ne[3] == 1 &&
src1->ne[2] == 1 && src1->ne[3] == 1 &&
ggml_is_contiguous(src0) && ggml_is_contiguous(src1) &&
backend_ctx->kernel_mul_mat_f16_f32_tiled != NULL) {
ggml_cl_mul_mat_f16_f32_tiled(backend, src0, src1, dst);
return;
}

if (!ggml_is_transposed(src0) &&
!ggml_is_transposed(src1) &&
src1t == GGML_TYPE_F32 &&
Expand Down
132 changes: 132 additions & 0 deletions ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl
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Original file line number Diff line number Diff line change
@@ -0,0 +1,132 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable

#define LOAD_VEC_A 4
#define LOAD_VEC_B 4

#define BM 64
#define BN 64
#define BK 16
#define TM 4
#define TN 8

kernel void kernel_mul_mm_f16_f32_l4_lm(
global half4 * src0,
ulong offset0,
global float4 * src1,
ulong offset1,
global float * dst,
ulong offsetd,

int ne00,
int ne01,
int ne02,
int ne11,
int ne12,

int stride_a,
int stride_b,
int stride_d,

int batch_stride_a,
int batch_stride_b,
int batch_stride_d,

int r2,
int r3
) {
src0 = (global half4*)((global char*)src0 + offset0);
src1 = (global float4*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);

local half buf_a[BM * BK];
local float buf_b[BN * BK];

const int batch_idx = get_global_id(2);

const int i13 = batch_idx / ne12;
const int i12 = batch_idx % ne12;

const int i03 = i13 / r3;
const int i02 = i12 / r2;

const int batch_idx_a = i03 * ne02 + i02;

const int ir = get_group_id(0);
const int ic = get_group_id(1);

const int tid = get_local_id(0);
const int th_r = tid % (BM / TM);
const int th_c = tid / (BM / TM);

const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);

const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;

int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
int pos_b = (batch_idx * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;

float sums[TM * TN];
half cache_a[TM];
float cache_b[TN];

for (int i = 0; i < TM * TN; i++) {
sums[i] = 0.0f;
}

for (int block = 0; block < ne00; block += BK) {
for (int l = 0; l < BM; l += loadstride_a) {
const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0;
buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1;
buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2;
buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = src0[idx].s3;
}

for (int l = 0; l < BN; l += loadstride_b) {
const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
}

barrier(CLK_LOCAL_MEM_FENCE);

pos_a += BK / LOAD_VEC_A;
pos_b += BK / LOAD_VEC_B;

for (int i = 0; i < BK; i++) {
for (int j = 0; j < TM; j++) {
cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
}
for (int j = 0; j < TN; j++) {
cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
}

for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
const int sums_idx = cc*TM + cr;
sums[sums_idx] = mad(convert_float(cache_a[cr]), cache_b[cc], sums[sums_idx]);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}

const int dr = ir * BM + th_r * TM;
const int dc = ic * BN + th_c * TN;

const int offsets = batch_idx * batch_stride_d;

for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
if (dr + cr < ne01 && dc + cc < ne11) {
dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
}
}
}
}
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