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OpenCL: Add concat, tsembd, upscale, tanh, pad and repeat (#13840)

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* add concat, pad, repeat, tsembd, tanh, upscale

* small fixes
This commit is contained in:
rmatif
2025-06-02 23:53:36 +00:00
committed by GitHub
parent 3637576288
commit bfb1e012a0
8 changed files with 1156 additions and 1 deletions
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6
ggml/src/ggml-opencl/CMakeLists.txt
View File

@@ -95,6 +95,12 @@ set(GGML_OPENCL_KERNELS
sub sub
sum_rows sum_rows
transpose transpose
concat
tsembd
upscale
tanh
pad
repeat
) )
foreach (K ${GGML_OPENCL_KERNELS}) foreach (K ${GGML_OPENCL_KERNELS})

739
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@@ -315,6 +315,12 @@ struct ggml_backend_opencl_context {
cl_program program_softmax_4_f16; cl_program program_softmax_4_f16;
cl_program program_argsort_f32_i32; cl_program program_argsort_f32_i32;
cl_program program_sum_rows_f32; cl_program program_sum_rows_f32;
cl_program program_repeat;
cl_program program_pad;
cl_program program_tanh;
cl_program program_upscale;
cl_program program_concat;
cl_program program_tsembd;
cl_kernel kernel_add, kernel_add_row; cl_kernel kernel_add, kernel_add_row;
cl_kernel kernel_mul, kernel_mul_row; cl_kernel kernel_mul, kernel_mul_row;
@@ -351,6 +357,15 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_im2col_f32, kernel_im2col_f16; cl_kernel kernel_im2col_f32, kernel_im2col_f16;
cl_kernel kernel_argsort_f32_i32; cl_kernel kernel_argsort_f32_i32;
cl_kernel kernel_sum_rows_f32; cl_kernel kernel_sum_rows_f32;
cl_kernel kernel_repeat;
cl_kernel kernel_pad;
cl_kernel kernel_tanh_f32_nd;
cl_kernel kernel_tanh_f16_nd;
cl_kernel kernel_upscale;
cl_kernel kernel_upscale_bilinear;
cl_kernel kernel_concat_f32_contiguous;
cl_kernel kernel_concat_f32_non_contiguous;
cl_kernel kernel_timestep_embedding;
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
// Transpose kernels // Transpose kernels
@@ -1097,6 +1112,150 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT("."); GGML_LOG_CONT(".");
} }
// repeat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "repeat.cl.h"
};
#else
const std::string kernel_src = read_file("repeat.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_repeat =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_repeat = clCreateKernel(backend_ctx->program_repeat, "kernel_repeat", &err), err));
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: repeat kernel source not found or empty. Repeat operations will not be available.\n");
backend_ctx->program_repeat = nullptr;
backend_ctx->kernel_repeat = nullptr;
}
}
// pad
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "pad.cl.h"
};
#else
const std::string kernel_src = read_file("pad.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_pad =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_pad = clCreateKernel(backend_ctx->program_pad, "kernel_pad", &err), err));
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: pad kernel source not found or empty. Pad operations will not be available.\n");
backend_ctx->program_pad = nullptr;
backend_ctx->kernel_pad = nullptr;
}
}
// tanh
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "tanh.cl.h"
};
#else
const std::string kernel_src = read_file("tanh.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_tanh =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_tanh_f32_nd = clCreateKernel(backend_ctx->program_tanh, "kernel_tanh_f32_nd", &err), err));
CL_CHECK((backend_ctx->kernel_tanh_f16_nd = clCreateKernel(backend_ctx->program_tanh, "kernel_tanh_f16_nd", &err), err));
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: tanh kernel source not found or empty. Tanh operation will not be available.\n");
backend_ctx->program_tanh = nullptr;
backend_ctx->kernel_tanh_f32_nd = nullptr;
backend_ctx->kernel_tanh_f16_nd = nullptr;
}
}
// upscale
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "upscale.cl.h"
};
#else
const std::string kernel_src = read_file("upscale.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_upscale =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_upscale = clCreateKernel(backend_ctx->program_upscale, "kernel_upscale", &err), err));
if (backend_ctx->program_upscale) {
cl_int err_bilinear;
backend_ctx->kernel_upscale_bilinear = clCreateKernel(backend_ctx->program_upscale, "kernel_upscale_bilinear", &err_bilinear);
if (err_bilinear != CL_SUCCESS) {
GGML_LOG_WARN("ggml_opencl: kernel_upscale_bilinear not found in upscale.cl. Bilinear upscale will not be available. Error: %d\n", err_bilinear);
backend_ctx->kernel_upscale_bilinear = nullptr;
}
} else {
backend_ctx->kernel_upscale_bilinear = nullptr;
}
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: upscale kernel source not found or empty. Upscale operations will not be available.\n");
backend_ctx->program_upscale = nullptr;
backend_ctx->kernel_upscale = nullptr;
backend_ctx->kernel_upscale_bilinear = nullptr;
}
}
// concat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "concat.cl.h"
};
#else
const std::string kernel_src = read_file("concat.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_concat =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_concat_f32_contiguous = clCreateKernel(backend_ctx->program_concat, "kernel_concat_f32_contiguous", &err), err));
CL_CHECK((backend_ctx->kernel_concat_f32_non_contiguous = clCreateKernel(backend_ctx->program_concat, "kernel_concat_f32_non_contiguous", &err), err));
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: concat kernel source not found or empty. Concat operations will not be available.\n");
backend_ctx->program_concat = nullptr;
backend_ctx->kernel_concat_f32_contiguous = nullptr;
backend_ctx->kernel_concat_f32_non_contiguous = nullptr;
}
}
// timestep_embedding
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "tsembd.cl.h"
};
#else
const std::string kernel_src = read_file("tsembd.cl");
#endif
if (!kernel_src.empty()) {
backend_ctx->program_tsembd =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_timestep_embedding = clCreateKernel(backend_ctx->program_tsembd, "kernel_timestep_embedding", &err), err));
GGML_LOG_CONT(".");
} else {
GGML_LOG_WARN("ggml_opencl: timestep_embedding kernel source not found or empty. This op will not be available.\n");
backend_ctx->program_tsembd = nullptr;
backend_ctx->kernel_timestep_embedding = nullptr;
}
}
// Adreno kernels // Adreno kernels
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
// transpose // transpose
@@ -1979,6 +2138,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_SIGMOID:
return ggml_is_contiguous(op->src[0]); return ggml_is_contiguous(op->src[0]);
case GGML_UNARY_OP_TANH:
return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
(op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16);
default: default:
return false; return false;
} }
@@ -1988,6 +2150,17 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
case GGML_OP_NORM: case GGML_OP_NORM:
case GGML_OP_RMS_NORM: case GGML_OP_RMS_NORM:
return true; return true;
case GGML_OP_REPEAT:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
case GGML_OP_PAD:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
op->src[0]->ne[3] == 1 && op->ne[3] == 1;
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
case GGML_OP_CONCAT:
return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
case GGML_OP_TIMESTEP_EMBEDDING:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
case GGML_OP_GROUP_NORM: case GGML_OP_GROUP_NORM:
return ggml_is_contiguous(op->src[0]); return ggml_is_contiguous(op->src[0]);
case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT:
@@ -4108,6 +4281,536 @@ static void ggml_cl_group_norm(ggml_backend_t backend, const ggml_tensor * src0,
#endif #endif
} }
static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
UNUSED(src1);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong offset0_abs = extra0->offset + src0->view_offs;
cl_ulong offsetd_abs = extrad->offset + dst->view_offs;
cl_kernel kernel;
if (dst->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_tanh_f32_nd;
} else if (dst->type == GGML_TYPE_F16) {
kernel = backend_ctx->kernel_tanh_f16_nd;
} else {
GGML_ASSERT(false && "Unsupported type for ggml_cl_tanh");
}
GGML_ASSERT(kernel != nullptr);
const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3];
const cl_ulong nb00 = src0->nb[0]; const cl_ulong nb01 = src0->nb[1]; const cl_ulong nb02 = src0->nb[2]; const cl_ulong nb03 = src0->nb[3];
const int ne10 = dst->ne[0]; const int ne11 = dst->ne[1]; const int ne12 = dst->ne[2]; const int ne13 = dst->ne[3];
const cl_ulong nb10 = dst->nb[0]; const cl_ulong nb11 = dst->nb[1]; const cl_ulong nb12 = dst->nb[2]; const cl_ulong nb13 = dst->nb[3];
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0_abs));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd_abs));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb00));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),&nb02));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong),&nb03));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne13));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong),&nb10));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong),&nb11));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong),&nb12));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong),&nb13));
size_t global_work_size[3];
if (ne10 == 0 || ne11 == 0 || ne12 == 0 || ne13 == 0) { // Handle case of 0 elements
return;
}
global_work_size[0] = (size_t)ne10;
global_work_size[1] = (size_t)ne11;
global_work_size[2] = (size_t)ne12;
size_t lws0 = 16, lws1 = 4, lws2 = 1;
if (ne10 < 16) lws0 = ne10;
if (ne11 < 4) lws1 = ne11;
if (ne12 < 1) lws2 = ne12 > 0 ? ne12 : 1;
while (lws0 * lws1 * lws2 > 256 && lws0 > 1) lws0 /= 2;
while (lws0 * lws1 * lws2 > 256 && lws1 > 1) lws1 /= 2;
while (lws0 * lws1 * lws2 > 256 && lws2 > 1) lws2 /= 2;
size_t local_work_size[] = {lws0, lws1, lws2};
size_t* local_work_size_ptr = local_work_size;
if (!backend_ctx->non_uniform_workgroups) {
if (global_work_size[0] % local_work_size[0] != 0 ||
global_work_size[1] % local_work_size[1] != 0 ||
global_work_size[2] % local_work_size[2] != 0) {
local_work_size_ptr = NULL;
}
}
if (global_work_size[0] == 0 || global_work_size[1] == 0 || global_work_size[2] == 0) return;
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, &evt));
g_profiling_info.emplace_back();
populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size_ptr ? local_work_size : (size_t[3]){0,0,0}, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, NULL));
#endif
}
static void ggml_cl_repeat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1_shape_def, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(dst->type == src0->type);
UNUSED(src1_shape_def);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
if (backend_ctx->kernel_repeat == nullptr) {
GGML_LOG_WARN("%s: repeat kernel not available, skipping OpenCL execution.\n", __func__);
return;
}
ggml_tensor_extra_cl * extra_src0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra_dst = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong off_src0 = extra_src0->offset + src0->view_offs;
cl_ulong off_dst = extra_dst->offset + dst->view_offs;
const int src0_ne0 = src0->ne[0]; const int src0_ne1 = src0->ne[1]; const int src0_ne2 = src0->ne[2]; const int src0_ne3 = src0->ne[3];
const cl_ulong src0_nb0 = src0->nb[0]; const cl_ulong src0_nb1 = src0->nb[1]; const cl_ulong src0_nb2 = src0->nb[2]; const cl_ulong src0_nb3 = src0->nb[3];
const int dst_ne0 = dst->ne[0]; const int dst_ne1 = dst->ne[1]; const int dst_ne2 = dst->ne[2]; const int dst_ne3 = dst->ne[3];
const cl_ulong dst_nb0 = dst->nb[0]; const cl_ulong dst_nb1 = dst->nb[1]; const cl_ulong dst_nb2 = dst->nb[2]; const cl_ulong dst_nb3 = dst->nb[3];
cl_kernel kernel = backend_ctx->kernel_repeat;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_src0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra_dst->data_device));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_ulong), &off_src0));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &src0_ne0));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &src0_ne1));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &src0_ne2));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &src0_ne3));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &src0_nb0));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &src0_nb1));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &src0_nb2));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &src0_nb3));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &dst_ne0));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &dst_ne1));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &dst_ne2));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &dst_ne3));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &dst_nb0));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &dst_nb1));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &dst_nb2));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &dst_nb3));
size_t gws0 = dst_ne1 > 0 ? (size_t)dst_ne1 : 1;
size_t gws1 = dst_ne2 > 0 ? (size_t)dst_ne2 : 1;
size_t gws2 = dst_ne3 > 0 ? (size_t)dst_ne3 : 1;
size_t global_work_size[] = { gws0, gws1, gws2 };
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, NULL, 0, NULL, &evt));
g_profiling_info.emplace_back();
populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, (size_t[3]){0,0,0}, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, NULL, 0, NULL, NULL));
#endif
}
static void ggml_cl_pad(ggml_backend_t backend, const ggml_tensor * src0, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
if (backend_ctx->kernel_pad == nullptr) {
GGML_LOG_WARN("%s: pad kernel not available, skipping OpenCL execution.\n", __func__);
return;
}
ggml_tensor_extra_cl * extra_src0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra_dst = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong off_src0 = extra_src0->offset + src0->view_offs;
cl_ulong off_dst = extra_dst->offset + dst->view_offs;
const int s_ne0 = src0->ne[0];
const int s_ne1 = src0->ne[1];
const int s_ne2 = src0->ne[2];
const int d_ne0 = dst->ne[0];
const int d_ne1 = dst->ne[1];
const int d_ne2 = dst->ne[2];
cl_kernel kernel = backend_ctx->kernel_pad;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_src0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &off_src0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra_dst->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &s_ne0));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &s_ne1));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &s_ne2));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &d_ne0));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &d_ne1));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &d_ne2));
size_t lws0 = 64;
size_t gws0 = (( (size_t)d_ne0 + lws0 - 1 ) / lws0) * lws0;
size_t global_work_size[] = { gws0, (size_t)d_ne1, (size_t)d_ne2 };
size_t local_work_size[] = { lws0, 1, 1 };
size_t * local_work_size_ptr = local_work_size;
if (d_ne0 % lws0 != 0 && !backend_ctx->non_uniform_workgroups) {
local_work_size_ptr = nullptr;
}
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, &evt));
g_profiling_info.emplace_back();
populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size_ptr ? local_work_size : (size_t[3]){0,0,0}, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, NULL));
#endif
}
static void ggml_cl_upscale(ggml_backend_t backend, const ggml_tensor * src0, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
const ggml_scale_mode mode = (ggml_scale_mode) ggml_get_op_params_i32(dst, 0);
cl_kernel kernel = nullptr;
if (mode == GGML_SCALE_MODE_NEAREST) {
kernel = backend_ctx->kernel_upscale;
if (kernel == nullptr) {
GGML_LOG_WARN("%s: nearest upscale kernel not available, skipping OpenCL execution.\n", __func__);
return;
}
} else if (mode == GGML_SCALE_MODE_BILINEAR) {
kernel = backend_ctx->kernel_upscale_bilinear;
if (kernel == nullptr) {
GGML_LOG_WARN("%s: bilinear upscale kernel not available, skipping OpenCL execution.\n", __func__);
return;
}
} else {
GGML_LOG_WARN("%s: unsupported upscale mode %d, skipping OpenCL execution.\n", __func__, mode);
return;
}
ggml_tensor_extra_cl * extra_src0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra_dst = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong off_src0 = extra_src0->offset + src0->view_offs;
cl_ulong off_dst = extra_dst->offset + dst->view_offs;
const cl_ulong nb00 = src0->nb[0];
const cl_ulong nb01 = src0->nb[1];
const cl_ulong nb02 = src0->nb[2];
const cl_ulong nb03 = src0->nb[3];
const int ne00_src = src0->ne[0];
const int ne01_src = src0->ne[1];
const int ne10_dst = dst->ne[0];
const int ne11_dst = dst->ne[1];
const int ne12_dst = dst->ne[2];
const int ne13_dst = dst->ne[3];
const float sf0 = (float)dst->ne[0] / src0->ne[0];
const float sf1 = (float)dst->ne[1] / src0->ne[1];
const float sf2 = (float)dst->ne[2] / src0->ne[2];
const float sf3 = (float)dst->ne[3] / src0->ne[3];
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_src0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &off_src0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra_dst->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &nb00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb03));
if (mode == GGML_SCALE_MODE_NEAREST) {
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne10_dst));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11_dst));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12_dst));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne13_dst));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float), &sf0));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float), &sf1));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(float), &sf2));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(float), &sf3));
} else if (mode == GGML_SCALE_MODE_BILINEAR) {
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00_src));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01_src));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne10_dst));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne11_dst));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne12_dst));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne13_dst));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(float), &sf0));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(float), &sf1));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(float), &sf2));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(float), &sf3));
}
size_t dst_total_elements = (size_t)ne10_dst * ne11_dst * ne12_dst * ne13_dst;
if (dst_total_elements == 0) {
return;
}
size_t global_work_size[] = { dst_total_elements, 1, 1 };
size_t local_work_size_pref = 256;
size_t local_work_size[] = { MIN(local_work_size_pref, dst_total_elements), 1, 1};
size_t * local_work_size_ptr = local_work_size;
if (dst_total_elements % local_work_size[0] != 0 && !backend_ctx->non_uniform_workgroups) {
local_work_size_ptr = nullptr;
}
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 1, NULL, global_work_size, local_work_size_ptr, 0, NULL, &evt));
g_profiling_info.emplace_back();
size_t profiling_gws[3] = {global_work_size[0], 1, 1};
size_t profiling_lws[3] = {local_work_size_ptr ? local_work_size[0] : 0, 1, 1};
populateProfilingInfo(g_profiling_info.back(), evt, kernel, profiling_gws, profiling_lws, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 1, NULL, global_work_size, local_work_size_ptr, 0, NULL, NULL));
#endif
}
static void ggml_cl_concat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(src1);
GGML_ASSERT(src1->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
if (backend_ctx->kernel_concat_f32_contiguous == nullptr || backend_ctx->kernel_concat_f32_non_contiguous == nullptr) {
GGML_LOG_WARN("%s: concat kernels not available, skipping OpenCL execution.\n", __func__);
return;
}
ggml_tensor_extra_cl * extra0_cl = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra1_cl = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad_cl = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong off_src0 = extra0_cl->offset + src0->view_offs;
cl_ulong off_src1 = extra1_cl->offset + src1->view_offs;
cl_ulong off_dst = extrad_cl->offset + dst->view_offs;
const int32_t dim = ((const int32_t *) dst->op_params)[0];
GGML_ASSERT(dim >= 0 && dim <= 3);
if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
if (dim == 3) {
size_t nbytes_src0 = ggml_nbytes(src0);
size_t nbytes_src1 = ggml_nbytes(src1);
CL_CHECK(clEnqueueCopyBuffer(queue, extra0_cl->data_device, extrad_cl->data_device,
off_src0, off_dst, nbytes_src0, 0, NULL, NULL));
CL_CHECK(clEnqueueCopyBuffer(queue, extra1_cl->data_device, extrad_cl->data_device,
off_src1, off_dst + nbytes_src0, nbytes_src1, 0, NULL, NULL));
} else {
cl_kernel kernel = backend_ctx->kernel_concat_f32_contiguous;
size_t global_work_size[3];
for (int i3 = 0; i3 < dst->ne[3]; ++i3) {
cl_ulong current_off_src0 = off_src0 + (i3 * src0->nb[3]);
cl_ulong current_off_src1 = off_src1 + (i3 * src1->nb[3]);
cl_ulong current_off_dst = off_dst + (i3 * dst->nb[3]);
int d_ne00 = src0->ne[0]; int d_ne01 = src0->ne[1]; int d_ne02 = src0->ne[2];
int d_ne10 = src1->ne[0]; int d_ne11 = src1->ne[1]; int d_ne12 = src1->ne[2];
int d_ne0 = dst->ne[0]; int d_ne1 = dst->ne[1]; int d_ne2 = dst->ne[2];
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &current_off_src0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &current_off_src1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &current_off_dst));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &d_ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &d_ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &d_ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &d_ne10));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &d_ne11));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &d_ne12));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &d_ne0));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &d_ne1));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &d_ne2));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &dim));
global_work_size[0] = d_ne0;
global_work_size[1] = d_ne1;
global_work_size[2] = d_ne2;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, NULL, 0, NULL, NULL));
}
}
} else {
cl_kernel kernel = backend_ctx->kernel_concat_f32_non_contiguous;
long ne00 = src0->ne[0], ne01 = src0->ne[1], ne02 = src0->ne[2], ne03 = src0->ne[3];
cl_ulong nb00 = src0->nb[0], nb01 = src0->nb[1], nb02 = src0->nb[2], nb03 = src0->nb[3];
cl_ulong nb10 = src1->nb[0], nb11 = src1->nb[1], nb12 = src1->nb[2], nb13 = src1->nb[3];
long d_ne0 = dst->ne[0], d_ne1 = dst->ne[1], d_ne2 = dst->ne[2], d_ne3 = dst->ne[3];
cl_ulong d_nb0 = dst->nb[0], d_nb1 = dst->nb[1], d_nb2 = dst->nb[2], d_nb3 = dst->nb[3];
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &off_src0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_src1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(long), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(long), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(long), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(long), &ne03));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb10));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(long), &d_ne0));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(long), &d_ne1));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(long), &d_ne2));
CL_CHECK(clSetKernelArg(kernel, 21, sizeof(long), &d_ne3));
CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &d_nb0));
CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &d_nb1));
CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &d_nb2));
CL_CHECK(clSetKernelArg(kernel, 25, sizeof(cl_ulong), &d_nb3));
CL_CHECK(clSetKernelArg(kernel, 26, sizeof(int), &dim));
size_t global_work_size_nc[] = { d_ne1 > 0 ? (size_t)d_ne1 : 1,
d_ne2 > 0 ? (size_t)d_ne2 : 1,
d_ne3 > 0 ? (size_t)d_ne3 : 1 };
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size_nc, NULL, 0, NULL, NULL));
}
}
static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor * src0, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
if (backend_ctx->kernel_timestep_embedding == nullptr) {
GGML_LOG_WARN("%s: timestep_embedding kernel not available, skipping OpenCL execution.\n", __func__);
return;
}
ggml_tensor_extra_cl * extra_src0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extra_dst = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong off_src0 = extra_src0->offset + src0->view_offs;
cl_ulong off_dst = extra_dst->offset + dst->view_offs;
const int logical_dim = dst->op_params[0];
const int max_period = dst->op_params[1];
const int dst_nb1_bytes = dst->nb[1];
cl_kernel kernel = backend_ctx->kernel_timestep_embedding;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_src0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &off_src0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra_dst->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &dst_nb1_bytes));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &logical_dim));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &max_period));
size_t gws0 = (size_t)(((logical_dim + 1) / 2) + 1);
size_t gws1 = (size_t)src0->ne[0];
size_t global_work_size[] = {gws0, gws1, 1};
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_work_size, NULL, 0, NULL, &evt)); // Pass 2 for 2D problem
g_profiling_info.emplace_back();
size_t profiling_gws[3] = {global_work_size[0], global_work_size[1], 1};
size_t profiling_lws[3] = {0,0,0}; // Reflects NULL LWS
populateProfilingInfo(g_profiling_info.back(), evt, kernel, profiling_gws, profiling_lws, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_work_size, NULL, 0, NULL, NULL)); // Pass 2 for 2D problem
#endif
}
static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0); GGML_ASSERT(src0);
GGML_ASSERT(src0->extra); GGML_ASSERT(src0->extra);
@@ -5667,6 +6370,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
} }
func = ggml_cl_sigmoid; func = ggml_cl_sigmoid;
break; break;
case GGML_UNARY_OP_TANH:
if (!any_on_device) {
return false;
}
func = ggml_cl_tanh;
break;
default: default:
return false; return false;
} break; } break;
@@ -5694,6 +6403,36 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
} }
func = ggml_cl_group_norm; func = ggml_cl_group_norm;
break; break;
case GGML_OP_REPEAT:
if (!any_on_device) {
return false;
}
func = ggml_cl_repeat;
break;
case GGML_OP_PAD:
if (!any_on_device) {
return false;
}
ggml_cl_pad(backend, tensor->src[0], tensor);
return true;
case GGML_OP_UPSCALE:
if (!any_on_device) {
return false;
}
ggml_cl_upscale(backend, tensor->src[0], tensor);
return true;
case GGML_OP_CONCAT:
if (!any_on_device) {
return false;
}
func = ggml_cl_concat;
break;
case GGML_OP_TIMESTEP_EMBEDDING:
if (!any_on_device) {
return false;
}
ggml_cl_timestep_embedding(backend, tensor->src[0], tensor);
return true;
case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT:
if (!any_on_device && !ggml_cl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { if (!any_on_device && !ggml_cl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
return false; return false;

109
ggml/src/ggml-opencl/kernels/concat.cl Normal file
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@@ -0,0 +1,109 @@
kernel void kernel_concat_f32_contiguous(
global const char * p_src0, ulong off_src0,
global const char * p_src1, ulong off_src1,
global char * p_dst, ulong off_dst,
int d_ne00, int d_ne01, int d_ne02, // src0->ne[0..2] for the slice
int d_ne10, int d_ne11, int d_ne12, // src1->ne[0..2] for the slice (d_ne1X must match d_ne0X on non-concat axes)
int d_ne0, int d_ne1, int d_ne2, // dst->ne[0..2] for the slice
int dim
) {
global const float * src0 = (global const float*)((global char*)p_src0 + off_src0);
global const float * src1 = (global const float*)((global char*)p_src1 + off_src1);
global float * dst = (global float*)((global char*)p_dst + off_dst);
int i0 = get_global_id(0); // Index along dst's 0th dimension
int i1 = get_global_id(1); // Index along dst's 1st dimension
int i2 = get_global_id(2); // Index along dst's 2nd dimension
if (i0 >= d_ne0 || i1 >= d_ne1 || i2 >= d_ne2) {
return;
}
ulong dst_idx = (ulong)i2 * d_ne0 * d_ne1 + (ulong)i1 * d_ne0 + i0;
ulong src_idx;
if (dim == 0) {
if (i0 < d_ne00) { // Data from src0
src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
dst[dst_idx] = src0[src_idx];
} else { // Data from src1
src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + (i0 - d_ne00);
dst[dst_idx] = src1[src_idx];
}
} else if (dim == 1) {
if (i1 < d_ne01) { // Data from src0
src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
dst[dst_idx] = src0[src_idx];
} else { // Data from src1
src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)(i1 - d_ne01) * d_ne10 + i0;
dst[dst_idx] = src1[src_idx];
}
} else if (dim == 2) {
if (i2 < d_ne02) { // Data from src0
src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
dst[dst_idx] = src0[src_idx];
} else { // Data from src1
src_idx = (ulong)(i2 - d_ne02) * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + i0;
dst[dst_idx] = src1[src_idx];
}
}
}
kernel void kernel_concat_f32_non_contiguous(
global const char * p_src0, ulong off_src0,
global const char * p_src1, ulong off_src1,
global char * p_dst, ulong off_dst,
long ne00, long ne01, long ne02, long ne03,
ulong nb00, ulong nb01, ulong nb02, ulong nb03,
ulong nb10, ulong nb11, ulong nb12, ulong nb13, // Strides for src1
long d_ne0, long d_ne1, long d_ne2, long d_ne3,
ulong d_nb0, ulong d_nb1, ulong d_nb2, ulong d_nb3,
int dim
) {
global const char * src0_base = p_src0 + off_src0;
global const char * src1_base = p_src1 + off_src1;
global char * dst_base = p_dst + off_dst;
long current_i1 = get_global_id(0); // Index for dst_dim_1
long current_i2 = get_global_id(1); // Index for dst_dim_2
long current_i3 = get_global_id(2); // Index for dst_dim_3
if (current_i1 >= d_ne1 || current_i2 >= d_ne2 || current_i3 >= d_ne3) {
return;
}
global const float * x_val_ptr;
global float * y_val_ptr;
for (long current_i0 = 0; current_i0 < d_ne0; ++current_i0) {
bool use_src0;
long s_i0 = current_i0, s_i1 = current_i1, s_i2 = current_i2, s_i3 = current_i3;
if (dim == 0) {
use_src0 = (current_i0 < ne00);
if (!use_src0) { s_i0 = current_i0 - ne00; }
} else if (dim == 1) {
use_src0 = (current_i1 < ne01);
if (!use_src0) { s_i1 = current_i1 - ne01; }
} else if (dim == 2) {
use_src0 = (current_i2 < ne02);
if (!use_src0) { s_i2 = current_i2 - ne02; }
} else { // dim == 3
use_src0 = (current_i3 < ne03);
if (!use_src0) { s_i3 = current_i3 - ne03; }
}
if (use_src0) {
x_val_ptr = (global const float *)(src0_base + (ulong)s_i3*nb03 + (ulong)s_i2*nb02 + (ulong)s_i1*nb01 + (ulong)s_i0*nb00);
} else {
x_val_ptr = (global const float *)(src1_base + (ulong)s_i3*nb13 + (ulong)s_i2*nb12 + (ulong)s_i1*nb11 + (ulong)s_i0*nb10);
}
y_val_ptr = (global float *)(dst_base + (ulong)current_i3*d_nb3 + (ulong)current_i2*d_nb2 + (ulong)current_i1*d_nb1 + (ulong)current_i0*d_nb0);
*y_val_ptr = *x_val_ptr;
}
}

30
ggml/src/ggml-opencl/kernels/pad.cl Normal file
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@@ -0,0 +1,30 @@
kernel void kernel_pad(
global const void * src0_ptr,
ulong src0_offset,
global void * dst_ptr,
ulong dst_offset,
int s_ne0, int s_ne1, int s_ne2,
int d_ne0, int d_ne1, int d_ne2
) {
global const float * src0 = (global const float *)((global const char *)src0_ptr + src0_offset);
global float * dst = (global float *)((global char *)dst_ptr + dst_offset);
int nidx = get_global_id(0);
int idx_d1 = get_group_id(1);
int idx_d2 = get_group_id(2);
if (nidx >= d_ne0) {
return;
}
int dst_el_offset = nidx + idx_d1 * d_ne0 + idx_d2 * d_ne0 * d_ne1;
bool in_src_bounds = (nidx < s_ne0) && (idx_d1 < s_ne1) && (idx_d2 < s_ne2);
if (in_src_bounds) {
int src_el_offset = nidx + idx_d1 * s_ne0 + idx_d2 * s_ne0 * s_ne1;
dst[dst_el_offset] = src0[src_el_offset];
} else {
dst[dst_el_offset] = 0.0f;
}
}

39
ggml/src/ggml-opencl/kernels/repeat.cl Normal file
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@@ -0,0 +1,39 @@
kernel void kernel_repeat(
global const char * src0_data_in,
global char * dst_data_in,
ulong src0_offset,
ulong dst_offset,
int src0_ne0, int src0_ne1, int src0_ne2, int src0_ne3,
ulong src0_nb0, ulong src0_nb1, ulong src0_nb2, ulong src0_nb3,
int dst_ne0, int dst_ne1, int dst_ne2, int dst_ne3,
ulong dst_nb0, ulong dst_nb1, ulong dst_nb2, ulong dst_nb3
) {
global const char * src0_data = src0_data_in + src0_offset;
global char * dst_data = dst_data_in + dst_offset;
const int d3 = get_global_id(2);
const int d2 = get_global_id(1);
const int d1 = get_global_id(0);
if (d3 >= dst_ne3 || d2 >= dst_ne2 || d1 >= dst_ne1) {
return;
}
const int s3 = d3 % src0_ne3;
const int s2 = d2 % src0_ne2;
const int s1 = d1 % src0_ne1;
const global char * p_src0_slice = src0_data + (ulong)s3*src0_nb3 + (ulong)s2*src0_nb2 + (ulong)s1*src0_nb1;
global char * p_dst_slice = dst_data + (ulong)d3*dst_nb3 + (ulong)d2*dst_nb2 + (ulong)d1*dst_nb1;
for (int d0 = 0; d0 < dst_ne0; ++d0) {
// Determine source index for dimension 0 based on tiling/broadcasting.
const int s0 = d0 % src0_ne0;
const global char * restrict current_src_el_ptr = p_src0_slice + (ulong)s0*src0_nb0;
global char * restrict current_dst_el_ptr = p_dst_slice + (ulong)d0*dst_nb0;
for (int k = 0; k < src0_nb0; ++k) {
current_dst_el_ptr[k] = current_src_el_ptr[k];
}
}
}

63
ggml/src/ggml-opencl/kernels/tanh.cl Normal file
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
kernel void kernel_tanh_f32_nd(
global void * p_src0_base, ulong off_src0_abs,
global void * p_dst_base, ulong off_dst_abs,
int ne00, int ne01, int ne02, int ne03,
ulong nb00, ulong nb01, ulong nb02, ulong nb03,
int ne10, int ne11, int ne12, int ne13,
ulong nb10, ulong nb11, ulong nb12, ulong nb13
) {
int i0 = get_global_id(0);
int i1 = get_global_id(1);
int i2 = get_global_id(2);
if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
for (int i3 = 0; i3 < ne13; ++i3) {
ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
global const float *src_val_ptr = (global const float *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
global float *dst_val_ptr = (global float *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
*dst_val_ptr = tanh(*src_val_ptr);
}
}
}
kernel void kernel_tanh_f16_nd(
global void * p_src0_base, ulong off_src0_abs,
global void * p_dst_base, ulong off_dst_abs,
int ne00, int ne01, int ne02, int ne03,
ulong nb00, ulong nb01, ulong nb02, ulong nb03,
int ne10, int ne11, int ne12, int ne13,
ulong nb10, ulong nb11, ulong nb12, ulong nb13
) {
int i0 = get_global_id(0);
int i1 = get_global_id(1);
int i2 = get_global_id(2);
if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
for (int i3 = 0; i3 < ne13; ++i3) {
ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
global const half *src_val_ptr = (global const half *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
global half *dst_val_ptr = (global half *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
*dst_val_ptr = tanh(*src_val_ptr);
}
}
}

48
ggml/src/ggml-opencl/kernels/tsembd.cl Normal file
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kernel void kernel_timestep_embedding(
global const void * p_timesteps,
ulong off_timesteps,
global void * p_dst,
ulong off_dst,
int dst_nb1_bytes,
int logical_dim,
int max_period
) {
int local_i;
int local_j;
int local_half_dim;
float local_timestep_val;
float local_freq;
float local_arg;
global float * local_embed_data_ptr;
global const float * local_timesteps_input_ptr;
global float * local_dst_output_base_ptr;
local_timesteps_input_ptr = (global const float *)((global char *)p_timesteps + off_timesteps);
local_dst_output_base_ptr = (global float *)((global char *)p_dst + off_dst);
local_i = get_global_id(1);
local_j = get_global_id(0);
local_half_dim = logical_dim / 2;
local_embed_data_ptr = (global float *)((global char *)local_dst_output_base_ptr + local_i * dst_nb1_bytes);
if (logical_dim % 2 != 0 && local_j == ((logical_dim + 1) / 2)) {
local_embed_data_ptr[logical_dim] = 0.0f;
}
if (local_j >= local_half_dim) {
return;
}
local_timestep_val = local_timesteps_input_ptr[local_i];
if (local_half_dim == 0) {
local_freq = 1.0f;
} else {
local_freq = exp(-log((float)max_period) * (float)local_j / (float)local_half_dim);
}
local_arg = local_timestep_val * local_freq;
local_embed_data_ptr[local_j] = cos(local_arg);
local_embed_data_ptr[local_j + local_half_dim] = sin(local_arg);
}

121
ggml/src/ggml-opencl/kernels/upscale.cl Normal file
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kernel void kernel_upscale(
global const void * p_src0,
ulong off_src0,
global void * p_dst,
ulong off_dst,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne10,
int ne11,
int ne12,
int ne13,
float sf0,
float sf1,
float sf2,
float sf3
) {
global const char * src_base = (global const char *)p_src0 + off_src0;
global float * dst_base = (global float *)((global char *)p_dst + off_dst);
int index = get_global_id(0);
int dst_total_elements = ne10 * ne11 * ne12 * ne13;
if (index >= dst_total_elements) {
return;
}
int i10 = index % ne10;
int i11 = (index / ne10) % ne11;
int i12 = (index / (ne10 * ne11)) % ne12;
int i13 = index / (ne10 * ne11 * ne12);
int i00 = (int)(i10 / sf0);
int i01 = (int)(i11 / sf1);
int i02 = (int)(i12 / sf2);
int i03 = (int)(i13 / sf3);
ulong offset_src_element = (ulong)i03 * nb03 + (ulong)i02 * nb02 + (ulong)i01 * nb01 + (ulong)i00 * nb00;
global const float * src_element_ptr = (global const float *)(src_base + offset_src_element);
dst_base[index] = *src_element_ptr;
}
kernel void kernel_upscale_bilinear(
global const void * p_src0,
ulong off_src0,
global void * p_dst,
ulong off_dst,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne00_src,
int ne01_src,
int ne10_dst,
int ne11_dst,
int ne12_dst,
int ne13_dst,
float sf0,
float sf1,
float sf2,
float sf3
) {
global const char * src_base = (global const char *)p_src0 + off_src0;
global float * dst_base = (global float *)((global char *)p_dst + off_dst);
int index = get_global_id(0);
int dst_total_elements = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
if (index >= dst_total_elements) {
return;
}
int i10_dst = index % ne10_dst;
int i11_dst = (index / ne10_dst) % ne11_dst;
int i12_dst = (index / (ne10_dst * ne11_dst)) % ne12_dst;
int i13_dst = index / (ne10_dst * ne11_dst * ne12_dst);
int i02_src = (int)(i12_dst / sf2);
int i03_src = (int)(i13_dst / sf3);
const float pixel_offset = 0.5f;
float y_src_f = ((float)i11_dst + pixel_offset) / sf1 - pixel_offset;
long y0_src = (long)floor(y_src_f);
long y1_src = y0_src + 1;
y0_src = max(0L, min(y0_src, (long)ne01_src - 1));
y1_src = max(0L, min(y1_src, (long)ne01_src - 1));
float dy = y_src_f - (float)y0_src;
dy = max(0.0f, min(dy, 1.0f));
float x_src_f = ((float)i10_dst + pixel_offset) / sf0 - pixel_offset;
long x0_src = (long)floor(x_src_f);
long x1_src = x0_src + 1;
x0_src = max(0L, min(x0_src, (long)ne00_src - 1));
x1_src = max(0L, min(x1_src, (long)ne00_src - 1));
float dx = x_src_f - (float)x0_src;
dx = max(0.0f, min(dx, 1.0f));
global const float * p_a = (global const float *)(src_base + (ulong)x0_src * nb00 + (ulong)y0_src * nb01 + (ulong)i02_src * nb02 + (ulong)i03_src * nb03);
global const float * p_b = (global const float *)(src_base + (ulong)x1_src * nb00 + (ulong)y0_src * nb01 + (ulong)i02_src * nb02 + (ulong)i03_src * nb03);
global const float * p_c = (global const float *)(src_base + (ulong)x0_src * nb00 + (ulong)y1_src * nb01 + (ulong)i02_src * nb02 + (ulong)i03_src * nb03);
global const float * p_d = (global const float *)(src_base + (ulong)x1_src * nb00 + (ulong)y1_src * nb01 + (ulong)i02_src * nb02 + (ulong)i03_src * nb03);
const float val_a = *p_a;
const float val_b = *p_b;
const float val_c = *p_c;
const float val_d = *p_d;
float result = val_a * (1.0f - dx) * (1.0f - dy) +
val_b * dx * (1.0f - dy) +
val_c * (1.0f - dx) * dy +
val_d * dx * dy;
dst_base[index] = result;
}