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opencl: add ABS op (#25115)

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This commit is contained in:
Gezahegne
2026-07-16 22:13:47 -07:00
committed by GitHub
parent e8f19cc0ad
commit b85833e934
3 changed files with 241 additions and 0 deletions
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ggml/src/ggml-opencl/ggml-opencl.cpp
+127
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@@ -845,6 +845,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_exp_f16, kernel_exp_f16_4, kernel_exp_f16_nc;
cl_kernel kernel_expm1_f32, kernel_expm1_f32_4, kernel_expm1_f32_nc;
cl_kernel kernel_expm1_f16, kernel_expm1_f16_4, kernel_expm1_f16_nc;
cl_kernel kernel_abs_f32, kernel_abs_f32_4, kernel_abs_f32_nc;
cl_kernel kernel_abs_f16, kernel_abs_f16_4, kernel_abs_f16_nc;
cl_kernel kernel_softplus_f32, kernel_softplus_f32_4, kernel_softplus_f32_nc;
cl_kernel kernel_softplus_f16, kernel_softplus_f16_4, kernel_softplus_f16_nc;
cl_kernel kernel_upscale;
@@ -2929,6 +2931,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
GGML_LOG_CONT(".");
}
// abs
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "abs.cl.h"
};
#else
const std::string kernel_src = read_file("abs.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_abs_f32 = clCreateKernel(prog, "kernel_abs_f32", &err), err));
CL_CHECK((backend_ctx->kernel_abs_f32_4 = clCreateKernel(prog, "kernel_abs_f32_4", &err), err));
CL_CHECK((backend_ctx->kernel_abs_f32_nc = clCreateKernel(prog, "kernel_abs_f32_nc", &err), err));
CL_CHECK((backend_ctx->kernel_abs_f16 = clCreateKernel(prog, "kernel_abs_f16", &err), err));
CL_CHECK((backend_ctx->kernel_abs_f16_4 = clCreateKernel(prog, "kernel_abs_f16_4", &err), err));
CL_CHECK((backend_ctx->kernel_abs_f16_nc = clCreateKernel(prog, "kernel_abs_f16_nc", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// softplus
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -7153,6 +7176,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return op->src[0]->type == GGML_TYPE_F32;
case GGML_UNARY_OP_EXPM1:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_UNARY_OP_ABS:
return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
case GGML_UNARY_OP_SOFTPLUS:
return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
default:
@@ -13387,6 +13412,102 @@ static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, cons
}
}
static void ggml_cl_abs(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;
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 = extra0->offset + src0->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;
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 cl_ulong nb0 = dst->nb[0];
const cl_ulong nb1 = dst->nb[1];
const cl_ulong nb2 = dst->nb[2];
const cl_ulong nb3 = dst->nb[3];
cl_kernel kernel;
if (ggml_is_contiguous(src0)) {
// Handle contiguous input
int n = ggml_nelements(dst);
if (n % 4 == 0) {
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_abs_f32_4;
} else {
kernel = backend_ctx->kernel_abs_f16_4;
}
n /= 4;
} else {
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_abs_f32;
} else {
kernel = backend_ctx->kernel_abs_f16;
}
}
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), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
size_t global_work_size[] = {(size_t)n, 1, 1};
size_t local_work_size[] = {64, 1, 1};
size_t * local_work_size_ptr = local_work_size;
if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) {
local_work_size_ptr = nullptr;
}
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst);
} else {
// Handle non-contiguous input
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_abs_f32_nc;
} else {
kernel = backend_ctx->kernel_abs_f16_nc;
}
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), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3));
int nth = 64;
size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
size_t local_work_size[] = {(size_t)nth, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
}
}
static void ggml_cl_softplus(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
@@ -24408,6 +24529,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
}
func = ggml_cl_expm1;
break;
case GGML_UNARY_OP_ABS:
if (!any_on_device) {
return false;
}
func = ggml_cl_abs;
break;
case GGML_UNARY_OP_SOFTPLUS:
if (!any_on_device) {
return false;