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opencl: add neg, exp and diag (#20127)

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* opencl: add `neg`

* opencl: add `exp`

* opencl: add `diag`
This commit is contained in:
lhez
2026-03-05 21:16:39 -08:00
committed by GitHub
parent 2b10b62677
commit 6c97bffd65
5 changed files with 573 additions and 0 deletions
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ggml/src/ggml-opencl/ggml-opencl.cpp
+293
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@@ -499,6 +499,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
cl_kernel kernel_group_norm, kernel_group_norm_mul_add;
cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
cl_kernel kernel_diag_f32;
cl_kernel kernel_soft_max, kernel_soft_max_4;
cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16;
@@ -549,6 +550,10 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_pad;
cl_kernel kernel_tanh_f32, kernel_tanh_f32_4, kernel_tanh_f32_nc;
cl_kernel kernel_tanh_f16, kernel_tanh_f16_4, kernel_tanh_f16_nc;
cl_kernel kernel_neg_f32, kernel_neg_f32_4, kernel_neg_f32_nc;
cl_kernel kernel_neg_f16, kernel_neg_f16_4, kernel_neg_f16_nc;
cl_kernel kernel_exp_f32, kernel_exp_f32_4, kernel_exp_f32_nc;
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_softplus_f32, kernel_softplus_f32_4, kernel_softplus_f32_nc;
@@ -932,6 +937,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// diag
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "diag.cl.h"
};
#else
const std::string kernel_src = read_file("diag.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_diag_f32 = clCreateKernel(prog, "kernel_diag_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// gelu
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1979,6 +2001,48 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// neg
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "neg.cl.h"
};
#else
const std::string kernel_src = read_file("neg.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_neg_f32 = clCreateKernel(prog, "kernel_neg_f32", &err), err));
CL_CHECK((backend_ctx->kernel_neg_f32_4 = clCreateKernel(prog, "kernel_neg_f32_4", &err), err));
CL_CHECK((backend_ctx->kernel_neg_f32_nc = clCreateKernel(prog, "kernel_neg_f32_nc", &err), err));
CL_CHECK((backend_ctx->kernel_neg_f16 = clCreateKernel(prog, "kernel_neg_f16", &err), err));
CL_CHECK((backend_ctx->kernel_neg_f16_4 = clCreateKernel(prog, "kernel_neg_f16_4", &err), err));
CL_CHECK((backend_ctx->kernel_neg_f16_nc = clCreateKernel(prog, "kernel_neg_f16_nc", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// exp
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "exp.cl.h"
};
#else
const std::string kernel_src = read_file("exp.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_exp_f32 = clCreateKernel(prog, "kernel_exp_f32", &err), err));
CL_CHECK((backend_ctx->kernel_exp_f32_4 = clCreateKernel(prog, "kernel_exp_f32_4", &err), err));
CL_CHECK((backend_ctx->kernel_exp_f32_nc = clCreateKernel(prog, "kernel_exp_f32_nc", &err), err));
CL_CHECK((backend_ctx->kernel_exp_f16 = clCreateKernel(prog, "kernel_exp_f16", &err), err));
CL_CHECK((backend_ctx->kernel_exp_f16_4 = clCreateKernel(prog, "kernel_exp_f16_4", &err), err));
CL_CHECK((backend_ctx->kernel_exp_f16_nc = clCreateKernel(prog, "kernel_exp_f16_nc", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// expm1
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3592,6 +3656,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
case GGML_UNARY_OP_SIGMOID:
return ggml_is_contiguous(op->src[0]);
case GGML_UNARY_OP_TANH:
case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_EXP:
return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
case GGML_UNARY_OP_EXPM1:
return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
@@ -3677,6 +3743,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_DIAG:
return true;
case GGML_OP_DIAG_MASK_INF:
return op->ne[3] == 1;
case GGML_OP_ROPE: {
@@ -7581,6 +7649,170 @@ static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const
}
}
static void ggml_cl_neg(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;
GGML_TENSOR_LOCALS(int, ne0, src0, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
GGML_TENSOR_LOCALS(int, ne, dst, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb);
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_neg_f32_4;
} else {
kernel = backend_ctx->kernel_neg_f16_4;
}
n /= 4;
} else {
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_neg_f32;
} else {
kernel = backend_ctx->kernel_neg_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));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &n));
size_t global_work_size[] = {(size_t)CEIL_DIV(n, 64)*64, 1, 1};
size_t local_work_size[] = {64, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
} else {
// Handle non-contiguous input
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_neg_f32_nc;
} else {
kernel = backend_ctx->kernel_neg_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_exp(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;
GGML_TENSOR_LOCALS(int, ne0, src0, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
GGML_TENSOR_LOCALS(int, ne, dst, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb);
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_exp_f32_4;
} else {
kernel = backend_ctx->kernel_exp_f16_4;
}
n /= 4;
} else {
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_exp_f32;
} else {
kernel = backend_ctx->kernel_exp_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));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &n));
size_t global_work_size[] = {(size_t)CEIL_DIV(n, 64)*64, 1, 1};
size_t local_work_size[] = {64, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
} else {
// Handle non-contiguous input
if (src0->type == GGML_TYPE_F32) {
kernel = backend_ctx->kernel_exp_f32_nc;
} else {
kernel = backend_ctx->kernel_exp_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_expm1(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
@@ -11029,6 +11261,49 @@ static void ggml_cl_diag_mask_inf(ggml_backend_t backend, const ggml_tensor * sr
}
}
static void ggml_cl_diag(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;
GGML_TENSOR_LOCALS(int, ne0, src0, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
GGML_TENSOR_LOCALS(int, ne, dst, ne);
GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb);
cl_kernel kernel = backend_ctx->kernel_diag_f32;
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(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb0));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb2));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb3));
int nth = 64;
size_t global_work_size[] = {(size_t)ne1*nth, (size_t)ne2, (size_t)ne3};
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_soft_max(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
@@ -11845,6 +12120,18 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
}
func = ggml_cl_tanh;
break;
case GGML_UNARY_OP_NEG:
if (!any_on_device) {
return false;
}
func = ggml_cl_neg;
break;
case GGML_UNARY_OP_EXP:
if (!any_on_device) {
return false;
}
func = ggml_cl_exp;
break;
case GGML_UNARY_OP_EXPM1:
if (!any_on_device) {
return false;
@@ -11971,6 +12258,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
}
func = ggml_cl_nop;
break;
case GGML_OP_DIAG:
if (!any_on_device) {
return false;
}
func = ggml_cl_diag;
break;
case GGML_OP_DIAG_MASK_INF:
if (!any_on_device) {
return false;