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llama.cpp
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OpenCL: add SOLVE_TRI op support (#18846)

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shaofeiqi 2026-01-15 11:17:17 -08:00 committed by GitHub
parent 6e7fc8a146
commit 785a710085
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3 changed files with 144 additions and 0 deletions
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1
ggml/src/ggml-opencl/CMakeLists.txt
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@ -69,6 +69,7 @@ set(GGML_OPENCL_KERNELS
get_rows get_rows
glu glu
group_norm group_norm
solve_tri
im2col_f32 im2col_f32
im2col_f16 im2col_f16
mean mean

92
ggml/src/ggml-opencl/ggml-opencl.cpp
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@ -531,6 +531,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mv_q6_K_f32; cl_kernel kernel_mul_mv_q6_K_f32;
cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat; cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat; cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat;
cl_kernel kernel_solve_tri_f32;
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;
@ -952,6 +953,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT("."); GGML_LOG_CONT(".");
} }
// solve_tri_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "solve_tri.cl.h"
};
#else
const std::string kernel_src = read_file("solve_tri.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_solve_tri_f32 = clCreateKernel(prog, "kernel_solve_tri_f32", &err), err));
GGML_LOG_CONT(".");
CL_CHECK(clReleaseProgram(prog));
}
// im2col_f32 // im2col_f32
{ {
#ifdef GGML_OPENCL_EMBED_KERNELS #ifdef GGML_OPENCL_EMBED_KERNELS
@ -3266,6 +3284,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
} }
return true; return true;
} }
case GGML_OP_SOLVE_TRI:
return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL: case GGML_OP_IM2COL:
return true; return true;
case GGML_OP_ARGSORT: { case GGML_OP_ARGSORT: {
@ -9474,6 +9494,72 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
} }
static void ggml_cl_solve_tri(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_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 * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong offset0 = extra0->offset + src0->view_offs;
cl_ulong offset1 = extra1->offset + src1->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;
cl_kernel kernel = backend_ctx->kernel_solve_tri_f32;
GGML_ASSERT(kernel != nullptr);
const int n = src0->ne[0];
const int k = src1->ne[0];
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 nb10 = src1->nb[0];
const cl_ulong nb11 = src1->nb[1];
const cl_ulong nb12 = src1->nb[2];
const cl_ulong nb13 = src1->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_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), &n));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &k));
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(cl_ulong),&nb10));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong),&nb11));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong),&nb12));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong),&nb13));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong),&nb0));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong),&nb1));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong),&nb2));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong),&nb3));
size_t global_work_size[3]= { (size_t)k, (size_t)dst->ne[2], (size_t)dst->ne[3]};
size_t local_work_size[] = {16, 4, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
}
static void ggml_cl_im2col(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { static void ggml_cl_im2col(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0); GGML_ASSERT(src0);
GGML_ASSERT(src1); GGML_ASSERT(src1);
@ -10039,6 +10125,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
} }
func = ggml_cl_rope; func = ggml_cl_rope;
break; break;
case GGML_OP_SOLVE_TRI:
if (!any_on_device) {
return false;
}
func = ggml_cl_solve_tri;
break;
case GGML_OP_IM2COL: case GGML_OP_IM2COL:
if (!any_on_device) { if (!any_on_device) {
return false; return false;

51
ggml/src/ggml-opencl/kernels/solve_tri.cl Normal file
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@ -0,0 +1,51 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
//------------------------------------------------------------------------------
// solve_tri
//------------------------------------------------------------------------------
kernel void kernel_solve_tri_f32(
global uchar * src0,
ulong offset0,
global uchar * src1,
ulong offset1,
global uchar * dst,
ulong offsetd,
int n,
int k,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
ulong nb10,
ulong nb11,
ulong nb12,
ulong nb13,
ulong nb0,
ulong nb1,
ulong nb2,
ulong nb3
) {
int col = get_global_id(0);
int i2 = get_global_id(1);
int i3 = get_global_id(2);
global const uchar * Lb = src0 + offset0 + i2 * nb02 + i3 * nb03;
global const uchar * Bb = src1 + offset1 + i2 * nb12 + i3 * nb13;
global uchar * Xb = dst + offsetd + i2 * nb2 + i3 * nb3;
for(int row = 0; row < n; ++row){
global const float *pB = (global const float *)(Bb + row * nb11 + col * nb10);
float sum = 0.0f;
for(int j = 0; j < row; ++j){
global const float *pL = (global const float *)(Lb + row * nb01 + j * nb00);
global const float *pX = (global const float *)(Xb + j * nb1 + col * nb0);
sum += (*pL) * (*pX);
}
global const float * pDiag = (global const float *)(Lb + row * nb01 + row *nb00);
global float * pOut = (global float *)(Xb + row * nb1 + col *nb0);
*pOut = ((* pB) - sum) / (*pDiag);
}
}