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@@ -351,6 +351,7 @@ struct ggml_backend_opencl_context {
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cl_program program_gemv_noshuffle_general;
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cl_program program_gemv_noshuffle;
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cl_program program_get_rows;
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+ cl_program program_set_rows;
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cl_program program_glu;
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cl_program program_im2col_f16;
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cl_program program_im2col_f32;
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@@ -412,6 +413,7 @@ struct ggml_backend_opencl_context {
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cl_kernel kernel_soft_max, kernel_soft_max_4;
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cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
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cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
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+ cl_kernel kernel_set_rows_f32, kernel_set_rows_f16;
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cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
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cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
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cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
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@@ -529,6 +531,16 @@ struct ggml_backend_opencl_context {
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fclose(ftrace);
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}
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+ size_t get_kernel_workgroup_size(cl_kernel kernel) const {
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+ size_t workgroup_size = 0;
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+ size_t ret_size = 0;
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+ CL_CHECK(
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+ clGetKernelWorkGroupInfo(kernel, device, CL_KERNEL_WORK_GROUP_SIZE,
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+ sizeof(size_t), &workgroup_size, &ret_size));
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+ GGML_ASSERT(sizeof(size_t) == ret_size);
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+ return workgroup_size;
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+ }
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+
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void enqueue_ndrange_kernel(cl_kernel kernel, cl_uint work_dim, size_t *global_work_size, size_t *local_work_size, const ggml_tensor * tensor) {
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#ifdef GGML_OPENCL_PROFILING
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cl_event evt;
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@@ -1431,6 +1443,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
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}
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}
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+ // set_rows
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+ {
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+#ifdef GGML_OPENCL_EMBED_KERNELS
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+ const std::string kernel_src {
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+ #include "set_rows.cl.h"
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+ };
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+#else
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+ const std::string kernel_src = read_file("set_rows.cl");
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+#endif
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+ backend_ctx->program_set_rows =
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+ build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
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+
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+ CL_CHECK((backend_ctx->kernel_set_rows_f32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32", &err), err));
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+ CL_CHECK((backend_ctx->kernel_set_rows_f16 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16", &err), err));
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+ GGML_LOG_CONT(".");
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+ }
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+
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// mul_mv_id_q4_0_f32_8x_flat
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{
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#ifdef GGML_OPENCL_EMBED_KERNELS
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@@ -2233,8 +2262,17 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
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{
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// TODO: add support
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// ref: https://github.com/ggml-org/llama.cpp/pull/14274
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- return false;
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- } break;
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+ if (op->src[0]->type != GGML_TYPE_F32) {
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+ return false;
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+ }
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+ switch (op->type) {
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+ case GGML_TYPE_F16:
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+ case GGML_TYPE_F32:
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+ return true;
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+ default:
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+ return false;
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+ }
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+ }
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case GGML_OP_CPY:
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case GGML_OP_DUP:
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case GGML_OP_CONT:
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@@ -3374,6 +3412,111 @@ static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, c
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backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
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}
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+static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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+ GGML_ASSERT(src0);
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+ GGML_ASSERT(src0->extra);
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+ GGML_ASSERT(src1);
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+ GGML_ASSERT(src1->extra);
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+ GGML_ASSERT(dst);
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+ GGML_ASSERT(dst->extra);
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+
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+ // ne0 = ne00
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+ // ne2 = ne02
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+ // ne3 = ne03
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+
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+ const int ne01 = src0->ne[1];
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+ const int ne02 = src0->ne[2];
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+ const int ne03 = src0->ne[3];
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+
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+ const cl_ulong nb01 = src0->nb[1];
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+ const cl_ulong nb02 = src0->nb[2];
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+ const cl_ulong nb03 = src0->nb[3];
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+
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+ const int ne11 = src1->ne[1];
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+ const int ne12 = src1->ne[2];
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+
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+ const cl_ulong nb10 = src1->nb[0];
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+ const cl_ulong nb11 = src1->nb[1];
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+ const cl_ulong nb12 = src1->nb[2];
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+
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+ const int ne0 = dst->ne[0];
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+
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+ const cl_ulong nb1 = dst->nb[1];
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+ const cl_ulong nb2 = dst->nb[2];
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+ const cl_ulong nb3 = dst->nb[3];
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+
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+ const int nblk0 = ne0/ggml_blck_size(dst->type);
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+
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+ ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
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+
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+ ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
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+ ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
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+ ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
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+
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+ cl_ulong offset0 = extra0->offset + src0->view_offs;
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+ cl_ulong offset1 = extra1->offset + src1->view_offs;
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+ cl_ulong offsetd = extrad->offset + dst->view_offs;
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+
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+ cl_kernel kernel;
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+
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+ switch (dst->type) {
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+ case GGML_TYPE_F32:
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+ kernel = backend_ctx->kernel_set_rows_f32;
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+ break;
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+ case GGML_TYPE_F16:
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+ kernel = backend_ctx->kernel_set_rows_f16;
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+ break;
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+ default:
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+ GGML_ABORT("not implemented");
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+ }
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+
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+ CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
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+ CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
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+ CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
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+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
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+ CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
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+ CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
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+ CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne01));
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+ CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb01));
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+ CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb02));
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+ CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb03));
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+ CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne11));
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+ CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
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+ CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb10));
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+ CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb11));
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+ CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb12));
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+ CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &nblk0));
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+ CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb1));
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+ CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb2));
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+ CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb3));
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+
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+ int nth0 = 64;
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+ if (backend_ctx->gpu_family == INTEL) {
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+ nth0 = 32;
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+ } else if (backend_ctx->gpu_family == ADRENO) {
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+ nth0 = 64;
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+ }
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+
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+ int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel);
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+ while (nth0 < nblk0 && nth0 < max_workgroup_size) {
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+ nth0 *= 2;
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+ }
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+
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+ int rows_per_workgroup = 1;
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+ if (nth0 > nblk0) {
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+ rows_per_workgroup = nth0 / nblk0;
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+ nth0 = nblk0;
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+ }
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+
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+ size_t global_work_size[] = {
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+ (size_t)(ne01 + rows_per_workgroup - 1)/rows_per_workgroup*nth0,
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+ (size_t)ne02*rows_per_workgroup,
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+ (size_t)ne03};
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+ size_t local_work_size[] = {(size_t)nth0, (size_t)rows_per_workgroup, 1};
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+
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+ backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
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+}
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+
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static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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GGML_ASSERT(src0);
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GGML_ASSERT(src0->extra);
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@@ -6388,6 +6531,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
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}
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func = ggml_cl_get_rows;
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break;
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+ case GGML_OP_SET_ROWS:
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+ if (!any_on_device) {
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+ return false;
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+ }
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+ func = ggml_cl_set_rows;
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+ break;
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case GGML_OP_CPY:
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if (!any_on_device) {
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return false;
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