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@@ -390,6 +390,9 @@ struct ggml_backend_opencl_context {
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cl_program program_tanh;
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cl_program program_upscale;
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cl_program program_concat;
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+ cl_program program_conv_2d_f16;
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+ cl_program program_conv_2d_f32;
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+ cl_program program_conv_2d_f16_f32;
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cl_program program_tsembd;
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cl_program program_mul_mv_id_q4_0_f32_8x_flat;
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@@ -441,6 +444,9 @@ struct ggml_backend_opencl_context {
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cl_kernel kernel_upscale_bilinear;
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cl_kernel kernel_concat_f32_contiguous;
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cl_kernel kernel_concat_f32_non_contiguous;
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+ cl_kernel kernel_conv_2d_f16;
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+ cl_kernel kernel_conv_2d_f32;
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+ cl_kernel kernel_conv_2d_f16_f32;
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cl_kernel kernel_timestep_embedding;
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cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
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@@ -1478,6 +1484,47 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
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GGML_LOG_CONT(".");
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}
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+ // conv2d
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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 "conv2d.cl.h"
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+ };
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+ const std::string kernel_src_f16_f32 {
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+ #include "conv2d_f16_f32.cl.h"
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+ };
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+ #else
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+ const std::string kernel_src = read_file("conv2d.cl");
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+ const std::string kernel_src_f16_f32 = read_file("conv2d_f16_f32.cl");
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+ #endif
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+ if (!kernel_src.empty()) {
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+ backend_ctx->program_conv_2d_f16 =
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+ build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), (std::string(compile_opts) + " -DUSE_FP16=1").c_str());
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+ CL_CHECK((backend_ctx->kernel_conv_2d_f16 = clCreateKernel(backend_ctx->program_conv_2d_f16, "kernel_conv_2d", &err), err));
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+ GGML_LOG_CONT(".");
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+ backend_ctx->program_conv_2d_f32 =
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+ build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
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+ CL_CHECK((backend_ctx->kernel_conv_2d_f32 = clCreateKernel(backend_ctx->program_conv_2d_f32, "kernel_conv_2d", &err), err));
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+ GGML_LOG_CONT(".");
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+ } else {
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+ GGML_LOG_WARN("ggml_opencl: conv2d kernel source not found or empty. This op will not be available.\n");
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+ backend_ctx->program_conv_2d_f16 = nullptr;
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+ backend_ctx->kernel_conv_2d_f16 = nullptr;
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+ backend_ctx->program_conv_2d_f32 = nullptr;
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+ backend_ctx->kernel_conv_2d_f32 = nullptr;
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+ }
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+ if (!kernel_src_f16_f32.empty()) {
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+ backend_ctx->program_conv_2d_f16_f32 =
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+ build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16_f32.c_str(), compile_opts);
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+ CL_CHECK((backend_ctx->kernel_conv_2d_f16_f32 = clCreateKernel(backend_ctx->program_conv_2d_f16_f32, "kernel_conv_2d", &err), err));
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+ GGML_LOG_CONT(".");
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+ } else {
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+ GGML_LOG_WARN("ggml_opencl: conv2d_f16_f32 kernel source not found or empty. This op will not be available.\n");
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+ backend_ctx->program_conv_2d_f16_f32 = nullptr;
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+ backend_ctx->kernel_conv_2d_f16_f32 = nullptr;
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+ }
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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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@@ -2361,6 +2408,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
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op->src[0]->ne[3] == 1 && op->ne[3] == 1;
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case GGML_OP_UPSCALE:
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return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
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+ case GGML_OP_CONV_2D:
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+ return (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16) ||
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+ (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
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+ (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32);
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case GGML_OP_CONCAT:
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return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
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case GGML_OP_TIMESTEP_EMBEDDING:
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@@ -4998,6 +5049,83 @@ static void ggml_cl_mul_mat_f16_f32_tiled(ggml_backend_t backend, const ggml_ten
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backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
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}
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+static void ggml_cl_conv_2d(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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+ GGML_TENSOR_BINARY_OP_LOCALS;
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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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+ const cl_uint Cout = ne03; const cl_uint Cin = ne02; const cl_uint N = ne13;
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+ const cl_uint KW = ne00; const cl_uint KH = ne01; const cl_uint W = ne10; const cl_uint H = ne11; const cl_uint OW = ne0; const cl_uint OH = ne1;
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+
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+ const cl_uint s0 = dst->op_params[0]; const cl_uint s1 = dst->op_params[1];
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+ const cl_uint p0 = dst->op_params[2]; const cl_uint p1 = dst->op_params[3];
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+ const cl_uint d0 = dst->op_params[4]; const cl_uint d1 = dst->op_params[5];
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+
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+ const cl_uint cl_nb01 = nb01/ggml_type_size(src0->type); const cl_uint cl_nb02 = nb02/ggml_type_size(src0->type); const cl_uint cl_nb03 = nb03/ggml_type_size(src0->type);
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+ const cl_uint cl_nb11 = nb11/ggml_type_size(src1->type); const cl_uint cl_nb12 = nb12/ggml_type_size(src1->type); const cl_uint cl_nb13 = nb13/ggml_type_size(src1->type);
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+ const cl_uint cl_nb1 = nb1/ggml_type_size(dst->type); const cl_uint cl_nb2 = nb2/ggml_type_size(dst->type); const cl_uint cl_nb3 = nb3/ggml_type_size(dst->type);
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+
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+ const int64_t NPQ = (int64_t)N * OW * OH;
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+
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+ const uint32_t BS_K = 64;
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+ const uint32_t BS_NPQ = 64;
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+ const uint32_t BS_CRS = 16;
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+ const uint32_t VEC_SIZE = 4;
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+
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+ const uint32_t TS_K = 4;
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+ const uint32_t TS_NPQ = 8;
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+
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+ const uint32_t WG_K = BS_K / TS_K;
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+ const uint32_t WG_NPQ = BS_NPQ / TS_NPQ;
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+
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+ auto splitWork = [](uint32_t work_size, uint32_t block_size) { return (block_size + work_size - 1) / block_size; };
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+ const uint32_t NB_K = splitWork(Cout, BS_K);
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+ const uint32_t NB_NPQ = splitWork(NPQ, BS_NPQ);
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+
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+ cl_kernel kernel;
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+ size_t shmem_size;
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+
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+ if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
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+ kernel = backend_ctx->kernel_conv_2d_f16;
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+ shmem_size = (size_t)(BS_K * BS_CRS * sizeof(cl_half) + BS_CRS * (BS_NPQ / VEC_SIZE) * sizeof(cl_half4));
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+ } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
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+ kernel = backend_ctx->kernel_conv_2d_f32;
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+ shmem_size = (size_t)(BS_K * BS_CRS * sizeof(cl_float) + BS_CRS * (BS_NPQ / VEC_SIZE) * sizeof(cl_float4));
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+ } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
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+ kernel = backend_ctx->kernel_conv_2d_f16_f32;
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+ shmem_size = (size_t)(BS_K * BS_CRS * sizeof(cl_half) + BS_CRS * (BS_NPQ / VEC_SIZE) * sizeof(cl_float4));
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+ } else {
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+ GGML_ASSERT(false && "Unsupported data type combination for conv2d");
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+ return;
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+ }
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+
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+ cl_uint idx = 0;
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_mem), &extra0->data_device)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_ulong), &offset0));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_mem), &extra1->data_device)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_ulong), &offset1));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_mem), &extrad->data_device)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_ulong), &offsetd));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, shmem_size, NULL));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &Cout)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &Cin)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &N));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &KW)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &KH)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &W)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &H));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &OW)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &OH));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &s0)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &s1)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &p0)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &p1));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &d0)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &d1));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb01)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb02)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb03));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb11)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb12)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb13));
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+ CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb1)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb2)); CL_CHECK(clSetKernelArg(kernel, idx++, sizeof(cl_uint), &cl_nb3));
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+
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+ size_t global_work_size[] = { (size_t)NB_K * WG_K, (size_t)NB_NPQ * WG_NPQ, 1 };
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+ size_t local_work_size[] = { (size_t)WG_K, (size_t)WG_NPQ, 1 };
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+
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+ backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
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+}
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+
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static void ggml_cl_mul_mat(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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@@ -6752,6 +6880,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
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}
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ggml_cl_upscale(backend, tensor->src[0], tensor);
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return true;
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+ case GGML_OP_CONV_2D:
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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_conv_2d;
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+ break;
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case GGML_OP_CONCAT:
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if (!any_on_device) {
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return false;
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