| /* |
| * Copyright (c) 2022 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #if defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION) |
| |
| #include "src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.h" |
| |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CL/ICLKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace experimental |
| { |
| namespace dynamic_fusion |
| { |
| ComponentType ClDirectConvolutionKernelComponent::get_component_type() const |
| { |
| return ComponentType::Complex; |
| } |
| |
| std::set<std::string> ClDirectConvolutionKernelComponent::get_headers_list() const |
| { |
| return std::set<std::string> { "helpers.h", "tile_helpers.h", "repeat.h" }; |
| } |
| |
| Window ClDirectConvolutionKernelComponent::get_window() const |
| { |
| const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); |
| const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); |
| auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); |
| |
| // Get dst shape |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src_info, *weight_info, _desc.pad_stride_info); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*dst_info, output_shape, |
| 1, |
| src_info->data_type(), |
| src_info->quantization_info()); |
| |
| const unsigned int vec_size = std::min(static_cast<unsigned int>(dst_info->tensor_shape()[0]), 4u); |
| const unsigned int num_rows = (dst_info->tensor_shape()[0] > 16) ? ((src_info->data_type() == DataType::F32) ? 2U : 4U) : 1U; |
| |
| // Create and configure kernel window |
| Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows)); |
| |
| const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], num_rows); |
| win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, num_rows)); |
| win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1)); |
| |
| return win; |
| } |
| |
| std::string ClDirectConvolutionKernelComponent::get_additional_macros() const |
| { |
| return R"_()_"; // no macros |
| } |
| |
| std::string ClDirectConvolutionKernelComponent::get_component_code() const |
| { |
| const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); |
| const auto bias_info = _blueprint->impl().get_kernel_argument_info(_bias.arg_id); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(src_info->data_layout() != DataLayout::NHWC, "Only NHWC data layout is supported by this component."); |
| |
| const auto channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); |
| const auto k0 = adjust_vec_size(is_data_type_quantized(src_info->data_type()) ? 16u : 8u, src_info->dimension(channel_idx)); |
| const bool leftover_loop = (src_info->dimension(channel_idx) % k0) != 0; |
| |
| std::string code = R"_( |
| //------------------ START KERNEL {{meta_kernel_id}} --------------------- |
| // IN_0(src) {{src}} |
| // IN_1(wei) {{weight}} |
| // IN_1(bia) {{bias}} |
| // OUT(dst, accum) {{dst}} |
| |
| const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM |
| const int mout = GET_SPATIAL_IDX(1, M0, 0); // WIDTH x HEIGHT |
| const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX |
| |
| // Initialize the accumulators |
| TILE({{ACC_DATA_TYPE}}, M0, N0, {{dst}}); |
| { |
| // All the tensor dimensions are passed at compile time. |
| // In case of dynamic tensor support, the following dimensions should be passed as function argument. |
| #define _I{{WEI_WIDTH}} {{WEI_WIDTH}} |
| #define _I{{WEI_HEIGHT}} {{WEI_HEIGHT}} |
| #define _ISRC_WIDTH {{src}}_w |
| #define _ISRC_HEIGHT {{src}}_h |
| #define _ISRC_CHANNELS {{src}}_c |
| #define _IDST_WIDTH {{dst_w}} |
| #define _IDST_HEIGHT {{dst_h}} |
| #define _IDST_CHANNELS {{dst_c}} |
| #define _IY_MULTIPLIER (_I{{WEI_WIDTH}} * _I{{WEI_HEIGHT}}) |
| |
| // .v = access the whole vector (OpenCL vector) |
| // .s[x] = access the vector element at position x (scalar access) |
| TILE(int, M0, 1, xi); |
| TILE(int, M0, 1, yi); |
| |
| // Convert the linear index to coordinate |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| xi[i].v = ((mout + i) % _IDST_WIDTH) * {{STRIDE_X}}; |
| yi[i].v = ((mout + i) / _IDST_WIDTH) * {{STRIDE_Y}}; |
| xi[i].v -= {{PAD_LEFT}}; |
| yi[i].v -= {{PAD_TOP}}; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| {{dst}}[i].v = 0; |
| }) |
| |
| uint cond = (get_global_id(0) == 0) && (get_global_id(1) == 0) && (get_global_id(2) == 0); |
| |
| for(int i = 0; i < (_I{{WEI_WIDTH}} * _I{{WEI_HEIGHT}}); ++i) |
| { |
| int ck = 0; |
| int xk = i % _I{{WEI_WIDTH}}; |
| int yk = i / _I{{WEI_WIDTH}}; |
| |
| int k = 0; |
| for(; k <= (_ISRC_CHANNELS - K0); k += K0) |
| { |
| TILE({{SRC_DATA_TYPE}}, M0, K0, a); |
| TILE({{WEI_DATA_TYPE}}, N0, K0, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, bout, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| |
| // Load tile from the weights tensor |
| T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, cout * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| |
| // Compute the matrix multiplication between two tiles |
| T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}}); |
| |
| ck += K0; |
| } |
| |
| // We voluntarily use SRC_CHANNELS rather than _DSRC_CHANNELS |
| // This #if directive should be removed in case of dynamic tensor support |
| )_"; |
| |
| if(leftover_loop) |
| { |
| code += R"_( |
| // Left-over accumulations |
| for(; k < _ISRC_CHANNELS; ++k) |
| { |
| TILE({{SRC_DATA_TYPE}}, M0, 1, a); |
| TILE({{WEI_DATA_TYPE}}, N0, 1, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, bout, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| |
| // Load tile from the weights tensor |
| // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration |
| T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, cout * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| |
| // Compute the matrix multiplication between two tiles |
| T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}}); |
| |
| ++ck; |
| } |
| )_"; |
| } |
| |
| code += R"_( |
| } |
| )_"; |
| |
| if(bias_info != nullptr) |
| { |
| code += R"_( |
| TILE({{BIA_DATA_TYPE}}, 1, N0, bias0); |
| |
| T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, cout, 0, 1, 0, bias0); |
| |
| // c = c + bias[broadcasted] |
| T_ADD_BROADCAST_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}}); |
| )_"; |
| } |
| |
| code += R"_( |
| #undef _I{{WEI_WIDTH}} |
| #undef _I{{WEI_HEIGHT}} |
| #undef _ISRC_WIDTH |
| #undef _ISRC_HEIGHT |
| #undef _ISRC_CHANNELS |
| #undef _IDST_WIDTH |
| #undef _IDST_HEIGHT |
| #undef _IDST_CHANNELS |
| #undef _IY_MULTIPLIER |
| } |
| |
| // Workaround for the discrepancy between tiles and repeats |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}0 = {{dst}}[0].v; |
| #if M0 >= 2 |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}1 = {{dst}}[1].v; |
| #endif // M0 >= 2 |
| #if M0 >= 3 |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}2 = {{dst}}[2].v; |
| #endif // M0 >= 3 |
| #if M0 >= 4 |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}3 = {{dst}}[3].v; |
| #endif // M0 >= 4 |
| #if M0 >= 8 |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}4 = {{dst}}[4].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}5 = {{dst}}[5].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}6 = {{dst}}[6].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}7 = {{dst}}[7].v; |
| #endif // M0 >= 8 |
| #if M0 == 16 |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}8 = {{dst}}[8].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}9 = {{dst}}[9].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}A = {{dst}}[10].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}B = {{dst}}[11].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}C = {{dst}}[12].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}D = {{dst}}[13].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}E = {{dst}}[14].v; |
| VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}F = {{dst}}[15].v; |
| #endif // M0 == 16 |
| //------------------ END KERNEL {{meta_kernel_id}} --------------------- |
| )_"; |
| return code.c_str(); |
| } |
| |
| bool export_to_cl_image_support(const ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout) |
| { |
| if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC)) |
| { |
| return false; |
| } |
| |
| // If not floating point |
| if(!is_data_type_float(tensor->data_type())) |
| { |
| return false; |
| } |
| |
| if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD) |
| { |
| return false; |
| } |
| |
| // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform |
| if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) |
| { |
| return false; |
| } |
| |
| // Check cl image pitch alignment |
| if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0) |
| { |
| return false; |
| } |
| |
| const size_t image_w = tensor->tensor_shape()[0] / 4; |
| const size_t image_h = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3]; |
| const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); |
| const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); |
| |
| if(image_w > max_image_w || image_h > max_image_h) |
| { |
| return false; |
| } |
| |
| return true; |
| } |
| |
| CLBuildOptions ClDirectConvolutionKernelComponent::generate_build_options() const |
| { |
| const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); |
| const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); |
| const auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); |
| |
| const unsigned int channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); |
| const DataType data_type = src_info->data_type(); |
| const GPUTarget gpu_target = ICLKernel().get_target(); |
| |
| Window win = get_window(); |
| |
| const unsigned int n0 = win.x().step(); |
| const unsigned int m0 = win.y().step(); |
| const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src_info->dimension(channel_idx)); |
| const unsigned int partial_store_n0 = dst_info->dimension(channel_idx) % n0; |
| const bool export_to_cl_image = export_to_cl_image_support(weight_info, gpu_target, src_info->data_layout()); |
| |
| // Update the padding for the weights tensor if we can export to cl_image |
| if(export_to_cl_image) |
| { |
| arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weight_info); |
| } |
| |
| CLBuildOptions build_opts{}; |
| build_opts.add_option("-cl-fast-relaxed-math"); |
| build_opts.add_option("-DIS_TILED"); |
| build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); |
| build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); |
| build_opts.add_option("-DK0=" + support::cpp11::to_string(k0)); |
| build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); |
| |
| return build_opts; |
| } |
| |
| ClDirectConvolutionKernelComponent::TagLUT ClDirectConvolutionKernelComponent::allocate_vars(SharedVarTable &vtable) const |
| { |
| TagLUT lut{}; |
| |
| const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); |
| const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); |
| const auto bias_info = _blueprint->impl().get_kernel_argument_info(_bias.arg_id); |
| const auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); |
| |
| const GPUTarget gpu_target = ICLKernel().get_target(); |
| const bool export_to_cl_image = export_to_cl_image_support(weight_info, gpu_target, src_info->data_layout()); |
| |
| const TensorArgType weight_type = export_to_cl_image ? TensorArgType::Tensor_4D_t_Image : TensorArgType::Tensor_4D_t_Buffer; |
| lut["meta_kernel_id"] = id(); |
| lut["src"] = vtable.add(_src, ClKernelArgRuntimeDescriptor(_src.arg_id, TensorArgType::Tensor_4D_t_Buffer), "src"); |
| lut["weight"] = vtable.add(_weight, ClKernelArgRuntimeDescriptor(_weight.arg_id, weight_type), "weight"); |
| |
| if(!_bias.is_empty()) // optional bias |
| { |
| lut["bias"] = vtable.add(_bias, ClKernelArgRuntimeDescriptor(_bias.arg_id, TensorArgType::Vector), "bias"); |
| lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(bias_info->data_type()); |
| } |
| lut["dst"] = vtable.add(_dst, ClKernelArgRuntimeDescriptor(_dst.arg_id, TensorArgType::Tensor_4D_t_Buffer), "dst"); |
| |
| // Local build options |
| const auto width_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::WIDTH); |
| const auto height_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::HEIGHT); |
| const auto channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| lut["dst_w"] = dst_info->dimension(width_idx); |
| lut["dst_h"] = dst_info->dimension(height_idx); |
| lut["dst_c"] = dst_info->dimension(channel_idx); |
| |
| lut["ACC_DATA_TYPE"] = src_info->data_type(); |
| lut["SRC_DATA_TYPE"] = src_info->data_type(); |
| lut["WEI_DATA_TYPE"] = weight_info->data_type(); |
| |
| lut["SRC_TENSOR_TYPE"] = "BUFFER"; |
| lut["WEI_TENSOR_TYPE"] = export_to_cl_image ? "IMAGE" : "BUFFER"; |
| |
| lut["WEI_WIDTH"] = weight_info->dimension(width_idx); |
| lut["WEI_HEIGHT"] = weight_info->dimension(height_idx); |
| |
| lut["STRIDE_X"] = std::get<0>(_desc.pad_stride_info.stride()); |
| lut["STRIDE_Y"] = std::get<1>(_desc.pad_stride_info.stride()); |
| |
| lut["PAD_LEFT"] = _desc.pad_stride_info.pad_left(); |
| lut["PAD_TOP"] = _desc.pad_stride_info.pad_top(); |
| |
| lut["ZERO_VALUE"] = 0; |
| |
| return lut; |
| } |
| } // namespace dynamic_fusion |
| } // namespace experimental |
| } // namespace arm_compute |
| |
| #endif // defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION) |