/* * Copyright (c) 2018-2019 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. */ #include "arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLValidate.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/IAccessWindow.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { if(act_info.enabled()) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG((input->data_type() == DataType::QASYMM8) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC), "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); } ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON(output->data_layout() != winograd_info.output_data_layout); const PadStrideInfo conv_info = winograd_info.convolution_info; const Size2D output_tile_size = winograd_info.output_tile_size; const Size2D kernel_size = winograd_info.kernel_size; const Size2D input_dimensions = winograd_info.input_dimensions; const unsigned int num_channels = (winograd_info.kernel_size.width + winograd_info.output_tile_size.width - 1) * (winograd_info.kernel_size.height + winograd_info.output_tile_size.height - 1); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, winograd_info.output_data_layout), "Winograd output transform not supported"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != num_channels, "Wrong number of channels"); // Compute number of elements to process in the X and Y direction // Compute the number of output tiles along the x and y direction of size "output_tile_size" const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions, kernel_size, output_tile_size, conv_info); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != static_cast((num_tiles.area()))); if(bias != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } // Checks performed when output is configured if(output->total_size() != 0) { const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(compute_winograd_output_transform_shape(*input, winograd_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const Size2D &output_tile_size) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); constexpr unsigned int num_elems_processed_per_iteration = 1; Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); bool window_changed = false; int output_static_window_end_x = 0; int output_static_window_end_y = 0; if(output->data_layout() == DataLayout::NCHW) { output_static_window_end_x = ceil_to_multiple(output->dimension(0), output_tile_size.width); output_static_window_end_y = ceil_to_multiple(output->dimension(1), output_tile_size.height); } else { output_static_window_end_x = output->dimension(0); output_static_window_end_y = std::max(ceil_to_multiple(output->dimension(1), output_tile_size.width), output->dimension(1) + 1 /* For out of bound reads towards the z axis */); } AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration); AccessWindowStatic output_access(output, 0, 0, output_static_window_end_x, output_static_window_end_y); window_changed = update_window_and_padding(win, input_access, output_access); output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); if(bias != nullptr) { AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); window_changed = window_changed || update_window_and_padding(win, bias_access); } Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, win); } } // namespace CLWinogradOutputTransformKernel::CLWinogradOutputTransformKernel() : _input(nullptr), _bias(nullptr), _output(nullptr), _is_nhwc(false) { } void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output tensor auto initialization if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_output_transform_shape(*input->info(), winograd_info))); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info, act_info)); _input = input; _bias = bias; _output = output; _is_nhwc = winograd_info.output_data_layout == DataLayout::NHWC; // Compute num_tiles_x const Size2D input_dimensions = winograd_info.input_dimensions; const Size2D kernel_size = winograd_info.kernel_size; const Size2D output_tile_size = winograd_info.output_tile_size; const PadStrideInfo conv_info = winograd_info.convolution_info; // Compute the number of output tiles along the x and y direction of size "output_tile_size" const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions, kernel_size, output_tile_size, conv_info); const size_t total_batches = output->info()->tensor_shape().total_size_upper(3); // Set build options CLBuildOptions build_opts; build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); if((output_tile_size.x() == 2) || (output_tile_size.x() == 1 && output_tile_size.y() == 2)) { build_opts.add_option("-DVEC_SIZE=2"); } else if((output_tile_size.x() == 4) || (output_tile_size.x() == 1 && output_tile_size.y() == 4)) { build_opts.add_option("-DVEC_SIZE=4"); } build_opts.add_option_if(_bias != nullptr, std::string("-DHAS_BIAS")); build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(num_tiles.width)); build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width)); build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height)); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(_input->info()->dimension(2))); build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL"); build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL"); // Create kernel std::string kernel_name = "winograd_output_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string() + "_" + lower_string(string_from_data_layout(winograd_info.output_data_layout)); _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Configure kernel window auto win_config = validate_and_configure_window(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info.output_tile_size); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(1)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); _config_id += "_"; _config_id += lower_string(string_from_data_layout(winograd_info.output_data_layout)); } Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info, act_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), winograd_info.output_tile_size).first); return Status{}; } void CLWinogradOutputTransformKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); // Collapse window Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); // Get initial windows Window slice = window_collapsed.first_slice_window_4D(); slice.set(Window::DimZ, Window::Dimension(0, 1, 1)); // Setup output slice Window slice_out(slice); slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); if(_bias != nullptr) { unsigned int idx1 = 2 * num_arguments_per_4D_tensor(); Window slice_biases; slice_biases.use_tensor_dimensions(_bias->info()->tensor_shape()); add_1D_tensor_argument(idx1, _bias, slice_biases); } if(_is_nhwc) { unsigned int idx2 = 2 * num_arguments_per_4D_tensor() + ((_bias != nullptr) ? num_arguments_per_1D_tensor() : 0); _kernel.setArg(idx2, static_cast(_output->info()->total_size() - _output->info()->strides_in_bytes().y())); } do { unsigned int idx = 0; add_4D_tensor_argument(idx, _input, slice); add_4D_tensor_argument(idx, _output, slice_out); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_out)); }