| /* |
| * Copyright (c) 2018 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/CLWinogradInputTransformKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW); |
| |
| 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; |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd input transform only supports 3x3 and 5x5 kernels"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U) |
| && output_tile_size != Size2D(4U, 4U), |
| "Winograd input transform only supports 2x2 or 4x4 output tile for 3x3 kernels"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd input transform only supports 4x4 output tile for 5x5 kernels"); |
| ARM_COMPUTE_UNUSED(conv_info); |
| ARM_COMPUTE_UNUSED(output_tile_size); |
| ARM_COMPUTE_UNUSED(kernel_size); |
| |
| // Validate configured output |
| if(output->total_size() != 0) |
| { |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_UNUSED(output); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| 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 unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1; |
| const unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1; |
| |
| Window win = calculate_max_window(*input, Steps(1, 1)); |
| |
| AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y); |
| |
| bool window_changed = update_window_and_padding(win, input_access); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| CLWinogradInputTransformKernel::CLWinogradInputTransformKernel() |
| : _border_size(0), _input(nullptr), _output(nullptr), _num_tiles_x(0), _num_tiles_y(0), _step_z(1) |
| { |
| } |
| |
| BorderSize CLWinogradInputTransformKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLWinogradInputTransformKernel::configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), winograd_info)); |
| |
| 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; |
| |
| // Compute number of elements to process in the X and Y direction |
| const int num_elements_x = input->info()->dimension(0) - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right(); |
| const int num_elements_y = input->info()->dimension(1) - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom(); |
| |
| // Check if we need to extend the right or bottom border |
| const unsigned int extra_border_right = ((num_elements_x % output_tile_size.width) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.width - 1); |
| const unsigned int extra_border_bottom = ((num_elements_y % output_tile_size.height) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.height - 1); |
| |
| _input = input; |
| _output = output; |
| _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right() + extra_border_right, conv_info.pad_bottom() + extra_border_bottom, conv_info.pad_left()); |
| _num_tiles_x = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width)); |
| _num_tiles_y = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height)); |
| |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input->info(), winograd_info); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); |
| |
| ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(output->info()->dimension(1))); |
| |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x)); |
| build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| |
| // Create kernel |
| std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string(); |
| |
| // Check optimized kernel if output_dims == 2x2 |
| if(output_tile_size == Size2D(2U, 2U)) |
| { |
| _step_z = (_input->info()->dimension(2) % 2) != 0 ? 1 : 2; |
| } |
| |
| _lws_hint = cl::NDRange(1, 1, 8); |
| |
| // Append stepz and data layout |
| kernel_name += "_stepz"; |
| kernel_name += support::cpp11::to_string(_step_z); |
| kernel_name += "_nchw"; |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Create window and update padding |
| auto win_config = validate_and_configure_window(input->info(), output->info(), winograd_info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure(win_config.second); |
| |
| _config_id = kernel_name; |
| _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(input->info()->dimension(2)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_info.pad_left()); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_info.pad_top()); |
| } |
| |
| Status CLWinogradInputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, winograd_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), winograd_info).first); |
| |
| return Status{}; |
| } |
| |
| void CLWinogradInputTransformKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| Window slice = window.first_slice_window_3D(); |
| slice.set(Window::DimX, Window::Dimension(0, _num_tiles_x, 1)); |
| slice.set(Window::DimY, Window::Dimension(0, _num_tiles_y, 1)); |
| |
| ARM_COMPUTE_ERROR_ON(((slice.z().end() - slice.z().start()) % _step_z) != 0); |
| slice.set(Window::DimZ, Window::Dimension(slice.z().start(), slice.z().end(), _step_z)); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window.slide_window_slice_3D(slice)); |
| } |