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Diffstat (limited to 'src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp | 548 |
1 files changed, 0 insertions, 548 deletions
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp deleted file mode 100644 index be34980663..0000000000 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ /dev/null @@ -1,548 +0,0 @@ -/* - * Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/NEON/kernels/convolution/common/utils.hpp" -#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <memory> - -namespace arm_compute -{ -//Batched Gemms - -namespace -{ -inline bool is_kernel_size_supported(DataType data_type, Size2D size) -{ - const std::array<Size2D, 8> f32_support = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } }; - const std::array<Size2D, 8> f16_support = { { Size2D(3, 3) } }; - - switch(data_type) - { - case DataType::F16: - return std::end(f16_support) != std::find(std::begin(f16_support), std::end(f16_support), size); - case DataType::F32: - return std::end(f32_support) != std::find(std::begin(f32_support), std::end(f32_support), size); - default: - return false; - } -} - -Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - - const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - const auto input_width = input->dimension(idx_width); - const auto input_height = input->dimension(idx_height); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(input_width, input_height)), - "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported"); - ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); - const Size2D &output_tile = winograd_info.output_tile_size; - const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } }; - ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile)); - - // Checks performed when output is configured - if(output->total_size() != 0) - { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_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<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info))); - const Window win = calculate_max_window(*input, Steps(), true /* skip border*/); - return std::make_pair(Status{}, win); -} - -Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const Size2D &kernel_dims = winograd_info.kernel_size; - const PadStrideInfo &conv_info = winograd_info.convolution_info; - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - 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(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)), - "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); - - // 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_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); - return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true)); -} - -Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const PadStrideInfo &conv_info = winograd_info.convolution_info; - const Size2D kernel_dims = winograd_info.kernel_size; - - // Number of tiles along the X and Y direction - const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float> - (winograd_info.output_tile_size.width)); - const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float> - (winograd_info.output_tile_size.height)); - const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y); - - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)), - "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); - - const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } }; - ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2))); - ARM_COMPUTE_UNUSED(kernel_dims); - if(bias != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1)); - } - - // Checks performed when output is configured - if(output->total_size() != 0) - { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::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<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info))); - - return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true)); -} -} // namespace - -Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - const DataLayout data_layout = input->data_layout(); - const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(weights->dimension(width_idx), weights->dimension(height_idx))), - "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - return Status{}; -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const -{ - const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels); - return static_cast<unsigned int>( - // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T - WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T)); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() - : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) -{ -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const -{ - return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels); -} - -#ifndef DOXYGEN_SKIP_THIS -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *weights_hwio, - ITensor *output, - const int matrix_stride, /** Stride across matrices in the output. */ - const int num_output_channels, /** Number of filters. */ - const int num_input_channels) /** Number of channels in each filter. */ -{ - _weights_hwio = weights_hwio; - _output = output; - _matrix_stride = matrix_stride; - _num_output_channels = num_output_channels; - _num_input_channels = num_input_channels; - _transform = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels); - - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} -#endif /* DOXYGEN_SKIP_THIS */ - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->set_weight_tensor(_weights_hwio->buffer()); - const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK); - _transform->set_output_matrices(_output->buffer(), _matrix_stride, matrix_row_stride); - _transform->set_working_space(_output->buffer()); - - _transform->run(fst, lst); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const -{ - return false; -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, - const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first); - return Status{}; -} - -template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>; - -template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -// Input transform - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( - int num_batches, /* Number of batches in the input tensor. */ - int num_channels, /* Number of feature maps in the input tensor. */ - int num_rows, /* Number of rows in each feature map. */ - int num_cols, /* Number of columns in each feature map. */ - bool same_padding /* Use "SAME" padding, otherwise use "VALID". */ -) const -{ - // Construct shapes for the input and kernel tensors. - const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels); - const KernelShape kern_shape(1, KernelRows, KernelCols, num_channels); - // Return the size, converted into units of TIn - return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T)); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const -{ - return _transform->get_working_space_size(num_threads) / sizeof(T); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( - int num_batches, /* Number of batches in the input tensor. */ - int num_channels, /* Number of feature maps in the input tensor. */ - int num_rows, /* Number of rows in each feature map. */ - int num_cols, /* Number of columns in each feature map. */ - bool same_padding /* Use "SAME" padding, otherwise use "VALID". */) const -{ - return WinogradConv::get_input_matrix_stride(num_batches, num_rows, num_cols, num_channels, same_padding); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() - : _transform(nullptr), _input_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0), _padding_top(), _padding_left(), - _padding_right(), _padding_bottom(), _workspace(nullptr) -{ -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *input_nhwc, - const int num_batches, /* Number of batches in input tensor. */ - const int num_rows, /* Number of rows in input tensor. */ - const int num_cols, /* Number of columns in input tensor. */ - const int num_channels, /* Number of channels in input tensor. */ - const PaddingType padding, /* Padding type. */ - ITensor *output, /* Base of output matrices. */ - const int matrix_stride, /* Stride between output matrices. */ - ITensor *workspace) -{ - _input_nhwc = input_nhwc; - _num_batches = num_batches; - _num_rows = num_rows; - _num_cols = num_cols; - _num_channels = num_channels; - _padding = padding; - _output = output; - _matrix_stride = matrix_stride; - _workspace = workspace; - - _padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0; - _padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0; - _padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0; - _padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0; - - _transform = std::make_unique<InputTransform>( - KernelRows, - KernelCols, - num_batches, - num_rows, - num_cols, - num_channels, - _padding_top, /**< Padding to apply to the top of the image. */ - _padding_left, /**< Padding to apply to the left of the image. */ - _padding_bottom, /**< Padding to apply to the bottom of the image. */ - _padding_right /**< Padding to apply to the right of the image. */ - ); - - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace); - - const int element_size_in_bytes = _input_nhwc->info()->element_size(); - const int input_col_stride = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes; - const int input_row_stride = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes; - const int input_batch_stride = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes; - const auto input_nhwc_ptr = reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes()); - auto output_ptr = reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes()); - ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr); - - _transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride); - _transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels); - - _transform->set_working_space(_workspace->buffer()); - - // The code below cannot be moved to configure because biases hasn't been allocated at that point - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->run(fst, lst, info.thread_id); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first); - - return Status{}; -} - -template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>; - -template class NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -// Output transform - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( - int num_batches, /* Number of batches in the output tensor. */ - int num_rows, /* Number of rows in each feature map of the input tensor. */ - int num_cols, /* Number of columns in each feature map of the input tensor. */ - int num_output_channels /* Number of feature maps in the output tensor. */ -) const -{ - // Construct shapes for the input and kernel tensors. - const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1); - const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1); - // Return the size, converted into units of TOut - return static_cast<unsigned int>( - WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T)); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() - : _transform(nullptr), _biases(nullptr), _transformed_output(nullptr), _workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), - _num_cols(0), _num_channels(0) -{ -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const -{ - return _transform->get_working_space_size(num_threads) / sizeof(T); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( - int num_batches, /* Number of batches in the output tensor. */ - int num_rows, /* Number of rows in each feature map of the input tensor. */ - int num_cols, /* Number of columns in each feature map of the input tensor. */ - int num_output_channels /* Number of feature maps in the output tensor. */ -) const -{ - return WinogradConv::get_output_matrix_stride(num_batches, num_rows, num_cols, num_output_channels); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( - int num_rows, /* Number of rows in each feature map of the input tensor. */ - int num_cols, /* Number of columns in each feature map of the input tensor. */ - bool padding_same) const -{ - return WinogradConv::get_output_shape(std::make_pair<unsigned int, unsigned int>(num_rows, num_cols), padding_same); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *biases, - const ITensor *transformed_output, - const int matrix_stride, - ITensor *output_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - ITensor *workspace, - const arm_gemm::Activation &activation) -{ - _biases = biases; - _workspace = workspace; - _transformed_output = transformed_output; - _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK); - _output_nhwc = output_nhwc; - _num_batches = num_batches; - _num_rows = num_rows; - _num_cols = num_cols; - _num_channels = num_channels; - // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window - _transform = std::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation); - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - - INEKernel::configure(win); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace); - ARM_COMPUTE_ERROR_ON_NULLPTR(_transformed_output); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc); - - const int out_batch_stride = _output_nhwc->info()->strides_in_bytes()[3] / sizeof(T); - const int out_row_stride = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T); - const int out_col_stride = _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T); - - _transform->set_input_matrices(_transformed_output->buffer(), _matrix_stride, _matrix_row_stride); - _transform->set_bias((_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr)); - _transform->set_output_tensor(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride); - _transform->set_working_space(_workspace->buffer()); - // The code below cannot be moved to configure because biases hasn't been allocated at that point - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->run(fst, lst, info.thread_id); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), output->clone().get(), winograd_info).first); - - return Status{}; -} - -template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>; - -template class NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -} // namespace arm_compute |