diff options
author | ramelg01 <ramy.elgammal@arm.com> | 2022-06-29 16:28:10 +0100 |
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committer | Ramy Elgammal <ramy.elgammal@arm.com> | 2022-07-14 17:56:01 +0000 |
commit | a1f7851e2f776610019db8725c2963c36b0c85eb (patch) | |
tree | eddf90d87594bec2a88d9ad76bf4d03907ff5958 /src/cpu/kernels/CpuWinogradConv2dKernel.cpp | |
parent | 4bfc70e31766587c951204c93a127a486e007d0c (diff) | |
download | ComputeLibrary-a1f7851e2f776610019db8725c2963c36b0c85eb.tar.gz |
Integrate new winograd APIs from MLTech
Resolves: COMPMID-5400
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: Ib4428436dd7a6e40d8b2d8a2f8dac1b079154551
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7894
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/cpu/kernels/CpuWinogradConv2dKernel.cpp')
-rw-r--r-- | src/cpu/kernels/CpuWinogradConv2dKernel.cpp | 568 |
1 files changed, 66 insertions, 502 deletions
diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp index 803af09a67..818d878119 100644 --- a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp +++ b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,531 +21,95 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/cpu/kernels/CpuWinogradConv2dKernel.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> +#include "src/cpu/kernels/CpuWinogradConv2dKernel.h" namespace arm_compute { namespace cpu { -//Batched Gemms - -namespace -{ -inline bool is_kernel_size_supported(DataType data_type, Size2D size) +CpuWinogradConv2dTransformInputKernel::CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads) + : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads } { - 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) +void CpuWinogradConv2dTransformInputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &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_UNUSED(window); + const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC); + const ITensor *winograd_input_transform = tensors.get_const_tensor(TensorType::ACL_DST); + const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT); - 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)); + const unsigned int width_idx = 1; + const unsigned int height_idx = 2; + const unsigned int batch_idx = 3; + int element_size_in_bytes = input_nhwc->info()->element_size(); + const auto src_strides = input_nhwc->info()->strides_in_bytes(); - // 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)); + const size_t input_row_stride = src_strides[height_idx] / element_size_in_bytes; + const size_t input_col_stride = src_strides[width_idx] / element_size_in_bytes; + const size_t input_batch_stride = src_strides[batch_idx] / element_size_in_bytes; + const auto input_nhwc_ptr = reinterpret_cast<const void *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes()); + auto win_transf_ptr = reinterpret_cast<void *>(winograd_input_transform->buffer() + winograd_input_transform->info()->offset_first_element_in_bytes()); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - - return Status{}; + _winograd_impl.input_transform->execute( + _conv_args, + input_nhwc_ptr, + input_batch_stride, + input_row_stride, + input_col_stride, + win_transf_ptr, + _winograd_impl.winograd_spec, + workspace->buffer(), + info.thread_id, + _nthreads); } -std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) +CpuWinogradConv2dTransformOutputKernel::CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads) + : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads } { - // 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) +// Inherited methods overridden: +void CpuWinogradConv2dTransformOutputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &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_UNUSED(window); + const ITensor *dst_nhwc = tensors.get_const_tensor(TensorType::ACL_DST); + const ITensor *winograd_output_transform = tensors.get_const_tensor(TensorType::ACL_SRC_0); + const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_1); + const ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - - return Status{}; -} + const unsigned int width_idx = 1; + const unsigned int height_idx = 2; + const unsigned int batch_idx = 3; + const int element_size_in_bytes = dst_nhwc->info()->element_size(); + const auto dst_strides = dst_nhwc->info()->strides_in_bytes(); -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 size_t out_row_stride = dst_strides[height_idx] / element_size_in_bytes; + const size_t out_col_stride = dst_strides[width_idx] / element_size_in_bytes; + const size_t out_batch_stride = dst_strides[batch_idx] / element_size_in_bytes; + const auto wout_transf_ptr = reinterpret_cast<const void *>(winograd_output_transform->buffer() + winograd_output_transform->info()->offset_first_element_in_bytes()); + auto dst_nhwc_ptr = reinterpret_cast<void *>(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes()); + void *biases_data_ptr = nullptr; + if(biases != nullptr) { - 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); + biases_data_ptr = reinterpret_cast<void *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()); } - 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 ICpuWinogradConv2dTransformWeightsKernel::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 CpuWinogradConv2dTransformWeightsKernel<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); - // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T - return static_cast<unsigned int>(WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T)); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformWeightsKernel() - : _transform(nullptr), _num_output_channels(0), _matrix_stride(0) -{ -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int CpuWinogradConv2dTransformWeightsKernel<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 CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensorInfo *weights_hwio, - ITensorInfo *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. */ -{ - ARM_COMPUTE_UNUSED(weights_hwio, output); - - _transform = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels); - _num_output_channels = num_output_channels; - _matrix_stride = matrix_stride; - - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - ICpuKernel::configure(win); -} -#endif /* DOXYGEN_SKIP_THIS */ - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON(tensors.empty()); - - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - - const ITensor *weights_hwio = tensors.get_const_tensor(TensorType::ACL_SRC); - ITensor *output = tensors.get_tensor(TensorType::ACL_DST); - - _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 CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const -{ - return false; -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status CpuWinogradConv2dTransformWeightsKernel<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 CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>; - -template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>; -template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class CpuWinogradConv2dTransformWeightsKernel<__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 CpuWinogradConv2dTransformInputKernel<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 CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const -{ - return _transform->get_working_space_size(num_threads); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int CpuWinogradConv2dTransformInputKernel<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> -CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformInputKernel() - : _transform(nullptr), _num_channels(0), _matrix_stride(0) -{ -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensorInfo *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. */ - ITensorInfo *output, /* Base of output matrices. */ - const int matrix_stride, /* Stride between output matrices. */ - ITensorInfo *workspace) -{ - ARM_COMPUTE_UNUSED(input_nhwc, output, matrix_stride, workspace); - - _num_channels = num_channels; - _matrix_stride = matrix_stride; - - const int padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0; - const int padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0; - const int padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0; - const int 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)); - ICpuKernel::configure(win); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON(tensors.empty()); - - const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC); - const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT); - ITensor *output = tensors.get_tensor(TensorType::ACL_DST); - - 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 CpuWinogradConv2dTransformInputKernel<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 CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>; -template class CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>; -template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>; -template class CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>; -template class CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>; - -template class CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>; -template class CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>; -template class CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>; -template class CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class CpuWinogradConv2dTransformInputKernel<__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 CpuWinogradConv2dTransformOutputKernel<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> -CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformOutputKernel() - : _transform(nullptr), _matrix_stride(0), _matrix_row_stride(0) -{ + // Output transform + _winograd_impl.output_transform->execute( + _conv_args, + wout_transf_ptr, + _winograd_impl.winograd_spec, + biases_data_ptr, + dst_nhwc_ptr, + out_batch_stride, + out_row_stride, + out_col_stride, + workspace->buffer(), + info.thread_id, + _nthreads); } -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const -{ - return _transform->get_working_space_size(num_threads); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int CpuWinogradConv2dTransformOutputKernel<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> CpuWinogradConv2dTransformOutputKernel<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 CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensorInfo *biases, - const ITensorInfo *transformed_output, - const int matrix_stride, - ITensorInfo *output_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - ITensorInfo *workspace, - const arm_gemm::Activation &activation) -{ - ARM_COMPUTE_UNUSED(biases, transformed_output, output_nhwc, num_batches, num_rows, num_cols, workspace, activation); - - _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK); - - // 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)); - - ICpuKernel::configure(win); -} - -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON(tensors.empty()); - - const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_0); - const ITensor *transformed_output = tensors.get_const_tensor(TensorType::ACL_SRC_1); - ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT); - ITensor *dst_nhwc = tensors.get_tensor(TensorType::ACL_DST); - - const int out_batch_stride = dst_nhwc->info()->strides_in_bytes()[3] / sizeof(T); - const int out_row_stride = dst_nhwc->info()->strides_in_bytes()[2] / sizeof(T); - const int out_col_stride = dst_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(dst_nhwc->buffer() + dst_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 CpuWinogradConv2dTransformOutputKernel<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 CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>; -template class CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>; -template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>; -template class CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>; -template class CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>; - -template class CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>; -template class CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>; -template class CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>; -template class CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>; - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template class CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC } // namespace cpu -} // namespace arm_compute +} // namespace arm_compute
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