diff options
| author | Pablo Tello <pablo.tello@arm.com> | 2019-03-27 09:28:32 +0000 |
|---|---|---|
| committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-04-16 11:31:40 +0000 |
| commit | 8f43d745b170aefca269a087fc045d8af3813c33 (patch) | |
| tree | 08df4a26c3fab575eb9bdf061be89d2a71fb3581 /src/core | |
| parent | 9e4824c909b14dbaf7106e9527b0ffa22ef09bdc (diff) | |
| download | ComputeLibrary-8f43d745b170aefca269a087fc045d8af3813c33.tar.gz | |
COMPMID-2063: New Winograd implementation
Refactoring of winograd code reducing the size of the binaries
about 8X.
Change-Id: If8845bda324573e1a5cf436f354ac8603e88a92e
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/959
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Anthony Barbier <Anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'src/core')
32 files changed, 5765 insertions, 3372 deletions
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp index 3e76a080fd..263ded0b84 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -238,8 +238,7 @@ unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTile template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() - : _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) - + : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) { } @@ -263,11 +262,10 @@ void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, Ke _matrix_stride = matrix_stride; _num_output_channels = num_output_channels; _num_input_channels = num_input_channels; + _transform = arm_compute::support::cpp14::make_unique<WeightsTransform>(num_output_channels, num_input_channels); - const int matrix_row_stride = roundup(num_output_channels, WinogradConv::N_BLOCK); - WeightsTransform transform(nullptr, nullptr, matrix_stride, matrix_row_stride, num_output_channels, num_input_channels); - Window win; - auto win_last = transform.get_window(); + Window win; + auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } @@ -278,12 +276,14 @@ void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, Ke { 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()); - const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK); - WeightsTransform transform(reinterpret_cast<T *>(_weights_hwio->buffer()), reinterpret_cast<T *>(_output->buffer()), _matrix_stride, matrix_row_stride, _num_output_channels, _num_input_channels); - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - transform.run(fst, lst); + _transform->run(fst, lst); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -331,6 +331,12 @@ unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCo } 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( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const { @@ -339,7 +345,8 @@ int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kerne template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() - : _input_nhwc(), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0) + : _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) { } @@ -352,7 +359,8 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern 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. */ + const int matrix_stride, /* Stride between output matrices. */ + ITensor *workspace) { _input_nhwc = input_nhwc; _num_batches = num_batches; @@ -362,9 +370,28 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern _padding = padding; _output = output; _matrix_stride = matrix_stride; - InputTransform transform(nullptr, num_batches, num_rows, num_cols, num_channels, padding, nullptr, matrix_stride, num_channels); - Window win; - auto win_last = transform.get_window(); + _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 = arm_compute::support::cpp14::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); } @@ -374,22 +401,25 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern { 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()); - InputTransform input_transform(input_nhwc_ptr, - _num_batches, _num_rows, _num_cols, _num_channels, _padding, - output_ptr, - _matrix_stride, _num_channels, input_batch_stride, input_row_stride, input_col_stride); + 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(); - input_transform.run(fst, lst); + _transform->run(fst, lst, info.thread_id); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -435,11 +465,18 @@ unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileC template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() - : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0) + : _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( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const { @@ -455,28 +492,29 @@ Tensor4DShape NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTile template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( const ITensor *biases, - const ITensor *output_workingspace, + 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) -{ - _biases = biases; - _output_workspace = output_workingspace; - _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; + const int num_channels, + ITensor *workspace) +{ + _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 - OutputTransform output_transform(nullptr, _matrix_stride, _matrix_row_stride, nullptr, nullptr, _num_batches, _num_rows, _num_cols, _num_channels); - + _transform = arm_compute::support::cpp14::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels); Window win; - auto win_last = output_transform.get_window(); + auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); _output_nhwc->info()->set_valid_region(ValidRegion(Coordinates(), _output_nhwc->info()->tensor_shape())); @@ -488,22 +526,22 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); + 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 = 0; + 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); - OutputTransform output_transform(reinterpret_cast<T *>(_output_workspace->buffer()), _matrix_stride, _matrix_row_stride, - (_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr), - reinterpret_cast<T *>(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes()), - _num_batches, _num_rows, _num_cols, _num_channels, out_batch_stride, out_row_stride, out_col_stride); - + _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(); - output_transform.run(fst, lst); + _transform->run(fst, lst, info.thread_id); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> diff --git a/src/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.cpp b/src/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.cpp deleted file mode 100644 index ac83bf9dd2..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.cpp +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/gemm.hpp" - -using namespace winograd; - -template <const int MB, const int NB, typename TIn, typename TOut> -BatchedBlockedGemm<MB, NB, TIn, TOut>::BatchedBlockedGemm( - const unsigned int n_gemms, - const int M, const int K, const int N, - const int a_matrix_stride, - const int a_row_stride, - const int b_matrix_stride, - const int b_row_stride, - const int c_matrix_stride, - const int c_row_stride, - const TIn* const a_ptr, - const TIn* const b_ptr, - TOut* const c_ptr -) : n_gemms(n_gemms), M(M), N(N), K(K), - a_matrix_stride(a_matrix_stride), - a_row_stride(a_row_stride), - b_matrix_stride(b_matrix_stride), - b_row_stride(b_row_stride), - c_matrix_stride(c_matrix_stride), - c_row_stride(c_row_stride), - a_ptr(a_ptr), b_ptr(b_ptr), c_ptr(c_ptr) -{ -} - -template <const int MBlock, const int NBlock, typename TIn, typename TOut> -unsigned int BatchedBlockedGemm<MBlock, NBlock, TIn, TOut>::get_window() const -{ - return n_gemms; -} - -template <const int MBlock, const int NBlock, typename TIn, typename TOut> -void BatchedBlockedGemm<MBlock, NBlock, TIn, TOut>::run( - const unsigned int start, const unsigned int stop -) -{ - // Perform the specified GEMMs - for (unsigned int i = start; i < stop; i++) - { - // Get pointers to the relevant matrices - const TIn* const mtr_a = a_ptr + i*a_matrix_stride; - const TIn* const mtr_b = b_ptr + i*b_matrix_stride; - TOut* const mtr_c = c_ptr + i*c_matrix_stride; - - // Perform the GEMM - BlockedGemm<MBlock, NBlock, TIn, TOut>( - mtr_a, mtr_b, mtr_c, M, K, N, - a_row_stride, b_row_stride, c_row_stride - ); - } -} - -template class winograd::BatchedBlockedGemm<4, 16, float, float>; - diff --git a/src/core/NEON/kernels/convolution/winograd/padding.cpp b/src/core/NEON/kernels/convolution/winograd/padding.cpp new file mode 100644 index 0000000000..46fe57c7c9 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/padding.cpp @@ -0,0 +1,166 @@ +/* + * Copyright (c) 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 <cstring> +#include <cstdint> + +#include "padding.hpp" + +namespace padding +{ + +template <typename T> +void copy_and_pad_tile( + const unsigned int tile_rows, + const unsigned int tile_cols, + const unsigned int n_channels, + const T* const inptr, + const unsigned int in_row_stride, + const unsigned int in_col_stride, + T* const outptr, + const unsigned int out_row_stride, + const unsigned int out_col_stride, + const unsigned int pad_top, + const unsigned int pad_left, + const unsigned int pad_bottom, + const unsigned int pad_right, + const T pad_value +) +{ + for (unsigned int out_i = 0; out_i < tile_rows; out_i++) + { + for (unsigned int out_j = 0; out_j < tile_cols; out_j++) + { + T* const output = outptr + out_i*out_row_stride + out_j*out_col_stride; + + if (out_i < pad_top || tile_rows - pad_bottom <= out_i || + out_j < pad_left || tile_cols - pad_right <= out_j) + { + for (unsigned int n = 0; n < n_channels; n++) + { + output[n] = pad_value; + } + } + else + { + const auto in_i = out_i - pad_top, in_j = out_j - pad_left; + const T* const input = inptr + in_i*in_row_stride + in_j*in_col_stride; + std::memcpy(output, input, n_channels * sizeof(T)); + } + } + } +} + +template void copy_and_pad_tile( + unsigned int, unsigned int, unsigned int, + const uint8_t *, unsigned int, unsigned int, + uint8_t *, unsigned int, unsigned int, + unsigned int, unsigned int, unsigned int, unsigned int, uint8_t +); + +template void copy_and_pad_tile( + unsigned int, unsigned int, unsigned int, + const float *, unsigned int, unsigned int, + float *, unsigned int, unsigned int, + unsigned int, unsigned int, unsigned int, unsigned int, float +); + +template <unsigned int TileRows, unsigned int TileCols> +void CopyCropped<TileRows, TileCols>::execute( + const size_t size, + const void * const inptr, + const size_t in_row_stride, + const size_t in_col_stride, + void * const outptr, + const size_t out_row_stride, + const size_t out_col_stride, + const unsigned int pad_top, + const unsigned int pad_left, + const unsigned int pad_bottom, + const unsigned int pad_right +) +{ + for (unsigned int out_i = 0, in_i = pad_top; in_i < TileRows - pad_bottom; out_i++, in_i++) + { + for (unsigned int out_j = 0, in_j = pad_left; in_j < TileCols - pad_right; out_j++, in_j++) + { + std::memcpy( + static_cast<uint8_t *>(outptr) + out_i*out_row_stride + out_j*out_col_stride, + static_cast<const uint8_t *>(inptr) + in_i*in_row_stride + in_j*in_col_stride, + size + ); + } + } +} + +template class CopyCropped<2, 2>; +template class CopyCropped<3, 3>; +template class CopyCropped<4, 4>; + +template <typename T> +void crop_and_copy_tile( + unsigned int tile_rows, + unsigned int tile_cols, + unsigned int n_channels, + const T *inptr, + unsigned int in_row_stride, + unsigned int in_col_stride, + T *outptr, + unsigned int out_row_stride, + unsigned int out_col_stride, + unsigned int crop_top, + unsigned int crop_left, + unsigned int crop_bottom, + unsigned int crop_right +) +{ + for (unsigned int out_i = 0, in_i = crop_top; in_i < tile_rows - crop_bottom; out_i++, in_i++) + { + for (unsigned int out_j = 0, in_j = crop_left; in_j < tile_cols - crop_right; out_j++, in_j++) + { + std::memcpy( + outptr + out_i*out_row_stride + out_j*out_col_stride, + inptr + in_i*in_row_stride + in_j*in_col_stride, + sizeof(T) * n_channels + ); + } + } +} + +template void crop_and_copy_tile( + unsigned int tile_rows, + unsigned int tile_cols, + unsigned int n_channels, + const float *inptr, + unsigned int in_row_stride, + unsigned int in_col_stride, + float *outptr, + unsigned int out_row_stride, + unsigned int out_col_stride, + unsigned int crop_top, + unsigned int crop_left, + unsigned int crop_bottom, + unsigned int crop_right +); + +} // namespace padding diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp deleted file mode 100644 index 893122cc45..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp +++ /dev/null @@ -1,376 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0> -void winograd_input_transform_6x6_fp32_process_tile( - int n_channels, - const float* const input_base, - const int input_row_stride, - const int input_col_stride, - float* const matrix_base, -const int matrix_stride, - const int _pad_top, - const int _pad_left, - const int _pad_bottom, - const int _pad_right -) -{ - const int pad_top = Specialized ? PadTop : _pad_top; - const int pad_left = Specialized ? PadLeft : _pad_left; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - constexpr int inner_tile_rows = 6; - constexpr int inner_tile_cols = 6; - - const int cells_i = inner_tile_rows - pad_bottom; - const int cells_j = inner_tile_cols - pad_right; - - float *outptr = matrix_base; - - // Get pointers into the input tile - const float *x_ptrs[inner_tile_rows][inner_tile_cols]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) - { - // Get a pointer into the row - const float* const row_ptr = input_base + xi*input_row_stride; - - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[i][j] = row_ptr + xj*input_col_stride; - } - } - - // Matrices used/computed in this kernel. - float x[inner_tile_rows][inner_tile_cols]; - float XTx[inner_tile_rows][inner_tile_cols]; - float U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = XTx[i][j] = 0.0f; - } - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used/computed in this kernel - float32x4_t x[inner_tile_rows][inner_tile_cols]; - float32x4_t XTx[inner_tile_rows][inner_tile_cols]; - float32x4_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = vdupq_n_f32(0.0f); - XTx[i][j] = vdupq_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1q_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 4; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used/computed in this kernel - float32x2_t x[inner_tile_rows][inner_tile_cols]; - float32x2_t XTx[inner_tile_rows][inner_tile_cols]; - float32x2_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = vdup_n_f32(0.0f); - XTx[i][j] = vdup_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 2; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = *(x_ptrs[i][j]++); - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - *(outptr + m*matrix_stride) = U[i][j]; - } - } - outptr++; - } -} -} - -namespace winograd -{ -template <int k> -using Tiles = InputTransformImplTiles<k, k, 6, 6, float>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_generic = winograd_input_transform_6x6_fp32_process_tile<false>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_unpadded = winograd_input_transform_6x6_fp32_process_tile<true>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_6x6_fp32_process_tile<true, 1, 0, 0, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 1, 0, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 4, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 5, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 6, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 6>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_generic = winograd_input_transform_6x6_fp32_process_tile<false>; - - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_unpadded = winograd_input_transform_6x6_fp32_process_tile<true>; - - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_6x6_fp32_process_tile<true, 2, 0, 0, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 2, 0, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 4, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 5, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 6, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 6>, -}; - -template class InputTransform<3, 3, 6, 6, float>; -template class InputTransform<5, 5, 6, 6, float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp deleted file mode 100644 index 597b074026..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp +++ /dev/null @@ -1,375 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_2x2_3x3_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - constexpr int OutputTileRows = 2, OutputTileCols = 2; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - const int cells_i = OutputTileRows - pad_bottom; - const int cells_j = OutputTileCols - pad_right; - - // Construct a map to the output cells - float *outptrs[OutputTileRows][OutputTileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vaddq_f32(vaddq_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsubq_f32(vsubq_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Load the bias vector - b = vld1q_f32(bptr); - bptr += 4; - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vadd_f32(vadd_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsub_f32(vsub_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Load the bias vector - b = vld1_f32(bptr); - bptr += 2; - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - } - - // Load the bias - b = *(bptr++); - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vaddq_f32(vaddq_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsubq_f32(vsubq_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vadd_f32(vadd_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsub_f32(vsub_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<3, 3, 4, 4, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_3x3_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_3x3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_2x2_3x3_fp32_process_tile<true, 1, 0> -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2x2_3x3_fp32_process_tile<true, 0, 1> -}; - -template class OutputTransform<3, 3, 4, 4, float>; -} // namespace winograd - diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp deleted file mode 100644 index 60d7181d97..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp +++ /dev/null @@ -1,369 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_2x2_5x5_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - constexpr int OutputTileRows = 2, OutputTileCols = 2; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - const int cells_i = 2 - pad_bottom; - const int cells_j = 2 - pad_right; - - // Construct a map to the output cells - float *outptrs[OutputTileRows][OutputTileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1q_f32(bptr); - bptr += 4; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1_f32(bptr); - bptr += 2; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - b = *(bptr++); - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<5, 5, 6, 6, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_5x5_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_5x5_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_2x2_5x5_fp32_process_tile<true, 1, 0> -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2x2_5x5_fp32_process_tile<true, 0, 1> -}; - -template class OutputTransform<5, 5, 6, 6, float>; -} // namespace winograd - diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp deleted file mode 100644 index 15cc04b352..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp +++ /dev/null @@ -1,428 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_4x4_3x3_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - constexpr int TileRows = 4, TileCols = 4; - - const int cells_i = TileRows - pad_bottom; - const int cells_j = TileCols - pad_right; - - // Construct a map to the output cells - float *outptrs[TileRows][TileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmlaq_n_f32(vaddq_f32(F[i][1], F[i][2]), vaddq_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmlaq_n_f32(vaddq_f32(FZ[1][j], FZ[2][j]), vaddq_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1q_f32(bptr); - bptr += 4; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmla_n_f32(vadd_f32(F[i][1], F[i][2]), vadd_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmla_n_f32(vadd_f32(FZ[1][j], FZ[2][j]), vadd_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1_f32(bptr); - bptr += 2; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - b = *(bptr++); - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmlaq_n_f32(vaddq_f32(F[i][1], F[i][2]), vaddq_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmlaq_n_f32(vaddq_f32(FZ[1][j], FZ[2][j]), vaddq_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmla_n_f32(vadd_f32(F[i][1], F[i][2]), vadd_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmla_n_f32(vadd_f32(FZ[1][j], FZ[2][j]), vadd_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<3, 3, 6, 6, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_4x4_3x3_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4x4_3x3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 1, 0>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 2, 0>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 3, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 1>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 2>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 3>, -}; - -template class OutputTransform<3, 3, 6, 6, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp deleted file mode 100644 index 85cf418656..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const float *inptrs[kernel_cols]; - for (int j = 0; j < kernel_cols; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[kernel_cols], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < kernel_cols; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36.0f; - V[1] = (w[1]*-1 + w[3]*-1 + w[5]*-1 + w[0]*1 + w[2]*1 + w[4]*1 + w[6]*1) / 48.0f; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1 + w[5]*1 + w[6]*1) / 48.0f; - V[3] = (w[0]*-1 + w[6]*-64 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8 + w[5]*32) / 120.0f; - V[4] = (w[0]*-1 + w[6]*-64 + w[5]*-32 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120.0f; - V[5] = (w[5]*-243 + w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[6]*729 + w[0]*1) / 720.0f; - V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[5]*243 + w[6]*729 + w[0]*1) / 720.0f; - V[7] = (w[6]*1) / 1.0f; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 2, 1, 7>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>; - template struct WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp deleted file mode 100644 index 6c71461f81..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp +++ /dev/null @@ -1,228 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - constexpr int inner_tile_i = 4; - constexpr int inner_tile_j = 4; - - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const auto weight_row_stride = 3 * weight_col_stride; - const float *inptrs[3][3]; - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; - } - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1q_f32(inptrs[i][j]); - inptrs[i][j] += 4; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = w[0][j]; - - // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[1][j] = vmulq_n_f32(vaddq_f32(vaddq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[2][j] = vmulq_n_f32(vaddq_f32(vsubq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - - // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][1] = vmulq_n_f32(vaddq_f32(vaddq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][2] = vmulq_n_f32(vaddq_f32(vsubq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = w[0][j]; - - // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[1][j] = vmul_n_f32(vadd_f32(vadd_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[2][j] = vmul_n_f32(vadd_f32(vsub_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - - // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][1] = vmul_n_f32(vadd_f32(vadd_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][2] = vmul_n_f32(vadd_f32(vsub_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = *(inptrs[i][j]++); - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = w[0][j]; - Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - *(outptr + m*matrix_stride) = V[i][j]; - } - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - const int channel_prod = shape.n_input_channels * shape.n_output_channels; - return 2 * 18 * channel_prod; - } - - template struct WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp deleted file mode 100644 index 2f4f6e1ba2..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp +++ /dev/null @@ -1,408 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const auto weight_row_stride = 5 * weight_col_stride; - const float *inptrs[5][5]; - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; - } - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_t w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = vld1q_f32(inptrs[i][j]); - inptrs[i][j] += 4; - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - // Ww[0][j] = w[0][j]/4.0f; - Ww[0][j] = vmulq_n_f32(w[0][j], 1.0f/4.0f); - - // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[1][j] = vmulq_n_f32( - vaddq_f32( - vaddq_f32( - vaddq_f32(w[1][j], w[0][j]), - vaddq_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - -1.0f/6.0f - ); - - // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; - Ww[2][j] = vmulq_n_f32( - vsubq_f32( - vaddq_f32( - vsubq_f32(w[1][j], w[0][j]), - vsubq_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - 1.0f/6.0f - ); - - // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[3][j] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vaddq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), - vaddq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vsubq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), - vsubq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[5][j] = w[4][j]; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - // V[i][0] = Ww[i][0]/4.0f; - V[i][0] = vmulq_n_f32(Ww[i][0], 1.0f/4.0f); - - // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][1] = vmulq_n_f32( - vaddq_f32( - vaddq_f32( - vaddq_f32(Ww[i][1], Ww[i][0]), - vaddq_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - -1.0f/6.0f - ); - - // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; - V[i][2] = vmulq_n_f32( - vsubq_f32( - vaddq_f32( - vsubq_f32(Ww[i][1], Ww[i][0]), - vsubq_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - 1.0f/6.0f - ); - - // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][3] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vaddq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), - vaddq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vsubq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), - vsubq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][5] = Ww[i][4]; - V[i][5] = Ww[i][4]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_t w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = vld1_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - // Ww[0][j] = w[0][j]/4.0f; - Ww[0][j] = vmul_n_f32(w[0][j], 1.0f/4.0f); - - // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[1][j] = vmul_n_f32( - vadd_f32( - vadd_f32( - vadd_f32(w[1][j], w[0][j]), - vadd_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - -1.0f/6.0f - ); - - // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; - Ww[2][j] = vmul_n_f32( - vsub_f32( - vadd_f32( - vsub_f32(w[1][j], w[0][j]), - vsub_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - 1.0f/6.0f - ); - - // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[3][j] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vadd_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), - vadd_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vsub_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), - vsub_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[5][j] = w[4][j]; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - // V[i][0] = Ww[i][0]/4.0f; - V[i][0] = vmul_n_f32(Ww[i][0], 1.0f/4.0f); - - // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][1] = vmul_n_f32( - vadd_f32( - vadd_f32( - vadd_f32(Ww[i][1], Ww[i][0]), - vadd_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - -1.0f/6.0f - ); - - // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; - V[i][2] = vmul_n_f32( - vsub_f32( - vadd_f32( - vsub_f32(Ww[i][1], Ww[i][0]), - vsub_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - 1.0f/6.0f - ); - - // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][3] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vadd_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), - vadd_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vsub_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), - vsub_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][5] = Ww[i][4]; - V[i][5] = Ww[i][4]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = *(inptrs[i][j]++); - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - Ww[0][j] = w[0][j]/4.0f; - Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - V[i][0] = Ww[i][0]/4.0f; - V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][5] = Ww[i][4]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - *(outptr + m*matrix_stride) = V[i][j]; - } - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - return 0; // TODO - } - - template class WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp deleted file mode 100644 index 2f14e20142..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const float *inptrs[kernel_cols]; - for (int j = 0; j < kernel_cols; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[kernel_cols], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < kernel_cols; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36; - V[1] = (w[1]*-1 + w[3]*-1 + w[0]*1 + w[2]*1 + w[4]*1) / 48; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1) / 48; - V[3] = (w[0]*-1 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8) / 120; - V[4] = (w[0]*-1 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120; - V[5] = (w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[0]*1) / 720; - V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[0]*1) / 720; - V[7] = (w[4]*1) / 1; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 4, 1, 5>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>; - template struct WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp deleted file mode 100644 index a56a475fc9..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp +++ /dev/null @@ -1,266 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - /* Float implementation for kernel transform F(4x4, 3x3) */ - template <> - template <> - void WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const auto weight_row_stride = 3 * weight_col_stride; - const float *inptrs[3][3]; - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; - } - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_t w[3][3], Ww[6][3], V[6][6]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1q_f32(inptrs[i][j]); - inptrs[i][j] += 4; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - // Ww[0][j] = 6*w[0][j]; - Ww[0][j] = vmulq_n_f32(w[0][j], 6.0); - - // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; - Ww[1][j] = vmulq_n_f32(vaddq_f32(vaddq_f32(w[0][j], w[1][j]), w[2][j]), -4.0); - - // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; - Ww[2][j] = vmulq_n_f32(vsubq_f32(vsubq_f32(w[1][j], w[0][j]), w[2][j]), 4.0); - - // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; - Ww[3][j] = vmlaq_n_f32(vmlaq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; - Ww[4][j] = vmlaq_n_f32(vmlsq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[5][j] = 24*w[2][j]; - Ww[5][j] = vmulq_n_f32(w[2][j], 24.0f); - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - const float recip576 = 1.0f / 576.0f; - - // V[i][0] = 6*Ww[i][0]; - V[i][0] = vmulq_n_f32(vmulq_n_f32(Ww[i][0], 6.0), recip576); - - // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; - V[i][1] = vmulq_n_f32(vmulq_n_f32(vaddq_f32(vaddq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); - - // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; - V[i][2] = vmulq_n_f32(vmulq_n_f32(vsubq_f32(vsubq_f32(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); - - // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; - V[i][3] = vmulq_n_f32(vmlaq_n_f32(vmlaq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; - V[i][4] = vmulq_n_f32(vmlaq_n_f32(vmlsq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][5] = 24*Ww[i][2]; - V[i][5] = vmulq_n_f32(vmulq_n_f32(Ww[i][2], 24.0f), recip576); - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_t w[3][3], Ww[6][3], V[6][6]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - // Ww[0][j] = 6*w[0][j]; - Ww[0][j] = vmul_n_f32(w[0][j], 6.0); - - // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; - Ww[1][j] = vmul_n_f32(vadd_f32(vadd_f32(w[0][j], w[1][j]), w[2][j]), -4.0); - - // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; - Ww[2][j] = vmul_n_f32(vsub_f32(vsub_f32(w[1][j], w[0][j]), w[2][j]), 4.0); - - // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; - Ww[3][j] = vmla_n_f32(vmla_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; - Ww[4][j] = vmla_n_f32(vmls_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[5][j] = 24*w[2][j]; - Ww[5][j] = vmul_n_f32(w[2][j], 24.0f); - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - const float recip576 = 1.0f / 576.0f; - - // V[i][0] = 6*Ww[i][0]; - V[i][0] = vmul_n_f32(vmul_n_f32(Ww[i][0], 6.0), recip576); - - // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; - V[i][1] = vmul_n_f32(vmul_n_f32(vadd_f32(vadd_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); - - // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; - V[i][2] = vmul_n_f32(vmul_n_f32(vsub_f32(vsub_f32(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); - - // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; - V[i][3] = vmul_n_f32(vmla_n_f32(vmla_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; - V[i][4] = vmul_n_f32(vmla_n_f32(vmls_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][5] = 24*Ww[i][2]; - V[i][5] = vmul_n_f32(vmul_n_f32(Ww[i][2], 24.0f), recip576); - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[3][3], Ww[6][3], V[6][6]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = *(inptrs[i][j]++); - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = 6*w[0][j]; - Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; - Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; - Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; - Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; - Ww[5][j] = 24*w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - V[i][0] = ( 6*Ww[i][0]) / 576.0; - V[i][1] = (-4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]) / 576.0; - V[i][2] = (-4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]) / 576.0; - V[i][3] = ( 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]) / 576.0; - V[i][4] = ( 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]) / 576.0; - V[i][5] = (24*Ww[i][2]) / 576.0; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - *(outptr + m*matrix_stride) = V[i][j]; - } - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - const int channel_prod = shape.n_input_channels * shape.n_output_channels; - return 9 * 16 * channel_prod; - } - - template struct WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp deleted file mode 100644 index c560aa8c8f..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp +++ /dev/null @@ -1,125 +0,0 @@ -/* - * Copyright (c) 2017 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/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const float *inptrs[3]; - for (int j = 0; j < 3; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[3], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < 3; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36.0f; - V[1] = (w[1]*-1 + w[0]*1 + w[2]*1) / 48.0f; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1) / 48.0f; - V[3] = (w[0]*-1 + w[2]*-4 + w[1]*2) / 120.0f; - V[4] = (w[0]*-1 + w[2]*-4 + w[1]*-2) / 120.0f; - V[5] = (w[1]*-3 + w[2]*9 + w[0]*1) / 720.0f; - V[6] = (w[1]*3 + w[2]*9 + w[0]*1) / 720.0f; - V[7] = (w[2]*1) / 1; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 6, 1, 3>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>; - template struct WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/convolution/winograd/winograd.cpp index a7de2fd3e5..226f303c7d 100644 --- a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,14 +22,13 @@ * SOFTWARE. */ #include <cstring> -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp" +#include "winograd.hpp" using namespace winograd; /** Get the output shape of a convolution. */ -template <int kr, int kc, int itr, int itc> -template <typename TOut, typename TIn> -Tensor4DShape WinogradGEMM<kr, kc, itr, itc>::Convolution<TOut, TIn>::get_output_shape( +template <int kr, int kc, int itr, int itc, WinogradRoots R> +template <typename TOut, typename TIn, typename TInGEMM, typename TOutGEMM> +Tensor4DShape WinogradGEMM<kr, kc, itr, itc, R>::Convolution<TOut, TIn, TInGEMM, TOutGEMM>::get_output_shape( const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding @@ -47,9 +46,9 @@ Tensor4DShape WinogradGEMM<kr, kc, itr, itc>::Convolution<TOut, TIn>::get_output /* Get the memory required to transform the kernel. */ template <int kernel_rows, int kernel_cols, - int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_transform_working_size(const KernelShape &shape) + int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_kernel_transform_working_size(const KernelShape &shape) { if (shape.ordering == HWIO) { @@ -68,17 +67,17 @@ size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols /** Get the memory required to store the kernel transformed into the * Winograd domain. */ -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_storage_size(const KernelShape &shape) +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_kernel_storage_size(const KernelShape &shape) { return N_GEMMS * get_kernel_matrix_size(shape); } -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_storage_size( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_input_storage_size( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding @@ -88,9 +87,9 @@ size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols } -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_storage_size( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_output_storage_size( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding @@ -102,9 +101,9 @@ size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols /** Get the memory required to apply a Winograd operator to some input. */ -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_working_space_size( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_working_space_size( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type @@ -139,20 +138,20 @@ size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols /* Get the memory required by a single "input" matrix. */ -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_matrix_size( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_input_matrix_size( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type ) { - return get_input_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TIn); + return get_input_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TGIn); } -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_matrix_stride( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_input_matrix_stride( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type @@ -171,21 +170,21 @@ int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>:: /* Get the memory required by a single "output" matrix. */ -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_matrix_size( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_output_matrix_size( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type ) { - return get_output_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TOut); + return get_output_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TGOut); } -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_matrix_stride( +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_output_matrix_stride( const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type @@ -204,16 +203,16 @@ int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>:: /* Get the memory required by a single "kernel" matrix. */ -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_matrix_size(const KernelShape &shape) +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_kernel_matrix_size(const KernelShape &shape) { - return sizeof(TIn) * get_kernel_matrix_stride(shape); + return sizeof(TGIn) * get_kernel_matrix_stride(shape); } -template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols> -template <typename TOut, typename TIn> -int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_matrix_stride(const KernelShape &shape) +template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols, WinogradRoots roots> +template <typename TOut, typename TIn, typename TGIn, typename TGOut> +int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols, roots>::Convolution<TOut, TIn, TGIn, TGOut>::get_kernel_matrix_stride(const KernelShape &shape) { const int K = shape.n_input_channels; const int N = roundup(shape.n_output_channels, N_BLOCK); @@ -222,19 +221,16 @@ int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>:: // Instantiate required implementations -template class WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>; -template class WinogradGEMM<4, 4, 3, 3>::Convolution<float, float>; +template class WinogradGEMM<2, 2, 3, 3, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<4, 4, 3, 3, WinogradRoots::Integers>::Convolution<float, float, float, float>; -template class WinogradGEMM<1, 6, 1, 3>::Convolution<float, float>; -template class WinogradGEMM<6, 1, 3, 1>::Convolution<float, float>; - -template class WinogradGEMM<2, 2, 5, 5>::Convolution<float, float>; - -template class WinogradGEMM<1, 4, 1, 5>::Convolution<float, float>; -template class WinogradGEMM<4, 1, 5, 1>::Convolution<float, float>; - -template class WinogradGEMM<1, 2, 1, 7>::Convolution<float, float>; -template class WinogradGEMM<2, 1, 7, 1>::Convolution<float, float>; +template class WinogradGEMM<1, 6, 1, 3, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<6, 1, 3, 1, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<2, 2, 5, 5, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<1, 4, 1, 5, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<4, 1, 5, 1, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<1, 2, 1, 7, WinogradRoots::Integers>::Convolution<float, float, float, float>; +template class WinogradGEMM<2, 1, 7, 1, WinogradRoots::Integers>::Convolution<float, float, float, float>; diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input.hpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input.hpp new file mode 100644 index 0000000000..fcbd21fcd0 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input.hpp @@ -0,0 +1,265 @@ +/* + * Copyright (c) 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. + */ + +#pragma once + +#include "winograd.hpp" +#include "padding.hpp" + +#define MEMBERFN(RTYPE) template <\ + int InnerTileRows, int InnerTileCols,\ + typename TIn, typename TOut, WinogradRoots Roots\ +> RTYPE InputTransform<InnerTileRows, InnerTileCols, TIn, TOut, Roots> + + +#define Nx1MEMBERFN(RTYPE) template <\ + int InnerTileRows, typename TIn, typename TOut, WinogradRoots Roots\ +> RTYPE InputTransform<InnerTileRows, 1, TIn, TOut, Roots> + +namespace winograd +{ + +MEMBERFN()::InputTransform( + const int kernel_rows, + const int kernel_cols, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels, + const int padding_top, + const int padding_left, + const int padding_bottom, + const int padding_right +) : _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels), + _inptr(nullptr), _outptr(nullptr), + _overlap_rows(kernel_rows - 1), _overlap_cols(kernel_cols - 1), + _padding_top(padding_top), _padding_left(padding_left), _padding_bottom(padding_bottom), _padding_right(padding_right), + _tiles_M(iceildiv(padding_top + n_rows + padding_bottom - kernel_rows + 1, InnerTileRows - kernel_rows + 1)), + _tiles_N(iceildiv(padding_left + n_cols + padding_right - kernel_cols + 1, InnerTileCols - kernel_cols + 1)), + _matrix_stride(0), _matrix_row_stride(0), _matrix_batch_stride(0), + _in_col_stride(0), _in_row_stride(0), _in_batch_stride(0), + _working_space_col_stride(n_channels), + _working_space_row_stride(InnerTileCols * _working_space_col_stride), + _working_space(nullptr) +{ +} + +MEMBERFN(void)::set_input_tensor(const void* const inptr) +{ + set_input_tensor(inptr, _n_channels); +} + +MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldcol) +{ + set_input_tensor(inptr, _n_cols * ldcol, ldcol); +} + +MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldrow, const int ldcol) +{ + set_input_tensor(inptr, _n_rows * ldrow, ldrow, ldcol); +} + +MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldbatch, const int ldrow, const int ldcol) +{ + _inptr = static_cast<const TIn *>(inptr); + _in_batch_stride = ldbatch; + _in_row_stride = ldrow; + _in_col_stride = ldcol; +} + +MEMBERFN(void)::set_output_matrices(void * const mptr, const int ldmatrix, const int ldrow) +{ + _outptr = static_cast<TOut *>(mptr); + _matrix_stride = ldmatrix; + _matrix_row_stride = ldrow; + _matrix_batch_stride = _tiles_M * _tiles_N * ldrow; +} + +Nx1MEMBERFN()::InputTransform( + const int kernel_rows, + const int kernel_cols, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels, + const int padding_top, + const int padding_left, + const int padding_bottom, + const int padding_right +) : InputTransform<1, InnerTileRows, TIn, TOut, Roots>::InputTransform( + /* Transpose rows and columns */ + kernel_cols, kernel_rows, n_batches, n_cols, n_rows, n_channels, + padding_left, padding_top, padding_right, padding_bottom + ) +{ +} + +Nx1MEMBERFN(void)::set_input_tensor(const void* const inptr) +{ + set_input_tensor(inptr, this->_n_channels); +} + +Nx1MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldcol) +{ + set_input_tensor(inptr, this->_n_cols * ldcol, ldcol); +} + +Nx1MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldrow, const int ldcol) +{ + set_input_tensor(inptr, this->_n_rows * ldrow, ldrow, ldcol); +} + +Nx1MEMBERFN(void)::set_input_tensor(const void* const inptr, const int ldbatch, const int ldrow, const int ldcol) +{ + // Transpose row and column strides + Base::set_input_tensor(inptr, ldbatch, ldcol, ldrow); +} + +MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const +{ + return sizeof(TIn) * InnerTileRows * _working_space_row_stride * nthreads; +} + +MEMBERFN(void)::set_working_space(void * const buffer) +{ + _working_space = static_cast<TIn *>(buffer); +} + +MEMBERFN(unsigned int)::get_window(void) const +{ + return iceildiv(_n_channels, WINDOW_BLOCK); +} + +MEMBERFN(void)::run( + const unsigned int start, + const unsigned int stop, + const unsigned int threadid +) +{ + // Determine the channels on which to work + if (start >= get_window()) + { + return; // No work to do beyond the end of the window + } + const unsigned int start_channel = start * WINDOW_BLOCK; + const unsigned int stop_channel = std::min<unsigned int>(_n_channels , stop * WINDOW_BLOCK); + const unsigned int n_channels = stop_channel - start_channel; + + // Loop over batches + for (int batch = 0; batch < _n_batches; batch++) + { + const TIn* const inptr_batch = _inptr + start_channel + batch*_in_batch_stride; + TOut* const outptr_batch = _outptr + start_channel + batch*_matrix_batch_stride; + + // Loop over rows of tiles + for (int tile_i = 0; tile_i < _tiles_M; tile_i++) + { + // Compute the starting and ending row of pixels within the row of tiles, + // hence compute the padding to apply to the top and bottom of each tile. + const int row_top = tile_i * (InnerTileRows - _overlap_rows) - _padding_top; + const int row_bottom = row_top + InnerTileRows; + const int row_pad_top = std::max(0, _padding_top - tile_i * (InnerTileRows - _overlap_rows)); + const int row_pad_bottom = std::max(0, row_bottom - _n_rows); + + // Get a pointer to the start of the row. + const int row_offset = std::min(0, row_pad_top - _padding_top); + const TIn* const inptr_row = inptr_batch + _in_row_stride*(row_offset + tile_i*(InnerTileRows - _overlap_rows)); + TOut* const outptr_row = outptr_batch + tile_i*_tiles_N*_matrix_row_stride; + + // Loop over tiles within the row + for (int tile_j = 0; tile_j < _tiles_N; tile_j++) + { + // Compute the starting and ending column of pixels within the tile, + // hence compute the padding to apply to the left and right of the + // tile. + const int tile_left = tile_j * (InnerTileCols - _overlap_cols) - _padding_left; + const int tile_right = tile_left + InnerTileCols; + const int tile_pad_left = std::max(0, _padding_left - tile_j * (InnerTileCols - _overlap_cols)); + const int tile_pad_right = std::max(0, tile_right - _n_cols); + + // Get a pointer to the start of the tile. + const int col_offset = std::min(0, tile_pad_left - _padding_left); + const TIn* const inptr_tile = inptr_row + _in_col_stride*(col_offset + tile_j*(InnerTileCols - _overlap_cols)); + TOut* const outptr_tile = outptr_row + tile_j * _matrix_row_stride; + + // Transform the tile, applying padding if necessary. + if (row_pad_top || tile_pad_left || row_pad_bottom || tile_pad_right) + { + transform_padded_tile( + threadid, n_channels, outptr_tile, inptr_tile, + row_pad_top, tile_pad_left, row_pad_bottom, tile_pad_right + ); + } + else + { + transform_unpadded_tile(threadid, n_channels, outptr_tile, inptr_tile); + } + } + } + } +} + +MEMBERFN(void)::transform_unpadded_tile( + const unsigned int /* threadid unused */, + const int n_channels, + TOut * const outptr, + const TIn * const inptr +) +{ + transform_tile( + n_channels, inptr, _in_row_stride, _in_col_stride, outptr, _matrix_stride + ); +} + +MEMBERFN(void)::transform_padded_tile( + const unsigned int threadid, + const int n_channels, + TOut * const outptr, + const TIn * const inptr, + const int padding_top, + const int padding_left, + const int padding_bottom, + const int padding_right +) +{ + padding::copy_and_pad_tile( + InnerTileRows, InnerTileCols, n_channels, + inptr, _in_row_stride, _in_col_stride, + static_cast<TIn *>(get_working_space(threadid)), _working_space_row_stride, _working_space_col_stride, + padding_top, padding_left, padding_bottom, padding_right + ); + + transform_tile( + n_channels, static_cast<const TIn *>(get_working_space(threadid)), + _working_space_row_stride, _working_space_col_stride, + outptr, _matrix_stride + ); +} + +MEMBERFN(void *)::get_working_space(const unsigned int threadid) const +{ + return _working_space + InnerTileRows * _working_space_row_stride * threadid; +} + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_1x8_fp32_fp32_integers.cpp index e66300d39a..5040ec1bd4 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_1x8_fp32_fp32_integers.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,43 +22,27 @@ * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" +#include "arm.hpp" +#include "input.hpp" -namespace +namespace winograd { -template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0> -void winograd_input_transform_1x8_fp32_process_tile( - int n_channels, +template <> +void InputTransform<1, 8, float, float, WinogradRoots::Integers>::transform_tile( + const int n_channels, const float* const input_base, - const int input_row_stride, + const int, // We don't need to stride over rows const int input_col_stride, - float* const matrix_base, - const int matrix_stride, - const int _pad_top, - const int _pad_left, - const int _pad_bottom, - const int _pad_right + float* outptr, + const int matrix_stride ) { - (void) input_row_stride; // No rows over which to stride - (void) _pad_top; // Never any top padding - (void) _pad_bottom; // Never any bottom padding - - // Extract padding arguments - const int pad_left = Specialized ? PadLeft : _pad_left; - const int pad_right = Specialized ? PadRight : _pad_right; - constexpr int inner_tile_cols = 8; - const int cells_j = inner_tile_cols - pad_right; - - float *outptr = matrix_base; // Get pointers into the input tile const float *x_ptrs[inner_tile_cols]; - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) + for (int j = 0, xj = 0; j < inner_tile_cols; j++, xj++) { x_ptrs[j] = input_base + xj*input_col_stride; } @@ -75,7 +59,7 @@ void winograd_input_transform_1x8_fp32_process_tile( // Perform the Winograd input transformation for each channel in the input // tensor. int channels_remaining = n_channels; -#ifdef __arm_any__ +#ifdef _arm_any_ for (; channels_remaining >= 4; channels_remaining -= 4) { float32x4_t x[inner_tile_cols], U[inner_tile_cols]; @@ -85,7 +69,7 @@ void winograd_input_transform_1x8_fp32_process_tile( } // Load x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[j] = vld1q_f32(x_ptrs[j]); x_ptrs[j] += 4; @@ -117,7 +101,7 @@ void winograd_input_transform_1x8_fp32_process_tile( } // Load x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[j] = vld1_f32(x_ptrs[j]); x_ptrs[j] += 2; @@ -140,11 +124,11 @@ void winograd_input_transform_1x8_fp32_process_tile( } outptr += 2; } -#endif // __arm_any__ +#endif // _arm_any_ for (; channels_remaining; channels_remaining--) { // Load x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[j] = *(x_ptrs[j]++); } @@ -168,94 +152,7 @@ void winograd_input_transform_1x8_fp32_process_tile( } } -} - -namespace winograd -{ -template <int x> -using Tiles = InputTransformImplTiles<1, x, 1, 8, float>; - -/*****************************************************************************/ -// 1x3 specialisations -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 1, 0, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>, -}; -/*****************************************************************************/ - -/*****************************************************************************/ -// 1x5 specialisations -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 2, 0, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>, -}; -/*****************************************************************************/ - -/*****************************************************************************/ -// 1x7 specialisations -template <> -const Tiles<7>::TileFn Tiles<7>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>; - -template <> -const Tiles<7>::TileFn Tiles<7>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>; - -template <> -const Tiles<7>::TileFn Tiles<7>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 1, 0, 0>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 3, 0, 0>, -}; - -template <> -const Tiles<7>::TileFn Tiles<7>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>, - winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>, -}; -/*****************************************************************************/ - +template class InputTransform<1, 8, float, float, WinogradRoots::Integers>; +template class InputTransform<8, 1, float, float, WinogradRoots::Integers>; -template class InputTransform<1, 3, 1, 8, float>; -template class InputTransform<3, 1, 8, 1, float>; -template class InputTransform<1, 5, 1, 8, float>; -template class InputTransform<5, 1, 8, 1, float>; -template class InputTransform<1, 7, 1, 8, float>; -template class InputTransform<7, 1, 8, 1, float>; } // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_4x4_fp32_fp32_integers.cpp index 4203945dd3..9393785dfc 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_4x4_fp32_fp32_integers.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,67 +22,45 @@ * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" +#include "input.hpp" +#include "arm.hpp" namespace winograd { -using Tiles = InputTransformImplTiles<3, 3, 4, 4, float>; - -namespace -{ - - -template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0> -void winograd_input_transform_4x4_fp32_process_tile( - int n_channels, +template <> +void InputTransform<4, 4, float, float, WinogradRoots::Integers>::transform_tile( + const int n_channels, const float* const input_base, const int input_row_stride, const int input_col_stride, - float* const matrix_base, - const int matrix_stride, - const int _pad_top, - const int _pad_left, - const int _pad_bottom, - const int _pad_right - ) + float* outptr, + const int matrix_stride +) { -const int pad_top = Specialized ? PadTop : _pad_top; - const int pad_left = Specialized ? PadLeft : _pad_left; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - constexpr int inner_tile_i = 4, inner_tile_j = 4; - const int cells_i = inner_tile_i - pad_bottom; - const int cells_j = inner_tile_i - pad_right; - - - - float *outptr = matrix_base; + constexpr int inner_tile_rows = 4, inner_tile_cols = 4; // Get pointers into the input tile - const float *x_ptrs[inner_tile_i][inner_tile_j]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) + const float *x_ptrs[inner_tile_rows][inner_tile_cols]; + for (int i = 0, xi = 0; i < inner_tile_rows; i++, xi++) { // Get a pointer into the row const float* const row_ptr = input_base + xi*input_row_stride; - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) + for (int j = 0, xj = 0; j < inner_tile_cols; j++, xj++) { x_ptrs[i][j] = row_ptr + xj*input_col_stride; } } // Matrices used/computed in this kernel. - float x[inner_tile_i][inner_tile_j]; - float XTx[inner_tile_i][inner_tile_j]; - float U[inner_tile_i][inner_tile_j]; + float x[inner_tile_rows][inner_tile_cols]; + float XTx[inner_tile_rows][inner_tile_cols]; + float U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = XTx[i][j] = 0.0f; } @@ -95,13 +73,13 @@ const int pad_top = Specialized ? PadTop : _pad_top; for (; channels_remaining >= 4; channels_remaining -= 4) { // Matrices used/computed in this kernel. - float32x4_t x[inner_tile_i][inner_tile_j]; - float32x4_t XTx[inner_tile_i][inner_tile_j]; - float32x4_t U[inner_tile_i][inner_tile_j]; + float32x4_t x[inner_tile_rows][inner_tile_cols]; + float32x4_t XTx[inner_tile_rows][inner_tile_cols]; + float32x4_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = vdupq_n_f32(0.0f); XTx[i][j] = vdupq_n_f32(0.0f); @@ -109,9 +87,9 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Load x - for (int i = pad_top; i < cells_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = vld1q_f32(x_ptrs[i][j]); x_ptrs[i][j] += 4; @@ -119,7 +97,7 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Compute XT . x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { // XTx[0][j] = x[0][j] - x[2][j]; XTx[0][j] = vsubq_f32(x[0][j], x[2][j]); @@ -135,7 +113,7 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { // U[i][0] = XTx[i][0] - XTx[i][2]; U[i][0] = vsubq_f32(XTx[i][0], XTx[i][2]); @@ -151,9 +129,9 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) + for (int i = 0, m = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++, m++) + for (int j = 0; j < inner_tile_cols; j++, m++) { vst1q_f32(outptr + m*matrix_stride, U[i][j]); } @@ -165,13 +143,13 @@ const int pad_top = Specialized ? PadTop : _pad_top; for (; channels_remaining >= 2; channels_remaining -= 2) { // Matrices used/computed in this kernel. - float32x2_t x[inner_tile_i][inner_tile_j]; - float32x2_t XTx[inner_tile_i][inner_tile_j]; - float32x2_t U[inner_tile_i][inner_tile_j]; + float32x2_t x[inner_tile_rows][inner_tile_cols]; + float32x2_t XTx[inner_tile_rows][inner_tile_cols]; + float32x2_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = vdup_n_f32(0.0f); XTx[i][j] = vdup_n_f32(0.0f); @@ -179,9 +157,9 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Load x - for (int i = pad_top; i < cells_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = vld1_f32(x_ptrs[i][j]); x_ptrs[i][j] += 2; @@ -189,7 +167,7 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Compute XT . x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { // XTx[0][j] = x[0][j] - x[2][j]; XTx[0][j] = vsub_f32(x[0][j], x[2][j]); @@ -205,7 +183,7 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { // U[i][0] = XTx[i][0] - XTx[i][2]; U[i][0] = vsub_f32(XTx[i][0], XTx[i][2]); @@ -221,9 +199,9 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) + for (int i = 0, m = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++, m++) + for (int j = 0; j < inner_tile_cols; j++, m++) { vst1_f32(outptr + m*matrix_stride, U[i][j]); } @@ -234,16 +212,16 @@ const int pad_top = Specialized ? PadTop : _pad_top; for (; channels_remaining; channels_remaining--) { // Load x - for (int i = pad_top; i < cells_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { x[i][j] = *(x_ptrs[i][j]++); } } // Compute XT . x - for (int j = pad_left; j < cells_j; j++) + for (int j = 0; j < inner_tile_cols; j++) { XTx[0][j] = x[0][j] - x[2][j]; XTx[1][j] = x[1][j] + x[2][j]; @@ -252,7 +230,7 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) + for (int i = 0; i < inner_tile_rows; i++) { U[i][0] = XTx[i][0] - XTx[i][2]; U[i][1] = XTx[i][1] + XTx[i][2]; @@ -261,9 +239,9 @@ const int pad_top = Specialized ? PadTop : _pad_top; } // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) + for (int i = 0, m = 0; i < inner_tile_rows; i++) { - for (int j = 0; j < inner_tile_j; j++, m++) + for (int j = 0; j < inner_tile_cols; j++, m++) { *(outptr + m*matrix_stride) = U[i][j]; } @@ -272,40 +250,6 @@ const int pad_top = Specialized ? PadTop : _pad_top; } } -} // namespace (anonymous) - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_input_transform_4x4_fp32_process_tile<false>; +template class InputTransform<4, 4, float, float, WinogradRoots::Integers>; -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_input_transform_4x4_fp32_process_tile<true>; - - -template <> -const Tiles::TileFn Tiles::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_4x4_fp32_process_tile<true, 1, 0, 0, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 1, 0, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 4, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 4>, -}; - -template class InputTransform<3, 3, 4, 4, float>; -} // namespace winograd +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..908fc8292a --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp32_fp32_integers.cpp @@ -0,0 +1,1308 @@ +/* + * Copyright (c) 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.hpp" +#include "input.hpp" + +namespace winograd +{ + +#ifdef __aarch64__ + +template <> +void InputTransform<6, 6, float, float, WinogradRoots::Integers>::transform_tile( + int n_channels, + const float* input_base, + const int input_row_stride, + const int input_col_stride, + float* matrix_base, + const int matrix_stride +) +{ + const float pcoeffs[4] = {1.0f, 2.0f, 4.0f, 5.0f}; + __asm__ __volatile__( + "ldr q0, [%[pcoeffs]]\n" + "add x25, %[inptr0], %[input_row_stride]\n" + "add x18, %[input_col_stride1], %[input_col_stride1]\n" + "add x16, x25, %[input_row_stride]\n" + "add x19, x18, %[input_col_stride1]\n" + "add x26, x16, %[input_row_stride]\n" + "add x20, x19, %[input_col_stride1]\n" + "add x17, x26, %[input_row_stride]\n" + "add x21, x20, %[input_col_stride1]\n" + "add x27, x17, %[input_row_stride]\n" + "add x28, %[outptr0], %[output_row_stride]\n" + "add x11, %[output_col_stride1], %[output_col_stride1]\n" + "add x22, x28, %[output_row_stride]\n" + "add x13, x11, %[output_col_stride1]\n" + "add x12, x22, %[output_row_stride]\n" + "add x23, x13, %[output_col_stride1]\n" + "add x14, x12, %[output_row_stride]\n" + "add x15, x23, %[output_col_stride1]\n" + "add x24, x14, %[output_row_stride]\n" + "cmp %w[n_channels], #4\n" + "blt 2f\n" + "1:\n" + "ldr q8, [%[inptr0], x20]\n" + "ldr q2, [%[inptr0], x18]\n" + "mov v14.16b, v8.16b\n" + "ldr q9, [%[inptr0]]\n" + "mov v10.16b, v8.16b\n" + "ldr q1, [%[inptr0], x21]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "ldr q4, [%[inptr0], x19]\n" + "mov v9.16b, v8.16b\n" + "ldr q12, [%[inptr0], %[input_col_stride1]]\n" + "fmls v10.4s, v12.4s, v0.s[2]\n" + "ldr q5, [x16, x20]\n" + "fmls v14.4s, v2.4s, v0.s[3]\n" + "ldr q20, [x16, x18]\n" + "fmla v9.4s, v12.4s, v0.s[2]\n" + "ldr q3, [x16]\n" + "fmls v10.4s, v2.4s, v0.s[2]\n" + "ldr q6, [x16, x21]\n" + "mov v7.16b, v8.16b\n" + "ldr q16, [x16, x19]\n" + "fmls v9.4s, v2.4s, v0.s[2]\n" + "ldr q22, [x16, %[input_col_stride1]]\n" + "fadd v10.4s, v10.4s, v4.4s\n" + "ldr q17, [x17, x20]\n" + "fmls v7.4s, v12.4s, v0.s[1]\n" + "ldr q15, [x17, x18]\n" + "fsub v9.4s, v9.4s, v4.4s\n" + "ldr q19, [x17]\n" + "mov v8.16b, v8.16b\n" + "ldr q18, [x17, x21]\n" + "fsub v7.4s, v7.4s, v2.4s\n" + "ldr q13, [x17, x19]\n" + "fmla v7.4s, v4.4s, v0.s[1]\n" + "ldr q21, [x17, %[input_col_stride1]]\n" + "fmla v8.4s, v12.4s, v0.s[1]\n" + "add %[inptr0], %[inptr0], #16\n" + "mov v11.16b, v1.16b\n" + "add x16, x16, #16\n" + "mov v1.16b, v5.16b\n" + "add x17, x17, #16\n" + "fsub v8.4s, v8.4s, v2.4s\n" + "fmla v11.4s, v12.4s, v0.s[2]\n" + "fmls v8.4s, v4.4s, v0.s[1]\n" + "fmla v1.4s, v3.4s, v0.s[2]\n" + "mov v2.16b, v5.16b\n" + "mov v3.16b, v5.16b\n" + "fmls v11.4s, v4.4s, v0.s[3]\n" + "mov v4.16b, v5.16b\n" + "fmls v1.4s, v20.4s, v0.s[3]\n" + "fmls v2.4s, v22.4s, v0.s[2]\n" + "fmla v3.4s, v22.4s, v0.s[2]\n" + "fmls v4.4s, v22.4s, v0.s[1]\n" + "mov v5.16b, v5.16b\n" + "mov v6.16b, v6.16b\n" + "fmls v2.4s, v20.4s, v0.s[2]\n" + "mov v12.16b, v17.16b\n" + "fmls v3.4s, v20.4s, v0.s[2]\n" + "fsub v4.4s, v4.4s, v20.4s\n" + "fmla v4.4s, v16.4s, v0.s[1]\n" + "fmla v5.4s, v22.4s, v0.s[1]\n" + "fadd v2.4s, v2.4s, v16.4s\n" + "fmla v6.4s, v22.4s, v0.s[2]\n" + "fsub v3.4s, v3.4s, v16.4s\n" + "fmla v12.4s, v19.4s, v0.s[2]\n" + "fsub v5.4s, v5.4s, v20.4s\n" + "mov v19.16b, v17.16b\n" + "fmls v5.4s, v16.4s, v0.s[1]\n" + "fmls v6.4s, v16.4s, v0.s[3]\n" + "fmls v12.4s, v15.4s, v0.s[3]\n" + "fmls v19.4s, v21.4s, v0.s[2]\n" + "mov v20.16b, v17.16b\n" + "mov v16.16b, v17.16b\n" + "mov v17.16b, v17.16b\n" + "mov v18.16b, v18.16b\n" + "fmls v19.4s, v15.4s, v0.s[2]\n" + "fmla v20.4s, v21.4s, v0.s[2]\n" + "fmls v16.4s, v21.4s, v0.s[1]\n" + "fmla v17.4s, v21.4s, v0.s[1]\n" + "fmla v18.4s, v21.4s, v0.s[2]\n" + "mov v23.16b, v12.16b\n" + "fadd v19.4s, v19.4s, v13.4s\n" + "fmls v20.4s, v15.4s, v0.s[2]\n" + "fsub v16.4s, v16.4s, v15.4s\n" + "fsub v17.4s, v17.4s, v15.4s\n" + "fmla v16.4s, v13.4s, v0.s[1]\n" + "fmls v17.4s, v13.4s, v0.s[1]\n" + "fsub v20.4s, v20.4s, v13.4s\n" + "fmls v18.4s, v13.4s, v0.s[3]\n" + "fmla v23.4s, v14.4s, v0.s[2]\n" + "mov v15.16b, v19.16b\n" + "mov v14.16b, v20.16b\n" + "mov v24.16b, v16.16b\n" + "fmla v15.4s, v10.4s, v0.s[2]\n" + "mov v10.16b, v17.16b\n" + "fmls v23.4s, v1.4s, v0.s[3]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "fmla v24.4s, v7.4s, v0.s[2]\n" + "fmla v10.4s, v8.4s, v0.s[2]\n" + "fmls v15.4s, v2.4s, v0.s[3]\n" + "mov v7.16b, v18.16b\n" + "str q23, [%[outptr0]]\n" + "fmls v14.4s, v3.4s, v0.s[3]\n" + "fmls v24.4s, v4.4s, v0.s[3]\n" + "fmls v10.4s, v5.4s, v0.s[3]\n" + "str q15, [%[outptr0], %[output_col_stride1]]\n" + "fmla v7.4s, v11.4s, v0.s[2]\n" + "str q14, [%[outptr0], x11]\n" + "str q24, [%[outptr0], x13]\n" + "str q10, [%[outptr0], x23]\n" + "fmls v7.4s, v6.4s, v0.s[3]\n" + "str q7, [%[outptr0], x15]\n" + "add %[outptr0], %[outptr0], #16\n" + "mov v26.16b, v12.16b\n" + "mov v25.16b, v19.16b\n" + "ldr q11, [x25, x20]\n" + "mov v10.16b, v11.16b\n" + "ldr q23, [x25, x18]\n" + "mov v9.16b, v11.16b\n" + "ldr q7, [x25]\n" + "fmla v10.4s, v7.4s, v0.s[2]\n" + "ldr q13, [x25, x21]\n" + "mov v7.16b, v11.16b\n" + "ldr q31, [x25, x19]\n" + "mov v8.16b, v11.16b\n" + "ldr q21, [x25, %[input_col_stride1]]\n" + "fmls v10.4s, v23.4s, v0.s[3]\n" + "ldr q30, [x26, x20]\n" + "fmls v9.4s, v21.4s, v0.s[2]\n" + "ldr q29, [x26, x18]\n" + "fmla v7.4s, v21.4s, v0.s[2]\n" + "ldr q22, [x26]\n" + "fmls v8.4s, v21.4s, v0.s[1]\n" + "ldr q24, [x26, x21]\n" + "fmls v9.4s, v23.4s, v0.s[2]\n" + "ldr q27, [x26, x19]\n" + "fmls v7.4s, v23.4s, v0.s[2]\n" + "ldr q28, [x26, %[input_col_stride1]]\n" + "fsub v8.4s, v8.4s, v23.4s\n" + "add x25, x25, #16\n" + "fadd v9.4s, v9.4s, v31.4s\n" + "add x26, x26, #16\n" + "fsub v7.4s, v7.4s, v31.4s\n" + "fmla v8.4s, v31.4s, v0.s[1]\n" + "mov v11.16b, v11.16b\n" + "mov v15.16b, v13.16b\n" + "mov v14.16b, v30.16b\n" + "mov v13.16b, v30.16b\n" + "fmla v11.4s, v21.4s, v0.s[1]\n" + "fmla v15.4s, v21.4s, v0.s[2]\n" + "fmla v14.4s, v22.4s, v0.s[2]\n" + "fmls v13.4s, v28.4s, v0.s[2]\n" + "mov v21.16b, v30.16b\n" + "mov v22.16b, v30.16b\n" + "fsub v11.4s, v11.4s, v23.4s\n" + "fmls v15.4s, v31.4s, v0.s[3]\n" + "fmls v11.4s, v31.4s, v0.s[1]\n" + "fmls v14.4s, v29.4s, v0.s[3]\n" + "fmls v13.4s, v29.4s, v0.s[2]\n" + "fmla v21.4s, v28.4s, v0.s[2]\n" + "fmls v22.4s, v28.4s, v0.s[1]\n" + "mov v23.16b, v30.16b\n" + "mov v24.16b, v24.16b\n" + "fmls v26.4s, v10.4s, v0.s[2]\n" + "fadd v13.4s, v13.4s, v27.4s\n" + "fmls v21.4s, v29.4s, v0.s[2]\n" + "fsub v22.4s, v22.4s, v29.4s\n" + "fmla v23.4s, v28.4s, v0.s[1]\n" + "fmla v22.4s, v27.4s, v0.s[1]\n" + "fmla v24.4s, v28.4s, v0.s[2]\n" + "fsub v21.4s, v21.4s, v27.4s\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fsub v23.4s, v23.4s, v29.4s\n" + "fmls v25.4s, v9.4s, v0.s[2]\n" + "fmls v23.4s, v27.4s, v0.s[1]\n" + "fmls v24.4s, v27.4s, v0.s[3]\n" + "fadd v26.4s, v26.4s, v14.4s\n" + "mov v27.16b, v20.16b\n" + "str q26, [x28]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "fmls v27.4s, v7.4s, v0.s[2]\n" + "mov v31.16b, v16.16b\n" + "mov v30.16b, v17.16b\n" + "mov v29.16b, v18.16b\n" + "fadd v25.4s, v25.4s, v13.4s\n" + "fmls v31.4s, v8.4s, v0.s[2]\n" + "str q25, [x28, %[output_col_stride1]]\n" + "fmls v27.4s, v3.4s, v0.s[2]\n" + "fmls v30.4s, v11.4s, v0.s[2]\n" + "fmls v29.4s, v15.4s, v0.s[2]\n" + "fmls v31.4s, v4.4s, v0.s[2]\n" + "mov v26.16b, v12.16b\n" + "fadd v27.4s, v27.4s, v21.4s\n" + "mov v25.16b, v19.16b\n" + "str q27, [x28, x11]\n" + "fmls v30.4s, v5.4s, v0.s[2]\n" + "fadd v31.4s, v31.4s, v22.4s\n" + "fmls v29.4s, v6.4s, v0.s[2]\n" + "str q31, [x28, x13]\n" + "fmla v26.4s, v10.4s, v0.s[2]\n" + "fadd v30.4s, v30.4s, v23.4s\n" + "fmla v25.4s, v9.4s, v0.s[2]\n" + "str q30, [x28, x23]\n" + "fadd v29.4s, v29.4s, v24.4s\n" + "str q29, [x28, x15]\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "add x28, x28, #16\n" + "mov v30.16b, v20.16b\n" + "mov v29.16b, v16.16b\n" + "fsub v26.4s, v26.4s, v14.4s\n" + "mov v28.16b, v17.16b\n" + "str q26, [x22]\n" + "fsub v25.4s, v25.4s, v13.4s\n" + "str q25, [x22, %[output_col_stride1]]\n" + "fmla v30.4s, v7.4s, v0.s[2]\n" + "fmla v29.4s, v8.4s, v0.s[2]\n" + "fmla v28.4s, v11.4s, v0.s[2]\n" + "mov v26.16b, v18.16b\n" + "mov v25.16b, v12.16b\n" + "fmls v30.4s, v3.4s, v0.s[2]\n" + "mov v31.16b, v19.16b\n" + "fmls v29.4s, v4.4s, v0.s[2]\n" + "fmls v28.4s, v5.4s, v0.s[2]\n" + "fmla v26.4s, v15.4s, v0.s[2]\n" + "fmls v25.4s, v10.4s, v0.s[1]\n" + "fsub v30.4s, v30.4s, v21.4s\n" + "fmls v31.4s, v9.4s, v0.s[1]\n" + "str q30, [x22, x11]\n" + "fsub v29.4s, v29.4s, v22.4s\n" + "str q29, [x22, x13]\n" + "fsub v28.4s, v28.4s, v23.4s\n" + "str q28, [x22, x23]\n" + "fmls v26.4s, v6.4s, v0.s[2]\n" + "fsub v25.4s, v25.4s, v1.4s\n" + "fsub v31.4s, v31.4s, v2.4s\n" + "fmla v25.4s, v14.4s, v0.s[1]\n" + "fmla v31.4s, v13.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v24.4s\n" + "mov v27.16b, v20.16b\n" + "str q26, [x22, x15]\n" + "mov v26.16b, v16.16b\n" + "str q25, [x12]\n" + "fmls v27.4s, v7.4s, v0.s[1]\n" + "str q31, [x12, %[output_col_stride1]]\n" + "fmls v26.4s, v8.4s, v0.s[1]\n" + "mov v25.16b, v17.16b\n" + "add x22, x22, #16\n" + "fsub v27.4s, v27.4s, v3.4s\n" + "mov v28.16b, v18.16b\n" + "fmla v27.4s, v21.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v4.4s\n" + "fmla v26.4s, v22.4s, v0.s[1]\n" + "fmls v25.4s, v11.4s, v0.s[1]\n" + "fmls v28.4s, v15.4s, v0.s[1]\n" + "mov v12.16b, v12.16b\n" + "str q27, [x12, x11]\n" + "mov v19.16b, v19.16b\n" + "str q26, [x12, x13]\n" + "fsub v25.4s, v25.4s, v5.4s\n" + "fmla v25.4s, v23.4s, v0.s[1]\n" + "fsub v28.4s, v28.4s, v6.4s\n" + "fmla v28.4s, v24.4s, v0.s[1]\n" + "fmla v12.4s, v10.4s, v0.s[1]\n" + "fmla v19.4s, v9.4s, v0.s[1]\n" + "mov v20.16b, v20.16b\n" + "str q25, [x12, x23]\n" + "mov v16.16b, v16.16b\n" + "str q28, [x12, x15]\n" + "fsub v12.4s, v12.4s, v1.4s\n" + "fmls v12.4s, v14.4s, v0.s[1]\n" + "add x12, x12, #16\n" + "fsub v19.4s, v19.4s, v2.4s\n" + "fmla v20.4s, v7.4s, v0.s[1]\n" + "fmls v19.4s, v13.4s, v0.s[1]\n" + "fmla v16.4s, v8.4s, v0.s[1]\n" + "str q12, [x14]\n" + "mov v1.16b, v17.16b\n" + "fsub v20.4s, v20.4s, v3.4s\n" + "mov v17.16b, v18.16b\n" + "str q19, [x14, %[output_col_stride1]]\n" + "fmls v20.4s, v21.4s, v0.s[1]\n" + "fsub v16.4s, v16.4s, v4.4s\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "fmls v16.4s, v22.4s, v0.s[1]\n" + "fmla v17.4s, v15.4s, v0.s[1]\n" + "str q20, [x14, x11]\n" + "fsub v1.4s, v1.4s, v5.4s\n" + "str q16, [x14, x13]\n" + "fmls v1.4s, v23.4s, v0.s[1]\n" + "fsub v17.4s, v17.4s, v6.4s\n" + "fmls v17.4s, v24.4s, v0.s[1]\n" + "str q1, [x14, x23]\n" + "str q17, [x14, x15]\n" + "add x14, x14, #16\n" + "ldr q2, [x27, x20]\n" + "mov v4.16b, v2.16b\n" + "ldr q17, [x27, x18]\n" + "mov v12.16b, v2.16b\n" + "ldr q18, [x27]\n" + "fmla v4.4s, v18.4s, v0.s[2]\n" + "ldr q3, [x27, x21]\n" + "mov v6.16b, v2.16b\n" + "ldr q5, [x27, x19]\n" + "mov v1.16b, v2.16b\n" + "ldr q18, [x27, %[input_col_stride1]]\n" + "fmls v4.4s, v17.4s, v0.s[3]\n" + "add x27, x27, #16\n" + "fmls v12.4s, v18.4s, v0.s[2]\n" + "sub %w[n_channels], %w[n_channels], #4\n" + "fmla v6.4s, v18.4s, v0.s[2]\n" + "cmp %w[n_channels], #4\n" + "fmls v1.4s, v18.4s, v0.s[1]\n" + "mov v2.16b, v2.16b\n" + "fmls v12.4s, v17.4s, v0.s[2]\n" + "mov v3.16b, v3.16b\n" + "fmls v6.4s, v17.4s, v0.s[2]\n" + "fmla v2.4s, v18.4s, v0.s[1]\n" + "fsub v1.4s, v1.4s, v17.4s\n" + "fmla v3.4s, v18.4s, v0.s[2]\n" + "fadd v12.4s, v12.4s, v5.4s\n" + "fmla v1.4s, v5.4s, v0.s[1]\n" + "fsub v6.4s, v6.4s, v5.4s\n" + "fsub v2.4s, v2.4s, v17.4s\n" + "fmls v2.4s, v5.4s, v0.s[1]\n" + "fmls v3.4s, v5.4s, v0.s[3]\n" + "mov v4.16b, v4.16b\n" + "mov v16.16b, v12.16b\n" + "mov v5.16b, v6.16b\n" + "mov v6.16b, v1.16b\n" + "fmla v4.4s, v10.4s, v0.s[2]\n" + "fmla v16.4s, v9.4s, v0.s[2]\n" + "fmla v5.4s, v7.4s, v0.s[2]\n" + "fmla v6.4s, v8.4s, v0.s[2]\n" + "mov v9.16b, v2.16b\n" + "mov v10.16b, v3.16b\n" + "fmls v4.4s, v14.4s, v0.s[3]\n" + "fmls v16.4s, v13.4s, v0.s[3]\n" + "fmls v5.4s, v21.4s, v0.s[3]\n" + "fmls v6.4s, v22.4s, v0.s[3]\n" + "fmla v9.4s, v11.4s, v0.s[2]\n" + "fmla v10.4s, v15.4s, v0.s[2]\n" + "str q4, [x24]\n" + "str q16, [x24, %[output_col_stride1]]\n" + "str q5, [x24, x11]\n" + "str q6, [x24, x13]\n" + "fmls v9.4s, v23.4s, v0.s[3]\n" + "fmls v10.4s, v24.4s, v0.s[3]\n" + "str q9, [x24, x23]\n" + "str q10, [x24, x15]\n" + "add x24, x24, #16\n" + "bge 1b\n" + "2:\n" + "cmp %w[n_channels], #2\n" + "blt 3f\n" + "ldr d8, [%[inptr0], x20]\n" + "mov v14.16b, v8.16b\n" + "ldr d2, [%[inptr0], x18]\n" + "mov v10.16b, v8.16b\n" + "ldr d9, [%[inptr0]]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "ldr d1, [%[inptr0], x21]\n" + "mov v9.16b, v8.16b\n" + "ldr d4, [%[inptr0], x19]\n" + "mov v7.16b, v8.16b\n" + "ldr d12, [%[inptr0], %[input_col_stride1]]\n" + "fmls v14.4s, v2.4s, v0.s[3]\n" + "ldr d5, [x16, x20]\n" + "fmls v10.4s, v12.4s, v0.s[2]\n" + "ldr d20, [x16, x18]\n" + "fmla v9.4s, v12.4s, v0.s[2]\n" + "ldr d3, [x16]\n" + "fmls v7.4s, v12.4s, v0.s[1]\n" + "ldr d6, [x16, x21]\n" + "fmls v10.4s, v2.4s, v0.s[2]\n" + "ldr d16, [x16, x19]\n" + "fmls v9.4s, v2.4s, v0.s[2]\n" + "ldr d22, [x16, %[input_col_stride1]]\n" + "fsub v7.4s, v7.4s, v2.4s\n" + "ldr d17, [x17, x20]\n" + "fadd v10.4s, v10.4s, v4.4s\n" + "ldr d15, [x17, x18]\n" + "fsub v9.4s, v9.4s, v4.4s\n" + "ldr d19, [x17]\n" + "fmla v7.4s, v4.4s, v0.s[1]\n" + "ldr d18, [x17, x21]\n" + "mov v8.16b, v8.16b\n" + "ldr d13, [x17, x19]\n" + "mov v11.16b, v1.16b\n" + "ldr d21, [x17, %[input_col_stride1]]\n" + "fmla v8.4s, v12.4s, v0.s[1]\n" + "add %[inptr0], %[inptr0], #8\n" + "fmla v11.4s, v12.4s, v0.s[2]\n" + "add x16, x16, #8\n" + "mov v1.16b, v5.16b\n" + "add x17, x17, #8\n" + "fsub v8.4s, v8.4s, v2.4s\n" + "mov v2.16b, v5.16b\n" + "fmls v8.4s, v4.4s, v0.s[1]\n" + "fmls v11.4s, v4.4s, v0.s[3]\n" + "fmla v1.4s, v3.4s, v0.s[2]\n" + "fmls v2.4s, v22.4s, v0.s[2]\n" + "mov v3.16b, v5.16b\n" + "mov v4.16b, v5.16b\n" + "mov v5.16b, v5.16b\n" + "mov v6.16b, v6.16b\n" + "fmls v1.4s, v20.4s, v0.s[3]\n" + "fmls v2.4s, v20.4s, v0.s[2]\n" + "fmla v3.4s, v22.4s, v0.s[2]\n" + "fmls v4.4s, v22.4s, v0.s[1]\n" + "fmla v5.4s, v22.4s, v0.s[1]\n" + "fmla v6.4s, v22.4s, v0.s[2]\n" + "fadd v2.4s, v2.4s, v16.4s\n" + "mov v12.16b, v17.16b\n" + "fmls v3.4s, v20.4s, v0.s[2]\n" + "fsub v4.4s, v4.4s, v20.4s\n" + "fmla v4.4s, v16.4s, v0.s[1]\n" + "fsub v5.4s, v5.4s, v20.4s\n" + "fmls v5.4s, v16.4s, v0.s[1]\n" + "fmls v6.4s, v16.4s, v0.s[3]\n" + "fsub v3.4s, v3.4s, v16.4s\n" + "fmla v12.4s, v19.4s, v0.s[2]\n" + "mov v19.16b, v17.16b\n" + "mov v20.16b, v17.16b\n" + "mov v16.16b, v17.16b\n" + "mov v17.16b, v17.16b\n" + "fmls v12.4s, v15.4s, v0.s[3]\n" + "fmls v19.4s, v21.4s, v0.s[2]\n" + "fmla v20.4s, v21.4s, v0.s[2]\n" + "fmls v16.4s, v21.4s, v0.s[1]\n" + "fmla v17.4s, v21.4s, v0.s[1]\n" + "mov v18.16b, v18.16b\n" + "fmls v19.4s, v15.4s, v0.s[2]\n" + "mov v23.16b, v12.16b\n" + "fmls v20.4s, v15.4s, v0.s[2]\n" + "fsub v16.4s, v16.4s, v15.4s\n" + "fmla v16.4s, v13.4s, v0.s[1]\n" + "fsub v17.4s, v17.4s, v15.4s\n" + "fadd v19.4s, v19.4s, v13.4s\n" + "fmls v17.4s, v13.4s, v0.s[1]\n" + "fsub v20.4s, v20.4s, v13.4s\n" + "fmla v18.4s, v21.4s, v0.s[2]\n" + "fmla v23.4s, v14.4s, v0.s[2]\n" + "mov v15.16b, v19.16b\n" + "mov v14.16b, v20.16b\n" + "mov v24.16b, v16.16b\n" + "fmls v18.4s, v13.4s, v0.s[3]\n" + "fmla v15.4s, v10.4s, v0.s[2]\n" + "fmls v23.4s, v1.4s, v0.s[3]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "fmla v24.4s, v7.4s, v0.s[2]\n" + "mov v10.16b, v17.16b\n" + "fmls v15.4s, v2.4s, v0.s[3]\n" + "mov v7.16b, v18.16b\n" + "str d23, [%[outptr0]]\n" + "fmls v14.4s, v3.4s, v0.s[3]\n" + "fmls v24.4s, v4.4s, v0.s[3]\n" + "fmla v10.4s, v8.4s, v0.s[2]\n" + "str d15, [%[outptr0], %[output_col_stride1]]\n" + "fmla v7.4s, v11.4s, v0.s[2]\n" + "str d14, [%[outptr0], x11]\n" + "fmls v10.4s, v5.4s, v0.s[3]\n" + "str d24, [%[outptr0], x13]\n" + "fmls v7.4s, v6.4s, v0.s[3]\n" + "str d10, [%[outptr0], x23]\n" + "str d7, [%[outptr0], x15]\n" + "add %[outptr0], %[outptr0], #8\n" + "mov v26.16b, v12.16b\n" + "mov v25.16b, v19.16b\n" + "ldr d11, [x25, x20]\n" + "mov v10.16b, v11.16b\n" + "ldr d23, [x25, x18]\n" + "mov v9.16b, v11.16b\n" + "ldr d7, [x25]\n" + "fmla v10.4s, v7.4s, v0.s[2]\n" + "ldr d13, [x25, x21]\n" + "mov v7.16b, v11.16b\n" + "ldr d31, [x25, x19]\n" + "mov v8.16b, v11.16b\n" + "ldr d21, [x25, %[input_col_stride1]]\n" + "fmls v10.4s, v23.4s, v0.s[3]\n" + "ldr d30, [x26, x20]\n" + "fmls v9.4s, v21.4s, v0.s[2]\n" + "ldr d29, [x26, x18]\n" + "fmla v7.4s, v21.4s, v0.s[2]\n" + "ldr d22, [x26]\n" + "fmls v8.4s, v21.4s, v0.s[1]\n" + "ldr d24, [x26, x21]\n" + "fmls v9.4s, v23.4s, v0.s[2]\n" + "ldr d27, [x26, x19]\n" + "fmls v7.4s, v23.4s, v0.s[2]\n" + "ldr d28, [x26, %[input_col_stride1]]\n" + "fsub v8.4s, v8.4s, v23.4s\n" + "add x25, x25, #8\n" + "fadd v9.4s, v9.4s, v31.4s\n" + "add x26, x26, #8\n" + "fsub v7.4s, v7.4s, v31.4s\n" + "fmla v8.4s, v31.4s, v0.s[1]\n" + "mov v11.16b, v11.16b\n" + "mov v15.16b, v13.16b\n" + "mov v14.16b, v30.16b\n" + "mov v13.16b, v30.16b\n" + "fmla v11.4s, v21.4s, v0.s[1]\n" + "fmla v15.4s, v21.4s, v0.s[2]\n" + "fmla v14.4s, v22.4s, v0.s[2]\n" + "fmls v13.4s, v28.4s, v0.s[2]\n" + "mov v21.16b, v30.16b\n" + "mov v22.16b, v30.16b\n" + "fsub v11.4s, v11.4s, v23.4s\n" + "fmls v15.4s, v31.4s, v0.s[3]\n" + "fmls v11.4s, v31.4s, v0.s[1]\n" + "fmls v14.4s, v29.4s, v0.s[3]\n" + "fmls v13.4s, v29.4s, v0.s[2]\n" + "fmla v21.4s, v28.4s, v0.s[2]\n" + "fmls v22.4s, v28.4s, v0.s[1]\n" + "mov v23.16b, v30.16b\n" + "mov v24.16b, v24.16b\n" + "fmls v26.4s, v10.4s, v0.s[2]\n" + "fadd v13.4s, v13.4s, v27.4s\n" + "fmls v21.4s, v29.4s, v0.s[2]\n" + "fsub v22.4s, v22.4s, v29.4s\n" + "fmla v23.4s, v28.4s, v0.s[1]\n" + "fmla v22.4s, v27.4s, v0.s[1]\n" + "fmla v24.4s, v28.4s, v0.s[2]\n" + "fsub v21.4s, v21.4s, v27.4s\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fsub v23.4s, v23.4s, v29.4s\n" + "fmls v25.4s, v9.4s, v0.s[2]\n" + "fmls v23.4s, v27.4s, v0.s[1]\n" + "fmls v24.4s, v27.4s, v0.s[3]\n" + "fadd v26.4s, v26.4s, v14.4s\n" + "mov v27.16b, v20.16b\n" + "str d26, [x28]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "fmls v27.4s, v7.4s, v0.s[2]\n" + "mov v31.16b, v16.16b\n" + "mov v30.16b, v17.16b\n" + "mov v29.16b, v18.16b\n" + "fadd v25.4s, v25.4s, v13.4s\n" + "fmls v31.4s, v8.4s, v0.s[2]\n" + "str d25, [x28, %[output_col_stride1]]\n" + "fmls v27.4s, v3.4s, v0.s[2]\n" + "fmls v30.4s, v11.4s, v0.s[2]\n" + "fmls v29.4s, v15.4s, v0.s[2]\n" + "fmls v31.4s, v4.4s, v0.s[2]\n" + "mov v26.16b, v12.16b\n" + "fadd v27.4s, v27.4s, v21.4s\n" + "mov v25.16b, v19.16b\n" + "str d27, [x28, x11]\n" + "fmls v30.4s, v5.4s, v0.s[2]\n" + "fadd v31.4s, v31.4s, v22.4s\n" + "fmls v29.4s, v6.4s, v0.s[2]\n" + "str d31, [x28, x13]\n" + "fmla v26.4s, v10.4s, v0.s[2]\n" + "fadd v30.4s, v30.4s, v23.4s\n" + "fmla v25.4s, v9.4s, v0.s[2]\n" + "str d30, [x28, x23]\n" + "fadd v29.4s, v29.4s, v24.4s\n" + "str d29, [x28, x15]\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "add x28, x28, #8\n" + "mov v30.16b, v20.16b\n" + "mov v29.16b, v16.16b\n" + "fsub v26.4s, v26.4s, v14.4s\n" + "mov v28.16b, v17.16b\n" + "str d26, [x22]\n" + "fsub v25.4s, v25.4s, v13.4s\n" + "str d25, [x22, %[output_col_stride1]]\n" + "fmla v30.4s, v7.4s, v0.s[2]\n" + "fmla v29.4s, v8.4s, v0.s[2]\n" + "fmla v28.4s, v11.4s, v0.s[2]\n" + "mov v26.16b, v18.16b\n" + "mov v25.16b, v12.16b\n" + "fmls v30.4s, v3.4s, v0.s[2]\n" + "mov v31.16b, v19.16b\n" + "fmls v29.4s, v4.4s, v0.s[2]\n" + "fmls v28.4s, v5.4s, v0.s[2]\n" + "fmla v26.4s, v15.4s, v0.s[2]\n" + "fmls v25.4s, v10.4s, v0.s[1]\n" + "fsub v30.4s, v30.4s, v21.4s\n" + "fmls v31.4s, v9.4s, v0.s[1]\n" + "str d30, [x22, x11]\n" + "fsub v29.4s, v29.4s, v22.4s\n" + "str d29, [x22, x13]\n" + "fsub v28.4s, v28.4s, v23.4s\n" + "str d28, [x22, x23]\n" + "fmls v26.4s, v6.4s, v0.s[2]\n" + "fsub v25.4s, v25.4s, v1.4s\n" + "fsub v31.4s, v31.4s, v2.4s\n" + "fmla v25.4s, v14.4s, v0.s[1]\n" + "fmla v31.4s, v13.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v24.4s\n" + "mov v27.16b, v20.16b\n" + "str d26, [x22, x15]\n" + "mov v26.16b, v16.16b\n" + "str d25, [x12]\n" + "fmls v27.4s, v7.4s, v0.s[1]\n" + "str d31, [x12, %[output_col_stride1]]\n" + "fmls v26.4s, v8.4s, v0.s[1]\n" + "mov v25.16b, v17.16b\n" + "add x22, x22, #8\n" + "fsub v27.4s, v27.4s, v3.4s\n" + "mov v28.16b, v18.16b\n" + "fmla v27.4s, v21.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v4.4s\n" + "fmla v26.4s, v22.4s, v0.s[1]\n" + "fmls v25.4s, v11.4s, v0.s[1]\n" + "fmls v28.4s, v15.4s, v0.s[1]\n" + "mov v12.16b, v12.16b\n" + "str d27, [x12, x11]\n" + "mov v19.16b, v19.16b\n" + "str d26, [x12, x13]\n" + "fsub v25.4s, v25.4s, v5.4s\n" + "fmla v25.4s, v23.4s, v0.s[1]\n" + "fsub v28.4s, v28.4s, v6.4s\n" + "fmla v28.4s, v24.4s, v0.s[1]\n" + "fmla v12.4s, v10.4s, v0.s[1]\n" + "fmla v19.4s, v9.4s, v0.s[1]\n" + "mov v20.16b, v20.16b\n" + "str d25, [x12, x23]\n" + "mov v16.16b, v16.16b\n" + "str d28, [x12, x15]\n" + "fsub v12.4s, v12.4s, v1.4s\n" + "fmls v12.4s, v14.4s, v0.s[1]\n" + "add x12, x12, #8\n" + "fsub v19.4s, v19.4s, v2.4s\n" + "fmla v20.4s, v7.4s, v0.s[1]\n" + "fmls v19.4s, v13.4s, v0.s[1]\n" + "fmla v16.4s, v8.4s, v0.s[1]\n" + "str d12, [x14]\n" + "mov v1.16b, v17.16b\n" + "fsub v20.4s, v20.4s, v3.4s\n" + "mov v17.16b, v18.16b\n" + "str d19, [x14, %[output_col_stride1]]\n" + "fmls v20.4s, v21.4s, v0.s[1]\n" + "fsub v16.4s, v16.4s, v4.4s\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "fmls v16.4s, v22.4s, v0.s[1]\n" + "fmla v17.4s, v15.4s, v0.s[1]\n" + "str d20, [x14, x11]\n" + "fsub v1.4s, v1.4s, v5.4s\n" + "str d16, [x14, x13]\n" + "fmls v1.4s, v23.4s, v0.s[1]\n" + "fsub v17.4s, v17.4s, v6.4s\n" + "fmls v17.4s, v24.4s, v0.s[1]\n" + "str d1, [x14, x23]\n" + "str d17, [x14, x15]\n" + "add x14, x14, #8\n" + "ldr d2, [x27, x20]\n" + "mov v4.16b, v2.16b\n" + "ldr d17, [x27, x18]\n" + "mov v12.16b, v2.16b\n" + "ldr d18, [x27]\n" + "fmla v4.4s, v18.4s, v0.s[2]\n" + "ldr d3, [x27, x21]\n" + "mov v6.16b, v2.16b\n" + "ldr d5, [x27, x19]\n" + "mov v1.16b, v2.16b\n" + "ldr d18, [x27, %[input_col_stride1]]\n" + "fmls v4.4s, v17.4s, v0.s[3]\n" + "add x27, x27, #8\n" + "fmls v12.4s, v18.4s, v0.s[2]\n" + "sub %w[n_channels], %w[n_channels], #2\n" + "fmla v6.4s, v18.4s, v0.s[2]\n" + "fmls v1.4s, v18.4s, v0.s[1]\n" + "mov v2.16b, v2.16b\n" + "mov v3.16b, v3.16b\n" + "fmls v12.4s, v17.4s, v0.s[2]\n" + "mov v4.16b, v4.16b\n" + "fmls v6.4s, v17.4s, v0.s[2]\n" + "fsub v1.4s, v1.4s, v17.4s\n" + "fmla v1.4s, v5.4s, v0.s[1]\n" + "fmla v2.4s, v18.4s, v0.s[1]\n" + "fadd v12.4s, v12.4s, v5.4s\n" + "fmla v3.4s, v18.4s, v0.s[2]\n" + "fsub v6.4s, v6.4s, v5.4s\n" + "fmla v4.4s, v10.4s, v0.s[2]\n" + "fsub v2.4s, v2.4s, v17.4s\n" + "mov v16.16b, v12.16b\n" + "fmls v2.4s, v5.4s, v0.s[1]\n" + "fmls v3.4s, v5.4s, v0.s[3]\n" + "fmls v4.4s, v14.4s, v0.s[3]\n" + "fmla v16.4s, v9.4s, v0.s[2]\n" + "mov v5.16b, v6.16b\n" + "mov v6.16b, v1.16b\n" + "mov v9.16b, v2.16b\n" + "mov v10.16b, v3.16b\n" + "str d4, [x24]\n" + "fmls v16.4s, v13.4s, v0.s[3]\n" + "fmla v5.4s, v7.4s, v0.s[2]\n" + "fmla v6.4s, v8.4s, v0.s[2]\n" + "fmla v9.4s, v11.4s, v0.s[2]\n" + "fmla v10.4s, v15.4s, v0.s[2]\n" + "str d16, [x24, %[output_col_stride1]]\n" + "fmls v5.4s, v21.4s, v0.s[3]\n" + "fmls v6.4s, v22.4s, v0.s[3]\n" + "fmls v9.4s, v23.4s, v0.s[3]\n" + "fmls v10.4s, v24.4s, v0.s[3]\n" + "str d5, [x24, x11]\n" + "str d6, [x24, x13]\n" + "str d9, [x24, x23]\n" + "str d10, [x24, x15]\n" + "add x24, x24, #8\n" + "3:\n" + "cbz %w[n_channels], 4f\n" + "ldr s8, [%[inptr0], x20]\n" + "mov v14.16b, v8.16b\n" + "ldr s2, [%[inptr0], x18]\n" + "mov v10.16b, v8.16b\n" + "ldr s9, [%[inptr0]]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "ldr s1, [%[inptr0], x21]\n" + "mov v9.16b, v8.16b\n" + "ldr s4, [%[inptr0], x19]\n" + "mov v7.16b, v8.16b\n" + "ldr s12, [%[inptr0], %[input_col_stride1]]\n" + "fmls v14.4s, v2.4s, v0.s[3]\n" + "ldr s5, [x16, x20]\n" + "fmls v10.4s, v12.4s, v0.s[2]\n" + "ldr s20, [x16, x18]\n" + "fmla v9.4s, v12.4s, v0.s[2]\n" + "ldr s3, [x16]\n" + "fmls v7.4s, v12.4s, v0.s[1]\n" + "ldr s6, [x16, x21]\n" + "fmls v10.4s, v2.4s, v0.s[2]\n" + "ldr s16, [x16, x19]\n" + "fmls v9.4s, v2.4s, v0.s[2]\n" + "ldr s22, [x16, %[input_col_stride1]]\n" + "fsub v7.4s, v7.4s, v2.4s\n" + "ldr s17, [x17, x20]\n" + "fadd v10.4s, v10.4s, v4.4s\n" + "ldr s15, [x17, x18]\n" + "fsub v9.4s, v9.4s, v4.4s\n" + "ldr s19, [x17]\n" + "fmla v7.4s, v4.4s, v0.s[1]\n" + "ldr s18, [x17, x21]\n" + "mov v8.16b, v8.16b\n" + "ldr s13, [x17, x19]\n" + "mov v11.16b, v1.16b\n" + "ldr s21, [x17, %[input_col_stride1]]\n" + "fmla v8.4s, v12.4s, v0.s[1]\n" + "add %[inptr0], %[inptr0], #4\n" + "fmla v11.4s, v12.4s, v0.s[2]\n" + "add x16, x16, #4\n" + "mov v1.16b, v5.16b\n" + "add x17, x17, #4\n" + "fsub v8.4s, v8.4s, v2.4s\n" + "mov v2.16b, v5.16b\n" + "fmls v8.4s, v4.4s, v0.s[1]\n" + "fmls v11.4s, v4.4s, v0.s[3]\n" + "fmla v1.4s, v3.4s, v0.s[2]\n" + "fmls v2.4s, v22.4s, v0.s[2]\n" + "mov v3.16b, v5.16b\n" + "mov v4.16b, v5.16b\n" + "mov v5.16b, v5.16b\n" + "mov v6.16b, v6.16b\n" + "fmls v1.4s, v20.4s, v0.s[3]\n" + "fmls v2.4s, v20.4s, v0.s[2]\n" + "fmla v3.4s, v22.4s, v0.s[2]\n" + "fmls v4.4s, v22.4s, v0.s[1]\n" + "fmla v5.4s, v22.4s, v0.s[1]\n" + "fmla v6.4s, v22.4s, v0.s[2]\n" + "fadd v2.4s, v2.4s, v16.4s\n" + "mov v12.16b, v17.16b\n" + "fmls v3.4s, v20.4s, v0.s[2]\n" + "fsub v4.4s, v4.4s, v20.4s\n" + "fmla v4.4s, v16.4s, v0.s[1]\n" + "fsub v5.4s, v5.4s, v20.4s\n" + "fmls v5.4s, v16.4s, v0.s[1]\n" + "fmls v6.4s, v16.4s, v0.s[3]\n" + "fsub v3.4s, v3.4s, v16.4s\n" + "fmla v12.4s, v19.4s, v0.s[2]\n" + "mov v19.16b, v17.16b\n" + "mov v20.16b, v17.16b\n" + "mov v16.16b, v17.16b\n" + "mov v17.16b, v17.16b\n" + "fmls v12.4s, v15.4s, v0.s[3]\n" + "fmls v19.4s, v21.4s, v0.s[2]\n" + "fmla v20.4s, v21.4s, v0.s[2]\n" + "fmls v16.4s, v21.4s, v0.s[1]\n" + "fmla v17.4s, v21.4s, v0.s[1]\n" + "mov v18.16b, v18.16b\n" + "fmls v19.4s, v15.4s, v0.s[2]\n" + "mov v23.16b, v12.16b\n" + "fmls v20.4s, v15.4s, v0.s[2]\n" + "fsub v16.4s, v16.4s, v15.4s\n" + "fmla v16.4s, v13.4s, v0.s[1]\n" + "fsub v17.4s, v17.4s, v15.4s\n" + "fadd v19.4s, v19.4s, v13.4s\n" + "fmls v17.4s, v13.4s, v0.s[1]\n" + "fsub v20.4s, v20.4s, v13.4s\n" + "fmla v18.4s, v21.4s, v0.s[2]\n" + "fmla v23.4s, v14.4s, v0.s[2]\n" + "mov v15.16b, v19.16b\n" + "mov v14.16b, v20.16b\n" + "mov v24.16b, v16.16b\n" + "fmls v18.4s, v13.4s, v0.s[3]\n" + "fmla v15.4s, v10.4s, v0.s[2]\n" + "fmls v23.4s, v1.4s, v0.s[3]\n" + "fmla v14.4s, v9.4s, v0.s[2]\n" + "fmla v24.4s, v7.4s, v0.s[2]\n" + "mov v10.16b, v17.16b\n" + "fmls v15.4s, v2.4s, v0.s[3]\n" + "mov v7.16b, v18.16b\n" + "str s23, [%[outptr0]]\n" + "fmls v14.4s, v3.4s, v0.s[3]\n" + "fmls v24.4s, v4.4s, v0.s[3]\n" + "fmla v10.4s, v8.4s, v0.s[2]\n" + "str s15, [%[outptr0], %[output_col_stride1]]\n" + "fmla v7.4s, v11.4s, v0.s[2]\n" + "str s14, [%[outptr0], x11]\n" + "fmls v10.4s, v5.4s, v0.s[3]\n" + "str s24, [%[outptr0], x13]\n" + "fmls v7.4s, v6.4s, v0.s[3]\n" + "str s10, [%[outptr0], x23]\n" + "str s7, [%[outptr0], x15]\n" + "add %[outptr0], %[outptr0], #4\n" + "mov v26.16b, v12.16b\n" + "mov v25.16b, v19.16b\n" + "ldr s11, [x25, x20]\n" + "mov v10.16b, v11.16b\n" + "ldr s23, [x25, x18]\n" + "mov v9.16b, v11.16b\n" + "ldr s7, [x25]\n" + "fmla v10.4s, v7.4s, v0.s[2]\n" + "ldr s13, [x25, x21]\n" + "mov v7.16b, v11.16b\n" + "ldr s31, [x25, x19]\n" + "mov v8.16b, v11.16b\n" + "ldr s21, [x25, %[input_col_stride1]]\n" + "fmls v10.4s, v23.4s, v0.s[3]\n" + "ldr s30, [x26, x20]\n" + "fmls v9.4s, v21.4s, v0.s[2]\n" + "ldr s29, [x26, x18]\n" + "fmla v7.4s, v21.4s, v0.s[2]\n" + "ldr s22, [x26]\n" + "fmls v8.4s, v21.4s, v0.s[1]\n" + "ldr s24, [x26, x21]\n" + "fmls v9.4s, v23.4s, v0.s[2]\n" + "ldr s27, [x26, x19]\n" + "fmls v7.4s, v23.4s, v0.s[2]\n" + "ldr s28, [x26, %[input_col_stride1]]\n" + "fsub v8.4s, v8.4s, v23.4s\n" + "add x25, x25, #4\n" + "fadd v9.4s, v9.4s, v31.4s\n" + "add x26, x26, #4\n" + "fsub v7.4s, v7.4s, v31.4s\n" + "fmla v8.4s, v31.4s, v0.s[1]\n" + "mov v11.16b, v11.16b\n" + "mov v15.16b, v13.16b\n" + "mov v14.16b, v30.16b\n" + "mov v13.16b, v30.16b\n" + "fmla v11.4s, v21.4s, v0.s[1]\n" + "fmla v15.4s, v21.4s, v0.s[2]\n" + "fmla v14.4s, v22.4s, v0.s[2]\n" + "fmls v13.4s, v28.4s, v0.s[2]\n" + "mov v21.16b, v30.16b\n" + "mov v22.16b, v30.16b\n" + "fsub v11.4s, v11.4s, v23.4s\n" + "fmls v15.4s, v31.4s, v0.s[3]\n" + "fmls v11.4s, v31.4s, v0.s[1]\n" + "fmls v14.4s, v29.4s, v0.s[3]\n" + "fmls v13.4s, v29.4s, v0.s[2]\n" + "fmla v21.4s, v28.4s, v0.s[2]\n" + "fmls v22.4s, v28.4s, v0.s[1]\n" + "mov v23.16b, v30.16b\n" + "mov v24.16b, v24.16b\n" + "fmls v26.4s, v10.4s, v0.s[2]\n" + "fadd v13.4s, v13.4s, v27.4s\n" + "fmls v21.4s, v29.4s, v0.s[2]\n" + "fsub v22.4s, v22.4s, v29.4s\n" + "fmla v23.4s, v28.4s, v0.s[1]\n" + "fmla v22.4s, v27.4s, v0.s[1]\n" + "fmla v24.4s, v28.4s, v0.s[2]\n" + "fsub v21.4s, v21.4s, v27.4s\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fsub v23.4s, v23.4s, v29.4s\n" + "fmls v25.4s, v9.4s, v0.s[2]\n" + "fmls v23.4s, v27.4s, v0.s[1]\n" + "fmls v24.4s, v27.4s, v0.s[3]\n" + "fadd v26.4s, v26.4s, v14.4s\n" + "mov v27.16b, v20.16b\n" + "str s26, [x28]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "fmls v27.4s, v7.4s, v0.s[2]\n" + "mov v31.16b, v16.16b\n" + "mov v30.16b, v17.16b\n" + "mov v29.16b, v18.16b\n" + "fadd v25.4s, v25.4s, v13.4s\n" + "fmls v31.4s, v8.4s, v0.s[2]\n" + "str s25, [x28, %[output_col_stride1]]\n" + "fmls v27.4s, v3.4s, v0.s[2]\n" + "fmls v30.4s, v11.4s, v0.s[2]\n" + "fmls v29.4s, v15.4s, v0.s[2]\n" + "fmls v31.4s, v4.4s, v0.s[2]\n" + "mov v26.16b, v12.16b\n" + "fadd v27.4s, v27.4s, v21.4s\n" + "mov v25.16b, v19.16b\n" + "str s27, [x28, x11]\n" + "fmls v30.4s, v5.4s, v0.s[2]\n" + "fadd v31.4s, v31.4s, v22.4s\n" + "fmls v29.4s, v6.4s, v0.s[2]\n" + "str s31, [x28, x13]\n" + "fmla v26.4s, v10.4s, v0.s[2]\n" + "fadd v30.4s, v30.4s, v23.4s\n" + "fmla v25.4s, v9.4s, v0.s[2]\n" + "str s30, [x28, x23]\n" + "fadd v29.4s, v29.4s, v24.4s\n" + "str s29, [x28, x15]\n" + "fmls v26.4s, v1.4s, v0.s[2]\n" + "fmls v25.4s, v2.4s, v0.s[2]\n" + "add x28, x28, #4\n" + "mov v30.16b, v20.16b\n" + "mov v29.16b, v16.16b\n" + "fsub v26.4s, v26.4s, v14.4s\n" + "mov v28.16b, v17.16b\n" + "str s26, [x22]\n" + "fsub v25.4s, v25.4s, v13.4s\n" + "str s25, [x22, %[output_col_stride1]]\n" + "fmla v30.4s, v7.4s, v0.s[2]\n" + "fmla v29.4s, v8.4s, v0.s[2]\n" + "fmla v28.4s, v11.4s, v0.s[2]\n" + "mov v26.16b, v18.16b\n" + "mov v25.16b, v12.16b\n" + "fmls v30.4s, v3.4s, v0.s[2]\n" + "mov v31.16b, v19.16b\n" + "fmls v29.4s, v4.4s, v0.s[2]\n" + "fmls v28.4s, v5.4s, v0.s[2]\n" + "fmla v26.4s, v15.4s, v0.s[2]\n" + "fmls v25.4s, v10.4s, v0.s[1]\n" + "fsub v30.4s, v30.4s, v21.4s\n" + "fmls v31.4s, v9.4s, v0.s[1]\n" + "str s30, [x22, x11]\n" + "fsub v29.4s, v29.4s, v22.4s\n" + "str s29, [x22, x13]\n" + "fsub v28.4s, v28.4s, v23.4s\n" + "str s28, [x22, x23]\n" + "fmls v26.4s, v6.4s, v0.s[2]\n" + "fsub v25.4s, v25.4s, v1.4s\n" + "fsub v31.4s, v31.4s, v2.4s\n" + "fmla v25.4s, v14.4s, v0.s[1]\n" + "fmla v31.4s, v13.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v24.4s\n" + "mov v27.16b, v20.16b\n" + "str s26, [x22, x15]\n" + "mov v26.16b, v16.16b\n" + "str s25, [x12]\n" + "fmls v27.4s, v7.4s, v0.s[1]\n" + "str s31, [x12, %[output_col_stride1]]\n" + "fmls v26.4s, v8.4s, v0.s[1]\n" + "mov v25.16b, v17.16b\n" + "add x22, x22, #4\n" + "fsub v27.4s, v27.4s, v3.4s\n" + "mov v28.16b, v18.16b\n" + "fmla v27.4s, v21.4s, v0.s[1]\n" + "fsub v26.4s, v26.4s, v4.4s\n" + "fmla v26.4s, v22.4s, v0.s[1]\n" + "fmls v25.4s, v11.4s, v0.s[1]\n" + "fmls v28.4s, v15.4s, v0.s[1]\n" + "mov v12.16b, v12.16b\n" + "str s27, [x12, x11]\n" + "mov v19.16b, v19.16b\n" + "str s26, [x12, x13]\n" + "fsub v25.4s, v25.4s, v5.4s\n" + "fmla v25.4s, v23.4s, v0.s[1]\n" + "fsub v28.4s, v28.4s, v6.4s\n" + "fmla v28.4s, v24.4s, v0.s[1]\n" + "fmla v12.4s, v10.4s, v0.s[1]\n" + "fmla v19.4s, v9.4s, v0.s[1]\n" + "mov v20.16b, v20.16b\n" + "str s25, [x12, x23]\n" + "mov v16.16b, v16.16b\n" + "str s28, [x12, x15]\n" + "fsub v12.4s, v12.4s, v1.4s\n" + "fmls v12.4s, v14.4s, v0.s[1]\n" + "add x12, x12, #4\n" + "fsub v19.4s, v19.4s, v2.4s\n" + "fmla v20.4s, v7.4s, v0.s[1]\n" + "fmls v19.4s, v13.4s, v0.s[1]\n" + "fmla v16.4s, v8.4s, v0.s[1]\n" + "str s12, [x14]\n" + "mov v1.16b, v17.16b\n" + "fsub v20.4s, v20.4s, v3.4s\n" + "mov v17.16b, v18.16b\n" + "str s19, [x14, %[output_col_stride1]]\n" + "fmls v20.4s, v21.4s, v0.s[1]\n" + "fsub v16.4s, v16.4s, v4.4s\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "fmls v16.4s, v22.4s, v0.s[1]\n" + "fmla v17.4s, v15.4s, v0.s[1]\n" + "str s20, [x14, x11]\n" + "fsub v1.4s, v1.4s, v5.4s\n" + "str s16, [x14, x13]\n" + "fmls v1.4s, v23.4s, v0.s[1]\n" + "fsub v17.4s, v17.4s, v6.4s\n" + "fmls v17.4s, v24.4s, v0.s[1]\n" + "str s1, [x14, x23]\n" + "str s17, [x14, x15]\n" + "add x14, x14, #4\n" + "ldr s2, [x27, x20]\n" + "mov v4.16b, v2.16b\n" + "ldr s17, [x27, x18]\n" + "mov v12.16b, v2.16b\n" + "ldr s18, [x27]\n" + "fmla v4.4s, v18.4s, v0.s[2]\n" + "ldr s3, [x27, x21]\n" + "mov v6.16b, v2.16b\n" + "ldr s5, [x27, x19]\n" + "mov v1.16b, v2.16b\n" + "ldr s18, [x27, %[input_col_stride1]]\n" + "fmls v4.4s, v17.4s, v0.s[3]\n" + "add x27, x27, #4\n" + "fmls v12.4s, v18.4s, v0.s[2]\n" + "fmla v6.4s, v18.4s, v0.s[2]\n" + "fmls v1.4s, v18.4s, v0.s[1]\n" + "mov v2.16b, v2.16b\n" + "mov v3.16b, v3.16b\n" + "mov v4.16b, v4.16b\n" + "fmls v12.4s, v17.4s, v0.s[2]\n" + "fmls v6.4s, v17.4s, v0.s[2]\n" + "fsub v1.4s, v1.4s, v17.4s\n" + "fmla v2.4s, v18.4s, v0.s[1]\n" + "fmla v1.4s, v5.4s, v0.s[1]\n" + "fmla v3.4s, v18.4s, v0.s[2]\n" + "fadd v12.4s, v12.4s, v5.4s\n" + "fsub v6.4s, v6.4s, v5.4s\n" + "fsub v2.4s, v2.4s, v17.4s\n" + "fmla v4.4s, v10.4s, v0.s[2]\n" + "fmls v2.4s, v5.4s, v0.s[1]\n" + "fmls v3.4s, v5.4s, v0.s[3]\n" + "mov v16.16b, v12.16b\n" + "mov v5.16b, v6.16b\n" + "fmls v4.4s, v14.4s, v0.s[3]\n" + "mov v6.16b, v1.16b\n" + "fmla v16.4s, v9.4s, v0.s[2]\n" + "fmla v5.4s, v7.4s, v0.s[2]\n" + "fmla v6.4s, v8.4s, v0.s[2]\n" + "mov v9.16b, v2.16b\n" + "str s4, [x24]\n" + "mov v10.16b, v3.16b\n" + "fmls v16.4s, v13.4s, v0.s[3]\n" + "fmls v5.4s, v21.4s, v0.s[3]\n" + "fmls v6.4s, v22.4s, v0.s[3]\n" + "fmla v9.4s, v11.4s, v0.s[2]\n" + "fmla v10.4s, v15.4s, v0.s[2]\n" + "str s16, [x24, %[output_col_stride1]]\n" + "str s5, [x24, x11]\n" + "fmls v9.4s, v23.4s, v0.s[3]\n" + "str s6, [x24, x13]\n" + "fmls v10.4s, v24.4s, v0.s[3]\n" + "str s9, [x24, x23]\n" + "str s10, [x24, x15]\n" + "add x24, x24, #4\n" + "4:\n" + : [outptr0] "+r" (matrix_base), + [n_channels] "+r" (n_channels), + [inptr0] "+r" (input_base) + : [pcoeffs] "r" (pcoeffs), + [output_row_stride] "r" (6 * matrix_stride * sizeof(float)), + [output_col_stride1] "r" (matrix_stride * sizeof(float)), + [input_row_stride] "r" (input_row_stride * sizeof(float)), + [input_col_stride1] "r" (input_col_stride * sizeof(float)) + : "cc", "v0", "v1", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", + "v18", "v19", "v2", "v20", "v21", "v22", "v23", "v24", "v25", "v26", + "v27", "v28", "v29", "v3", "v30", "v31", "v4", "v5", "v6", "v7", "v8", + "v9", "x11", "x12", "x13", "x14", "x15", "x16", "x17", "x18", "x19", + "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "memory" + ); +} + +#else // __arm__ not __aarch64__ + +template <> +void InputTransform<6, 6, float, float, WinogradRoots::Integers>::transform_tile( + const int n_channels, + const float* const input_base, + const int input_row_stride, + const int input_col_stride, + float* outptr, + const int matrix_stride +) +{ + constexpr int inner_tile_rows = 6; + constexpr int inner_tile_cols = 6; + + // Get pointers into the input tile + const float *x_ptrs[inner_tile_rows][inner_tile_cols]; + for (int i = 0, xi = 0; i < inner_tile_rows; i++, xi++) + { + // Get a pointer into the row + const float* const row_ptr = input_base + xi*input_row_stride; + + for (int j = 0, xj = 0; j < inner_tile_cols; j++, xj++) + { + x_ptrs[i][j] = row_ptr + xj*input_col_stride; + } + } + + // Matrices used/computed in this kernel. + float x[inner_tile_rows][inner_tile_cols]; + float XTx[inner_tile_rows][inner_tile_cols]; + float U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = XTx[i][j] = 0.0f; + } + } + + // Perform the Winograd input transformation for each channel in the input + // tensor. + int channels_remaining = n_channels; + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used/computed in this kernel + float32x2_t x[inner_tile_rows][inner_tile_cols]; + float32x2_t XTx[inner_tile_rows][inner_tile_cols]; + float32x2_t U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdup_n_f32(0.0f); + XTx[i][j] = vdup_n_f32(0.0f); + } + } + + // Read a 6x6 tile in the Winograd domain + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vld1_f32(x_ptrs[i][j]); + x_ptrs[i][j] += 2; + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); + + // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); + + // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); + + // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); + + // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); + + // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); + + // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1_f32(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 2; + } + for (; channels_remaining; channels_remaining--) + { + // Load x + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = *(x_ptrs[i][j]++); + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + *(outptr + m*matrix_stride) = U[i][j]; + } + } + outptr++; + } +} + +#endif + +template class InputTransform<6, 6, float, float, WinogradRoots::Integers>; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/kernel.hpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/kernel.hpp new file mode 100644 index 0000000000..e45f1863e3 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/kernel.hpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 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. + */ + +#pragma once +#include "winograd.hpp" +using namespace winograd; + +#define MEMBERFN(RTYPE) template <\ + int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename TIn, typename TOut, WinogradRoots Roots\ +> RTYPE WeightTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, TIn, TOut, Roots> + +MEMBERFN()::WeightTransform( + const int n_output_channels, + const int n_input_channels +) : _n_output_channels(n_output_channels), _n_input_channels(n_input_channels), + _matrices(nullptr), _matrix_stride(0), _matrix_row_stride(0), _weights(nullptr) +{ + +} + +MEMBERFN(void)::set_weight_tensor(const void * const weights) +{ + _weights = static_cast<const TIn *>(weights); +} + +MEMBERFN(void)::set_output_matrices(void * const mptr, const int ldmatrix, const int ldrow) +{ + _matrices = static_cast<TOut *>(mptr); + _matrix_stride = ldmatrix; + _matrix_row_stride = ldrow; +} + +MEMBERFN(size_t)::get_working_space_size(unsigned int) const +{ + return 0; +} + +MEMBERFN(void)::set_working_space(void *) +{ +} + +MEMBERFN(unsigned int)::get_window(void) const +{ + // TODO When the weights transform supports multithreading, return the number + // of output channels. For now we return 1 to indicate that the weights must + // be transformed as a single block. + // return n_output_channels; + return 1; +} + +MEMBERFN(void)::run(const unsigned int, const unsigned int, unsigned int) +{ + execute( + _n_output_channels, _n_input_channels, _weights, + _matrices, _matrix_stride, _matrix_row_stride + ); +} diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp new file mode 100644 index 0000000000..d97af21a43 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp @@ -0,0 +1,249 @@ +/* + * Copyright (c) 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. + */ + +#pragma once + +#include <algorithm> +#include "winograd.hpp" +#include "padding.hpp" +#include "utils.hpp" + +#define MEMBERFN(RTYPE) template<\ + int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols,\ + typename TIn, typename TOut, WinogradRoots Roots\ +> RTYPE OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, TIn, TOut, Roots> + +#define Nx1MEMBERFN(RTYPE) template<\ + int KernelRows, int InnerTileRows, typename TIn, typename TOut, WinogradRoots Roots\ +> RTYPE OutputTransform<KernelRows, 1, InnerTileRows, 1, TIn, TOut, Roots> + +namespace winograd +{ + +MEMBERFN()::OutputTransform( + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels +) : _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels), + _matrix_base(nullptr), + _biases(nullptr), + _matrix_stride(0), _matrix_row_stride(0), _matrix_batch_stride(0), + _outptr(nullptr), + _tiles_M(iceildiv(n_rows, output_tile_rows)), + _tiles_N(iceildiv(n_cols, output_tile_cols)), + _out_col_stride(0), _out_row_stride(0), _out_batch_stride(0), + _working_space_col_stride(n_channels), + _working_space_row_stride(output_tile_cols * _working_space_col_stride), + _working_space(nullptr) +{ +} + +MEMBERFN(void)::set_input_matrices(const void * const mptr, const int ldmatrix, const int ldrow) +{ + _matrix_base = static_cast<const TIn *>(mptr); + _matrix_stride = ldmatrix; + _matrix_row_stride = ldrow; + _matrix_batch_stride = _tiles_M * _tiles_N * ldrow; +} + +MEMBERFN(void)::set_bias(const void * const bias) +{ + _biases = static_cast<const TOut *>(bias); +} + +MEMBERFN(void)::set_output_tensor(void * const outptr) +{ + set_output_tensor(outptr, _n_channels); +} + +MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldcol) +{ + set_output_tensor(outptr, _n_cols * ldcol, ldcol); +} + +MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldrow, const int ldcol) +{ + set_output_tensor(outptr, _n_rows * ldrow, ldrow, ldcol); +} + +MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldbatch, const int ldrow, const int ldcol) +{ + _outptr = static_cast<TOut *>(outptr); + _out_batch_stride = ldbatch; + _out_row_stride = ldrow; + _out_col_stride = ldcol; +} + +Nx1MEMBERFN()::OutputTransform( + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels +) : OutputTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>::OutputTransform( + n_batches, n_cols, n_rows, n_channels /* Transpose rows and columns */ + ) +{ +} + +Nx1MEMBERFN(void)::set_output_tensor(void * const outptr) +{ + set_output_tensor(outptr, this->_n_channels); +} + +Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldcol) +{ + set_output_tensor(outptr, this->_n_cols * ldcol, ldcol); +} + +Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldrow, const int ldcol) +{ + set_output_tensor(outptr, this->_n_rows * ldrow, ldrow, ldcol); +} + +Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldbatch, const int ldrow, const int ldcol) +{ + // Transpose rows and columns + Base::set_output_tensor(outptr, ldbatch, ldcol, ldrow); +} + +MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const +{ + return sizeof(TOut) * output_tile_rows * _working_space_row_stride * nthreads; +} + +MEMBERFN(void)::set_working_space(void * const buffer) +{ + _working_space = static_cast<TOut *>(buffer); +} + +MEMBERFN(unsigned int)::get_window(void) const +{ + return iceildiv(_n_channels, WINDOW_BLOCK); +} + +MEMBERFN(void)::run( + const unsigned int start, + const unsigned int stop, + const unsigned int threadid +) +{ + // Determine the channels on which to work + if (start >= get_window()) + { + return; // No work to do beyond the end of the window + } + const unsigned int start_channel = start * WINDOW_BLOCK; + const unsigned int stop_channel = std::min<unsigned int>(_n_channels, stop * WINDOW_BLOCK); + const unsigned int n_channels = stop_channel - start_channel; + + const auto matrix_tile_col_stride = _matrix_row_stride; + const auto matrix_tile_row_stride = _tiles_N * matrix_tile_col_stride; + + const TOut* const bptr = (_biases == nullptr) ? nullptr : _biases + start_channel; + + // Loop over batches + for (int batch = 0; batch < _n_batches; batch++) + { + const TIn* const matrix_batch = _matrix_base + start_channel + batch * _matrix_batch_stride; + TOut* const outptr_batch = _outptr + start_channel + batch * _out_batch_stride; + + for (int tile_i = 0; tile_i < _tiles_M; tile_i++) + { + // Compute properties of the row of output tiles + const int row_pad_bottom = std::max(0, (tile_i + 1)*output_tile_rows - _n_rows); + const TIn* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride; + TOut* const outptr_row = outptr_batch + tile_i * output_tile_rows * _out_row_stride; + + for (int tile_j = 0; tile_j < _tiles_N; tile_j++) + { + // Compute property of this specific tile + const int tile_pad_right = std::max(0, (tile_j + 1)*output_tile_cols - _n_cols); + const TIn* const matrix_tile = matrix_tile_row + tile_j * matrix_tile_col_stride; + TOut* const outptr_tile = outptr_row + tile_j * output_tile_cols * _out_col_stride; + + // Perform the transformation + if (row_pad_bottom || tile_pad_right) + { + transform_cropped_tile( + threadid, n_channels, outptr_tile, matrix_tile, bptr, + row_pad_bottom, tile_pad_right + ); + } + else + { + transform_uncropped_tile( + threadid, n_channels, outptr_tile, matrix_tile, bptr + ); + } + } + } + } +} + +MEMBERFN(void)::transform_uncropped_tile( + const unsigned int /* threadid unused */, + const int n_channels, + TOut * const outptr, + const TIn * const inptr, + const TOut * const biases +) +{ + transform_tile( + n_channels, inptr, _matrix_stride, biases, + outptr, _out_row_stride, _out_col_stride + ); +} + +MEMBERFN(void)::transform_cropped_tile( + const unsigned int threadid, + const int n_channels, + TOut * const outptr, + const TIn * const inptr, + const TOut * const biases, + const int pad_bottom, + const int pad_right +) +{ + // Transform into working space and then copy the relevant section out. + TOut *wsptr = static_cast<TOut *>(get_working_space(threadid)); + transform_tile( + n_channels, inptr, _matrix_stride, biases, + wsptr, _working_space_row_stride, _working_space_col_stride + ); + + padding::crop_and_copy_tile( + output_tile_rows, output_tile_cols, n_channels, + wsptr, _working_space_row_stride, _working_space_col_stride, + outptr, _out_row_stride, _out_col_stride, + 0u, 0u, pad_bottom, pad_right + ); +} + +MEMBERFN(void *)::get_working_space(const unsigned int threadid) const +{ + return _working_space + output_tile_rows * _working_space_row_stride * threadid; +} + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2_7_fp32_fp32_integers.cpp index ea842a45ee..c32d7f2f58 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2_7_fp32_fp32_integers.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,43 +22,29 @@ * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" +#include "arm.hpp" +#include "output.hpp" -namespace +namespace winograd { -template <bool Specialized, int PadRight=0> -void winograd_output_transform_2_7_fp32_process_tile( +template <> +void OutputTransform<1, 7, 1, 8, float, float, WinogradRoots::Integers>::transform_tile( const int n_channels, - const float* const matrix_base, + const float* inptr, const int matrix_stride, - const float* const biases, + const float* bptr, float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right + const int, // No need to stride across rows + const int output_col_stride ) { - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 2; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) + float *outptrs[output_tile_cols]; + for (int j = 0; j < output_tile_cols; j++) { outptrs[j] = output + j*output_col_stride; } - const float *inptr = matrix_base; - const float *bptr = biases; // For each channel of the output int channels_remaining = n_channels; @@ -84,7 +70,7 @@ void winograd_output_transform_2_7_fp32_process_tile( b = vld1q_f32(bptr); bptr += 4; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1q_f32(outptrs[j], f[j] + b); outptrs[j] += 4; @@ -111,7 +97,7 @@ void winograd_output_transform_2_7_fp32_process_tile( b = vld1_f32(bptr); bptr += 2; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1_f32(outptrs[j], f[j] + b); outptrs[j] += 2; @@ -138,26 +124,14 @@ void winograd_output_transform_2_7_fp32_process_tile( { b = *(bptr++); } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { *(outptrs[j]++) = f[j] + b; } } } -} // namespace (anonymous) -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 7, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2_7_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2_7_fp32_process_tile<true, 1> -}; +template class OutputTransform<1, 7, 1, 8, float, float, WinogradRoots::Integers>; +template class OutputTransform<7, 1, 8, 1, float, float, WinogradRoots::Integers>; -template class OutputTransform<1, 7, 1, 8, float>; -template class OutputTransform<7, 1, 8, 1, float>; } // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_3x3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_3x3_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..d6ebf44f41 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_3x3_fp32_fp32_integers.cpp @@ -0,0 +1,222 @@ +/* + * Copyright (c) 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.hpp" +#include "output.hpp" + +namespace winograd +{ + +template <> +void OutputTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>::transform_tile( + const int n_channels, + const float* inptr, + const int matrix_stride, + const float* bptr, + float* const output, + const int output_row_stride, + const int output_col_stride +) +{ + // Construct a map to the output cells + float *outptrs[output_tile_rows][output_tile_cols]; + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; + } + } + + // For each channel of the output + int channels_remaining = n_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed during this transform + float32x4_t F[4][4], FZ[4][2], f[2][2], b; + + // Read a 4x4 tile in the Winograd domain + for (int i = 0, m = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++, m++) + { + F[i][j] = vld1q_f32(inptr + m*matrix_stride); + } + } + inptr += 4; + + // Compute the matrix F Z + for (int i = 0; i < 4; i++) + { + // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; + FZ[i][0] = vaddq_f32(vaddq_f32(F[i][0], F[i][1]), F[i][2]); + + // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; + FZ[i][1] = vsubq_f32(vsubq_f32(F[i][1], F[i][2]), F[i][3]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; + f[0][j] = vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); + + // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; + f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); + } + + // Load the bias vector + if (bptr != nullptr) + { + b = vld1q_f32(bptr); + bptr += 4; + } + else + { + b = vdupq_n_f32(0.0f); + } + + // Write out the output tile + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); + outptrs[i][j] += 4; + } + } + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed during this transform + float32x2_t F[4][4], FZ[4][2], f[2][2], b; + + // Read a 4x4 tile in the Winograd domain + for (int i = 0, m = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++, m++) + { + F[i][j] = vld1_f32(inptr + m*matrix_stride); + } + } + inptr += 2; + + // Compute the matrix F Z + for (int i = 0; i < 4; i++) + { + // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; + FZ[i][0] = vadd_f32(vadd_f32(F[i][0], F[i][1]), F[i][2]); + + // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; + FZ[i][1] = vsub_f32(vsub_f32(F[i][1], F[i][2]), F[i][3]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; + f[0][j] = vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); + + // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; + f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); + } + + // Load the bias vector + if (bptr != nullptr) + { + b = vld1_f32(bptr); + bptr += 2; + } + else + { + b = vdup_n_f32(0.0f); + } + + // Write out the output tile + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); + outptrs[i][j] += 2; + } + } + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed during this transform + float F[4][4], FZ[4][2], f[2][2], b; + + // Read a 4x4 tile in the Winograd domain + for (int i = 0, m = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++, m++) + { + F[i][j] = *(inptr + m*matrix_stride); + } + } + inptr++; + + // Compute the matrix F Z + for (int i = 0; i < 4; i++) + { + FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; + FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; + f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; + } + + // Load the bias + if (bptr != nullptr) + { + b = *(bptr++); + } + else + { + b = 0.0f; + } + + // Write out the output tile + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + *(outptrs[i][j]++) = f[i][j] + b; + } + } + } +} + +template class OutputTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>; + +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_5x5_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_5x5_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..d93d9e234a --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_2x2_5x5_fp32_fp32_integers.cpp @@ -0,0 +1,216 @@ +/* + * Copyright (c) 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 "output.hpp" +#include "arm.hpp" + +namespace winograd +{ + +template <> +void OutputTransform<5, 5, 6, 6, float, float, WinogradRoots::Integers>::transform_tile( + const int n_channels, + const float* inptr, + const int matrix_stride, + const float* bptr, + float* const output, + const int output_row_stride, + const int output_col_stride +) +{ + // Construct a map to the output cells + float *outptrs[output_tile_rows][output_tile_cols]; + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; + } + } + + // For each channel of the output + int channels_remaining = n_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed during this transform + float32x4_t F[6][6], FZ[6][2], f[2][2], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = vld1q_f32(inptr + m*matrix_stride); + } + } + inptr += 4; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); + + // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; + FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); + + // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; + f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); + } + + // Write out the output tile + if (bptr != nullptr) + { + b = vld1q_f32(bptr); + bptr += 4; + } + else + { + b = vdupq_n_f32(0.0f); + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); + outptrs[i][j] += 4; + } + } + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed during this transform + float32x2_t F[6][6], FZ[6][2], f[2][2], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = vld1_f32(inptr + m*matrix_stride); + } + } + inptr += 2; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); + + // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; + FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); + + // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; + f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); + } + + // Write out the output tile + if (bptr != nullptr) + { + b = vld1_f32(bptr); + bptr += 2; + } + else + { + b = vdup_n_f32(0.0f); + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); + outptrs[i][j] += 2; + } + } + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed during this transform + float F[6][6], FZ[6][2], f[2][2], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = *(inptr + m*matrix_stride); + } + } + inptr++; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 2; j++) + { + f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; + } + + // Write out the output tile + if (bptr != nullptr) + { + b = *(bptr++); + } + else + { + b = 0.0f; + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + *(outptrs[i][j]++) = f[i][j] + b; + } + } + } +} + +template class OutputTransform<5, 5, 6, 6, float, float, WinogradRoots::Integers>; + +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4_5_fp32_fp32_integers.cpp index 911759b128..7187ef2d20 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4_5_fp32_fp32_integers.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,42 +22,29 @@ * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" +#include "output.hpp" +#include "arm.hpp" -namespace +namespace winograd { -template <bool Specialized, int PadRight=0> -void winograd_output_transform_4_5_fp32_process_tile( +template <> +void OutputTransform<1, 5, 1, 8, float, float, WinogradRoots::Integers>::transform_tile( const int n_channels, - const float* const matrix_base, + const float* inptr, const int matrix_stride, - const float* const biases, + const float* bptr, float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right + const int, // No need to stride across rows + const int output_col_stride ) { - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 4; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) + float *outptrs[output_tile_cols]; + for (int j = 0; j < output_tile_cols; j++) { outptrs[j] = output + j*output_col_stride; } - const float *inptr = matrix_base; - const float *bptr = biases; // For each channel of the output int channels_remaining = n_channels; @@ -85,7 +72,7 @@ void winograd_output_transform_4_5_fp32_process_tile( b = vld1q_f32(bptr); bptr += 4; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1q_f32(outptrs[j], f[j] + b); outptrs[j] += 4; @@ -114,7 +101,7 @@ void winograd_output_transform_4_5_fp32_process_tile( b = vld1_f32(bptr); bptr += 2; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1_f32(outptrs[j], f[j] + b); outptrs[j] += 2; @@ -143,29 +130,14 @@ void winograd_output_transform_4_5_fp32_process_tile( { b = *(bptr++); } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { *(outptrs[j]++) = f[j] + b; } } } -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 5, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4_5_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_4_5_fp32_process_tile<true, 1>, - winograd_output_transform_4_5_fp32_process_tile<true, 2>, - winograd_output_transform_4_5_fp32_process_tile<true, 3> -}; +template class OutputTransform<1, 5, 1, 8, float, float, WinogradRoots::Integers>; +template class OutputTransform<5, 1, 8, 1, float, float, WinogradRoots::Integers>; -template class OutputTransform<1, 5, 1, 8, float>; -template class OutputTransform<5, 1, 8, 1, float>; } // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..fd16a4df1c --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp32_fp32_integers.cpp @@ -0,0 +1,1855 @@ +/* + * Copyright (c) 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.hpp" +#include "output.hpp" + +namespace winograd +{ + +#ifdef __aarch64__ + +template <> +void OutputTransform<3, 3, 6, 6, float, float, winograd::WinogradRoots::Integers>::transform_tile( + int n_channels, + const float* inptr, + const int matrix_stride, + const float* bptr, + float* output, + const int output_row_stride, + const int output_col_stride +) +{ + const float coeffs[2] = {2.0f, 4.0f}; + if (bptr != nullptr) + { + __asm__ __volatile__ ( + "ldr d0, [%[pcoeffs]]\n" + "add x21, %[in_col_stride1], %[in_col_stride1]\n" + "add x22, x21, %[in_col_stride1]\n" + "add x25, %[inptr0], %[in_row_stride]\n" + "add x15, %[output_col_stride1], %[output_col_stride1]\n" + "add x23, x22, %[in_col_stride1]\n" + "add x13, x25, %[in_row_stride]\n" + "add x16, x15, %[output_col_stride1]\n" + "add x24, x23, %[in_col_stride1]\n" + "add x26, x13, %[in_row_stride]\n" + "add x17, %[outptr0], %[output_row_stride]\n" + "add x14, x26, %[in_row_stride]\n" + "add x28, x17, %[output_row_stride]\n" + "lsr x19, %[n_channels], #2\n" + "add x27, x14, %[in_row_stride]\n" + "add x18, x28, %[output_row_stride]\n" + "and x20, %[n_channels], #3\n" + "cbz x19, 4f\n" + "1:\n" + "ldr q19, [%[inptr0]]\n" + "subs x19, x19, #1\n" + "ldr q20, [%[inptr0], %[in_col_stride1]]\n" + "ldr q4, [%[inptr0], x21]\n" + "fadd v1.4s, v20.4s, v4.4s\n" + "ldr q17, [%[inptr0], x22]\n" + "fsub v7.4s, v20.4s, v4.4s\n" + "ldr q22, [%[inptr0], x23]\n" + "fadd v5.4s, v17.4s, v22.4s\n" + "ldr q18, [%[inptr0], x24]\n" + "fsub v10.4s, v17.4s, v22.4s\n" + "ldr q25, [x25]\n" + "fadd v8.4s, v19.4s, v1.4s\n" + "ldr q12, [x25, %[in_col_stride1]]\n" + "mov v4.16b, v1.16b\n" + "ldr q23, [x25, x21]\n" + "mov v1.16b, v7.16b\n" + "ldr q9, [x25, x22]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "ldr q11, [x25, x23]\n" + "fadd v8.4s, v8.4s, v5.4s\n" + "ldr q6, [x25, x24]\n" + "fmla v4.4s, v5.4s, v0.s[1]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "fmla v1.4s, v10.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v18.4s\n" + "beq 3f\n" + "2:\n" + "fadd v3.4s, v12.4s, v23.4s\n" + "ldr q2, [x13]\n" + "fadd v27.4s, v9.4s, v11.4s\n" + "ldr q21, [x13, %[in_col_stride1]]\n" + "fsub v16.4s, v12.4s, v23.4s\n" + "ldr q26, [x13, x21]\n" + "fsub v9.4s, v9.4s, v11.4s\n" + "ldr q17, [x13, x22]\n" + "fadd v14.4s, v25.4s, v3.4s\n" + "ldr q19, [x13, x23]\n" + "mov v11.16b, v3.16b\n" + "ldr q10, [x13, x24]\n" + "mov v3.16b, v16.16b\n" + "ldr q15, [x26]\n" + "fmul v9.4s, v9.4s, v0.s[0]\n" + "ldr q12, [x26, %[in_col_stride1]]\n" + "fadd v14.4s, v14.4s, v27.4s\n" + "ldr q20, [x26, x21]\n" + "fmla v11.4s, v27.4s, v0.s[1]\n" + "ldr q24, [x26, x22]\n" + "fadd v23.4s, v21.4s, v26.4s\n" + "ldr q29, [x26, x23]\n" + "fadd v13.4s, v16.4s, v9.4s\n" + "ldr q5, [x26, x24]\n" + "fmla v3.4s, v9.4s, v0.s[1]\n" + "ldr q18, [x14]\n" + "fadd v30.4s, v17.4s, v19.4s\n" + "add %[inptr0], %[inptr0], #16\n" + "fadd v16.4s, v2.4s, v23.4s\n" + "add x25, x25, #16\n" + "fsub v21.4s, v21.4s, v26.4s\n" + "ldr q22, [x14, %[in_col_stride1]]\n" + "fadd v3.4s, v3.4s, v6.4s\n" + "ldr q28, [x14, x21]\n" + "fsub v19.4s, v17.4s, v19.4s\n" + "add x13, x13, #16\n" + "fadd v16.4s, v16.4s, v30.4s\n" + "add x26, x26, #16\n" + "mov v17.16b, v23.16b\n" + "subs x19, x19, #1\n" + "fadd v26.4s, v12.4s, v20.4s\n" + "fsub v9.4s, v12.4s, v20.4s\n" + "fmul v19.4s, v19.4s, v0.s[0]\n" + "ldr q20, [x14, x22]\n" + "fmla v17.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v24.4s, v29.4s\n" + "fsub v12.4s, v24.4s, v29.4s\n" + "fadd v24.4s, v22.4s, v28.4s\n" + "fadd v23.4s, v15.4s, v26.4s\n" + "mov v15.16b, v26.16b\n" + "fsub v22.4s, v22.4s, v28.4s\n" + "fadd v29.4s, v14.4s, v16.4s\n" + "fsub v16.4s, v14.4s, v16.4s\n" + "ldr q28, [x14, x23]\n" + "fmul v12.4s, v12.4s, v0.s[0]\n" + "fmla v15.4s, v25.4s, v0.s[1]\n" + "fadd v23.4s, v23.4s, v25.4s\n" + "mov v6.16b, v21.16b\n" + "fadd v30.4s, v21.4s, v19.4s\n" + "fadd v26.4s, v18.4s, v24.4s\n" + "mov v25.16b, v24.16b\n" + "fadd v18.4s, v8.4s, v29.4s\n" + "fmla v6.4s, v19.4s, v0.s[1]\n" + "fadd v27.4s, v20.4s, v28.4s\n" + "fsub v21.4s, v20.4s, v28.4s\n" + "mov v19.16b, v29.16b\n" + "fadd v29.4s, v13.4s, v30.4s\n" + "fsub v8.4s, v13.4s, v30.4s\n" + "fadd v14.4s, v9.4s, v12.4s\n" + "fadd v6.4s, v6.4s, v10.4s\n" + "ldr q20, [x14, x24]\n" + "fadd v26.4s, v26.4s, v27.4s\n" + "add x14, x14, #16\n" + "fmla v9.4s, v12.4s, v0.s[1]\n" + "ldr q24, [x27]\n" + "fmul v21.4s, v21.4s, v0.s[0]\n" + "fmla v25.4s, v27.4s, v0.s[1]\n" + "fadd v10.4s, v7.4s, v29.4s\n" + "ldr q2, [%[bptr]]\n" + "mov v7.16b, v29.16b\n" + "add %[bptr], %[bptr], #16\n" + "fadd v9.4s, v9.4s, v5.4s\n" + "fadd v13.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "fadd v27.4s, v11.4s, v17.4s\n" + "fsub v11.4s, v11.4s, v17.4s\n" + "fadd v30.4s, v15.4s, v25.4s\n" + "fsub v15.4s, v15.4s, v25.4s\n" + "ldr q28, [x27, %[in_col_stride1]]\n" + "fadd v18.4s, v18.4s, v13.4s\n" + "fmla v19.4s, v13.4s, v0.s[1]\n" + "fadd v26.4s, v22.4s, v21.4s\n" + "mov v12.16b, v22.16b\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fadd v17.4s, v4.4s, v27.4s\n" + "fmul v15.4s, v15.4s, v0.s[0]\n" + "mov v4.16b, v27.16b\n" + "fmla v12.4s, v21.4s, v0.s[1]\n" + "ldr q22, [x27, x21]\n" + "fadd v18.4s, v18.4s, v2.4s\n" + "fadd v19.4s, v19.4s, v2.4s\n" + "fadd v17.4s, v17.4s, v30.4s\n" + "fmla v4.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v28.4s, v22.4s\n" + "fsub v27.4s, v28.4s, v22.4s\n" + "fadd v12.4s, v12.4s, v20.4s\n" + "ldr q29, [x27, x22]\n" + "str q18, [%[outptr0]]\n" + "fadd v22.4s, v16.4s, v23.4s\n" + "str q19, [x28]\n" + "fadd v28.4s, v24.4s, v25.4s\n" + "ldr q30, [x27, x23]\n" + "fadd v20.4s, v29.4s, v30.4s\n" + "fsub v18.4s, v29.4s, v30.4s\n" + "mov v21.16b, v25.16b\n" + "ldr q25, [x27, x24]\n" + "fmla v16.4s, v23.4s, v0.s[1]\n" + "ldr q19, [%[inptr0]]\n" + "fadd v17.4s, v17.4s, v2.4s\n" + "add x27, x27, #16\n" + "fadd v28.4s, v28.4s, v20.4s\n" + "fmul v18.4s, v18.4s, v0.s[0]\n" + "fmla v21.4s, v20.4s, v0.s[1]\n" + "ldr q20, [%[inptr0], %[in_col_stride1]]\n" + "fadd v22.4s, v22.4s, v2.4s\n" + "fadd v4.4s, v4.4s, v2.4s\n" + "str q17, [%[outptr0], x15]\n" + "mov v24.16b, v27.16b\n" + "fadd v23.4s, v27.4s, v18.4s\n" + "fadd v16.4s, v16.4s, v28.4s\n" + "fadd v13.4s, v14.4s, v26.4s\n" + "fsub v30.4s, v14.4s, v26.4s\n" + "str q22, [x17]\n" + "fmla v24.4s, v18.4s, v0.s[1]\n" + "str q4, [x28, x15]\n" + "mov v14.16b, v8.16b\n" + "fadd v29.4s, v11.4s, v15.4s\n" + "ldr q4, [%[inptr0], x21]\n" + "fadd v10.4s, v10.4s, v13.4s\n" + "ldr q17, [%[inptr0], x22]\n" + "fadd v24.4s, v24.4s, v25.4s\n" + "ldr q22, [%[inptr0], x23]\n" + "fmul v30.4s, v30.4s, v0.s[0]\n" + "fmla v7.4s, v13.4s, v0.s[1]\n" + "mov v26.16b, v11.16b\n" + "fadd v13.4s, v3.4s, v6.4s\n" + "fsub v3.4s, v3.4s, v6.4s\n" + "ldr q18, [%[inptr0], x24]\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "fadd v29.4s, v29.4s, v2.4s\n" + "fadd v8.4s, v8.4s, v30.4s\n" + "fmla v14.4s, v30.4s, v0.s[1]\n" + "fmla v26.4s, v15.4s, v0.s[1]\n" + "ldr q25, [x25]\n" + "fadd v27.4s, v9.4s, v12.4s\n" + "fadd v1.4s, v1.4s, v13.4s\n" + "str q10, [%[outptr0], %[output_col_stride1]]\n" + "fsub v6.4s, v9.4s, v12.4s\n" + "str q29, [x17, x15]\n" + "fadd v14.4s, v14.4s, v23.4s\n" + "fadd v26.4s, v26.4s, v21.4s\n" + "ldr q12, [x25, %[in_col_stride1]]\n" + "fadd v1.4s, v1.4s, v27.4s\n" + "ldr q23, [x25, x21]\n" + "fmul v6.4s, v6.4s, v0.s[0]\n" + "ldr q9, [x25, x22]\n" + "mov v5.16b, v13.16b\n" + "ldr q11, [x25, x23]\n" + "mov v13.16b, v3.16b\n" + "fadd v8.4s, v8.4s, v2.4s\n" + "fadd v1.4s, v1.4s, v2.4s\n" + "fadd v7.4s, v7.4s, v2.4s\n" + "fadd v10.4s, v3.4s, v6.4s\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "fmla v13.4s, v6.4s, v0.s[1]\n" + "ldr q6, [x25, x24]\n" + "str q8, [x17, %[output_col_stride1]]\n" + "fadd v16.4s, v16.4s, v2.4s\n" + "str q1, [%[outptr0], x16]\n" + "fadd v14.4s, v14.4s, v2.4s\n" + "str q7, [x28, %[output_col_stride1]]\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "fadd v13.4s, v13.4s, v24.4s\n" + "add %[outptr0], %[outptr0], #16\n" + "str q16, [x18]\n" + "fadd v5.4s, v5.4s, v2.4s\n" + "str q14, [x18, %[output_col_stride1]]\n" + "fadd v26.4s, v26.4s, v2.4s\n" + "str q10, [x17, x16]\n" + "fadd v1.4s, v20.4s, v4.4s\n" + "fadd v13.4s, v13.4s, v2.4s\n" + "add x17, x17, #16\n" + "str q5, [x28, x16]\n" + "fadd v5.4s, v17.4s, v22.4s\n" + "str q26, [x18, x15]\n" + "fsub v7.4s, v20.4s, v4.4s\n" + "fadd v8.4s, v19.4s, v1.4s\n" + "add x28, x28, #16\n" + "str q13, [x18, x16]\n" + "mov v4.16b, v1.16b\n" + "fsub v10.4s, v17.4s, v22.4s\n" + "add x18, x18, #16\n" + "mov v1.16b, v7.16b\n" + "fadd v8.4s, v8.4s, v5.4s\n" + "fmla v4.4s, v5.4s, v0.s[1]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "fmla v1.4s, v10.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v18.4s\n" + "bne 2b\n" + "3:\n" + "fadd v3.4s, v12.4s, v23.4s\n" + "ldr q2, [x13]\n" + "fadd v27.4s, v9.4s, v11.4s\n" + "ldr q21, [x13, %[in_col_stride1]]\n" + "fsub v16.4s, v12.4s, v23.4s\n" + "ldr q26, [x13, x21]\n" + "fsub v9.4s, v9.4s, v11.4s\n" + "ldr q17, [x13, x22]\n" + "fadd v14.4s, v25.4s, v3.4s\n" + "ldr q19, [x13, x23]\n" + "mov v11.16b, v3.16b\n" + "ldr q10, [x13, x24]\n" + "mov v3.16b, v16.16b\n" + "ldr q15, [x26]\n" + "fmul v9.4s, v9.4s, v0.s[0]\n" + "ldr q12, [x26, %[in_col_stride1]]\n" + "fadd v14.4s, v14.4s, v27.4s\n" + "ldr q20, [x26, x21]\n" + "fmla v11.4s, v27.4s, v0.s[1]\n" + "ldr q24, [x26, x22]\n" + "fadd v23.4s, v21.4s, v26.4s\n" + "ldr q29, [x26, x23]\n" + "fadd v13.4s, v16.4s, v9.4s\n" + "ldr q5, [x26, x24]\n" + "fmla v3.4s, v9.4s, v0.s[1]\n" + "ldr q18, [x14]\n" + "fadd v30.4s, v17.4s, v19.4s\n" + "add %[inptr0], %[inptr0], #16\n" + "fadd v16.4s, v2.4s, v23.4s\n" + "add x25, x25, #16\n" + "fsub v21.4s, v21.4s, v26.4s\n" + "ldr q22, [x14, %[in_col_stride1]]\n" + "fadd v3.4s, v3.4s, v6.4s\n" + "ldr q28, [x14, x21]\n" + "fsub v19.4s, v17.4s, v19.4s\n" + "add x13, x13, #16\n" + "fadd v16.4s, v16.4s, v30.4s\n" + "add x26, x26, #16\n" + "mov v17.16b, v23.16b\n" + "fadd v26.4s, v12.4s, v20.4s\n" + "fsub v9.4s, v12.4s, v20.4s\n" + "ldr q2, [%[bptr]]\n" + "fmul v19.4s, v19.4s, v0.s[0]\n" + "add %[bptr], %[bptr], #16\n" + "fmla v17.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v24.4s, v29.4s\n" + "fadd v23.4s, v15.4s, v26.4s\n" + "fsub v12.4s, v24.4s, v29.4s\n" + "mov v15.16b, v26.16b\n" + "fadd v24.4s, v22.4s, v28.4s\n" + "fsub v22.4s, v22.4s, v28.4s\n" + "fadd v29.4s, v14.4s, v16.4s\n" + "fsub v16.4s, v14.4s, v16.4s\n" + "ldr q20, [x14, x22]\n" + "fadd v23.4s, v23.4s, v25.4s\n" + "fmul v12.4s, v12.4s, v0.s[0]\n" + "fmla v15.4s, v25.4s, v0.s[1]\n" + "mov v6.16b, v21.16b\n" + "fadd v30.4s, v21.4s, v19.4s\n" + "fadd v26.4s, v18.4s, v24.4s\n" + "mov v25.16b, v24.16b\n" + "fadd v18.4s, v8.4s, v29.4s\n" + "fmla v6.4s, v19.4s, v0.s[1]\n" + "mov v19.16b, v29.16b\n" + "fadd v27.4s, v11.4s, v17.4s\n" + "fsub v11.4s, v11.4s, v17.4s\n" + "fadd v29.4s, v13.4s, v30.4s\n" + "fsub v8.4s, v13.4s, v30.4s\n" + "fadd v14.4s, v9.4s, v12.4s\n" + "fadd v6.4s, v6.4s, v10.4s\n" + "ldr q28, [x14, x23]\n" + "fadd v17.4s, v4.4s, v27.4s\n" + "mov v4.16b, v27.16b\n" + "fmla v9.4s, v12.4s, v0.s[1]\n" + "fadd v27.4s, v20.4s, v28.4s\n" + "fsub v21.4s, v20.4s, v28.4s\n" + "fadd v10.4s, v7.4s, v29.4s\n" + "mov v7.16b, v29.16b\n" + "fadd v13.4s, v3.4s, v6.4s\n" + "fsub v3.4s, v3.4s, v6.4s\n" + "ldr q20, [x14, x24]\n" + "fadd v9.4s, v9.4s, v5.4s\n" + "fadd v26.4s, v26.4s, v27.4s\n" + "fmul v21.4s, v21.4s, v0.s[0]\n" + "add x14, x14, #16\n" + "fmla v25.4s, v27.4s, v0.s[1]\n" + "mov v12.16b, v22.16b\n" + "fadd v1.4s, v1.4s, v13.4s\n" + "mov v5.16b, v13.16b\n" + "fadd v13.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "fadd v26.4s, v22.4s, v21.4s\n" + "ldr q24, [x27]\n" + "fmla v12.4s, v21.4s, v0.s[1]\n" + "fadd v30.4s, v15.4s, v25.4s\n" + "fsub v15.4s, v15.4s, v25.4s\n" + "ldr q28, [x27, %[in_col_stride1]]\n" + "fadd v18.4s, v18.4s, v13.4s\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fmla v19.4s, v13.4s, v0.s[1]\n" + "ldr q22, [x27, x21]\n" + "fadd v12.4s, v12.4s, v20.4s\n" + "ldr q29, [x27, x22]\n" + "fadd v17.4s, v17.4s, v30.4s\n" + "fmul v15.4s, v15.4s, v0.s[0]\n" + "fmla v4.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v28.4s, v22.4s\n" + "fsub v27.4s, v28.4s, v22.4s\n" + "fadd v22.4s, v16.4s, v23.4s\n" + "fadd v18.4s, v18.4s, v2.4s\n" + "fadd v17.4s, v17.4s, v2.4s\n" + "fadd v19.4s, v19.4s, v2.4s\n" + "fadd v28.4s, v24.4s, v25.4s\n" + "mov v21.16b, v25.16b\n" + "fmla v16.4s, v23.4s, v0.s[1]\n" + "ldr q30, [x27, x23]\n" + "str q18, [%[outptr0]]\n" + "fadd v20.4s, v29.4s, v30.4s\n" + "str q17, [%[outptr0], x15]\n" + "fsub v18.4s, v29.4s, v30.4s\n" + "str q19, [x28]\n" + "mov v24.16b, v27.16b\n" + "fadd v13.4s, v14.4s, v26.4s\n" + "ldr q25, [x27, x24]\n" + "fadd v28.4s, v28.4s, v20.4s\n" + "add x27, x27, #16\n" + "fmul v18.4s, v18.4s, v0.s[0]\n" + "fmla v21.4s, v20.4s, v0.s[1]\n" + "fsub v30.4s, v14.4s, v26.4s\n" + "mov v14.16b, v8.16b\n" + "fadd v10.4s, v10.4s, v13.4s\n" + "fmla v7.4s, v13.4s, v0.s[1]\n" + "fadd v16.4s, v16.4s, v28.4s\n" + "fadd v29.4s, v11.4s, v15.4s\n" + "fadd v23.4s, v27.4s, v18.4s\n" + "fmla v24.4s, v18.4s, v0.s[1]\n" + "fmul v30.4s, v30.4s, v0.s[0]\n" + "mov v26.16b, v11.16b\n" + "fadd v27.4s, v9.4s, v12.4s\n" + "fsub v6.4s, v9.4s, v12.4s\n" + "mov v13.16b, v3.16b\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "fadd v24.4s, v24.4s, v25.4s\n" + "fmla v26.4s, v15.4s, v0.s[1]\n" + "fadd v8.4s, v8.4s, v30.4s\n" + "fmla v14.4s, v30.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v27.4s\n" + "fmul v6.4s, v6.4s, v0.s[0]\n" + "str q10, [%[outptr0], %[output_col_stride1]]\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "fadd v26.4s, v26.4s, v21.4s\n" + "fadd v22.4s, v22.4s, v2.4s\n" + "fadd v14.4s, v14.4s, v23.4s\n" + "fadd v8.4s, v8.4s, v2.4s\n" + "fadd v10.4s, v3.4s, v6.4s\n" + "fmla v13.4s, v6.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v2.4s\n" + "fadd v29.4s, v29.4s, v2.4s\n" + "str q22, [x17]\n" + "fadd v7.4s, v7.4s, v2.4s\n" + "str q8, [x17, %[output_col_stride1]]\n" + "fadd v4.4s, v4.4s, v2.4s\n" + "fadd v13.4s, v13.4s, v24.4s\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "str q1, [%[outptr0], x16]\n" + "fadd v5.4s, v5.4s, v2.4s\n" + "str q29, [x17, x15]\n" + "fadd v16.4s, v16.4s, v2.4s\n" + "str q7, [x28, %[output_col_stride1]]\n" + "fadd v14.4s, v14.4s, v2.4s\n" + "str q10, [x17, x16]\n" + "fadd v26.4s, v26.4s, v2.4s\n" + "str q4, [x28, x15]\n" + "fadd v13.4s, v13.4s, v2.4s\n" + "str q5, [x28, x16]\n" + "add %[outptr0], %[outptr0], #16\n" + "str q16, [x18]\n" + "add x17, x17, #16\n" + "str q14, [x18, %[output_col_stride1]]\n" + "add x28, x28, #16\n" + "str q26, [x18, x15]\n" + "str q13, [x18, x16]\n" + "add x18, x18, #16\n" + "4:\n" + "cmp x20, #2\n" + "blt 5f\n" + "ldr d19, [%[inptr0]]\n" + "ldr d20, [%[inptr0], %[in_col_stride1]]\n" + "sub x20, x20, #2\n" + "ldr d4, [%[inptr0], x21]\n" + "ldr d17, [%[inptr0], x22]\n" + "fadd v1.4s, v20.4s, v4.4s\n" + "ldr d22, [%[inptr0], x23]\n" + "fadd v5.4s, v17.4s, v22.4s\n" + "ldr d18, [%[inptr0], x24]\n" + "fsub v7.4s, v20.4s, v4.4s\n" + "ldr d25, [x25]\n" + "fsub v10.4s, v17.4s, v22.4s\n" + "ldr d12, [x25, %[in_col_stride1]]\n" + "fadd v8.4s, v19.4s, v1.4s\n" + "ldr d23, [x25, x21]\n" + "mov v4.16b, v1.16b\n" + "ldr d9, [x25, x22]\n" + "mov v1.16b, v7.16b\n" + "ldr d11, [x25, x23]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "ldr d6, [x25, x24]\n" + "fadd v8.4s, v8.4s, v5.4s\n" + "ldr d2, [x13]\n" + "fmla v4.4s, v5.4s, v0.s[1]\n" + "ldr d21, [x13, %[in_col_stride1]]\n" + "fadd v3.4s, v12.4s, v23.4s\n" + "ldr d26, [x13, x21]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "ldr d17, [x13, x22]\n" + "fmla v1.4s, v10.4s, v0.s[1]\n" + "ldr d19, [x13, x23]\n" + "fadd v27.4s, v9.4s, v11.4s\n" + "ldr d10, [x13, x24]\n" + "fadd v14.4s, v25.4s, v3.4s\n" + "ldr d15, [x26]\n" + "fsub v16.4s, v12.4s, v23.4s\n" + "ldr d12, [x26, %[in_col_stride1]]\n" + "fadd v1.4s, v1.4s, v18.4s\n" + "ldr d20, [x26, x21]\n" + "fsub v9.4s, v9.4s, v11.4s\n" + "ldr d24, [x26, x22]\n" + "fadd v14.4s, v14.4s, v27.4s\n" + "ldr d29, [x26, x23]\n" + "mov v11.16b, v3.16b\n" + "ldr d5, [x26, x24]\n" + "mov v3.16b, v16.16b\n" + "ldr d18, [x14]\n" + "fmul v9.4s, v9.4s, v0.s[0]\n" + "add %[inptr0], %[inptr0], #8\n" + "fmla v11.4s, v27.4s, v0.s[1]\n" + "add x25, x25, #8\n" + "fadd v23.4s, v21.4s, v26.4s\n" + "add x13, x13, #8\n" + "fsub v21.4s, v21.4s, v26.4s\n" + "ldr d22, [x14, %[in_col_stride1]]\n" + "fadd v13.4s, v16.4s, v9.4s\n" + "add x26, x26, #8\n" + "fmla v3.4s, v9.4s, v0.s[1]\n" + "fadd v30.4s, v17.4s, v19.4s\n" + "fadd v16.4s, v2.4s, v23.4s\n" + "fsub v19.4s, v17.4s, v19.4s\n" + "mov v17.16b, v23.16b\n" + "fadd v26.4s, v12.4s, v20.4s\n" + "fsub v9.4s, v12.4s, v20.4s\n" + "ldr d28, [x14, x21]\n" + "fadd v3.4s, v3.4s, v6.4s\n" + "ldr d20, [x14, x22]\n" + "fadd v16.4s, v16.4s, v30.4s\n" + "fmul v19.4s, v19.4s, v0.s[0]\n" + "fmla v17.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v24.4s, v29.4s\n" + "fadd v23.4s, v15.4s, v26.4s\n" + "fsub v12.4s, v24.4s, v29.4s\n" + "mov v15.16b, v26.16b\n" + "fadd v24.4s, v22.4s, v28.4s\n" + "fsub v22.4s, v22.4s, v28.4s\n" + "fadd v29.4s, v14.4s, v16.4s\n" + "fsub v16.4s, v14.4s, v16.4s\n" + "ldr d28, [x14, x23]\n" + "fadd v23.4s, v23.4s, v25.4s\n" + "fmul v12.4s, v12.4s, v0.s[0]\n" + "fmla v15.4s, v25.4s, v0.s[1]\n" + "mov v6.16b, v21.16b\n" + "fadd v30.4s, v21.4s, v19.4s\n" + "fadd v26.4s, v18.4s, v24.4s\n" + "mov v25.16b, v24.16b\n" + "fadd v18.4s, v8.4s, v29.4s\n" + "fmla v6.4s, v19.4s, v0.s[1]\n" + "fadd v27.4s, v20.4s, v28.4s\n" + "fsub v21.4s, v20.4s, v28.4s\n" + "mov v19.16b, v29.16b\n" + "fadd v29.4s, v13.4s, v30.4s\n" + "fsub v8.4s, v13.4s, v30.4s\n" + "fadd v14.4s, v9.4s, v12.4s\n" + "fadd v6.4s, v6.4s, v10.4s\n" + "ldr d20, [x14, x24]\n" + "fadd v26.4s, v26.4s, v27.4s\n" + "add x14, x14, #8\n" + "fmla v9.4s, v12.4s, v0.s[1]\n" + "ldr d24, [x27]\n" + "fmul v21.4s, v21.4s, v0.s[0]\n" + "fmla v25.4s, v27.4s, v0.s[1]\n" + "fadd v10.4s, v7.4s, v29.4s\n" + "ldr d2, [%[bptr]]\n" + "mov v7.16b, v29.16b\n" + "add %[bptr], %[bptr], #8\n" + "fadd v9.4s, v9.4s, v5.4s\n" + "fadd v13.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "fadd v27.4s, v11.4s, v17.4s\n" + "fsub v11.4s, v11.4s, v17.4s\n" + "fadd v30.4s, v15.4s, v25.4s\n" + "fsub v15.4s, v15.4s, v25.4s\n" + "ldr d28, [x27, %[in_col_stride1]]\n" + "fadd v18.4s, v18.4s, v13.4s\n" + "fmla v19.4s, v13.4s, v0.s[1]\n" + "fadd v26.4s, v22.4s, v21.4s\n" + "mov v12.16b, v22.16b\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fadd v17.4s, v4.4s, v27.4s\n" + "fmul v15.4s, v15.4s, v0.s[0]\n" + "mov v4.16b, v27.16b\n" + "fmla v12.4s, v21.4s, v0.s[1]\n" + "ldr d22, [x27, x21]\n" + "fadd v18.4s, v18.4s, v2.4s\n" + "fadd v19.4s, v19.4s, v2.4s\n" + "fadd v17.4s, v17.4s, v30.4s\n" + "fmla v4.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v28.4s, v22.4s\n" + "fsub v27.4s, v28.4s, v22.4s\n" + "fadd v12.4s, v12.4s, v20.4s\n" + "ldr d29, [x27, x22]\n" + "str d18, [%[outptr0]]\n" + "fadd v22.4s, v16.4s, v23.4s\n" + "str d19, [x28]\n" + "fadd v28.4s, v24.4s, v25.4s\n" + "ldr d30, [x27, x23]\n" + "fadd v20.4s, v29.4s, v30.4s\n" + "fsub v18.4s, v29.4s, v30.4s\n" + "mov v21.16b, v25.16b\n" + "ldr d25, [x27, x24]\n" + "fmla v16.4s, v23.4s, v0.s[1]\n" + "add x27, x27, #8\n" + "mov v24.16b, v27.16b\n" + "fadd v17.4s, v17.4s, v2.4s\n" + "fadd v28.4s, v28.4s, v20.4s\n" + "fmul v18.4s, v18.4s, v0.s[0]\n" + "fmla v21.4s, v20.4s, v0.s[1]\n" + "fadd v13.4s, v14.4s, v26.4s\n" + "fsub v30.4s, v14.4s, v26.4s\n" + "mov v14.16b, v8.16b\n" + "str d17, [%[outptr0], x15]\n" + "fadd v29.4s, v11.4s, v15.4s\n" + "fadd v23.4s, v27.4s, v18.4s\n" + "fmla v24.4s, v18.4s, v0.s[1]\n" + "fadd v16.4s, v16.4s, v28.4s\n" + "fadd v10.4s, v10.4s, v13.4s\n" + "fmul v30.4s, v30.4s, v0.s[0]\n" + "fmla v7.4s, v13.4s, v0.s[1]\n" + "mov v26.16b, v11.16b\n" + "fadd v13.4s, v3.4s, v6.4s\n" + "fadd v24.4s, v24.4s, v25.4s\n" + "fadd v27.4s, v9.4s, v12.4s\n" + "fsub v3.4s, v3.4s, v6.4s\n" + "fsub v6.4s, v9.4s, v12.4s\n" + "fadd v8.4s, v8.4s, v30.4s\n" + "fmla v14.4s, v30.4s, v0.s[1]\n" + "fmla v26.4s, v15.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v13.4s\n" + "mov v5.16b, v13.16b\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "fmul v6.4s, v6.4s, v0.s[0]\n" + "mov v13.16b, v3.16b\n" + "fadd v14.4s, v14.4s, v23.4s\n" + "fadd v22.4s, v22.4s, v2.4s\n" + "fadd v26.4s, v26.4s, v21.4s\n" + "fadd v1.4s, v1.4s, v27.4s\n" + "str d10, [%[outptr0], %[output_col_stride1]]\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "fadd v10.4s, v3.4s, v6.4s\n" + "fmla v13.4s, v6.4s, v0.s[1]\n" + "str d22, [x17]\n" + "fadd v8.4s, v8.4s, v2.4s\n" + "fadd v1.4s, v1.4s, v2.4s\n" + "fadd v29.4s, v29.4s, v2.4s\n" + "fadd v7.4s, v7.4s, v2.4s\n" + "fadd v4.4s, v4.4s, v2.4s\n" + "fadd v13.4s, v13.4s, v24.4s\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "str d8, [x17, %[output_col_stride1]]\n" + "fadd v5.4s, v5.4s, v2.4s\n" + "str d1, [%[outptr0], x16]\n" + "fadd v16.4s, v16.4s, v2.4s\n" + "str d29, [x17, x15]\n" + "fadd v14.4s, v14.4s, v2.4s\n" + "str d10, [x17, x16]\n" + "fadd v26.4s, v26.4s, v2.4s\n" + "str d7, [x28, %[output_col_stride1]]\n" + "fadd v13.4s, v13.4s, v2.4s\n" + "str d4, [x28, x15]\n" + "add %[outptr0], %[outptr0], #8\n" + "str d5, [x28, x16]\n" + "add x17, x17, #8\n" + "str d16, [x18]\n" + "add x28, x28, #8\n" + "str d14, [x18, %[output_col_stride1]]\n" + "str d26, [x18, x15]\n" + "str d13, [x18, x16]\n" + "add x18, x18, #8\n" + "5:\n" + "cbz x20, 6f\n" + "ldr s19, [%[inptr0]]\n" + "ldr s20, [%[inptr0], %[in_col_stride1]]\n" + "ldr s4, [%[inptr0], x21]\n" + "fadd v1.4s, v20.4s, v4.4s\n" + "ldr s17, [%[inptr0], x22]\n" + "fsub v7.4s, v20.4s, v4.4s\n" + "ldr s22, [%[inptr0], x23]\n" + "fadd v5.4s, v17.4s, v22.4s\n" + "ldr s18, [%[inptr0], x24]\n" + "fsub v10.4s, v17.4s, v22.4s\n" + "ldr s25, [x25]\n" + "fadd v8.4s, v19.4s, v1.4s\n" + "ldr s12, [x25, %[in_col_stride1]]\n" + "mov v4.16b, v1.16b\n" + "ldr s23, [x25, x21]\n" + "mov v1.16b, v7.16b\n" + "ldr s9, [x25, x22]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "ldr s11, [x25, x23]\n" + "fadd v8.4s, v8.4s, v5.4s\n" + "ldr s6, [x25, x24]\n" + "fmla v4.4s, v5.4s, v0.s[1]\n" + "ldr s2, [x13]\n" + "fadd v3.4s, v12.4s, v23.4s\n" + "ldr s21, [x13, %[in_col_stride1]]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "ldr s26, [x13, x21]\n" + "fmla v1.4s, v10.4s, v0.s[1]\n" + "ldr s17, [x13, x22]\n" + "fadd v27.4s, v9.4s, v11.4s\n" + "ldr s19, [x13, x23]\n" + "fadd v14.4s, v25.4s, v3.4s\n" + "ldr s10, [x13, x24]\n" + "fsub v16.4s, v12.4s, v23.4s\n" + "ldr s15, [x26]\n" + "fadd v1.4s, v1.4s, v18.4s\n" + "ldr s12, [x26, %[in_col_stride1]]\n" + "fsub v9.4s, v9.4s, v11.4s\n" + "ldr s20, [x26, x21]\n" + "fadd v14.4s, v14.4s, v27.4s\n" + "ldr s24, [x26, x22]\n" + "mov v11.16b, v3.16b\n" + "ldr s29, [x26, x23]\n" + "mov v3.16b, v16.16b\n" + "ldr s5, [x26, x24]\n" + "fmul v9.4s, v9.4s, v0.s[0]\n" + "ldr s18, [x14]\n" + "fmla v11.4s, v27.4s, v0.s[1]\n" + "fadd v23.4s, v21.4s, v26.4s\n" + "fsub v21.4s, v21.4s, v26.4s\n" + "fadd v30.4s, v17.4s, v19.4s\n" + "fsub v19.4s, v17.4s, v19.4s\n" + "ldr s22, [x14, %[in_col_stride1]]\n" + "fadd v13.4s, v16.4s, v9.4s\n" + "fmla v3.4s, v9.4s, v0.s[1]\n" + "fadd v16.4s, v2.4s, v23.4s\n" + "mov v17.16b, v23.16b\n" + "fadd v26.4s, v12.4s, v20.4s\n" + "fsub v9.4s, v12.4s, v20.4s\n" + "fmul v19.4s, v19.4s, v0.s[0]\n" + "ldr s28, [x14, x21]\n" + "fadd v3.4s, v3.4s, v6.4s\n" + "ldr s20, [x14, x22]\n" + "fadd v16.4s, v16.4s, v30.4s\n" + "fmla v17.4s, v30.4s, v0.s[1]\n" + "fadd v25.4s, v24.4s, v29.4s\n" + "fadd v23.4s, v15.4s, v26.4s\n" + "fsub v12.4s, v24.4s, v29.4s\n" + "mov v15.16b, v26.16b\n" + "fadd v24.4s, v22.4s, v28.4s\n" + "fsub v22.4s, v22.4s, v28.4s\n" + "fadd v30.4s, v21.4s, v19.4s\n" + "mov v6.16b, v21.16b\n" + "fadd v23.4s, v23.4s, v25.4s\n" + "fmla v15.4s, v25.4s, v0.s[1]\n" + "fmul v12.4s, v12.4s, v0.s[0]\n" + "ldr s28, [x14, x23]\n" + "fmla v6.4s, v19.4s, v0.s[1]\n" + "fadd v27.4s, v20.4s, v28.4s\n" + "fadd v26.4s, v18.4s, v24.4s\n" + "fsub v21.4s, v20.4s, v28.4s\n" + "mov v25.16b, v24.16b\n" + "fadd v29.4s, v14.4s, v16.4s\n" + "fsub v16.4s, v14.4s, v16.4s\n" + "ldr s20, [x14, x24]\n" + "fadd v6.4s, v6.4s, v10.4s\n" + "ldr s24, [x27]\n" + "fadd v26.4s, v26.4s, v27.4s\n" + "fmul v21.4s, v21.4s, v0.s[0]\n" + "fmla v25.4s, v27.4s, v0.s[1]\n" + "fadd v18.4s, v8.4s, v29.4s\n" + "mov v19.16b, v29.16b\n" + "fadd v29.4s, v13.4s, v30.4s\n" + "fsub v8.4s, v13.4s, v30.4s\n" + "fadd v27.4s, v11.4s, v17.4s\n" + "fsub v11.4s, v11.4s, v17.4s\n" + "fadd v13.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "ldr s28, [x27, %[in_col_stride1]]\n" + "fadd v10.4s, v7.4s, v29.4s\n" + "mov v7.16b, v29.16b\n" + "fadd v17.4s, v4.4s, v27.4s\n" + "mov v4.16b, v27.16b\n" + "fadd v18.4s, v18.4s, v13.4s\n" + "fmla v19.4s, v13.4s, v0.s[1]\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fadd v30.4s, v15.4s, v25.4s\n" + "fsub v15.4s, v15.4s, v25.4s\n" + "fadd v13.4s, v3.4s, v6.4s\n" + "fsub v3.4s, v3.4s, v6.4s\n" + "ldr s2, [%[bptr]]\n" + "fadd v18.4s, v18.4s, v2.4s\n" + "fadd v19.4s, v19.4s, v2.4s\n" + "fadd v17.4s, v17.4s, v30.4s\n" + "fmla v4.4s, v30.4s, v0.s[1]\n" + "fadd v14.4s, v9.4s, v12.4s\n" + "fmul v15.4s, v15.4s, v0.s[0]\n" + "fadd v1.4s, v1.4s, v13.4s\n" + "str s18, [%[outptr0]]\n" + "fadd v26.4s, v22.4s, v21.4s\n" + "str s19, [x28]\n" + "fmla v9.4s, v12.4s, v0.s[1]\n" + "mov v12.16b, v22.16b\n" + "ldr s22, [x27, x21]\n" + "fadd v25.4s, v28.4s, v22.4s\n" + "fsub v27.4s, v28.4s, v22.4s\n" + "fadd v22.4s, v16.4s, v23.4s\n" + "fadd v9.4s, v9.4s, v5.4s\n" + "ldr s29, [x27, x22]\n" + "fmla v12.4s, v21.4s, v0.s[1]\n" + "ldr s30, [x27, x23]\n" + "fadd v28.4s, v24.4s, v25.4s\n" + "mov v21.16b, v25.16b\n" + "fmla v16.4s, v23.4s, v0.s[1]\n" + "ldr s25, [x27, x24]\n" + "mov v5.16b, v13.16b\n" + "fadd v17.4s, v17.4s, v2.4s\n" + "fadd v12.4s, v12.4s, v20.4s\n" + "fadd v20.4s, v29.4s, v30.4s\n" + "fsub v18.4s, v29.4s, v30.4s\n" + "mov v24.16b, v27.16b\n" + "fadd v22.4s, v22.4s, v2.4s\n" + "fadd v4.4s, v4.4s, v2.4s\n" + "str s17, [%[outptr0], x15]\n" + "fadd v13.4s, v14.4s, v26.4s\n" + "fadd v28.4s, v28.4s, v20.4s\n" + "fmla v21.4s, v20.4s, v0.s[1]\n" + "fmul v18.4s, v18.4s, v0.s[0]\n" + "fsub v30.4s, v14.4s, v26.4s\n" + "str s22, [x17]\n" + "mov v14.16b, v8.16b\n" + "str s4, [x28, x15]\n" + "fadd v10.4s, v10.4s, v13.4s\n" + "fadd v16.4s, v16.4s, v28.4s\n" + "fmla v7.4s, v13.4s, v0.s[1]\n" + "fadd v23.4s, v27.4s, v18.4s\n" + "fmla v24.4s, v18.4s, v0.s[1]\n" + "fmul v30.4s, v30.4s, v0.s[0]\n" + "fadd v29.4s, v11.4s, v15.4s\n" + "mov v26.16b, v11.16b\n" + "fadd v27.4s, v9.4s, v12.4s\n" + "fsub v6.4s, v9.4s, v12.4s\n" + "mov v13.16b, v3.16b\n" + "fadd v24.4s, v24.4s, v25.4s\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "fadd v8.4s, v8.4s, v30.4s\n" + "fmla v14.4s, v30.4s, v0.s[1]\n" + "fmla v26.4s, v15.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v27.4s\n" + "fmul v6.4s, v6.4s, v0.s[0]\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "str s10, [%[outptr0], %[output_col_stride1]]\n" + "fadd v29.4s, v29.4s, v2.4s\n" + "fadd v14.4s, v14.4s, v23.4s\n" + "fadd v8.4s, v8.4s, v2.4s\n" + "fadd v26.4s, v26.4s, v21.4s\n" + "fadd v1.4s, v1.4s, v2.4s\n" + "fadd v10.4s, v3.4s, v6.4s\n" + "fmla v13.4s, v6.4s, v0.s[1]\n" + "str s29, [x17, x15]\n" + "fadd v7.4s, v7.4s, v2.4s\n" + "str s8, [x17, %[output_col_stride1]]\n" + "fadd v5.4s, v5.4s, v2.4s\n" + "str s1, [%[outptr0], x16]\n" + "fadd v16.4s, v16.4s, v2.4s\n" + "fadd v13.4s, v13.4s, v24.4s\n" + "fadd v10.4s, v10.4s, v2.4s\n" + "str s7, [x28, %[output_col_stride1]]\n" + "fadd v14.4s, v14.4s, v2.4s\n" + "str s5, [x28, x16]\n" + "fadd v26.4s, v26.4s, v2.4s\n" + "str s16, [x18]\n" + "fadd v13.4s, v13.4s, v2.4s\n" + "str s10, [x17, x16]\n" + "str s14, [x18, %[output_col_stride1]]\n" + "str s26, [x18, x15]\n" + "str s13, [x18, x16]\n" + "6:\n" + : [bptr] "+r" (bptr), [outptr0] "+r" (output), [inptr0] "+r" (inptr) + : [output_row_stride] "r" (output_row_stride * sizeof(float)), [output_col_stride1] "r" (output_col_stride * sizeof(float)), [pcoeffs] "r" (coeffs), [n_channels] "r" ((long) n_channels), [in_row_stride] "r" (6 * matrix_stride * sizeof(float)), [in_col_stride1] "r" (matrix_stride * sizeof(float)) + : "cc", "v0", "v1", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v2", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v3", "v30", "v4", "v5", "v6", "v7", "v8", "v9", "x13", "x14", "x15", "x16", "x17", "x18", "x19", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "memory" + ); + } + else + { + __asm__ __volatile__ ( + "ldr d0, [%[pcoeffs]]\n" + "add x21, %[in_col_stride1], %[in_col_stride1]\n" // Compute input column stride 2 + "add x22, x21, %[in_col_stride1]\n" // Compute input column stride 3 + "add x25, %[inptr0], %[in_row_stride]\n" // Compute input row pointers + "add x15, %[output_col_stride1], %[output_col_stride1]\n" // Compute output column stride 2 + "add x23, x22, %[in_col_stride1]\n" // Compute input column stride 4 + "add x13, x25, %[in_row_stride]\n" // Compute input row pointers + "add x16, x15, %[output_col_stride1]\n" // Compute output column stride 3 + "add x24, x23, %[in_col_stride1]\n" // Compute input column stride 5 + "add x26, x13, %[in_row_stride]\n" // Compute input row pointers + "add x17, %[outptr0], %[output_row_stride]\n" // Compute output row pointer 1 + "add x14, x26, %[in_row_stride]\n" // Compute input row pointers + "add x28, x17, %[output_row_stride]\n" // Compute output row pointer 2 + "lsr x19, %[n_channels], #2\n" + "add x27, x14, %[in_row_stride]\n" // Compute input row pointers + "add x18, x28, %[output_row_stride]\n" // Compute output row pointer 3 + "and x20, %[n_channels], #3\n" + "cbz x19, 4f\n" + "1:\n" // Quad head + "ldr q17, [%[inptr0]]\n" + "subs x19, x19, #1\n" + "ldr q23, [%[inptr0], %[in_col_stride1]]\n" + "ldr q27, [%[inptr0], x21]\n" + "fadd v4.4s, v23.4s, v27.4s\n" + "ldr q24, [%[inptr0], x22]\n" + "fsub v13.4s, v23.4s, v27.4s\n" + "ldr q11, [%[inptr0], x23]\n" + "fadd v10.4s, v24.4s, v11.4s\n" + "ldr q12, [%[inptr0], x24]\n" + "fsub v11.4s, v24.4s, v11.4s\n" + "ldr q20, [x25]\n" + "fadd v7.4s, v17.4s, v4.4s\n" + "ldr q19, [x25, %[in_col_stride1]]\n" + "mov v4.16b, v4.16b\n" + "ldr q22, [x25, x21]\n" + "mov v1.16b, v13.16b\n" + "ldr q14, [x25, x22]\n" + "fmul v11.4s, v11.4s, v0.s[0]\n" + "ldr q18, [x25, x23]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "ldr q3, [x25, x24]\n" + "fmla v4.4s, v10.4s, v0.s[1]\n" + "fadd v8.4s, v13.4s, v11.4s\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v12.4s\n" + "beq 3f\n" + "2:\n" // Quad loop + "fadd v2.4s, v19.4s, v22.4s\n" + "ldr q16, [x13]\n" + "fadd v23.4s, v14.4s, v18.4s\n" + "ldr q21, [x13, %[in_col_stride1]]\n" + "fsub v15.4s, v19.4s, v22.4s\n" + "ldr q24, [x13, x21]\n" + "fsub v31.4s, v14.4s, v18.4s\n" + "ldr q25, [x13, x22]\n" + "fadd v11.4s, v20.4s, v2.4s\n" + "ldr q17, [x13, x23]\n" + "mov v13.16b, v2.16b\n" + "ldr q9, [x13, x24]\n" + "mov v2.16b, v15.16b\n" + "ldr q6, [x26]\n" + "fmul v31.4s, v31.4s, v0.s[0]\n" + "ldr q19, [x26, %[in_col_stride1]]\n" + "fadd v11.4s, v11.4s, v23.4s\n" + "ldr q22, [x26, x21]\n" + "fmla v13.4s, v23.4s, v0.s[1]\n" + "ldr q12, [x26, x22]\n" + "fadd v29.4s, v21.4s, v24.4s\n" + "ldr q26, [x26, x23]\n" + "fadd v15.4s, v15.4s, v31.4s\n" + "ldr q5, [x26, x24]\n" + "fmla v2.4s, v31.4s, v0.s[1]\n" + "ldr q10, [x14]\n" + "fadd v18.4s, v25.4s, v17.4s\n" + "add %[inptr0], %[inptr0], #16\n" + "fadd v27.4s, v16.4s, v29.4s\n" + "add x25, x25, #16\n" + "fsub v14.4s, v21.4s, v24.4s\n" + "ldr q30, [x14, %[in_col_stride1]]\n" + "fadd v2.4s, v2.4s, v3.4s\n" + "ldr q31, [x14, x21]\n" + "fsub v28.4s, v25.4s, v17.4s\n" + "add x13, x13, #16\n" + "fadd v27.4s, v27.4s, v18.4s\n" + "add x26, x26, #16\n" + "mov v21.16b, v29.16b\n" + "subs x19, x19, #1\n" + "fadd v20.4s, v19.4s, v22.4s\n" + "fsub v17.4s, v19.4s, v22.4s\n" + "fmul v28.4s, v28.4s, v0.s[0]\n" + "ldr q23, [x14, x22]\n" + "fmla v21.4s, v18.4s, v0.s[1]\n" + "fadd v29.4s, v12.4s, v26.4s\n" + "fsub v16.4s, v12.4s, v26.4s\n" + "fadd v25.4s, v30.4s, v31.4s\n" + "fadd v24.4s, v6.4s, v20.4s\n" + "mov v6.16b, v20.16b\n" + "fsub v22.4s, v30.4s, v31.4s\n" + "fadd v31.4s, v11.4s, v27.4s\n" + "fsub v12.4s, v11.4s, v27.4s\n" + "ldr q26, [x14, x23]\n" + "fmul v16.4s, v16.4s, v0.s[0]\n" + "fmla v6.4s, v29.4s, v0.s[1]\n" + "fadd v24.4s, v24.4s, v29.4s\n" + "mov v3.16b, v14.16b\n" + "fadd v20.4s, v14.4s, v28.4s\n" + "fadd v29.4s, v10.4s, v25.4s\n" + "mov v10.16b, v25.16b\n" + "fadd v25.4s, v7.4s, v31.4s\n" + "fmla v3.4s, v28.4s, v0.s[1]\n" + "fadd v14.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "mov v26.16b, v31.16b\n" + "fadd v31.4s, v15.4s, v20.4s\n" + "fsub v11.4s, v15.4s, v20.4s\n" + "fadd v20.4s, v17.4s, v16.4s\n" + "mov v7.16b, v17.16b\n" + "fadd v3.4s, v3.4s, v9.4s\n" + "ldr q18, [x14, x24]\n" + "fadd v29.4s, v29.4s, v14.4s\n" + "add x14, x14, #16\n" + "fmla v7.4s, v16.4s, v0.s[1]\n" + "ldr q19, [x27]\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fmla v10.4s, v14.4s, v0.s[1]\n" + "fadd v15.4s, v8.4s, v31.4s\n" + "mov v14.16b, v31.16b\n" + "fadd v28.4s, v24.4s, v29.4s\n" + "fsub v24.4s, v24.4s, v29.4s\n" + "fadd v7.4s, v7.4s, v5.4s\n" + "ldr q27, [x27, %[in_col_stride1]]\n" + "fadd v30.4s, v13.4s, v21.4s\n" + "fsub v9.4s, v13.4s, v21.4s\n" + "fadd v17.4s, v22.4s, v23.4s\n" + "mov v8.16b, v22.16b\n" + "fadd v25.4s, v25.4s, v28.4s\n" + "fmul v24.4s, v24.4s, v0.s[0]\n" + "fmla v26.4s, v28.4s, v0.s[1]\n" + "ldr q29, [x27, x21]\n" + "fmla v8.4s, v23.4s, v0.s[1]\n" + "ldr q28, [x27, x22]\n" + "fadd v13.4s, v4.4s, v30.4s\n" + "mov v4.16b, v30.16b\n" + "str q25, [%[outptr0]]\n" // Store output (0, 0) + "fadd v16.4s, v27.4s, v29.4s\n" + "str q26, [x28]\n" // Store output (2, 0) + "fsub v29.4s, v27.4s, v29.4s\n" + "fadd v8.4s, v8.4s, v18.4s\n" + "ldr q23, [x27, x23]\n" + "fadd v30.4s, v28.4s, v23.4s\n" + "ldr q25, [x27, x24]\n" + "fadd v19.4s, v19.4s, v16.4s\n" + "add x27, x27, #16\n" + "fsub v27.4s, v28.4s, v23.4s\n" + "mov v16.16b, v16.16b\n" + "fadd v22.4s, v20.4s, v17.4s\n" + "fsub v20.4s, v20.4s, v17.4s\n" + "fadd v21.4s, v12.4s, v24.4s\n" + "mov v26.16b, v12.16b\n" + "fadd v19.4s, v19.4s, v30.4s\n" + "fmla v16.4s, v30.4s, v0.s[1]\n" + "fmul v27.4s, v27.4s, v0.s[0]\n" + "ldr q17, [%[inptr0]]\n" + "fmla v26.4s, v24.4s, v0.s[1]\n" + "ldr q23, [%[inptr0], %[in_col_stride1]]\n" + "str q21, [x17]\n" // Store output (1, 0) + "mov v5.16b, v29.16b\n" + "fadd v15.4s, v15.4s, v22.4s\n" + "fmul v20.4s, v20.4s, v0.s[0]\n" + "fadd v18.4s, v29.4s, v27.4s\n" + "fmla v14.4s, v22.4s, v0.s[1]\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "ldr q27, [%[inptr0], x21]\n" + "fadd v26.4s, v26.4s, v19.4s\n" + "ldr q24, [%[inptr0], x22]\n" + "str q15, [%[outptr0], %[output_col_stride1]]\n" // Store output (0, 1) + "fadd v12.4s, v11.4s, v20.4s\n" + "str q14, [x28, %[output_col_stride1]]\n" // Store output (2, 1) + "mov v28.16b, v11.16b\n" + "fadd v5.4s, v5.4s, v25.4s\n" + "ldr q11, [%[inptr0], x23]\n" + "str q26, [x18]\n" // Store output (3, 0) + "fadd v21.4s, v6.4s, v10.4s\n" + "str q12, [x17, %[output_col_stride1]]\n" // Store output (1, 1) + "fmla v28.4s, v20.4s, v0.s[1]\n" + "fsub v10.4s, v6.4s, v10.4s\n" + "ldr q12, [%[inptr0], x24]\n" + "mov v15.16b, v9.16b\n" + "ldr q20, [x25]\n" + "fadd v13.4s, v13.4s, v21.4s\n" + "ldr q19, [x25, %[in_col_stride1]]\n" + "fadd v28.4s, v28.4s, v18.4s\n" + "ldr q22, [x25, x21]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "ldr q14, [x25, x22]\n" + "fmla v4.4s, v21.4s, v0.s[1]\n" + "ldr q18, [x25, x23]\n" + "str q13, [%[outptr0], x15]\n" // Store output (0, 2) + "fadd v6.4s, v2.4s, v3.4s\n" + "str q28, [x18, %[output_col_stride1]]\n" // Store output (3, 1) + "fadd v30.4s, v7.4s, v8.4s\n" + "fadd v13.4s, v9.4s, v10.4s\n" + "fmla v15.4s, v10.4s, v0.s[1]\n" + "str q4, [x28, x15]\n" // Store output (2, 2) + "fsub v2.4s, v2.4s, v3.4s\n" + "fadd v1.4s, v1.4s, v6.4s\n" + "ldr q3, [x25, x24]\n" + "fsub v8.4s, v7.4s, v8.4s\n" + "mov v6.16b, v6.16b\n" + "str q13, [x17, x15]\n" // Store output (1, 2) + "fadd v15.4s, v15.4s, v16.4s\n" + "mov v9.16b, v2.16b\n" + "fadd v4.4s, v23.4s, v27.4s\n" + "fadd v1.4s, v1.4s, v30.4s\n" + "fmla v6.4s, v30.4s, v0.s[1]\n" + "fmul v8.4s, v8.4s, v0.s[0]\n" + "fadd v10.4s, v24.4s, v11.4s\n" + "str q15, [x18, x15]\n" // Store output (3, 2) + "fsub v13.4s, v23.4s, v27.4s\n" + "fadd v7.4s, v17.4s, v4.4s\n" + "fsub v11.4s, v24.4s, v11.4s\n" + "str q1, [%[outptr0], x16]\n" // Store output (0, 3) + "mov v4.16b, v4.16b\n" + "str q6, [x28, x16]\n" // Store output (2, 3) + "fadd v2.4s, v2.4s, v8.4s\n" + "fmla v9.4s, v8.4s, v0.s[1]\n" + "add %[outptr0], %[outptr0], #16\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "add x28, x28, #16\n" + "fmul v11.4s, v11.4s, v0.s[0]\n" + "fmla v4.4s, v10.4s, v0.s[1]\n" + "str q2, [x17, x16]\n" // Store output (1, 3) + "mov v1.16b, v13.16b\n" + "fadd v9.4s, v9.4s, v5.4s\n" + "add x17, x17, #16\n" + "fadd v8.4s, v13.4s, v11.4s\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "str q9, [x18, x16]\n" // Store output (3, 3) + "add x18, x18, #16\n" + "fadd v1.4s, v1.4s, v12.4s\n" + "bne 2b\n" + "3:\n" // Quad tail + "fadd v2.4s, v19.4s, v22.4s\n" + "ldr q16, [x13]\n" + "fadd v23.4s, v14.4s, v18.4s\n" + "ldr q21, [x13, %[in_col_stride1]]\n" + "fsub v15.4s, v19.4s, v22.4s\n" + "ldr q24, [x13, x21]\n" + "fsub v31.4s, v14.4s, v18.4s\n" + "ldr q25, [x13, x22]\n" + "fadd v11.4s, v20.4s, v2.4s\n" + "ldr q17, [x13, x23]\n" + "mov v13.16b, v2.16b\n" + "ldr q9, [x13, x24]\n" + "mov v2.16b, v15.16b\n" + "ldr q6, [x26]\n" + "fmul v31.4s, v31.4s, v0.s[0]\n" + "ldr q19, [x26, %[in_col_stride1]]\n" + "fadd v11.4s, v11.4s, v23.4s\n" + "ldr q22, [x26, x21]\n" + "fmla v13.4s, v23.4s, v0.s[1]\n" + "ldr q12, [x26, x22]\n" + "fadd v29.4s, v21.4s, v24.4s\n" + "ldr q26, [x26, x23]\n" + "fadd v15.4s, v15.4s, v31.4s\n" + "ldr q5, [x26, x24]\n" + "fmla v2.4s, v31.4s, v0.s[1]\n" + "ldr q10, [x14]\n" + "fadd v18.4s, v25.4s, v17.4s\n" + "add %[inptr0], %[inptr0], #16\n" + "fadd v27.4s, v16.4s, v29.4s\n" + "add x25, x25, #16\n" + "fsub v14.4s, v21.4s, v24.4s\n" + "ldr q30, [x14, %[in_col_stride1]]\n" + "fadd v2.4s, v2.4s, v3.4s\n" + "ldr q31, [x14, x21]\n" + "fsub v28.4s, v25.4s, v17.4s\n" + "add x13, x13, #16\n" + "fadd v27.4s, v27.4s, v18.4s\n" + "add x26, x26, #16\n" + "mov v21.16b, v29.16b\n" + "fadd v20.4s, v19.4s, v22.4s\n" + "fsub v17.4s, v19.4s, v22.4s\n" + "fadd v29.4s, v12.4s, v26.4s\n" + "fmul v28.4s, v28.4s, v0.s[0]\n" + "fsub v16.4s, v12.4s, v26.4s\n" + "fmla v21.4s, v18.4s, v0.s[1]\n" + "ldr q23, [x14, x22]\n" + "fadd v24.4s, v6.4s, v20.4s\n" + "mov v6.16b, v20.16b\n" + "fadd v25.4s, v30.4s, v31.4s\n" + "fsub v22.4s, v30.4s, v31.4s\n" + "fadd v20.4s, v14.4s, v28.4s\n" + "mov v3.16b, v14.16b\n" + "fmul v16.4s, v16.4s, v0.s[0]\n" + "fmla v6.4s, v29.4s, v0.s[1]\n" + "fadd v24.4s, v24.4s, v29.4s\n" + "ldr q26, [x14, x23]\n" + "fmla v3.4s, v28.4s, v0.s[1]\n" + "fadd v14.4s, v23.4s, v26.4s\n" + "fadd v29.4s, v10.4s, v25.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "mov v10.16b, v25.16b\n" + "fadd v31.4s, v11.4s, v27.4s\n" + "fsub v12.4s, v11.4s, v27.4s\n" + "ldr q18, [x14, x24]\n" + "fadd v3.4s, v3.4s, v9.4s\n" + "ldr q19, [x27]\n" + "fadd v29.4s, v29.4s, v14.4s\n" + "add x14, x14, #16\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fmla v10.4s, v14.4s, v0.s[1]\n" + "fadd v25.4s, v7.4s, v31.4s\n" + "mov v26.16b, v31.16b\n" + "fadd v31.4s, v15.4s, v20.4s\n" + "fsub v11.4s, v15.4s, v20.4s\n" + "fadd v28.4s, v24.4s, v29.4s\n" + "fsub v24.4s, v24.4s, v29.4s\n" + "fadd v30.4s, v13.4s, v21.4s\n" + "fsub v9.4s, v13.4s, v21.4s\n" + "fadd v20.4s, v17.4s, v16.4s\n" + "mov v7.16b, v17.16b\n" + "fadd v15.4s, v8.4s, v31.4s\n" + "mov v14.16b, v31.16b\n" + "fadd v25.4s, v25.4s, v28.4s\n" + "fmul v24.4s, v24.4s, v0.s[0]\n" + "fmla v7.4s, v16.4s, v0.s[1]\n" + "ldr q27, [x27, %[in_col_stride1]]\n" + "fmla v26.4s, v28.4s, v0.s[1]\n" + "ldr q29, [x27, x21]\n" + "fadd v13.4s, v4.4s, v30.4s\n" + "mov v4.16b, v30.16b\n" + "str q25, [%[outptr0]]\n" // Store output (0, 0) + "fadd v17.4s, v22.4s, v23.4s\n" + "fadd v7.4s, v7.4s, v5.4s\n" + "ldr q28, [x27, x22]\n" + "str q26, [x28]\n" // Store output (2, 0) + "mov v8.16b, v22.16b\n" + "fadd v16.4s, v27.4s, v29.4s\n" + "fsub v29.4s, v27.4s, v29.4s\n" + "fadd v21.4s, v12.4s, v24.4s\n" + "mov v26.16b, v12.16b\n" + "fmla v8.4s, v23.4s, v0.s[1]\n" + "fadd v22.4s, v20.4s, v17.4s\n" + "fsub v20.4s, v20.4s, v17.4s\n" + "ldr q23, [x27, x23]\n" + "fadd v19.4s, v19.4s, v16.4s\n" + "mov v16.16b, v16.16b\n" + "str q21, [x17]\n" // Store output (1, 0) + "fadd v30.4s, v28.4s, v23.4s\n" + "fadd v8.4s, v8.4s, v18.4s\n" + "ldr q25, [x27, x24]\n" + "fsub v27.4s, v28.4s, v23.4s\n" + "add x27, x27, #16\n" + "mov v5.16b, v29.16b\n" + "fmla v26.4s, v24.4s, v0.s[1]\n" + "fadd v19.4s, v19.4s, v30.4s\n" + "fmla v16.4s, v30.4s, v0.s[1]\n" + "fadd v15.4s, v15.4s, v22.4s\n" + "fmul v20.4s, v20.4s, v0.s[0]\n" + "fmul v27.4s, v27.4s, v0.s[0]\n" + "fmla v14.4s, v22.4s, v0.s[1]\n" + "mov v28.16b, v11.16b\n" + "fadd v21.4s, v6.4s, v10.4s\n" + "fadd v26.4s, v26.4s, v19.4s\n" + "fsub v10.4s, v6.4s, v10.4s\n" + "str q15, [%[outptr0], %[output_col_stride1]]\n" // Store output (0, 1) + "fadd v12.4s, v11.4s, v20.4s\n" + "str q14, [x28, %[output_col_stride1]]\n" // Store output (2, 1) + "fadd v18.4s, v29.4s, v27.4s\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "fmla v28.4s, v20.4s, v0.s[1]\n" + "str q26, [x18]\n" // Store output (3, 0) + "fadd v13.4s, v13.4s, v21.4s\n" + "str q12, [x17, %[output_col_stride1]]\n" // Store output (1, 1) + "fmul v10.4s, v10.4s, v0.s[0]\n" + "fmla v4.4s, v21.4s, v0.s[1]\n" + "mov v15.16b, v9.16b\n" + "fadd v5.4s, v5.4s, v25.4s\n" + "fadd v28.4s, v28.4s, v18.4s\n" + "str q13, [%[outptr0], x15]\n" // Store output (0, 2) + "fadd v6.4s, v2.4s, v3.4s\n" + "fadd v13.4s, v9.4s, v10.4s\n" + "fmla v15.4s, v10.4s, v0.s[1]\n" + "str q4, [x28, x15]\n" // Store output (2, 2) + "fadd v30.4s, v7.4s, v8.4s\n" + "str q28, [x18, %[output_col_stride1]]\n" // Store output (3, 1) + "fsub v2.4s, v2.4s, v3.4s\n" + "fadd v1.4s, v1.4s, v6.4s\n" + "fsub v8.4s, v7.4s, v8.4s\n" + "str q13, [x17, x15]\n" // Store output (1, 2) + "fadd v15.4s, v15.4s, v16.4s\n" + "mov v6.16b, v6.16b\n" + "mov v9.16b, v2.16b\n" + "fadd v1.4s, v1.4s, v30.4s\n" + "fmul v8.4s, v8.4s, v0.s[0]\n" + "str q15, [x18, x15]\n" // Store output (3, 2) + "fmla v6.4s, v30.4s, v0.s[1]\n" + "str q1, [%[outptr0], x16]\n" // Store output (0, 3) + "fadd v2.4s, v2.4s, v8.4s\n" + "str q6, [x28, x16]\n" // Store output (2, 3) + "fmla v9.4s, v8.4s, v0.s[1]\n" + "add %[outptr0], %[outptr0], #16\n" + "add x28, x28, #16\n" + "str q2, [x17, x16]\n" // Store output (1, 3) + "fadd v9.4s, v9.4s, v5.4s\n" + "add x17, x17, #16\n" + "str q9, [x18, x16]\n" // Store output (3, 3) + "add x18, x18, #16\n" + "4:\n" // Double + "cmp x20, #2\n" + "blt 5f\n" + "ldr d17, [%[inptr0]]\n" + "ldr d23, [%[inptr0], %[in_col_stride1]]\n" + "sub x20, x20, #2\n" + "ldr d27, [%[inptr0], x21]\n" + "ldr d24, [%[inptr0], x22]\n" + "fadd v4.4s, v23.4s, v27.4s\n" + "ldr d11, [%[inptr0], x23]\n" + "fadd v10.4s, v24.4s, v11.4s\n" + "ldr d12, [%[inptr0], x24]\n" + "fsub v13.4s, v23.4s, v27.4s\n" + "ldr d20, [x25]\n" + "fsub v11.4s, v24.4s, v11.4s\n" + "ldr d19, [x25, %[in_col_stride1]]\n" + "fadd v7.4s, v17.4s, v4.4s\n" + "ldr d22, [x25, x21]\n" + "mov v4.16b, v4.16b\n" + "ldr d14, [x25, x22]\n" + "mov v1.16b, v13.16b\n" + "ldr d18, [x25, x23]\n" + "fmul v11.4s, v11.4s, v0.s[0]\n" + "ldr d3, [x25, x24]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "ldr d16, [x13]\n" + "fmla v4.4s, v10.4s, v0.s[1]\n" + "ldr d21, [x13, %[in_col_stride1]]\n" + "fadd v2.4s, v19.4s, v22.4s\n" + "ldr d24, [x13, x21]\n" + "fadd v8.4s, v13.4s, v11.4s\n" + "ldr d25, [x13, x22]\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "ldr d17, [x13, x23]\n" + "fadd v23.4s, v14.4s, v18.4s\n" + "ldr d9, [x13, x24]\n" + "fadd v11.4s, v20.4s, v2.4s\n" + "ldr d6, [x26]\n" + "fsub v15.4s, v19.4s, v22.4s\n" + "ldr d19, [x26, %[in_col_stride1]]\n" + "fadd v1.4s, v1.4s, v12.4s\n" + "ldr d22, [x26, x21]\n" + "fsub v31.4s, v14.4s, v18.4s\n" + "ldr d12, [x26, x22]\n" + "fadd v11.4s, v11.4s, v23.4s\n" + "ldr d26, [x26, x23]\n" + "mov v13.16b, v2.16b\n" + "ldr d5, [x26, x24]\n" + "mov v2.16b, v15.16b\n" + "ldr d10, [x14]\n" + "fmul v31.4s, v31.4s, v0.s[0]\n" + "add %[inptr0], %[inptr0], #8\n" + "fmla v13.4s, v23.4s, v0.s[1]\n" + "add x25, x25, #8\n" + "fadd v29.4s, v21.4s, v24.4s\n" + "add x13, x13, #8\n" + "fsub v14.4s, v21.4s, v24.4s\n" + "ldr d30, [x14, %[in_col_stride1]]\n" + "fadd v15.4s, v15.4s, v31.4s\n" + "add x26, x26, #8\n" + "fmla v2.4s, v31.4s, v0.s[1]\n" + "fadd v18.4s, v25.4s, v17.4s\n" + "fadd v27.4s, v16.4s, v29.4s\n" + "fsub v28.4s, v25.4s, v17.4s\n" + "mov v21.16b, v29.16b\n" + "fadd v20.4s, v19.4s, v22.4s\n" + "fsub v17.4s, v19.4s, v22.4s\n" + "ldr d31, [x14, x21]\n" + "fadd v2.4s, v2.4s, v3.4s\n" + "ldr d23, [x14, x22]\n" + "fadd v27.4s, v27.4s, v18.4s\n" + "fmul v28.4s, v28.4s, v0.s[0]\n" + "fmla v21.4s, v18.4s, v0.s[1]\n" + "fadd v29.4s, v12.4s, v26.4s\n" + "fadd v24.4s, v6.4s, v20.4s\n" + "fsub v16.4s, v12.4s, v26.4s\n" + "mov v6.16b, v20.16b\n" + "fadd v25.4s, v30.4s, v31.4s\n" + "fsub v22.4s, v30.4s, v31.4s\n" + "fadd v31.4s, v11.4s, v27.4s\n" + "fsub v12.4s, v11.4s, v27.4s\n" + "ldr d26, [x14, x23]\n" + "fadd v24.4s, v24.4s, v29.4s\n" + "fmul v16.4s, v16.4s, v0.s[0]\n" + "fmla v6.4s, v29.4s, v0.s[1]\n" + "mov v3.16b, v14.16b\n" + "fadd v20.4s, v14.4s, v28.4s\n" + "fadd v29.4s, v10.4s, v25.4s\n" + "mov v10.16b, v25.16b\n" + "fadd v25.4s, v7.4s, v31.4s\n" + "fmla v3.4s, v28.4s, v0.s[1]\n" + "fadd v14.4s, v23.4s, v26.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "mov v26.16b, v31.16b\n" + "fadd v31.4s, v15.4s, v20.4s\n" + "fsub v11.4s, v15.4s, v20.4s\n" + "fadd v20.4s, v17.4s, v16.4s\n" + "mov v7.16b, v17.16b\n" + "fadd v3.4s, v3.4s, v9.4s\n" + "ldr d18, [x14, x24]\n" + "fadd v29.4s, v29.4s, v14.4s\n" + "add x14, x14, #8\n" + "fmla v7.4s, v16.4s, v0.s[1]\n" + "ldr d19, [x27]\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fmla v10.4s, v14.4s, v0.s[1]\n" + "fadd v15.4s, v8.4s, v31.4s\n" + "mov v14.16b, v31.16b\n" + "fadd v28.4s, v24.4s, v29.4s\n" + "fsub v24.4s, v24.4s, v29.4s\n" + "fadd v7.4s, v7.4s, v5.4s\n" + "ldr d27, [x27, %[in_col_stride1]]\n" + "fadd v30.4s, v13.4s, v21.4s\n" + "fsub v9.4s, v13.4s, v21.4s\n" + "fadd v17.4s, v22.4s, v23.4s\n" + "mov v8.16b, v22.16b\n" + "fadd v25.4s, v25.4s, v28.4s\n" + "fmul v24.4s, v24.4s, v0.s[0]\n" + "fmla v26.4s, v28.4s, v0.s[1]\n" + "ldr d29, [x27, x21]\n" + "fmla v8.4s, v23.4s, v0.s[1]\n" + "ldr d28, [x27, x22]\n" + "fadd v13.4s, v4.4s, v30.4s\n" + "mov v4.16b, v30.16b\n" + "str d25, [%[outptr0]]\n" // Store output (0, 0) + "fadd v16.4s, v27.4s, v29.4s\n" + "str d26, [x28]\n" // Store output (2, 0) + "fsub v29.4s, v27.4s, v29.4s\n" + "fadd v8.4s, v8.4s, v18.4s\n" + "ldr d23, [x27, x23]\n" + "fadd v30.4s, v28.4s, v23.4s\n" + "ldr d25, [x27, x24]\n" + "fadd v19.4s, v19.4s, v16.4s\n" + "add x27, x27, #8\n" + "fsub v27.4s, v28.4s, v23.4s\n" + "mov v16.16b, v16.16b\n" + "fadd v22.4s, v20.4s, v17.4s\n" + "fsub v20.4s, v20.4s, v17.4s\n" + "fadd v21.4s, v12.4s, v24.4s\n" + "mov v26.16b, v12.16b\n" + "fadd v19.4s, v19.4s, v30.4s\n" + "fmla v16.4s, v30.4s, v0.s[1]\n" + "fmul v27.4s, v27.4s, v0.s[0]\n" + "mov v5.16b, v29.16b\n" + "fmla v26.4s, v24.4s, v0.s[1]\n" + "fadd v15.4s, v15.4s, v22.4s\n" + "str d21, [x17]\n" // Store output (1, 0) + "fmul v20.4s, v20.4s, v0.s[0]\n" + "fmla v14.4s, v22.4s, v0.s[1]\n" + "mov v28.16b, v11.16b\n" + "fadd v18.4s, v29.4s, v27.4s\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "str d15, [%[outptr0], %[output_col_stride1]]\n" // Store output (0, 1) + "fadd v26.4s, v26.4s, v19.4s\n" + "fadd v12.4s, v11.4s, v20.4s\n" + "fmla v28.4s, v20.4s, v0.s[1]\n" + "str d14, [x28, %[output_col_stride1]]\n" // Store output (2, 1) + "fadd v21.4s, v6.4s, v10.4s\n" + "fadd v5.4s, v5.4s, v25.4s\n" + "fsub v10.4s, v6.4s, v10.4s\n" + "str d26, [x18]\n" // Store output (3, 0) + "mov v15.16b, v9.16b\n" + "str d12, [x17, %[output_col_stride1]]\n" // Store output (1, 1) + "fadd v28.4s, v28.4s, v18.4s\n" + "fadd v13.4s, v13.4s, v21.4s\n" + "fmla v4.4s, v21.4s, v0.s[1]\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "fadd v6.4s, v2.4s, v3.4s\n" + "fadd v30.4s, v7.4s, v8.4s\n" + "fsub v2.4s, v2.4s, v3.4s\n" + "str d28, [x18, %[output_col_stride1]]\n" // Store output (3, 1) + "fsub v8.4s, v7.4s, v8.4s\n" + "str d13, [%[outptr0], x15]\n" // Store output (0, 2) + "str d4, [x28, x15]\n" // Store output (2, 2) + "fadd v13.4s, v9.4s, v10.4s\n" + "fmla v15.4s, v10.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v6.4s\n" + "mov v6.16b, v6.16b\n" + "fmul v8.4s, v8.4s, v0.s[0]\n" + "mov v9.16b, v2.16b\n" + "str d13, [x17, x15]\n" // Store output (1, 2) + "fadd v15.4s, v15.4s, v16.4s\n" + "fadd v1.4s, v1.4s, v30.4s\n" + "fmla v6.4s, v30.4s, v0.s[1]\n" + "fadd v2.4s, v2.4s, v8.4s\n" + "fmla v9.4s, v8.4s, v0.s[1]\n" + "str d15, [x18, x15]\n" // Store output (3, 2) + "str d1, [%[outptr0], x16]\n" // Store output (0, 3) + "str d2, [x17, x16]\n" // Store output (1, 3) + "fadd v9.4s, v9.4s, v5.4s\n" + "str d6, [x28, x16]\n" // Store output (2, 3) + "add %[outptr0], %[outptr0], #8\n" + "add x17, x17, #8\n" + "add x28, x28, #8\n" + "str d9, [x18, x16]\n" // Store output (3, 3) + "add x18, x18, #8\n" + "5:\n" // Scalar + "cbz x20, 6f\n" + "ldr s17, [%[inptr0]]\n" + "ldr s23, [%[inptr0], %[in_col_stride1]]\n" + "ldr s27, [%[inptr0], x21]\n" + "fadd v4.4s, v23.4s, v27.4s\n" + "ldr s24, [%[inptr0], x22]\n" + "fsub v13.4s, v23.4s, v27.4s\n" + "ldr s11, [%[inptr0], x23]\n" + "fadd v10.4s, v24.4s, v11.4s\n" + "ldr s12, [%[inptr0], x24]\n" + "fsub v11.4s, v24.4s, v11.4s\n" + "ldr s20, [x25]\n" + "fadd v7.4s, v17.4s, v4.4s\n" + "ldr s19, [x25, %[in_col_stride1]]\n" + "mov v4.16b, v4.16b\n" + "ldr s22, [x25, x21]\n" + "mov v1.16b, v13.16b\n" + "ldr s14, [x25, x22]\n" + "fmul v11.4s, v11.4s, v0.s[0]\n" + "ldr s18, [x25, x23]\n" + "fadd v7.4s, v7.4s, v10.4s\n" + "ldr s3, [x25, x24]\n" + "fmla v4.4s, v10.4s, v0.s[1]\n" + "ldr s16, [x13]\n" + "fadd v2.4s, v19.4s, v22.4s\n" + "ldr s21, [x13, %[in_col_stride1]]\n" + "fadd v8.4s, v13.4s, v11.4s\n" + "ldr s24, [x13, x21]\n" + "fmla v1.4s, v11.4s, v0.s[1]\n" + "ldr s25, [x13, x22]\n" + "fadd v23.4s, v14.4s, v18.4s\n" + "ldr s17, [x13, x23]\n" + "fadd v11.4s, v20.4s, v2.4s\n" + "ldr s9, [x13, x24]\n" + "fsub v15.4s, v19.4s, v22.4s\n" + "ldr s6, [x26]\n" + "fadd v1.4s, v1.4s, v12.4s\n" + "ldr s19, [x26, %[in_col_stride1]]\n" + "fsub v31.4s, v14.4s, v18.4s\n" + "ldr s22, [x26, x21]\n" + "fadd v11.4s, v11.4s, v23.4s\n" + "ldr s12, [x26, x22]\n" + "mov v13.16b, v2.16b\n" + "ldr s26, [x26, x23]\n" + "mov v2.16b, v15.16b\n" + "ldr s5, [x26, x24]\n" + "fmul v31.4s, v31.4s, v0.s[0]\n" + "ldr s10, [x14]\n" + "fmla v13.4s, v23.4s, v0.s[1]\n" + "fadd v29.4s, v21.4s, v24.4s\n" + "fsub v14.4s, v21.4s, v24.4s\n" + "fadd v18.4s, v25.4s, v17.4s\n" + "fsub v28.4s, v25.4s, v17.4s\n" + "ldr s30, [x14, %[in_col_stride1]]\n" + "fadd v15.4s, v15.4s, v31.4s\n" + "fmla v2.4s, v31.4s, v0.s[1]\n" + "fadd v27.4s, v16.4s, v29.4s\n" + "mov v21.16b, v29.16b\n" + "fadd v20.4s, v19.4s, v22.4s\n" + "fsub v17.4s, v19.4s, v22.4s\n" + "fmul v28.4s, v28.4s, v0.s[0]\n" + "ldr s31, [x14, x21]\n" + "fadd v2.4s, v2.4s, v3.4s\n" + "ldr s23, [x14, x22]\n" + "fadd v27.4s, v27.4s, v18.4s\n" + "fmla v21.4s, v18.4s, v0.s[1]\n" + "fadd v29.4s, v12.4s, v26.4s\n" + "fadd v24.4s, v6.4s, v20.4s\n" + "fsub v16.4s, v12.4s, v26.4s\n" + "mov v6.16b, v20.16b\n" + "fadd v25.4s, v30.4s, v31.4s\n" + "fsub v22.4s, v30.4s, v31.4s\n" + "fadd v20.4s, v14.4s, v28.4s\n" + "mov v3.16b, v14.16b\n" + "fadd v24.4s, v24.4s, v29.4s\n" + "fmla v6.4s, v29.4s, v0.s[1]\n" + "fmul v16.4s, v16.4s, v0.s[0]\n" + "ldr s26, [x14, x23]\n" + "fmla v3.4s, v28.4s, v0.s[1]\n" + "fadd v14.4s, v23.4s, v26.4s\n" + "fadd v29.4s, v10.4s, v25.4s\n" + "fsub v23.4s, v23.4s, v26.4s\n" + "mov v10.16b, v25.16b\n" + "fadd v31.4s, v11.4s, v27.4s\n" + "fsub v12.4s, v11.4s, v27.4s\n" + "ldr s18, [x14, x24]\n" + "fadd v3.4s, v3.4s, v9.4s\n" + "ldr s19, [x27]\n" + "fadd v29.4s, v29.4s, v14.4s\n" + "fmul v23.4s, v23.4s, v0.s[0]\n" + "fmla v10.4s, v14.4s, v0.s[1]\n" + "fadd v25.4s, v7.4s, v31.4s\n" + "mov v26.16b, v31.16b\n" + "fadd v31.4s, v15.4s, v20.4s\n" + "fsub v11.4s, v15.4s, v20.4s\n" + "fadd v30.4s, v13.4s, v21.4s\n" + "fsub v9.4s, v13.4s, v21.4s\n" + "fadd v28.4s, v24.4s, v29.4s\n" + "fsub v24.4s, v24.4s, v29.4s\n" + "ldr s27, [x27, %[in_col_stride1]]\n" + "fadd v15.4s, v8.4s, v31.4s\n" + "mov v14.16b, v31.16b\n" + "fadd v13.4s, v4.4s, v30.4s\n" + "mov v4.16b, v30.16b\n" + "fadd v25.4s, v25.4s, v28.4s\n" + "fmla v26.4s, v28.4s, v0.s[1]\n" + "fmul v24.4s, v24.4s, v0.s[0]\n" + "fadd v21.4s, v6.4s, v10.4s\n" + "fsub v10.4s, v6.4s, v10.4s\n" + "fadd v6.4s, v2.4s, v3.4s\n" + "fsub v2.4s, v2.4s, v3.4s\n" + "ldr s29, [x27, x21]\n" + "str s25, [%[outptr0]]\n" // Store output (0, 0) + "fadd v20.4s, v17.4s, v16.4s\n" + "str s26, [x28]\n" // Store output (2, 0) + "mov v7.16b, v17.16b\n" + "fadd v17.4s, v22.4s, v23.4s\n" + "mov v8.16b, v22.16b\n" + "fadd v13.4s, v13.4s, v21.4s\n" + "fmul v10.4s, v10.4s, v0.s[0]\n" + "fmla v7.4s, v16.4s, v0.s[1]\n" + "ldr s28, [x27, x22]\n" + "fmla v8.4s, v23.4s, v0.s[1]\n" + "ldr s23, [x27, x23]\n" + "fmla v4.4s, v21.4s, v0.s[1]\n" + "ldr s25, [x27, x24]\n" + "str s13, [%[outptr0], x15]\n" // Store output (0, 2) + "fadd v16.4s, v27.4s, v29.4s\n" + "fadd v7.4s, v7.4s, v5.4s\n" + "fadd v30.4s, v28.4s, v23.4s\n" + "fadd v8.4s, v8.4s, v18.4s\n" + "fsub v29.4s, v27.4s, v29.4s\n" + "str s4, [x28, x15]\n" // Store output (2, 2) + "fsub v27.4s, v28.4s, v23.4s\n" + "fadd v19.4s, v19.4s, v16.4s\n" + "mov v16.16b, v16.16b\n" + "fadd v21.4s, v12.4s, v24.4s\n" + "mov v26.16b, v12.16b\n" + "mov v5.16b, v29.16b\n" + "fadd v22.4s, v20.4s, v17.4s\n" + "fmul v27.4s, v27.4s, v0.s[0]\n" + "fmla v16.4s, v30.4s, v0.s[1]\n" + "fadd v19.4s, v19.4s, v30.4s\n" + "fmla v26.4s, v24.4s, v0.s[1]\n" + "str s21, [x17]\n" // Store output (1, 0) + "fsub v20.4s, v20.4s, v17.4s\n" + "fadd v15.4s, v15.4s, v22.4s\n" + "fmla v14.4s, v22.4s, v0.s[1]\n" + "fadd v18.4s, v29.4s, v27.4s\n" + "fmla v5.4s, v27.4s, v0.s[1]\n" + "fadd v26.4s, v26.4s, v19.4s\n" + "mov v28.16b, v11.16b\n" + "fmul v20.4s, v20.4s, v0.s[0]\n" + "fadd v13.4s, v9.4s, v10.4s\n" + "str s15, [%[outptr0], %[output_col_stride1]]\n" // Store output (0, 1) + "mov v15.16b, v9.16b\n" + "str s14, [x28, %[output_col_stride1]]\n" // Store output (2, 1) + "fadd v5.4s, v5.4s, v25.4s\n" + "str s26, [x18]\n" // Store output (3, 0) + "fadd v30.4s, v7.4s, v8.4s\n" + "str s13, [x17, x15]\n" // Store output (1, 2) + "fadd v12.4s, v11.4s, v20.4s\n" + "fmla v28.4s, v20.4s, v0.s[1]\n" + "fmla v15.4s, v10.4s, v0.s[1]\n" + "fadd v1.4s, v1.4s, v6.4s\n" + "fsub v8.4s, v7.4s, v8.4s\n" + "mov v6.16b, v6.16b\n" + "mov v9.16b, v2.16b\n" + "str s12, [x17, %[output_col_stride1]]\n" // Store output (1, 1) + "fadd v28.4s, v28.4s, v18.4s\n" + "fadd v15.4s, v15.4s, v16.4s\n" + "fadd v1.4s, v1.4s, v30.4s\n" + "fmul v8.4s, v8.4s, v0.s[0]\n" + "fmla v6.4s, v30.4s, v0.s[1]\n" + "str s28, [x18, %[output_col_stride1]]\n" // Store output (3, 1) + "str s1, [%[outptr0], x16]\n" // Store output (0, 3) + "str s6, [x28, x16]\n" // Store output (2, 3) + "fadd v2.4s, v2.4s, v8.4s\n" + "str s15, [x18, x15]\n" // Store output (3, 2) + "fmla v9.4s, v8.4s, v0.s[1]\n" + "str s2, [x17, x16]\n" // Store output (1, 3) + "fadd v9.4s, v9.4s, v5.4s\n" + "str s9, [x18, x16]\n" // Store output (3, 3) + "6:\n" // End + : [outptr0] "+r" (output), [inptr0] "+r" (inptr) + : [output_col_stride1] "r" (output_col_stride * sizeof(float)), [pcoeffs] "r" (coeffs), [n_channels] "r" ((long) n_channels), [in_row_stride] "r" (6 * matrix_stride * sizeof(float)), [in_col_stride1] "r" (matrix_stride * sizeof(float)), [output_row_stride] "r" (output_row_stride * sizeof(float)) + : "cc", "v0", "v1", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v2", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v3", "v30", "v31", "v4", "v5", "v6", "v7", "v8", "v9", "x13", "x14", "x15", "x16", "x17", "x18", "x19", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "memory" + ); + } +} + +#else + +template <> +void winograd::OutputTransform<3, 3, 6, 6, float, float, winograd::WinogradRoots::Integers>::transform_tile( + const int n_channels, + const float* inptr, + const int matrix_stride, + const float* bptr, + float* const output, + const int output_row_stride, + const int output_col_stride +) +{ + // Construct a map to the output cells + float *outptrs[output_tile_rows][output_tile_cols]; + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; + } + } + + // For each channel of the output + int channels_remaining = n_channels; +#ifdef __arm__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed during this transform + float32x2_t F[6][6], FZ[6][4], f[4][4], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = vld1_f32(inptr + m*matrix_stride); + } + } + inptr += 2; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); + + // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; + FZ[i][1] = vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f); + + // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; + FZ[i][2] = vmla_n_f32(vadd_f32(F[i][1], F[i][2]), vadd_f32(F[i][3], F[i][4]), 4.0f); + + // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; + FZ[i][3] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 4; j++) + { + // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); + + // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; + f[1][j] = vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f); + + // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; + f[2][j] = vmla_n_f32(vadd_f32(FZ[1][j], FZ[2][j]), vadd_f32(FZ[3][j], FZ[4][j]), 4.0f); + + // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; + f[3][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); + } + + // Write out the output tile + if (bptr != nullptr) + { + b = vld1_f32(bptr); + bptr += 2; + } + else + { + b = vdup_n_f32(0.0f); + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); + outptrs[i][j] += 2; + } + } + } +#endif // __arm__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed during this transform + float F[6][6], FZ[6][4], f[4][4], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = *(inptr + m*matrix_stride); + } + } + inptr++; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; + FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; + FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 4; j++) + { + f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; + f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; + f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; + } + + // Write out the output tile + if (bptr != nullptr) + { + b = *(bptr++); + } + else + { + b = 0.0f; + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + *(outptrs[i][j]++) = f[i][j] + b; + } + } + } +} + +#endif + +template class OutputTransform<3, 3, 6, 6, float, float, winograd::WinogradRoots::Integers>; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_6_3_fp32_fp32_integers.cpp index 58bed71a47..ce921cea01 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_6_3_fp32_fp32_integers.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -22,42 +22,29 @@ * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" +#include "output.hpp" +#include "arm.hpp" -namespace +namespace winograd { -template <bool Specialized, int PadRight=0> -void winograd_output_transform_6_3_fp32_process_tile( +template <> +void OutputTransform<1, 3, 1, 8, float, float, WinogradRoots::Integers>::transform_tile( const int n_channels, - const float* const matrix_base, + const float* inptr, const int matrix_stride, - const float* const biases, + const float* bptr, float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right + const int, // No need to stride across rows + const int output_col_stride ) { - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 6; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) + float *outptrs[output_tile_cols]; + for (int j = 0; j < output_tile_cols; j++) { outptrs[j] = output + j*output_col_stride; } - const float *inptr = matrix_base; - const float *bptr = biases; // For each channel of the output int channels_remaining = n_channels; @@ -87,7 +74,7 @@ void winograd_output_transform_6_3_fp32_process_tile( b = vld1q_f32(bptr); bptr += 4; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1q_f32(outptrs[j], f[j] + b); outptrs[j] += 4; @@ -118,7 +105,7 @@ void winograd_output_transform_6_3_fp32_process_tile( b = vld1_f32(bptr); bptr += 2; } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { vst1_f32(outptrs[j], f[j] + b); outptrs[j] += 2; @@ -149,31 +136,14 @@ void winograd_output_transform_6_3_fp32_process_tile( { b = *(bptr++); } - for (int j = 0; j < cells_j; j++) + for (int j = 0; j < output_tile_cols; j++) { *(outptrs[j]++) = f[j] + b; } } } -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 3, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_6_3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_6_3_fp32_process_tile<true, 1>, - winograd_output_transform_6_3_fp32_process_tile<true, 2>, - winograd_output_transform_6_3_fp32_process_tile<true, 3>, - winograd_output_transform_6_3_fp32_process_tile<true, 4>, - winograd_output_transform_6_3_fp32_process_tile<true, 5>, -}; +template class OutputTransform<1, 3, 1, 8, float, float, WinogradRoots::Integers>; +template class OutputTransform<3, 1, 8, 1, float, float, WinogradRoots::Integers>; -template class OutputTransform<1, 3, 1, 8, float>; -template class OutputTransform<3, 1, 8, 1, float>; -} // namespace winograd +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2_7_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2_7_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..37ae43fdb0 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2_7_fp32_fp32_integers.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<1, 7, 1, 8, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, // NOTE: Data in HWIO order + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const float *inptrs[kernel_cols]; + for (int j = 0; j < kernel_cols; j++) + { + inptrs[j] = input + j*weight_col_stride; + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[kernel_cols], V[inner_tile_cols]; + + // Read weights + for (int j = 0; j < kernel_cols; j++) + { + w[j] = *(inptrs[j]++); + } + + // Compute V = w WT + V[0] = (w[0]*-1) / 36.0f; + V[1] = (w[1]*-1 + w[3]*-1 + w[5]*-1 + w[0]*1 + w[2]*1 + w[4]*1 + w[6]*1) / 48.0f; + V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1 + w[5]*1 + w[6]*1) / 48.0f; + V[3] = (w[0]*-1 + w[6]*-64 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8 + w[5]*32) / 120.0f; + V[4] = (w[0]*-1 + w[6]*-64 + w[5]*-32 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120.0f; + V[5] = (w[5]*-243 + w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[6]*729 + w[0]*1) / 720.0f; + V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[5]*243 + w[6]*729 + w[0]*1) / 720.0f; + V[7] = (w[6]*1) / 1.0f; + + // Store the transformed weights + for (int j = 0; j < inner_tile_cols; j++) + { + *(outptr + j*matrix_stride) = V[j]; + } + outptr++; + } + } +} + +template class WeightTransform<1, 7, 1, 8, float, float, WinogradRoots::Integers>; +template class WeightTransform<7, 1, 8, 1, float, float, WinogradRoots::Integers>; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..8fab6db1ba --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp @@ -0,0 +1,220 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + constexpr int inner_tile_i = 4; + constexpr int inner_tile_j = 4; + + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const auto weight_row_stride = 3 * weight_col_stride; + const float *inptrs[3][3]; + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; + } + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed in this kernel + float32x4_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1q_f32(inptrs[i][j]); + inptrs[i][j] += 4; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + Ww[0][j] = w[0][j]; + + // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); + Ww[1][j] = vmulq_n_f32(vaddq_f32(vaddq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); + + // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); + Ww[2][j] = vmulq_n_f32(vaddq_f32(vsubq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); + + Ww[3][j] = w[2][j]; + } + + // Compute V = W w WT + for (int i = 0; i < inner_tile_i; i++) + { + V[i][0] = Ww[i][0]; + + // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); + V[i][1] = vmulq_n_f32(vaddq_f32(vaddq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); + + // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); + V[i][2] = vmulq_n_f32(vaddq_f32(vsubq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); + + V[i][3] = Ww[i][2]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < inner_tile_i; i++) + { + for (int j = 0; j < inner_tile_j; j++, m++) + { + vst1q_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 4; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed in this kernel + float32x2_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1_f32(inptrs[i][j]); + inptrs[i][j] += 2; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + Ww[0][j] = w[0][j]; + + // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); + Ww[1][j] = vmul_n_f32(vadd_f32(vadd_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); + + // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); + Ww[2][j] = vmul_n_f32(vadd_f32(vsub_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); + + Ww[3][j] = w[2][j]; + } + + // Compute V = W w WT + for (int i = 0; i < inner_tile_i; i++) + { + V[i][0] = Ww[i][0]; + + // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); + V[i][1] = vmul_n_f32(vadd_f32(vadd_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); + + // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); + V[i][2] = vmul_n_f32(vadd_f32(vsub_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); + + V[i][3] = Ww[i][2]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < inner_tile_i; i++) + { + for (int j = 0; j < inner_tile_j; j++, m++) + { + vst1_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 2; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = *(inptrs[i][j]++); + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + Ww[0][j] = w[0][j]; + Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); + Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); + Ww[3][j] = w[2][j]; + } + + // Compute V = W w WT + for (int i = 0; i < inner_tile_i; i++) + { + V[i][0] = Ww[i][0]; + V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); + V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); + V[i][3] = Ww[i][2]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < inner_tile_i; i++) + { + for (int j = 0; j < inner_tile_j; j++, m++) + { + *(outptr + m*matrix_stride) = V[i][j]; + } + } + outptr++; + } + } +} + +template class WeightTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>; + +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_5x5_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_5x5_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..79f4fa30c4 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_5x5_fp32_fp32_integers.cpp @@ -0,0 +1,401 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<5, 5, 6, 6, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const auto weight_row_stride = 5 * weight_col_stride; + const float *inptrs[5][5]; + for (int i = 0; i < 5; i++) + { + for (int j = 0; j < 5; j++) + { + inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; + } + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed in this kernel + float32x4_t w[5][5], Ww[6][5], V[6][6]; + + // Read weights + for (int i = 0; i < 5; i++) + { + for (int j = 0; j < 5; j++) + { + w[i][j] = vld1q_f32(inptrs[i][j]); + inptrs[i][j] += 4; + } + } + + // Compute the matrix W w + for (int j = 0; j < 5; j++) + { + // Ww[0][j] = w[0][j]/4.0f; + Ww[0][j] = vmulq_n_f32(w[0][j], 1.0f/4.0f); + + // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; + Ww[1][j] = vmulq_n_f32( + vaddq_f32( + vaddq_f32( + vaddq_f32(w[1][j], w[0][j]), + vaddq_f32(w[3][j], w[2][j]) + ), + w[4][j] + ), + -1.0f/6.0f + ); + + // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; + // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; + Ww[2][j] = vmulq_n_f32( + vsubq_f32( + vaddq_f32( + vsubq_f32(w[1][j], w[0][j]), + vsubq_f32(w[3][j], w[2][j]) + ), + w[4][j] + ), + 1.0f/6.0f + ); + + // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; + Ww[3][j] = vmulq_n_f32( + vmlaq_n_f32( + vaddq_f32( + vaddq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), + vaddq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) + ), + w[4][j], 2.0f + ), + 1.0f/3.0f + ); + + // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; + Ww[4][j] = vmulq_n_f32( + vmlaq_n_f32( + vaddq_f32( + vsubq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), + vsubq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) + ), + w[4][j], 2.0f + ), + 1.0f/3.0f + ); + + // Ww[5][j] = w[4][j]; + Ww[5][j] = w[4][j]; + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + // V[i][0] = Ww[i][0]/4.0f; + V[i][0] = vmulq_n_f32(Ww[i][0], 1.0f/4.0f); + + // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; + V[i][1] = vmulq_n_f32( + vaddq_f32( + vaddq_f32( + vaddq_f32(Ww[i][1], Ww[i][0]), + vaddq_f32(Ww[i][3], Ww[i][2]) + ), + Ww[i][4] + ), + -1.0f/6.0f + ); + + // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; + // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; + V[i][2] = vmulq_n_f32( + vsubq_f32( + vaddq_f32( + vsubq_f32(Ww[i][1], Ww[i][0]), + vsubq_f32(Ww[i][3], Ww[i][2]) + ), + Ww[i][4] + ), + 1.0f/6.0f + ); + + // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][3] = vmulq_n_f32( + vmlaq_n_f32( + vaddq_f32( + vaddq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), + vaddq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) + ), + Ww[i][4], 2.0f + ), + 1.0f/3.0f + ); + + // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][4] = vmulq_n_f32( + vmlaq_n_f32( + vaddq_f32( + vsubq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), + vsubq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) + ), + Ww[i][4], 2.0f + ), + 1.0f/3.0f + ); + + // V[i][5] = Ww[i][4]; + V[i][5] = Ww[i][4]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1q_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 4; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed in this kernel + float32x2_t w[5][5], Ww[6][5], V[6][6]; + + // Read weights + for (int i = 0; i < 5; i++) + { + for (int j = 0; j < 5; j++) + { + w[i][j] = vld1_f32(inptrs[i][j]); + inptrs[i][j] += 2; + } + } + + // Compute the matrix W w + for (int j = 0; j < 5; j++) + { + // Ww[0][j] = w[0][j]/4.0f; + Ww[0][j] = vmul_n_f32(w[0][j], 1.0f/4.0f); + + // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; + Ww[1][j] = vmul_n_f32( + vadd_f32( + vadd_f32( + vadd_f32(w[1][j], w[0][j]), + vadd_f32(w[3][j], w[2][j]) + ), + w[4][j] + ), + -1.0f/6.0f + ); + + // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; + // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; + Ww[2][j] = vmul_n_f32( + vsub_f32( + vadd_f32( + vsub_f32(w[1][j], w[0][j]), + vsub_f32(w[3][j], w[2][j]) + ), + w[4][j] + ), + 1.0f/6.0f + ); + + // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; + Ww[3][j] = vmul_n_f32( + vmla_n_f32( + vadd_f32( + vadd_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), + vadd_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) + ), + w[4][j], 2.0f + ), + 1.0f/3.0f + ); + + // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; + Ww[4][j] = vmul_n_f32( + vmla_n_f32( + vadd_f32( + vsub_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), + vsub_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) + ), + w[4][j], 2.0f + ), + 1.0f/3.0f + ); + + // Ww[5][j] = w[4][j]; + Ww[5][j] = w[4][j]; + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + // V[i][0] = Ww[i][0]/4.0f; + V[i][0] = vmul_n_f32(Ww[i][0], 1.0f/4.0f); + + // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; + V[i][1] = vmul_n_f32( + vadd_f32( + vadd_f32( + vadd_f32(Ww[i][1], Ww[i][0]), + vadd_f32(Ww[i][3], Ww[i][2]) + ), + Ww[i][4] + ), + -1.0f/6.0f + ); + + // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; + // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; + V[i][2] = vmul_n_f32( + vsub_f32( + vadd_f32( + vsub_f32(Ww[i][1], Ww[i][0]), + vsub_f32(Ww[i][3], Ww[i][2]) + ), + Ww[i][4] + ), + 1.0f/6.0f + ); + + // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][3] = vmul_n_f32( + vmla_n_f32( + vadd_f32( + vadd_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), + vadd_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) + ), + Ww[i][4], 2.0f + ), + 1.0f/3.0f + ); + + // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][4] = vmul_n_f32( + vmla_n_f32( + vadd_f32( + vsub_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), + vsub_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) + ), + Ww[i][4], 2.0f + ), + 1.0f/3.0f + ); + + // V[i][5] = Ww[i][4]; + V[i][5] = Ww[i][4]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 2; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[5][5], Ww[6][5], V[6][6]; + + // Read weights + for (int i = 0; i < 5; i++) + { + for (int j = 0; j < 5; j++) + { + w[i][j] = *(inptrs[i][j]++); + } + } + + // Compute the matrix W w + for (int j = 0; j < 5; j++) + { + Ww[0][j] = w[0][j]/4.0f; + Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; + Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; + Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; + Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; + Ww[5][j] = w[4][j]; + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + V[i][0] = Ww[i][0]/4.0f; + V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; + V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; + V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; + V[i][5] = Ww[i][4]; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + *(outptr + m*matrix_stride) = V[i][j]; + } + } + outptr++; + } + } +} + +template class WeightTransform<5, 5, 6, 6, float, float, WinogradRoots::Integers>; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4_5_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4_5_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..fb3d712954 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4_5_fp32_fp32_integers.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<1, 5, 1, 8, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, // NOTE: Data in HWIO order + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const float *inptrs[kernel_cols]; + for (int j = 0; j < kernel_cols; j++) + { + inptrs[j] = input + j*weight_col_stride; + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[kernel_cols], V[inner_tile_cols]; + + // Read weights + for (int j = 0; j < kernel_cols; j++) + { + w[j] = *(inptrs[j]++); + } + + // Compute V = w WT + V[0] = (w[0]*-1) / 36; + V[1] = (w[1]*-1 + w[3]*-1 + w[0]*1 + w[2]*1 + w[4]*1) / 48; + V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1) / 48; + V[3] = (w[0]*-1 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8) / 120; + V[4] = (w[0]*-1 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120; + V[5] = (w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[0]*1) / 720; + V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[0]*1) / 720; + V[7] = (w[4]*1) / 1; + + // Store the transformed weights + for (int j = 0; j < inner_tile_cols; j++) + { + *(outptr + j*matrix_stride) = V[j]; + } + outptr++; + } + } +} + +template class WeightTransform<1, 5, 1, 8, float, float, WinogradRoots::Integers>; +template class WeightTransform<5, 1, 8, 1, float, float, WinogradRoots::Integers>; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..9e7040bca8 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp32_fp32_integers.cpp @@ -0,0 +1,257 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<3, 3, 6, 6, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, // NOTE: Data in HWIO order + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const auto weight_row_stride = 3 * weight_col_stride; + const float *inptrs[3][3]; + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; + } + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed in this kernel + float32x4_t w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1q_f32(inptrs[i][j]); + inptrs[i][j] += 4; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + // Ww[0][j] = 6*w[0][j]; + Ww[0][j] = vmulq_n_f32(w[0][j], 6.0); + + // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[1][j] = vmulq_n_f32(vaddq_f32(vaddq_f32(w[0][j], w[1][j]), w[2][j]), -4.0); + + // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[2][j] = vmulq_n_f32(vsubq_f32(vsubq_f32(w[1][j], w[0][j]), w[2][j]), 4.0); + + // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[3][j] = vmlaq_n_f32(vmlaq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); + + // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[4][j] = vmlaq_n_f32(vmlsq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); + + // Ww[5][j] = 24*w[2][j]; + Ww[5][j] = vmulq_n_f32(w[2][j], 24.0f); + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + const float recip576 = 1.0f / 576.0f; + + // V[i][0] = 6*Ww[i][0]; + V[i][0] = vmulq_n_f32(vmulq_n_f32(Ww[i][0], 6.0), recip576); + + // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; + V[i][1] = vmulq_n_f32(vmulq_n_f32(vaddq_f32(vaddq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); + + // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; + V[i][2] = vmulq_n_f32(vmulq_n_f32(vsubq_f32(vsubq_f32(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); + + // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; + V[i][3] = vmulq_n_f32(vmlaq_n_f32(vmlaq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); + + // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; + V[i][4] = vmulq_n_f32(vmlaq_n_f32(vmlsq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); + + // V[i][5] = 24*Ww[i][2]; + V[i][5] = vmulq_n_f32(vmulq_n_f32(Ww[i][2], 24.0f), recip576); + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1q_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 4; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used and computed in this kernel + float32x2_t w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1_f32(inptrs[i][j]); + inptrs[i][j] += 2; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + // Ww[0][j] = 6*w[0][j]; + Ww[0][j] = vmul_n_f32(w[0][j], 6.0); + + // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[1][j] = vmul_n_f32(vadd_f32(vadd_f32(w[0][j], w[1][j]), w[2][j]), -4.0); + + // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[2][j] = vmul_n_f32(vsub_f32(vsub_f32(w[1][j], w[0][j]), w[2][j]), 4.0); + + // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[3][j] = vmla_n_f32(vmla_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); + + // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[4][j] = vmla_n_f32(vmls_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); + + // Ww[5][j] = 24*w[2][j]; + Ww[5][j] = vmul_n_f32(w[2][j], 24.0f); + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + const float recip576 = 1.0f / 576.0f; + + // V[i][0] = 6*Ww[i][0]; + V[i][0] = vmul_n_f32(vmul_n_f32(Ww[i][0], 6.0), recip576); + + // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; + V[i][1] = vmul_n_f32(vmul_n_f32(vadd_f32(vadd_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); + + // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; + V[i][2] = vmul_n_f32(vmul_n_f32(vsub_f32(vsub_f32(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); + + // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; + V[i][3] = vmul_n_f32(vmla_n_f32(vmla_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); + + // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; + V[i][4] = vmul_n_f32(vmla_n_f32(vmls_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); + + // V[i][5] = 24*Ww[i][2]; + V[i][5] = vmul_n_f32(vmul_n_f32(Ww[i][2], 24.0f), recip576); + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1_f32(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 2; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = *(inptrs[i][j]++); + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + Ww[0][j] = 6*w[0][j]; + Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[5][j] = 24*w[2][j]; + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + V[i][0] = ( 6*Ww[i][0]) / 576.0; + V[i][1] = (-4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]) / 576.0; + V[i][2] = (-4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]) / 576.0; + V[i][3] = ( 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]) / 576.0; + V[i][4] = ( 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]) / 576.0; + V[i][5] = (24*Ww[i][2]) / 576.0; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + *(outptr + m*matrix_stride) = V[i][j]; + } + } + outptr++; + } + } +} + +template class WeightTransform<3, 3, 6, 6, float, float, WinogradRoots::Integers>; + +} // namespace diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_6_3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_6_3_fp32_fp32_integers.cpp new file mode 100644 index 0000000000..45723482a2 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_6_3_fp32_fp32_integers.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 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.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<1, 3, 1, 8, float, float, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const float* const input, // NOTE: Data in HWIO order + float* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const float *inptrs[3]; + for (int j = 0; j < 3; j++) + { + inptrs[j] = input + j*weight_col_stride; + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + float *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + float w[3], V[inner_tile_cols]; + + // Read weights + for (int j = 0; j < 3; j++) + { + w[j] = *(inptrs[j]++); + } + + // Compute V = w WT + V[0] = (w[0]*-1) / 36.0f; + V[1] = (w[1]*-1 + w[0]*1 + w[2]*1) / 48.0f; + V[2] = (w[0]*1 + w[1]*1 + w[2]*1) / 48.0f; + V[3] = (w[0]*-1 + w[2]*-4 + w[1]*2) / 120.0f; + V[4] = (w[0]*-1 + w[2]*-4 + w[1]*-2) / 120.0f; + V[5] = (w[1]*-3 + w[2]*9 + w[0]*1) / 720.0f; + V[6] = (w[1]*3 + w[2]*9 + w[0]*1) / 720.0f; + V[7] = (w[2]*1) / 1; + + // Store the transformed weights + for (int j = 0; j < inner_tile_cols; j++) + { + *(outptr + j*matrix_stride) = V[j]; + } + outptr++; + } + } +} + +template class WeightTransform<1, 3, 1, 8, float, float, WinogradRoots::Integers>; +template class WeightTransform<3, 1, 8, 1, float, float, WinogradRoots::Integers>; + +} // namespace |