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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-01-30 18:13:46 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:46:07 +0000 |
commit | 4074c995d2a88684fd4a9d1aa36d51de56bb8dab (patch) | |
tree | 280a15ca10ff88c5eb432be011ccb721660a3349 /arm_compute/core/NEON/kernels/winograd/transforms | |
parent | c5694afca3f937f8c9b3ec328da9394f11f9af2d (diff) | |
download | ComputeLibrary-4074c995d2a88684fd4a9d1aa36d51de56bb8dab.tar.gz |
COMPMID-873: Integrate RSH NEON Depthwise Convolution routine
Change-Id: Ida1e9a836bc518bfe5563e16bf7f92bde5fc13f7
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118472
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Diffstat (limited to 'arm_compute/core/NEON/kernels/winograd/transforms')
3 files changed, 0 insertions, 453 deletions
diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp deleted file mode 100644 index 075765a513..0000000000 --- a/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp +++ /dev/null @@ -1,195 +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. - */ - -#pragma once -#include "../winograd_gemm.hpp" - -namespace winograd -{ - /***************************************************************************/ - /* Instance-less API */ - template <int output_tile_rows, int output_tile_cols, - int kernel_rows, int kernel_cols> - template <typename T> - void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::execute( - const T *inptr, - const Tensor4DShape& input_shape, - const PaddingType padding_type, - const int tile_M, - const int tile_N, - T *outptr_base, - const int matrix_stride, - const int matrix_batch_stride, - const int matrix_row_stride - ) - { - // Compute the padding required on each edge of the image - const bool base_padding = (padding_type == PADDING_SAME) ? 1 : 0; - const int pad_top = base_padding; - const int pad_left = base_padding; - const int tile_overlap = kernel_rows - 1; - - // Compute striding values (assuming NHWC ordered data) - const int input_col_stride = input_shape.n_channels; - const int input_row_stride = input_shape.n_cols * input_col_stride; - const int input_batch_stride = input_shape.n_rows * input_row_stride; - const int output_col_stride = matrix_row_stride; - const int output_row_stride = tile_N * output_col_stride; - - // Loop over batches - for (int batch = 0; batch < input_shape.n_batches; batch++) - { - // Pointer to the batch - const T* const input_base_batch = inptr + batch * input_batch_stride; - T* const outptr_base_batch = outptr_base + batch * matrix_batch_stride; - - // Loop over rows of tiles - for (int tile_i = 0; tile_i < tile_M; tile_i++) - { - // Pointer to the row - const int row_offset = (tile_i == 0) ? - 0 : ((padding_type == PADDING_VALID) ? 0 : 1); - const T* const input_base_row = ( - input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*input_row_stride - ); - T* const outptr_base_row = outptr_base_batch + tile_i*output_row_stride; - - // Padding (top + bottom) for the row - const int row_top = tile_i*(inner_tile_rows - tile_overlap) - pad_top; - const int row_bottom = row_top + inner_tile_rows; - const int row_pad_top = (tile_i == 0) ? pad_top : 0; - const int row_pad_bottom = (row_bottom <= input_shape.n_rows) ? 0 : row_bottom - input_shape.n_rows; - - // Process the row - process_tile_row( - tile_N, input_shape.n_channels, - input_base_row, input_row_stride, input_col_stride, - outptr_base_row, matrix_stride, matrix_row_stride, - row_pad_top, pad_left, row_pad_bottom, input_shape.n_cols - ); - } - } - } - - template <int output_tile_rows, int output_tile_cols, - int kernel_rows, int kernel_cols> - template <typename T> - void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::process_tile_row( - const int tile_N, - int n_channels, - const T* const input_base, - const int input_row_stride, - const int input_col_stride, - T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const int pad_top, - const int row_pad_left, - const int pad_bottom, - const int n_cols - ) - { - constexpr int tile_overlap = kernel_cols - 1; - - // Loop over columns of tiles - for (int tile_j = 0; tile_j < tile_N; tile_j++) - { - // Padding (left + right) for the tile - const int t_pad_left = (tile_j == 0) ? row_pad_left : 0; - const int t_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_left; - const int t_end = t_start + inner_tile_cols; - const int t_pad_right = (t_end <= n_cols) ? 0 : t_end - n_cols; - - // Get pointers into the inputs and outputs - const int col_offset = (tile_j == 0) ? 0 : row_pad_left; - const T* const input_base_col = ( - input_base + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*input_col_stride - ); - T* const outptr = matrix_base + tile_j*matrix_row_stride; - - // Apply the specific tile processing function - tile_fns[pad_top][t_pad_left][pad_bottom][t_pad_right]( - n_channels, - input_base_col, - input_row_stride, - input_col_stride, - outptr, - matrix_stride - ); - } - } - - /***************************************************************************/ - template <int otr, int otc, int kr, int kc> - template <typename T> - WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::InputTransform( - const T* const input, /** Input tensor data */ - const int n_batches, /** Number of batches in input tensor. */ - const int n_rows, /** Number of rows in input tensor. */ - const int n_cols, /** Number of columns in input tensor. */ - const int n_channels, /** Number of channels in input tensor. */ - const PaddingType padding, /** Padding type. */ - T* const output, /** Base of output matrices. */ - const int matrix_stride, /** Stride between output matrices. */ - const int matrix_row_stride /** Stride within matrices. */ - ) : _inptr(input), _outptr(output), - _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels), - _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride), - _tiles_M(iceildiv((padding == PADDING_SAME) ? n_rows : n_rows - 2, output_tile_rows)), - _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - 2, output_tile_cols)), - _padding_type(padding) - { - } - - template <int otr, int otc, int kr, int kc> - template <typename T> - unsigned int WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::get_window() const - { - // TODO When the input transform supports multithreading, return the total - // number of tile rows (allowing for multiple batches). For now we return 1 - // to indicate that the activations must be transformed as a single block. - return 1; // TODO _tiles_M * _n_batches; - } - - template <int otr, int otc, int kr, int kc> - template <typename T> - void WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::run( - const unsigned int start, const unsigned int stop - ) - { - // TODO When the input transform supports multithreading call execute for a - // portion of the tile rows. - (void) start; - (void) stop; - - // For now, just do all of the work. - const Tensor4DShape input_shape = { - _n_batches, _n_rows, _n_cols, _n_channels, NHWC - }; - execute( - _inptr, input_shape, _padding_type, _tiles_M, _tiles_N, _outptr, - _matrix_stride, _matrix_row_stride * _tiles_M * _tiles_N, _matrix_row_stride - ); - } -} diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp deleted file mode 100644 index 4b54dfdf08..0000000000 --- a/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp +++ /dev/null @@ -1,77 +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 "winograd_gemm.hpp" -using namespace winograd; - - -template <int otr, int otc, int kr, int kc> -template <typename T> -WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::WeightsTransform( - const T* const input, - T* const output, - const int matrix_stride, /** Stride across matrices in the output. */ - const int matrix_row_stride, /** Stride across rows of the matrix. */ - const int n_output_channels, - const int n_input_channels -) : inptr(input), outptr(output), - matrix_stride(matrix_stride), matrix_row_stride(matrix_row_stride), - n_output_channels(n_output_channels), n_input_channels(n_input_channels) -{ -} - - -template <int otr, int otc, int kr, int kc> -template <typename T> -unsigned int WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::get_window() 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; -} - - -template <int otr, int otc, int kr, int kc> -template <typename T> -void WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::run( - const unsigned int start, const unsigned int stop -) -{ - // TODO When the weights transform supports multithreading call execute for a - // portion of the output channels. - (void) start; - (void) stop; - - // For now, just do all of the work. - execute( - n_output_channels, - n_input_channels, - inptr, - outptr, - matrix_stride, - matrix_row_stride - ); -} diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp deleted file mode 100644 index 0dd719751b..0000000000 --- a/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp +++ /dev/null @@ -1,181 +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. - */ - -#pragma once -#include "../winograd_gemm.hpp" - -namespace winograd -{ - template <int output_tile_rows, int output_tile_cols, - int kernel_rows, int kernel_cols> - template <typename T> - void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::execute( - const Tensor4DShape &output_shape, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output - ) - { - // Compute the number of tiles and hence the padding required on the bottom - // and right of the image. - const int tile_M = iceildiv(output_shape.n_rows, output_tile_rows); - const int tile_N = iceildiv(output_shape.n_cols, output_tile_cols); - const int pad_bottom = output_tile_rows*tile_M - output_shape.n_rows; - const int pad_right = output_tile_cols*tile_N - output_shape.n_cols; - - const int matrix_tile_row_stride = tile_N * matrix_row_stride; - const int matrix_batch_stride = tile_M * matrix_tile_row_stride; - const int output_col_stride = output_shape.n_channels; - const int output_row_stride = output_shape.n_cols * output_col_stride; - const int output_batch_stride = output_shape.n_rows * output_row_stride; - - // Perform the output transformation for each batch - for (int batch = 0; batch < output_shape.n_batches; batch++) - { - // Get batch offset for input and outputs. - const T* const matrix_batch = matrix_base + batch*matrix_batch_stride; - T* const outptr_batch = output + batch*output_batch_stride; - - // Perform the output transformation for each row of the output tensor. - for (int tile_i = 0; tile_i < tile_M; tile_i++) - { - // Compute properties of this row of output tiles - const int row_pad_bottom = (tile_i < tile_M - 1) ? 0: pad_bottom; - const T* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride; - T* const outptr_row = outptr_batch + output_tile_rows*tile_i*output_row_stride; - - // Process the row - process_tile_row( - tile_N, output_shape.n_channels, matrix_tile_row, matrix_stride, - matrix_row_stride, biases, - outptr_row, output_row_stride, output_col_stride, row_pad_bottom, - pad_right - ); - } - } - } - - template <int output_tile_rows, int output_tile_cols, - int kernel_rows, int kernel_cols> - template <typename T> - void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::process_tile_row( - const int tile_N, - const int n_channels, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output, - const int output_row_stride, - const int output_col_stride, - const int row_pad_bottom, - const int row_pad_right - ) - { - // Loop over columns of tiles - for (int tile_j = 0; tile_j < tile_N; tile_j++) - { - // Properties of this tile - const int tile_pad_right = (tile_j < tile_N - 1) ? 0 : row_pad_right; - const T* const matrix_row = matrix_base + tile_j * matrix_row_stride; - T* const outptr = output + output_tile_cols*tile_j*output_col_stride; - - // Perform the output transformation - tile_fns[row_pad_bottom][tile_pad_right]( - n_channels, matrix_row, matrix_stride, biases, - outptr, output_row_stride, output_col_stride - ); - } - } - - template <int output_tile_rows, int output_tile_cols, int kr, int kc> - template <typename T> - size_t WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::bytes_read(const Tensor4DShape &shape) - { - const int M = iceildiv(shape.n_rows, output_tile_rows) * - iceildiv(shape.n_cols, output_tile_cols); - const int N = shape.n_channels; - return inner_tile_rows * inner_tile_cols * M * N * sizeof(T); - } - - template <int otr, int otc, int kr, int kc> - template <typename T> - size_t WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::bytes_written(const Tensor4DShape &shape) - { - return shape.size() * sizeof(T); - } - - template <int output_tile_rows, int output_tile_cols, int kr, int kc> - template <typename T> - WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::OutputTransform( - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels - ) : _matrix_base(matrix_base), _biases(biases), - _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride), - _outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), - _n_channels(n_channels), _tile_M(iceildiv(n_rows, output_tile_rows)), - _tile_N(iceildiv(n_cols, output_tile_cols)) - { - } - - template <int otr, int otc, int kr, int kc> - template <typename T> - unsigned int WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::get_window() const - { - // TODO When the output transform supports multithreading, return the total - // number of tile rows (allowing for multiple batches). For now we return 1 - // to indicate that the activations must be transformed as a single block. - return 1; // TODO _tile_M * _n_batches; - } - - template <int otr, int otc, int kr, int kc> - template <typename T> - void WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::run( - const unsigned int start, const unsigned int stop - ) - { - // TODO When the output transform supports multithreading call execute for a - // portion of the tile rows. - (void) start; - (void) stop; - - // For now, just do all of the work. - const Tensor4DShape output_shape = { - _n_batches, _n_rows, _n_cols, _n_channels, NHWC - }; - execute( - output_shape, _matrix_base, _matrix_stride, _matrix_row_stride, _biases, - _outptr - ); - } -} // namespace winograd |