| /* |
| * 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 |
| ); |
| } |
| } |