| /* |
| * 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 KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| void InputTransformImpl<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::execute( |
| const T* const input, /** Input tensor data */ |
| const int n_batches, /** Number of batches in input tensor. */ |
| const int in_batch_stride, /** Stride between batches of the input. */ |
| const int n_rows, /** Number of rows in input tensor. */ |
| const int in_row_stride, /** Stride between rows of the input. */ |
| const int n_cols, /** Number of columns in input tensor. */ |
| const int in_col_stride, /** Stride between columns of the input. */ |
| const int n_channels, /** Number of channels in input tensor. */ |
| const PaddingType padding, /** Padding type. */ |
| const int tile_M, |
| const int tile_N, |
| T* const output, /** Base of output matrices. */ |
| const int matrix_stride, /** Stride between output matrices. */ |
| const int matrix_batch_stride, /** Stride between batches within the matrix. */ |
| const int matrix_row_stride /** Stride within matrices. */ |
| ) |
| { |
| // Compute the padding required on each edge of the image |
| const int pad_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0; |
| const int pad_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0; |
| |
| // Compute striding values (assuming NHWC ordered data) |
| 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 < n_batches; batch++) |
| { |
| // Pointer to the batch |
| const T* const input_base_batch = input + batch * in_batch_stride; |
| T* const outptr_base_batch = output + batch * matrix_batch_stride; |
| |
| // Loop over rows of tiles |
| for (int tile_i = 0; tile_i < tile_M; tile_i++) |
| { |
| // Padding (top + bottom) for the row |
| const int row_top = tile_i*(InnerTileRows - overlap_rows) - pad_top; |
| const int row_bottom = row_top + InnerTileRows; |
| const int row_pad_top = std::max(0, pad_top - tile_i*(InnerTileRows - overlap_rows)); |
| const int row_pad_bottom = (row_bottom <= n_rows) ? 0 : row_bottom - n_rows; |
| |
| // Pointer to the row |
| const int row_offset = std::min(0, row_pad_top - pad_top); |
| const T* const input_base_row = ( |
| input_base_batch + ((InnerTileRows - overlap_rows)*tile_i + row_offset)*in_row_stride |
| ); |
| T* const outptr_base_row = outptr_base_batch + tile_i*output_row_stride; |
| |
| // Process the row |
| process_tile_row( |
| tile_N, n_channels, |
| input_base_row, in_row_stride, in_col_stride, |
| outptr_base_row, matrix_stride, matrix_row_stride, |
| row_pad_top, pad_left, row_pad_bottom, n_cols |
| ); |
| } |
| } |
| } |
| |
| |
| template <int KernelRows, int InnerTileRows, typename T> |
| void InputTransformImpl<KernelRows, 1, InnerTileRows, 1, T>::execute( |
| const T* const input, /** Input tensor data */ |
| const int n_batches, /** Number of batches in input tensor. */ |
| const int in_batch_stride, /** Stride between batches of the input. */ |
| const int n_rows, /** Number of rows in input tensor. */ |
| const int in_row_stride, /** Stride between rows of the input. */ |
| const int n_cols, /** Number of columns in input tensor. */ |
| const int in_col_stride, /** Stride between columns of the input. */ |
| const int n_channels, /** Number of channels in input tensor. */ |
| const PaddingType padding, /** Padding type. */ |
| const int tile_M, |
| const int tile_N, |
| T* const output, /** Base of output matrices. */ |
| const int matrix_stride, /** Stride between output matrices. */ |
| const int matrix_batch_stride, /** Stride between batches within the matrix. */ |
| const int matrix_row_stride /** Stride within matrices. */ |
| ) |
| { |
| // If an Nx1 kernel then transpose and redirect to the 1xN implementation |
| InputTransformImpl<1, KernelRows, 1, InnerTileRows, T>::execute( |
| input, |
| n_batches, in_batch_stride, |
| n_cols, in_col_stride, |
| n_rows, in_row_stride, |
| n_channels, padding, |
| tile_N, tile_M, |
| output, matrix_stride, matrix_batch_stride, matrix_row_stride |
| ); |
| } |
| |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| void InputTransformImpl<KernelRows, KernelCols, InnerTileRows, InnerTileCols, 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 |
| ) |
| { |
| // 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_start = tile_j*(InnerTileCols - overlap_cols) - row_pad_left; |
| const int t_end = t_start + InnerTileCols; |
| const int t_pad_left = std::max(0, row_pad_left - tile_j*(InnerTileCols - overlap_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 = std::min(0, t_pad_left - row_pad_left); |
| const T* const input_base_col = ( |
| input_base + ((InnerTileCols - overlap_cols)*tile_j + col_offset)*input_col_stride |
| ); |
| T* const outptr = matrix_base + tile_j*matrix_row_stride; |
| |
| // Apply the specific tile processing function |
| const typename Tiles::TileFn tilefn = Tiles::get_tile_specialization( |
| pad_top, t_pad_left, pad_bottom, t_pad_right |
| ); |
| |
| tilefn( |
| n_channels, |
| input_base_col, input_row_stride, input_col_stride, |
| outptr, matrix_stride, |
| pad_top, t_pad_left, pad_bottom, t_pad_right |
| ); |
| } |
| } |
| |
| /***************************************************************************/ |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| InputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, 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. */ |
| const int in_batch_stride, /** Stride between input batches. */ |
| const int in_row_stride, /** Stride between input rows. */ |
| const int in_col_stride /** Stride between input columns. */ |
| ) : _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 - KernelRows + 1, |
| InnerTileRows - KernelRows + 1)), |
| _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - KernelCols + 1, |
| InnerTileCols - KernelCols + 1)), |
| _in_col_stride(in_col_stride ? in_col_stride : n_channels), |
| _in_row_stride(in_row_stride ? in_row_stride : n_cols * _in_col_stride), |
| _in_batch_stride(in_batch_stride ? in_batch_stride : n_rows * _in_row_stride), |
| _padding_type(padding) |
| { |
| } |
| |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| unsigned int InputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::get_window() const |
| { |
| // The final window includes the tail, all other windows will be a multiple |
| // of the window block in size. |
| return iceildiv(_n_channels, WINDOW_BLOCK); |
| } |
| |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| void InputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::run( |
| const unsigned int start, const unsigned int stop |
| ) |
| { |
| if (start >= get_window()) |
| { |
| return; |
| } |
| |
| // Determine the window of work to perform |
| const unsigned int start_channel = start * WINDOW_BLOCK; |
| const unsigned int stop_channel = std::min<const unsigned int>( |
| stop * WINDOW_BLOCK, _n_channels |
| ); |
| const unsigned int n_channels = stop_channel - start_channel; |
| |
| // Perform the work |
| execute( |
| _inptr + start_channel, |
| _n_batches, _in_batch_stride, |
| _n_rows, _in_row_stride, |
| _n_cols, _in_col_stride, |
| n_channels, |
| _padding_type, |
| _tiles_M, |
| _tiles_N, |
| _outptr + start_channel, |
| _matrix_stride, |
| _matrix_row_stride * _tiles_M * _tiles_N, |
| _matrix_row_stride |
| ); |
| } |
| |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| void InputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::execute( |
| const T* const input, /** Input tensor data */ |
| const int n_batches, /** Number of batches in input tensor. */ |
| const int in_batch_stride, /** Stride between batches of the input. */ |
| const int n_rows, /** Number of rows in input tensor. */ |
| const int in_row_stride, /** Stride between rows of the input. */ |
| const int n_cols, /** Number of columns in input tensor. */ |
| const int in_col_stride, /** Stride between columns of the input. */ |
| const int n_channels, /** Number of channels in input tensor. */ |
| const PaddingType padding, /** Padding type. */ |
| const int tile_M, |
| const int tile_N, |
| T* const output, /** Base of output matrices. */ |
| const int matrix_stride, /** Stride between output matrices. */ |
| const int matrix_batch_stride, /** Stride between batches within the matrix. */ |
| const int matrix_row_stride /** Stride within matrices. */ |
| ) |
| { |
| Transform::execute( |
| input, n_batches, in_batch_stride, n_rows, in_row_stride, n_cols, |
| in_col_stride, n_channels, padding, tile_M, tile_N, output, |
| matrix_stride, matrix_batch_stride, matrix_row_stride |
| ); |
| } |
| |
| template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> |
| typename InputTransformImplTiles<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::TileFn |
| InputTransformImplTiles<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>:: |
| get_tile_specialization( |
| const int pad_top, |
| const int pad_left, |
| const int pad_bottom, |
| const int pad_right |
| ) |
| { |
| if (!(pad_top || pad_left || pad_bottom || pad_right)) |
| { |
| // No padding, return unpadded specialisation |
| return tilefn_unpadded; |
| } |
| else if (pad_top && !(pad_left || pad_bottom || pad_right)) |
| { |
| // Top padding only |
| const int index = (pad_top - min_pad_top) / (InnerTileRows - overlap_rows); |
| return tilefn_top_padded[index]; |
| } |
| else if (!(pad_top) && pad_left && !(pad_bottom || pad_right)) |
| { |
| // Left padding only |
| const int index = (pad_left - min_pad_left) / (InnerTileCols - overlap_cols); |
| return tilefn_left_padded[index]; |
| } |
| else if (!(pad_top || pad_left) && pad_bottom && !(pad_right)) |
| { |
| // Bottom padding only |
| return tilefn_bottom_padded[pad_bottom - 1]; |
| } |
| else if (!(pad_top || pad_left || pad_bottom) && pad_right) |
| { |
| // Right padding only |
| return tilefn_right_padded[pad_right - 1]; |
| } |
| else |
| { |
| // Combination of paddings, return an unspecialised method |
| return tilefn_generic; |
| } |
| } |
| |
| template <int KernelCols, int InnerTileCols, typename T> |
| typename InputTransformImplTiles<1, KernelCols, 1, InnerTileCols, T>::TileFn |
| InputTransformImplTiles<1, KernelCols, 1, InnerTileCols, T>:: |
| get_tile_specialization( |
| const int pad_top, |
| const int pad_left, |
| const int pad_bottom, |
| const int pad_right |
| ) |
| { |
| (void) pad_top; |
| (void) pad_bottom; |
| |
| if (!(pad_left || pad_right)) |
| { |
| // No padding, return unpadded specialisation |
| return tilefn_unpadded; |
| } |
| else if (pad_left && !pad_right) |
| { |
| // Left padding only |
| const int index = (pad_left - min_pad_left) / (InnerTileCols - overlap_cols); |
| return tilefn_left_padded[index]; |
| } |
| else if (!pad_left && pad_right) |
| { |
| // Right padding only |
| return tilefn_right_padded[pad_right - 1]; |
| } |
| else |
| { |
| // Combination of paddings, return an unspecialised method |
| return tilefn_generic; |
| } |
| } |
| } |
| |
| |