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Diffstat (limited to 'src/core/NEON/kernels/winograd/winograd_layer.cpp')
| -rw-r--r-- | src/core/NEON/kernels/winograd/winograd_layer.cpp | 204 |
1 files changed, 204 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/winograd/winograd_layer.cpp b/src/core/NEON/kernels/winograd/winograd_layer.cpp new file mode 100644 index 0000000000..689ecba5fb --- /dev/null +++ b/src/core/NEON/kernels/winograd/winograd_layer.cpp @@ -0,0 +1,204 @@ +/* + * 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 "convolution.hpp" +#include "winograd_layer.hpp" +#include "tensor.hpp" + + +/** Determine how much memory (in units of TIn) to allocate for the transformed + * weights. + */ +template < + int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TOut +> +unsigned int WinogradConvolutionLayer< + OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut +>::get_weight_storage_size( + const int n_output_channels, /** Number of output feature maps. */ + const int n_input_channels /** Number of input feature maps. */ +) +{ + const KernelShape shape( + n_output_channels, KernelRows, KernelCols, n_input_channels + ); + return static_cast<unsigned int>( + // WinogradConv returns the size in bytes, we divide by `sizeof(TIn)` to + // express that in units of TIn. + WinogradConv::get_kernel_storage_size(shape) / sizeof(TIn) + ); +} + + +/** Determine how much memory (in units of TIn) to allocate for the transformed + * input. + */ +template < + int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TOut +> +unsigned int WinogradConvolutionLayer< + OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut +>::get_input_storage_size( + const int n_batches, /** Number of batches in the input tensor. */ + const int n_channels, /** Number of feature maps in the input tensor. */ + const int n_rows, /** Number of rows in each feature map. */ + const int n_cols, /** Number of columns in each feature map. */ + const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ +) +{ + // Construct shapes for the input and kernel tensors. + const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels); + const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels); + const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; + + // Return the size, converted into units of TIn + return static_cast<unsigned int>( + WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / + sizeof(TIn) + ); +} + + +/** Determine how much memory (in units of TOut) to allocate for the (Winograd + * domain) output. + */ +template < + int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TOut +> +unsigned int WinogradConvolutionLayer< + OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut +>::get_output_storage_size( + const int n_batches, /** Number of batches in the output tensor. */ + const int n_rows, /** Number of rows in each feature map of the input tensor. */ + const int n_cols, /** Number of columns in each feature map of the input tensor. */ + const int n_output_channels, /** Number of feature maps in the output tensor. */ + const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ +) +{ + // Construct shapes for the input and kernel tensors. + const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1); + const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1); + const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; + + // Return the size, converted into units of TOut + return static_cast<unsigned int>( + WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / + sizeof(TOut) + ); +} + + +/** Get the shape (rows, cols) of a feature map of the output tensor. */ +template < + int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TOut +> +std::pair<int, int> WinogradConvolutionLayer< + OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut +>::get_output_feature_map_shape( + const int n_input_rows, /** Number of rows in the input feature map. */ + const int n_input_cols, /** Number of columns in the input feature map. */ + const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ +) +{ + // Construct shapes for the input and kernel tensors. + const Tensor4DShape input_shape(1, n_input_rows, n_input_cols, 1); + const KernelShape kern_shape(1, KernelRows, KernelCols, 1); + const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; + + // Compute the new shape + const auto output_shape = WinogradConv::get_output_shape( + kern_shape, input_shape, padding + ); + + return std::make_pair(output_shape.n_rows, output_shape.n_cols); +} + + +/** Create a new Winograd convolution layer. + */ +template < + int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TOut +> +WinogradConvolutionLayer<OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut>:: +WinogradConvolutionLayer( + const int n_batches, /** Number of batches in the input and output tensors. */ + const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */ + const int n_input_rows, /** Number of rows in a feature map of the input tensor. */ + const int n_input_cols, /** Number of columns in a feature map of the input tensor. */ + const int n_output_channels, /** Number of feature maps in the output tensor. */ + const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */ + const TIn* const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ + TIn* const winograd_weights, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */ + const TIn* const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ + TIn* const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */ + TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ + TOut* const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */ +) : _kernel_shape(n_output_channels, KernelRows, KernelCols, n_input_channels), + _input_shape(n_batches, n_input_rows, n_input_cols, n_input_channels), + _padding(same_padding ? PADDING_SAME : PADDING_VALID), + _output_shape(WinogradConv::get_output_shape(_kernel_shape, _input_shape, _padding)), + _n_output_rows(_output_shape.n_rows), + _n_output_cols(_output_shape.n_cols), + _kernel_matrix_stride(WinogradConv::get_kernel_matrix_stride(_kernel_shape)), + _kernel_matrix_row_stride(roundup(n_output_channels, WinogradConv::N_BLOCK)), + _input_matrix_stride(WinogradConv::get_input_matrix_stride(_kernel_shape, _input_shape, _padding)), + _input_matrix_row_stride(n_input_channels), + _output_matrix_stride(WinogradConv::get_output_matrix_stride(_kernel_shape, _input_shape, _padding)), + _output_matrix_row_stride(_kernel_matrix_row_stride), + _tile_rows(iceildiv(_n_output_rows, OutputTileRows)), + _tile_cols(iceildiv(_n_output_cols, OutputTileCols)), + _m(n_batches * _tile_rows * _tile_cols), + _k(n_input_channels), + _n(n_output_channels), + weights_transform( + weights, winograd_weights, + _kernel_matrix_stride, _kernel_matrix_row_stride, + n_output_channels, n_input_channels + ), + input_transform( + input, n_batches, n_input_rows, n_input_cols, n_input_channels, _padding, + winograd_input, _input_matrix_stride, _input_matrix_row_stride + ), + gemms( + WinogradBase::N_GEMMS, _m, _k, _n, + _input_matrix_stride, _input_matrix_row_stride, + _kernel_matrix_stride, _kernel_matrix_row_stride, + _output_matrix_stride, _output_matrix_row_stride, + winograd_input, winograd_weights, winograd_output + ), + output_transform( + winograd_output, _output_matrix_stride, _output_matrix_row_stride, + output, n_batches, _n_output_rows, _n_output_cols, n_output_channels + ) +{ +} + +// Instantiate valid implementations. +template class WinogradConvolutionLayer<2, 2, 3, 3, float, float>; +template class WinogradConvolutionLayer<4, 4, 3, 3, float, float>; |