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Diffstat (limited to 'arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp')
| -rw-r--r-- | arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp | 211 |
1 files changed, 211 insertions, 0 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp new file mode 100644 index 0000000000..9d418bebb4 --- /dev/null +++ b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp @@ -0,0 +1,211 @@ +/* + * 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 <utility> + +#include "arm_gemm_local.hpp" +#include "arm_gemm.hpp" +#include "winograd.hpp" + +namespace winograd +{ + + +class IWinogradConvolutionLayer +{ + public: + virtual ~IWinogradConvolutionLayer() = default; + + virtual unsigned int weight_transform_get_window(void) const = 0; + virtual void weight_transform_run(unsigned int start, unsigned int stop) = 0; + + virtual ITransform& input_transform(void) = 0; // Expose the input transform + virtual ITransform& output_transform(void) = 0; // Expose the output transform + virtual arm_gemm::IGemmCommon *gemm(void) = 0; // Expose the underlying GEMM +}; + +/** Example of how to construct an ACL-like interface. + * + * Use `get_weight_storage_size`, `get_input_storage_size` and + * `get_output_storage_size` to allocate memory for the convolution engine. + * Then create a `WinogradConvolutionLayer`. + * + * Initialise the weights using `weights_transform.run(...)`. + * + * For each inference: + * 1. Transform the inputs to the Winograd domain using `input_transform.run(...)` + * 2. Perform a number of GEMMs using `gemms.run(...)` + * 3. Transform the output to the spatial domain using `output_transform.run(...)` + */ +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, + typename TIn, typename TInGEMM, typename TOutGEMM, typename TOut, + WinogradRoots Roots> +class WinogradConvolutionLayer : public IWinogradConvolutionLayer +{ + private: + static constexpr int InnerTileRows = OutputTileRows + KernelRows - 1; + static constexpr int InnerTileCols = OutputTileCols + KernelCols - 1; + static constexpr int N_GEMMS = InnerTileRows * InnerTileCols; + + const KernelShape _kernel_shape; + const Tensor4DShape _input_shape; + const PaddingType _padding; + const Tensor4DShape _output_shape; + const int _n_output_rows, _n_output_cols; + const int _kernel_matrix_stride, _kernel_matrix_row_stride; + const int _input_matrix_stride, _input_matrix_row_stride; + const int _output_matrix_stride, _output_matrix_row_stride; + const int _tile_rows, _tile_cols; + const int _m, _k, _n; + + public: + using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, Roots>; + using WeightsTransform = typename WinogradBase::template WeightsTransform<TIn, TInGEMM>; + using InputTransform = typename WinogradBase::template InputTransform<TIn, TInGEMM>; + using WinogradConv = typename WinogradBase::template Convolution<TOut, TIn, TInGEMM, TOutGEMM>; + using OutputTransform = typename WinogradBase::template OutputTransform<TOutGEMM, TOut>; + + /* Public member variables. */ + WeightsTransform weights_transform; /** Operator to transform weights to Winograd domain. */ + InputTransform _input_transform; /** Operator to transform input to Winograd domain. */ + arm_gemm::UniqueGemmCommon<TInGEMM, TOutGEMM> gemms; /** Operator to perform multiple GEMMs. */ + OutputTransform _output_transform; /** Operator to transform output from Winograd domain. */ + + /** Determine how much memory (in units of TIn) to allocate for the + * transformed weights. + */ + static unsigned int get_weight_storage_size( + const int n_output_channels, /** Number of output feature maps. */ + const int n_input_channels /** Number of input feature maps. */ + ); + + static unsigned int get_weight_stride( + const int n_output_channels, /** Number of output feature maps. */ + const int n_input_channels /** Number of input feature maps. */ + ); + + static unsigned int get_weight_multi_stride( + const int n_output_channels, /** Number of output feature maps. */ + const int n_input_channels /** Number of input feature maps. */ + ); + + /** Determine how much memory (in units of TIn) to allocate for the + * transformed input. + */ + static unsigned int 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". */ + ); + + /** Get the row stride for the A matrix in the Winograd domain. */ + static unsigned int get_input_stride( + 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". */ + ); + + /** Get the stride between A matrices in the Winograd domain. */ + static unsigned int get_input_multi_stride( + 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". */ + ); + + /** Determine how much memory (in units of TOut) to allocate for the + * (Winograd domain) output. + */ + static unsigned int 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". */ + ); + + static unsigned int get_output_stride( + 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". */ + ); + + static unsigned int get_output_multi_stride( + 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". */ + ); + + /** Get the shape (rows, cols) of a feature map of the output tensor. */ + static std::pair<int, int> 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". */ + ); + + /** Create a new Winograd convolution layer. + */ + WinogradConvolutionLayer( + const arm_gemm::CPUInfo &cpuinfo, /** Describes CPU properties. */ + const int n_threads, /** Maximum number of threads used to execute the convolution. */ + 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. */ + TInGEMM* const weights_storage, /** 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. */ + TInGEMM* 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`. */ + const TOut* const biases, /** Pointer to biases vector. Pass nullptr if no bias is provided. */ + TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ + TOutGEMM* 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`. */ + const bool pretranspose_B=true, /** Hint that the B matrix can be pretransposed. */ + arm_gemm::GemmConfig *gemm_cfg=nullptr /** Pointer to GEMM configuration. */ + ); + + /* Utility methods for interacting with the layer. */ + unsigned int weight_transform_get_window(void) const; + void weight_transform_run(const unsigned int start, const unsigned int stop); + + ITransform& input_transform(void); + ITransform& output_transform(void); + + /* Get a pointer to the GEMM underlying the Winograd transform. */ + arm_gemm::IGemmCommon *gemm(void); +}; + +} |