diff options
author | Pablo Tello <pablo.tello@arm.com> | 2018-01-30 14:48:11 +0000 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:45:00 +0000 |
commit | 52140b42f4f663da7f4537abbdebd13df541bcea (patch) | |
tree | 16c7e4b8969830fcb65860cdffdcc06c2265180c /src/core/NEON | |
parent | 054a7144cf9c9cf7ed25adcb7e8095b9bcf866bf (diff) | |
download | ComputeLibrary-52140b42f4f663da7f4537abbdebd13df541bcea.tar.gz |
COMPMID-784: Winograd tramsforms refactoring
1) Removed the example files winograd_layer.hpp/cpp
2) Teplatized winograd transform kernels
Change-Id: I7045fa0b801b9d30a11275914aaa2dafd254aed2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118332
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src/core/NEON')
-rw-r--r-- | src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 242 | ||||
-rw-r--r-- | src/core/NEON/kernels/winograd/winograd_gemm.cpp | 4 | ||||
-rw-r--r-- | src/core/NEON/kernels/winograd/winograd_layer.cpp | 206 |
3 files changed, 132 insertions, 320 deletions
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp index e2e4e40fe4..b0a36ff46a 100644 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp @@ -29,173 +29,193 @@ #include "arm_compute/core/TensorInfo.h" #include "support/ToolchainSupport.h" -#include "arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp" - -namespace -{ -using T = WinogradConvolutionLayer<2, 2, 3, 3, float, float>; -} // namespace - namespace arm_compute { -class Winograd3x3F32::Private -{ -public: - Private( - 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 float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ - float *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 float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ - float *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`. */ - float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ - float *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`. */ - ) - : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, nullptr, output, winograd_output) - { - } - T convolver; -}; - -Winograd3x3F32::~Winograd3x3F32() -{ -} - -Winograd3x3F32::Winograd3x3F32( - 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 float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ - float *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 float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ - float *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`. */ - float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ - float *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`. */ -) - : _pimpl(support::cpp14::make_unique<Private>(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output, - winograd_output)) -{ -} - -unsigned int NEWinogradLayerKernel::get_input_storage_size(const int n_batches, const int n_channels, const int n_rows, const int n_cols, const bool same_padding) -{ - return T::get_input_storage_size(n_batches, n_channels, n_rows, n_cols, same_padding); -} - -unsigned int NEWinogradLayerKernel::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". */ -) +//Batched Gemms +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerKernel() + : _gemms() { - return T::get_output_storage_size(n_batches, n_rows, n_cols, n_output_channels, same_padding); } -unsigned int NEWinogradLayerKernel::get_weight_storage_size(const int n_output_channels, const int n_input_channels) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const unsigned int n_gemms, + const int M, const int K, const int N, + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const float *const a_ptr, + const float *const b_ptr, + float *const c_ptr) { - return T::get_weight_storage_size(n_output_channels, n_input_channels); -} - -NEWinogradLayerKernel::NEWinogradLayerKernel() - : _convolver(nullptr) -{ -} - -void NEWinogradLayerKernel::configure(Winograd3x3F32 *convolver) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(convolver); - _convolver = convolver; + _gemms = support::cpp14::make_unique<MultiGEMM>(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr); Window win; - auto win_last = _convolver->_pimpl->convolver.gemms.get_window(); + auto win_last = _gemms->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } -void NEWinogradLayerKernel::run(const Window &window, const ThreadInfo &info) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); const size_t first_gemm = window.x().start(); const size_t last_gemm = window.x().end(); - _convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm); + _gemms->run(first_gemm, last_gemm); } -INEWinogradLayerTransformKernel::INEWinogradLayerTransformKernel() - : _convolver(nullptr) +template class NEWinogradLayerKernel<2, 2, 3, 3>; + +// Weights transform + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels) { + 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(float)` to + // express that in units of float. + WinogradConv::get_kernel_storage_size(shape) / sizeof(float)); } -void INEWinogradLayerTransformKernel::configure(Winograd3x3F32 *convolver) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() + : _transform() { - ARM_COMPUTE_ERROR_ON_NULLPTR(convolver); - _convolver = convolver; } -// Weights transform - -void NEWinogradLayerTransformWeightsKernel::configure(Winograd3x3F32 *convolver) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const ITensor *weights_hwio, + float *const output, + const int matrix_stride, /** Stride across matrices in the output. */ + const int n_output_channels, /** Number of filters. */ + const int n_input_channels) /** Number of channels in each filter. */ { - INEWinogradLayerTransformKernel::configure(convolver); + const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK); + _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<float *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, + n_input_channels); Window win; - auto win_last = _convolver->_pimpl->convolver.weights_transform.get_window(); + auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } -void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); const size_t fst = window.x().start(); const size_t lst = window.x().end(); - _convolver->_pimpl->convolver.weights_transform.run(fst, lst); + _transform->run(fst, lst); } -bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } +template class NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>; + // Input transform -void NEWinogradLayerTransformInputKernel::configure(Winograd3x3F32 *convolver) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( + int n_batches, /** Number of batches in the input tensor. */ + int n_channels, /** Number of feature maps in the input tensor. */ + int n_rows, /** Number of rows in each feature map. */ + int n_cols, /** Number of columns in each feature map. */ + 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(float)); +} + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() + : _transform() { - INEWinogradLayerTransformKernel::configure(convolver); +} + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const float *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. */ + float *const output, /** Base of output matrices. */ + const int matrix_stride) /** Stride between output matrices. */ +{ + // _input_matrix_row_stride(n_input_channels), + _transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels); Window win; - auto win_last = _convolver->_pimpl->convolver.input_transform.get_window(); + auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } -void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); const size_t fst = window.x().start(); const size_t lst = window.x().end(); - _convolver->_pimpl->convolver.input_transform.run(fst, lst); + _transform->run(fst, lst); } -bool NEWinogradLayerTransformInputKernel::is_parallelisable() const + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } +template class NEWinogradLayerTransformInputKernel<2, 2, 3, 3>; + // Output transform -NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel() + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( + int n_batches, /** Number of batches in the output tensor. */ + int n_rows, /** Number of rows in each feature map of the input tensor. */ + int n_cols, /** Number of columns in each feature map of the input tensor. */ + int n_output_channels, /** Number of feature maps in the output tensor. */ + 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(float)); +} + +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0) { } -void NEWinogradLayerTransformOutputKernel::configure( +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( const ITensor *biases, const float *const output_workingspace, const int matrix_stride, @@ -205,13 +225,10 @@ void NEWinogradLayerTransformOutputKernel::configure( const int n_cols, const int n_channels) { - using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>; - using OutputTransform = typename WinogradBase::template OutputTransform<float>; - _biases = biases; _output_workspace = output_workingspace; _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(n_channels, WinogradBase::Convolution<float, float>::N_BLOCK); + _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK); _output = output; _n_batches = n_batches; _n_rows = n_rows; @@ -226,7 +243,8 @@ void NEWinogradLayerTransformOutputKernel::configure( INEKernel::configure(win); } -void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info) +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -234,9 +252,6 @@ void NEWinogradLayerTransformOutputKernel::run(const Window &window, const Threa ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); ARM_COMPUTE_ERROR_ON_NULLPTR(_output); - using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>; - using OutputTransform = typename WinogradBase::template OutputTransform<float>; - OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, reinterpret_cast<float *>(_biases->buffer()), _output, _n_batches, _n_rows, _n_cols, _n_channels); @@ -247,9 +262,12 @@ void NEWinogradLayerTransformOutputKernel::run(const Window &window, const Threa output_transform.run(fst, lst); } -bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } +template class NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>; + } // namespace arm_compute diff --git a/src/core/NEON/kernels/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/winograd/winograd_gemm.cpp index b45f6f55d9..05426450a6 100644 --- a/src/core/NEON/kernels/winograd/winograd_gemm.cpp +++ b/src/core/NEON/kernels/winograd/winograd_gemm.cpp @@ -36,8 +36,8 @@ Tensor4DShape WinogradGEMM<kr, kc, itr, itc>::Convolution<TOut, TIn>::get_output { return Tensor4DShape { in_shape.n_batches, - (padding == PADDING_SAME) ? in_shape.n_rows : in_shape.n_rows - (kernel_rows - 2), - (padding == PADDING_SAME) ? in_shape.n_cols : in_shape.n_cols - (kernel_cols - 2), + (padding == PADDING_SAME) ? in_shape.n_rows : in_shape.n_rows - (kernel_rows - 1), + (padding == PADDING_SAME) ? in_shape.n_cols : in_shape.n_cols - (kernel_cols - 1), kernel_shape.n_output_channels, in_shape.ordering }; diff --git a/src/core/NEON/kernels/winograd/winograd_layer.cpp b/src/core/NEON/kernels/winograd/winograd_layer.cpp deleted file mode 100644 index f16d62c0ef..0000000000 --- a/src/core/NEON/kernels/winograd/winograd_layer.cpp +++ /dev/null @@ -1,206 +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 "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`. */ - const TOut* const biases, /** Pointer to biases vector. */ - 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, biases, - 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>; -template class WinogradConvolutionLayer<2, 2, 5, 5, float, float>; |