From 72686fa6ee0f04d458ed2274b4d34917628ef14d Mon Sep 17 00:00:00 2001 From: Pablo Tello Date: Mon, 3 Sep 2018 11:40:33 +0100 Subject: COMPMID-1550: Winograd integrate RSH changes. Refactors the transforms to make use of partial specialization. Change-Id: Idff68d22817a00a7ee9eef5351a5a9fd33147540 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146635 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- .../convolution/winograd/transforms/input.hpp | 152 +++--- .../kernels/convolution/winograd/winograd_gemm.hpp | 123 +---- .../winograd/winograd_input_transform.hpp | 184 +++++++ .../winograd/transforms/input_1x8_fp32.cpp | 274 ++++++++++ .../winograd/transforms/input_2x2_3x3_fp32.cpp | 21 +- .../winograd/transforms/input_2x2_5x5_fp32.cpp | 458 ---------------- .../winograd/transforms/input_4x4_3x3_fp32.cpp | 486 ---------------- .../winograd/transforms/input_6_3_fp32.cpp | 226 -------- .../winograd/transforms/input_6x6_fp32.cpp | 608 +++++++++++++++++++++ 9 files changed, 1164 insertions(+), 1368 deletions(-) create mode 100644 arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp create mode 100644 src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp delete mode 100644 src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp delete mode 100644 src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp delete mode 100644 src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp create mode 100644 src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp index 369c2ff48f..473a13c3b0 100644 --- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp +++ b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp @@ -29,10 +29,8 @@ namespace winograd { /***************************************************************************/ /* Instance-less API */ - template - template - void WinogradGEMM::InputTransform::execute( + template + void InputTransformImpl::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. */ @@ -50,26 +48,9 @@ namespace winograd const int matrix_row_stride /** Stride within matrices. */ ) { - // If an Nx1 kernel then transpose and redirect to the 1xN implementation - if (kernel_cols == 1) - { - WinogradGEMM:: - template InputTransform::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 - ); - return; - } - // Compute the padding required on each edge of the image - const int pad_top = (padding == PADDING_SAME) ? (kernel_rows - 1) / 2 : 0; - const int pad_left = (padding == PADDING_SAME) ? (kernel_cols - 1) / 2 : 0; - const int tile_overlap = kernel_rows - 1; + 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; @@ -85,19 +66,19 @@ namespace winograd // 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 = (tile_i == 0) ? 0 : pad_top; + const int row_offset = std::min(0, row_pad_top - pad_top); const T* const input_base_row = ( - input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*in_row_stride + 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; - // 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 <= n_rows) ? 0 : row_bottom - n_rows; - // Process the row process_tile_row( tile_N, n_channels, @@ -109,10 +90,40 @@ namespace winograd } } - template - template - void WinogradGEMM::InputTransform::process_tile_row( + + template + void InputTransformImpl::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 + void InputTransformImpl::process_tile_row( const int tile_N, int n_channels, const T* const input_base, @@ -127,33 +138,25 @@ namespace winograd const int n_cols ) { - if (kernel_cols == 1) - { - // If an Nx1 implementation then this should never be reached. - return; - } - - 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_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 = (tile_j == 0) ? 0 : row_pad_left; + const int col_offset = std::min(0, t_pad_left - row_pad_left); const T* const input_base_col = ( - input_base + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*input_col_stride + 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 int f_pad_top = pad_top ? 1 : 0; - const int f_pad_left = t_pad_left ? 1 : 0; + const int f_pad_top = iceildiv(pad_top, 2); + const int f_pad_left = iceildiv(t_pad_left, 2); tile_fns[f_pad_top][f_pad_left][pad_bottom][t_pad_right]( n_channels, input_base_col, @@ -166,9 +169,8 @@ namespace winograd } /***************************************************************************/ - template - template - WinogradGEMM::InputTransform::InputTransform( + template + InputTransform::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. */ @@ -184,10 +186,10 @@ namespace winograd ) : _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 - kr + 1, - output_tile_rows)), - _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - kc + 1, - output_tile_cols)), + _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), @@ -195,18 +197,16 @@ namespace winograd { } - template - template - unsigned int WinogradGEMM::InputTransform::get_window() const + template + unsigned int InputTransform::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 - template - void WinogradGEMM::InputTransform::run( + template + void InputTransform::run( const unsigned int start, const unsigned int stop ) { @@ -238,4 +238,30 @@ namespace winograd _matrix_row_stride ); } + + template + void InputTransform::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 + ); + } } diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp index 7098fc48a1..31aee35fab 100644 --- a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp +++ b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp @@ -30,7 +30,7 @@ #include "arm_compute/core/NEON/kernels/convolution/common/shims.hpp" #include "arm_compute/core/NEON/kernels/convolution/common/tensor.hpp" #include "arm_compute/core/NEON/kernels/convolution/common/utils.hpp" - +#include "winograd_input_transform.hpp" #include #include @@ -114,121 +114,12 @@ class WinogradGEMM /** Transform input feature maps from the spatial to the Winograd domain. */ template - struct InputTransform - { - /** Get the bytes read during the transform. */ - static size_t bytes_read(const Tensor4DShape &shape) - { - return shape.size() * sizeof(T); - } - - /** Get the bytes written during the transform. */ - static size_t bytes_written(const Tensor4DShape &shape) - { - const int M = iceildiv(shape.n_rows, inner_tile_rows) * - iceildiv(shape.n_cols, inner_tile_cols); - const int K = shape.n_channels; - return inner_tile_rows * inner_tile_cols * M * K * sizeof(T); - } - - /** Get the count of operations performed by the transform. */ - static int ops_performed(const Tensor4DShape &shape); - - /** Apply the transform to a tensor. */ - static void 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. */ - ); - - /***********************************************************************/ - /** Create an InputTransform operator fixed on a given problem and set of - * pointers. - */ - 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=0, /** Stride between input batches. */ - const int in_row_stride=0, /** Stride between input rows. */ - const int in_col_stride=0 /** Stride between input columns. */ - ); - - /** Get the window of work a given operator can perform. */ - unsigned int get_window() const; - static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window - - /** Perform work upon a window of the input. */ - void run(const unsigned int start, const unsigned int stop); - /***********************************************************************/ - - private: - static void 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 row_pad_top, - const int row_pad_left, - const int row_pad_bottom, - const int n_cols - ); - - // Tile overlaps - static constexpr int overlap_rows = kernel_rows - 1; - static constexpr int overlap_cols = kernel_cols - 1; - - // Maximum padding and number of distinct paddings - static constexpr int max_pad_top = kernel_rows / 2; - static constexpr int n_pad_top = 1 + iceildiv(max_pad_top, inner_tile_rows - overlap_rows); - - static constexpr int max_pad_left = kernel_cols / 2; - static constexpr int n_pad_left = 1 + iceildiv(max_pad_left, inner_tile_cols - overlap_cols); - - static constexpr int n_pad_bottom = inner_tile_rows; - static constexpr int n_pad_right = inner_tile_cols; - - - - /** Process a single tile of the input tensor. */ - template - static void process_tile(int, const T*, int, int, T*, int); - - // Array of methods to transform tiles of the input tensor. - typedef void (*TileFn)(int, const T*, int, int, T*, int); - static const TileFn - tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right]; - - /* Member values for instance-based API. */ - const T* const _inptr; - T* const _outptr; - const int _n_batches, _n_rows, _n_cols, _n_channels, _matrix_stride, - _matrix_row_stride, _tiles_M, _tiles_N; - const int _in_col_stride, _in_row_stride, _in_batch_stride; - const PaddingType _padding_type; - }; + using InputTransform = InputTransform< + KernelRows, KernelCols, + (OutputTileRows + KernelRows - 1), + (OutputTileCols + KernelCols - 1), + T + >; /** Transform output feature maps from the Winograd to the spatial domain. */ diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp new file mode 100644 index 0000000000..abcda53534 --- /dev/null +++ b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp @@ -0,0 +1,184 @@ +/* + * Copyright (c) 2018 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 + +namespace winograd +{ + +namespace +{ + +template +class InputTransformImpl +{ + public: + /** Apply the transform to a tensor. */ + static void 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. */ + ); + + private: + static void 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 row_pad_top, + const int row_pad_left, + const int row_pad_bottom, + const int n_cols + ); + + // Tile overlaps + static constexpr int overlap_rows = KernelRows - 1; + static constexpr int overlap_cols = KernelCols - 1; + + // Maximum padding and number of distinct paddings + static constexpr int max_pad_top = KernelRows / 2; + static constexpr int n_pad_top = 1 + iceildiv(max_pad_top, InnerTileRows - overlap_rows); + + static constexpr int max_pad_left = KernelCols / 2; + static constexpr int n_pad_left = 1 + iceildiv(max_pad_left, InnerTileCols - overlap_cols); + + static constexpr int n_pad_bottom = InnerTileRows; + static constexpr int n_pad_right = InnerTileCols; + + /** Process a single tile of the input tensor. */ + template + static void process_tile(int, const T*, int, int, T*, int); + + // Array of methods to transform tiles of the input tensor. + typedef void (*TileFn)(int, const T*, int, int, T*, int); + static const TileFn + tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right]; +}; + + +template +class InputTransformImpl +{ + public: + /** Apply the transform to a tensor. */ + static void 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. */ + ); +}; + +} // namespace (anonymous) + +template +class InputTransform +{ + public: + /***********************************************************************/ + /** Create an InputTransform operator fixed on a given problem and set of + * pointers. + */ + 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=0, /** Stride between input batches. */ + const int in_row_stride=0, /** Stride between input rows. */ + const int in_col_stride=0 /** Stride between input columns. */ + ); + + /** Get the window of work a given operator can perform. */ + unsigned int get_window() const; + static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window + + /** Perform work upon a window of the input. */ + void run(const unsigned int start, const unsigned int stop); + + /** Apply the transform to a tensor. */ + static void 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. */ + ); + + protected: + using Transform = InputTransformImpl; + + /* Member values for instance-based API. */ + const T* const _inptr; + T* const _outptr; + const int _n_batches, _n_rows, _n_cols, _n_channels, _matrix_stride, + _matrix_row_stride, _tiles_M, _tiles_N; + const int _in_col_stride, _in_row_stride, _in_batch_stride; + const PaddingType _padding_type; +}; + +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp new file mode 100644 index 0000000000..042d4debbc --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp @@ -0,0 +1,274 @@ +/* + * 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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" +#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" +#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" + +namespace +{ + +template +void winograd_input_transform_1x8_fp32_process_tile( + int n_channels, + const float* const input_base, + const int input_row_stride, + const int input_col_stride, + float* const matrix_base, + const int matrix_stride +) +{ + (void) input_row_stride; // No rows over which to stride + constexpr int inner_tile_cols = 8; + constexpr int cells_j = inner_tile_cols - pad_right; + + float *outptr = matrix_base; + + // Get pointers into the input tile + const float *x_ptrs[inner_tile_cols]; + for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) + { + x_ptrs[j] = input_base + xj*input_col_stride; + } + + // Vectors used/computed in this kernel. + float x[inner_tile_cols]; + float U[inner_tile_cols]; + + for (int j = 0; j < inner_tile_cols; j++) + { + x[j] = 0.0f; + } + + // Perform the Winograd input transformation for each channel in the input + // tensor. + int channels_remaining = n_channels; +#ifdef __arm_any__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + float32x4_t x[inner_tile_cols], U[inner_tile_cols]; + for (int j = 0; j < inner_tile_cols; j++) + { + x[j] = vdupq_n_f32(0.0f); + } + + // Load x + for (int j = pad_left; j < cells_j; j++) + { + x[j] = vld1q_f32(x_ptrs[j]); + x_ptrs[j] += 4; + } + + // Compute U = x . X + U[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36); + U[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1); + U[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13); + U[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18); + U[4] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20); + U[5] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12); + U[6] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15); + U[7] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36); + + // Store the transformed vector + for (int j = 0; j < inner_tile_cols; j++) + { + vst1q_f32(outptr + j*matrix_stride, U[j]); + } + outptr += 4; + } + for (; channels_remaining >= 2; channels_remaining -= 2) + { + float32x2_t x[inner_tile_cols], U[inner_tile_cols]; + for (int j = 0; j < inner_tile_cols; j++) + { + x[j] = vdup_n_f32(0.0f); + } + + // Load x + for (int j = pad_left; j < cells_j; j++) + { + x[j] = vld1_f32(x_ptrs[j]); + x_ptrs[j] += 2; + } + + // Compute U = x . X + U[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36); + U[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1); + U[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13); + U[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18); + U[4] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20); + U[5] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12); + U[6] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15); + U[7] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36); + + // Store the transformed vector + for (int j = 0; j < inner_tile_cols; j++) + { + vst1_f32(outptr + j*matrix_stride, U[j]); + } + outptr += 2; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Load x + for (int j = pad_left; j < cells_j; j++) + { + x[j] = *(x_ptrs[j]++); + } + + // Compute U = x . X + U[0] = x[0]*-36 + x[4]*-14 + x[2]*49 + x[6]*1; + U[1] = x[5]*-1 + x[1]*-36 + x[4]*-13 + x[3]*13 + x[2]*36 + x[6]*1; + U[2] = x[3]*-13 + x[4]*-13 + x[1]*36 + x[2]*36 + x[5]*1 + x[6]*1; + U[3] = x[1]*-18 + x[4]*-10 + x[5]*-2 + x[2]*9 + x[3]*20 + x[6]*1; + U[4] = x[3]*-20 + x[4]*-10 + x[5]*2 + x[2]*9 + x[1]*18 + x[6]*1; + U[5] = x[1]*-12 + x[4]*-5 + x[5]*-3 + x[2]*4 + x[3]*15 + x[6]*1; + U[6] = x[3]*-15 + x[4]*-5 + x[5]*3 + x[2]*4 + x[1]*12 + x[6]*1; + U[7] = x[1]*-36 + x[5]*-14 + x[3]*49 + x[7]*1; + + // Store the transformed vector + for (int j = 0; j < inner_tile_cols; j++) + { + *(outptr + j*matrix_stride) = U[j]; + } + outptr++; + } +} + +} + +namespace winograd +{ +template +using Transform = InputTransformImpl<1, x, 1, 8, float>; + +template <> +const Transform<3>::TileFn + Transform<3>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = +{ + { + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 6>, + } + }, + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 6>, + } + } + } +}; + +template <> +const Transform<5>::TileFn + Transform<5>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = +{ + { + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 6>, + } + }, + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 2, 0, 6>, + } + } + } +}; + +template <> +const Transform<7>::TileFn + Transform<7>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = +{ + { + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 0, 0, 6>, + } + }, + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 1, 0, 6>, + } + }, + { + { + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 0>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 1>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 2>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 3>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 4>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 5>, + winograd_input_transform_1x8_fp32_process_tile<0, 3, 0, 6>, + } + } + } +}; + +template class InputTransform<1, 3, 1, 8, float>; +template class InputTransform<3, 1, 8, 1, float>; +template class InputTransform<1, 5, 1, 8, float>; +template class InputTransform<5, 1, 8, 1, float>; +template class InputTransform<1, 7, 1, 8, float>; +template class InputTransform<7, 1, 8, 1, float>; +} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp index 97b2695d69..a9d5d52d15 100644 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp +++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp @@ -29,22 +29,7 @@ namespace winograd { -using Transform = WinogradGEMM<2, 2, 3, 3>::InputTransform; - -/****************************************************************************** - * Cost methods for the input transform. - * ===================================== - */ -template <> -template <> -int Transform::ops_performed(const Tensor4DShape &input_shape) -{ - // NOTE: Cost in FLOPs rather than instructions or uops. - const int tile_M = iceildiv(input_shape.n_rows, inner_tile_rows); - const int tile_N = iceildiv(input_shape.n_cols, inner_tile_cols); - return 16 * 16 * tile_M * tile_N * input_shape.n_channels; -} -/*****************************************************************************/ +using Transform = InputTransformImpl<3, 3, 4, 4, float>; /***************************************************************************** * F(2x2, 3x3) implies the use of a 4x4 input tile. Such tiles can require a @@ -100,7 +85,6 @@ int Transform::ops_performed(const Tensor4DShape &input_shape) * Padding right in {0, 1, 2} */ template <> -template <> template void Transform::process_tile( int n_channels, @@ -327,7 +311,6 @@ void Transform::process_tile( } } -template <> template <> const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = { @@ -405,5 +388,5 @@ const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom] } }; -template struct WinogradGEMM<2, 2, 3, 3>::InputTransform; +template class InputTransform<3, 3, 4, 4, float>; } // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp deleted file mode 100644 index 30c9463bb8..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp +++ /dev/null @@ -1,458 +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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace winograd -{ - -using Transform = WinogradGEMM<2, 2, 5, 5>::InputTransform; - -template <> -template <> -int Transform::ops_performed(const Tensor4DShape &input_shape) -{ - (void) input_shape; - return 0; // TODO -} - -/***************************************************************************** -* F(2x2, 5x5) implies the use of a 6x6 input tile. -* -* Build an array of the specialised methods that deal with each of the -* different padding combinations which may be required. These padding -* constraints are the space: -* -* Padding top in {0, 2} -* Padding left in {0, 2} -* Padding bottom in {0, 1, 2, 3, 4} -* Padding right in {0, 1, 2, 3, 4} -*/ -template <> -template <> -template -void Transform::process_tile( - int n_channels, - const float* const input_base, - const int input_row_stride, - const int input_col_stride, - float* const matrix_base, - const int matrix_stride -) -{ - constexpr int cells_i = 6 - pad_bottom; - constexpr int cells_j = 6 - pad_right; - - float *outptr = matrix_base; - - // Get pointers into the input tile - const float *x_ptrs[6][6]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) - { - // Get a pointer into the row - const float* const row_ptr = input_base + xi*input_row_stride; - - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[i][j] = row_ptr + xj*input_col_stride; - } - } - - // Matrices used/computed in this kernel. - float x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = XTx[i][j] = 0.0f; - } - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used/computed in this kernel - float32x4_t x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = vdupq_n_f32(0.0f); - XTx[i][j] = vdupq_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1q_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 4; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used/computed in this kernel - float32x2_t x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = vdup_n_f32(0.0f); - XTx[i][j] = vdup_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 2; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = *(x_ptrs[i][j]++); - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - *(outptr + m*matrix_stride) = U[i][j]; - } - } - outptr++; - } -} - -template <> -template <> -const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = -{ - { - { - { - Transform::template process_tile<0, 0, 0, 0>, // No padding - Transform::template process_tile<0, 0, 0, 1>, // Right - Transform::template process_tile<0, 0, 0, 2>, // " " - Transform::template process_tile<0, 0, 0, 3>, // " " - Transform::template process_tile<0, 0, 0, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 1, 0>, // Bottom - Transform::template process_tile<0, 0, 1, 1>, // Bottom right - Transform::template process_tile<0, 0, 1, 2>, // " " - Transform::template process_tile<0, 0, 1, 3>, // " " - Transform::template process_tile<0, 0, 1, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 2, 0>, // Bottom - Transform::template process_tile<0, 0, 2, 1>, // Bottom right - Transform::template process_tile<0, 0, 2, 2>, // " " - Transform::template process_tile<0, 0, 2, 3>, // " " - Transform::template process_tile<0, 0, 2, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 3, 0>, // Bottom - Transform::template process_tile<0, 0, 3, 1>, // Bottom right - Transform::template process_tile<0, 0, 3, 2>, // " " - Transform::template process_tile<0, 0, 3, 3>, // " " - Transform::template process_tile<0, 0, 3, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 4, 0>, // Bottom - Transform::template process_tile<0, 0, 4, 1>, // Bottom right - Transform::template process_tile<0, 0, 4, 2>, // " " - Transform::template process_tile<0, 0, 4, 3>, // " " - Transform::template process_tile<0, 0, 4, 4>, // " " - } - }, - { - { - Transform::template process_tile<0, 2, 0, 0>, // Left - Transform::template process_tile<0, 2, 0, 1>, - Transform::template process_tile<0, 2, 0, 2>, - Transform::template process_tile<0, 2, 0, 3>, - Transform::template process_tile<0, 2, 0, 4>, - }, - { - Transform::template process_tile<0, 2, 1, 0>, // Bottom left - Transform::template process_tile<0, 2, 1, 1>, - Transform::template process_tile<0, 2, 1, 2>, - Transform::template process_tile<0, 2, 1, 3>, - Transform::template process_tile<0, 2, 1, 4>, - }, - { - Transform::template process_tile<0, 2, 2, 0>, // " " - Transform::template process_tile<0, 2, 2, 1>, - Transform::template process_tile<0, 2, 2, 2>, - Transform::template process_tile<0, 2, 2, 3>, - Transform::template process_tile<0, 2, 2, 4>, - }, - { - Transform::template process_tile<0, 2, 3, 0>, // " " - Transform::template process_tile<0, 2, 3, 1>, - Transform::template process_tile<0, 2, 3, 2>, - Transform::template process_tile<0, 2, 3, 3>, - Transform::template process_tile<0, 2, 3, 4>, - }, - { - Transform::template process_tile<0, 2, 4, 0>, // " " - Transform::template process_tile<0, 2, 4, 1>, - Transform::template process_tile<0, 2, 4, 2>, - Transform::template process_tile<0, 2, 4, 3>, - Transform::template process_tile<0, 2, 4, 4>, - } - } - }, - { - { - { - Transform::template process_tile<2, 0, 0, 0>, // Top - Transform::template process_tile<2, 0, 0, 1>, // Top right - Transform::template process_tile<2, 0, 0, 2>, // " " - Transform::template process_tile<2, 0, 0, 3>, // " " - Transform::template process_tile<2, 0, 0, 4>, // " " - }, - { - Transform::template process_tile<2, 0, 1, 0>, - Transform::template process_tile<2, 0, 1, 1>, - Transform::template process_tile<2, 0, 1, 2>, - Transform::template process_tile<2, 0, 1, 3>, - Transform::template process_tile<2, 0, 1, 4>, - }, - { - Transform::template process_tile<2, 0, 2, 0>, - Transform::template process_tile<2, 0, 2, 1>, - Transform::template process_tile<2, 0, 2, 2>, - Transform::template process_tile<2, 0, 2, 3>, - Transform::template process_tile<2, 0, 2, 4>, - }, - { - Transform::template process_tile<2, 0, 3, 0>, - Transform::template process_tile<2, 0, 3, 1>, - Transform::template process_tile<2, 0, 3, 2>, - Transform::template process_tile<2, 0, 3, 3>, - Transform::template process_tile<2, 0, 3, 4>, - }, - { - Transform::template process_tile<2, 0, 4, 0>, - Transform::template process_tile<2, 0, 4, 1>, - Transform::template process_tile<2, 0, 4, 2>, - Transform::template process_tile<2, 0, 4, 3>, - Transform::template process_tile<2, 0, 4, 4>, - }, - }, - { - { - Transform::template process_tile<2, 2, 0, 0>, // Top left - Transform::template process_tile<2, 2, 0, 1>, - Transform::template process_tile<2, 2, 0, 2>, - Transform::template process_tile<2, 2, 0, 3>, - Transform::template process_tile<2, 2, 0, 4>, - }, - { - Transform::template process_tile<2, 2, 1, 0>, - Transform::template process_tile<2, 2, 1, 1>, - Transform::template process_tile<2, 2, 1, 2>, - Transform::template process_tile<2, 2, 1, 3>, - Transform::template process_tile<2, 2, 1, 4>, - }, - { - Transform::template process_tile<2, 2, 2, 0>, - Transform::template process_tile<2, 2, 2, 1>, - Transform::template process_tile<2, 2, 2, 2>, - Transform::template process_tile<2, 2, 2, 3>, - Transform::template process_tile<2, 2, 2, 4>, - }, - { - Transform::template process_tile<2, 2, 3, 0>, - Transform::template process_tile<2, 2, 3, 1>, - Transform::template process_tile<2, 2, 3, 2>, - Transform::template process_tile<2, 2, 3, 3>, - Transform::template process_tile<2, 2, 3, 4>, - }, - { - Transform::template process_tile<2, 2, 4, 0>, - Transform::template process_tile<2, 2, 4, 1>, - Transform::template process_tile<2, 2, 4, 2>, - Transform::template process_tile<2, 2, 4, 3>, - Transform::template process_tile<2, 2, 4, 4>, - } - } - } -}; - -template struct WinogradGEMM<2, 2, 5, 5>::InputTransform; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp deleted file mode 100644 index 7f93187132..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp +++ /dev/null @@ -1,486 +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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace winograd -{ - -using Transform = WinogradGEMM<4, 4, 3, 3>::InputTransform; - -template <> -template <> -int Transform::ops_performed(const Tensor4DShape &input_shape) -{ - // NOTE: Cost in FLOPs rather than instructions or uops. - const int tile_M = iceildiv(input_shape.n_rows, inner_tile_rows); - const int tile_N = iceildiv(input_shape.n_cols, inner_tile_cols); - return 12 * 24 * tile_M * tile_N * input_shape.n_channels; -} - -/* F(4x4, 3x3) implies the use of a 6x6 input tile. Such tiles can require a -* variety of padding types. For example, tiles at the top and left of an -* image can require one row or column of padding on their top and left sides -* if the padding type is SAME (where X represents a padded value): -* -* ___________ ___________ -* |X X X X X X| |X X X X X X| -* |X | | | -* |X | | | -* |X | | | -* |X | | | -* |X__________| |___________| -* ___________ -* |X | -* |X | -* |X | -* |X | -* |X | -* |X__________| -* -* For tiles near the right or bottom of the image it is more complicated. -* Such tiles might require padding by 0, 1, 2 or 3 rows or columns if the -* padding type is VALID or 1, 2, 3 or 4 rows or columns if the padding -* type is SAME. -* -* Build an array of the specialised methods that deal with each of the -* different padding combinations which may be required. These padding -* constraints are the space: -* -* Padding top in {0, 1} -* Padding left in {0, 1} -* Padding bottom in {0, 1, 2, 3, 4} -* Padding right in {0, 1, 2, 3, 4} -*/ -template <> -template <> -template -void Transform::process_tile( - int n_channels, - const float* const input_base, - const int input_row_stride, - const int input_col_stride, - float* const matrix_base, - const int matrix_stride -) -{ - constexpr int cells_i = 6 - pad_bottom; - constexpr int cells_j = 6 - pad_right; - - float *outptr = matrix_base; - - // Get pointers into the input tile - const float *x_ptrs[6][6]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) - { - // Get a pointer into the row - const float* const row_ptr = input_base + xi*input_row_stride; - - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[i][j] = row_ptr + xj*input_col_stride; - } - } - - // Matrices used/computed in this kernel. - float x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = XTx[i][j] = 0.0f; - } - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used/computed in this kernel - float32x4_t x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = vdupq_n_f32(0.0f); - XTx[i][j] = vdupq_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1q_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 4; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used/computed in this kernel - float32x2_t x[6][6], XTx[6][6], U[6][6]; - for (int i = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++) - { - x[i][j] = vdup_n_f32(0.0f); - XTx[i][j] = vdup_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 2; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = *(x_ptrs[i][j]++); - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - } - - // Compute U = XT . x . X - for (int i = 0; i < 6; i++) - { - U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - *(outptr + m*matrix_stride) = U[i][j]; - } - } - outptr++; - } -} - -/* In the below, unusual or especially small tiles are routed via the slow - * path whereas common or large tiles are routed through a faster path. - */ -template <> -template <> -const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = -{ - { - { - { - Transform::template process_tile<0, 0, 0, 0>, // No padding - Transform::template process_tile<0, 0, 0, 1>, // Right - Transform::template process_tile<0, 0, 0, 2>, // " " - Transform::template process_tile<0, 0, 0, 3>, // " " - Transform::template process_tile<0, 0, 0, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 1, 0>, // Bottom - Transform::template process_tile<0, 0, 1, 1>, // Bottom right - Transform::template process_tile<0, 0, 1, 2>, // " " - Transform::template process_tile<0, 0, 1, 3>, // " " - Transform::template process_tile<0, 0, 1, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 2, 0>, // Bottom - Transform::template process_tile<0, 0, 2, 1>, // Bottom right - Transform::template process_tile<0, 0, 2, 2>, // " " - Transform::template process_tile<0, 0, 2, 3>, // " " - Transform::template process_tile<0, 0, 2, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 3, 0>, // Bottom - Transform::template process_tile<0, 0, 3, 1>, // Bottom right - Transform::template process_tile<0, 0, 3, 2>, // " " - Transform::template process_tile<0, 0, 3, 3>, // " " - Transform::template process_tile<0, 0, 3, 4>, // " " - }, - { - Transform::template process_tile<0, 0, 4, 0>, // Bottom - Transform::template process_tile<0, 0, 4, 1>, // Bottom right - Transform::template process_tile<0, 0, 4, 2>, // " " - Transform::template process_tile<0, 0, 4, 3>, // " " - Transform::template process_tile<0, 0, 4, 4>, // " " - } - }, - { - { - Transform::template process_tile<0, 1, 0, 0>, // Left - Transform::template process_tile<0, 1, 0, 1>, - Transform::template process_tile<0, 1, 0, 2>, - Transform::template process_tile<0, 1, 0, 3>, - Transform::template process_tile<0, 1, 0, 4>, - }, - { - Transform::template process_tile<0, 1, 1, 0>, // Bottom left - Transform::template process_tile<0, 1, 1, 1>, - Transform::template process_tile<0, 1, 1, 2>, - Transform::template process_tile<0, 1, 1, 3>, - Transform::template process_tile<0, 1, 1, 4>, - }, - { - Transform::template process_tile<0, 1, 2, 0>, // " " - Transform::template process_tile<0, 1, 2, 1>, - Transform::template process_tile<0, 1, 2, 2>, - Transform::template process_tile<0, 1, 2, 3>, - Transform::template process_tile<0, 1, 2, 4>, - }, - { - Transform::template process_tile<0, 1, 3, 0>, // " " - Transform::template process_tile<0, 1, 3, 1>, - Transform::template process_tile<0, 1, 3, 2>, - Transform::template process_tile<0, 1, 3, 3>, - Transform::template process_tile<0, 1, 3, 4>, - }, - { - Transform::template process_tile<0, 1, 4, 0>, // " " - Transform::template process_tile<0, 1, 4, 1>, - Transform::template process_tile<0, 1, 4, 2>, - Transform::template process_tile<0, 1, 4, 3>, - Transform::template process_tile<0, 1, 4, 4>, - } - } - }, - { - { - { - Transform::template process_tile<1, 0, 0, 0>, // Top - Transform::template process_tile<1, 0, 0, 1>, // Top right - Transform::template process_tile<1, 0, 0, 2>, // " " - Transform::template process_tile<1, 0, 0, 3>, // " " - Transform::template process_tile<1, 0, 0, 4>, // " " - }, - { - Transform::template process_tile<1, 0, 1, 0>, - Transform::template process_tile<1, 0, 1, 1>, - Transform::template process_tile<1, 0, 1, 2>, - Transform::template process_tile<1, 0, 1, 3>, - Transform::template process_tile<1, 0, 1, 4>, - }, - { - Transform::template process_tile<1, 0, 2, 0>, - Transform::template process_tile<1, 0, 2, 1>, - Transform::template process_tile<1, 0, 2, 2>, - Transform::template process_tile<1, 0, 2, 3>, - Transform::template process_tile<1, 0, 2, 4>, - }, - { - Transform::template process_tile<1, 0, 3, 0>, - Transform::template process_tile<1, 0, 3, 1>, - Transform::template process_tile<1, 0, 3, 2>, - Transform::template process_tile<1, 0, 3, 3>, - Transform::template process_tile<1, 0, 3, 4>, - }, - { - Transform::template process_tile<1, 0, 4, 0>, - Transform::template process_tile<1, 0, 4, 1>, - Transform::template process_tile<1, 0, 4, 2>, - Transform::template process_tile<1, 0, 4, 3>, - Transform::template process_tile<1, 0, 4, 4>, - }, - }, - { - { - Transform::template process_tile<1, 1, 0, 0>, // Top left - Transform::template process_tile<1, 1, 0, 1>, - Transform::template process_tile<1, 1, 0, 2>, - Transform::template process_tile<1, 1, 0, 3>, - Transform::template process_tile<1, 1, 0, 4>, - }, - { - Transform::template process_tile<1, 1, 1, 0>, - Transform::template process_tile<1, 1, 1, 1>, - Transform::template process_tile<1, 1, 1, 2>, - Transform::template process_tile<1, 1, 1, 3>, - Transform::template process_tile<1, 1, 1, 4>, - }, - { - Transform::template process_tile<1, 1, 2, 0>, - Transform::template process_tile<1, 1, 2, 1>, - Transform::template process_tile<1, 1, 2, 2>, - Transform::template process_tile<1, 1, 2, 3>, - Transform::template process_tile<1, 1, 2, 4>, - }, - { - Transform::template process_tile<1, 1, 3, 0>, - Transform::template process_tile<1, 1, 3, 1>, - Transform::template process_tile<1, 1, 3, 2>, - Transform::template process_tile<1, 1, 3, 3>, - Transform::template process_tile<1, 1, 3, 4>, - }, - { - Transform::template process_tile<1, 1, 4, 0>, - Transform::template process_tile<1, 1, 4, 1>, - Transform::template process_tile<1, 1, 4, 2>, - Transform::template process_tile<1, 1, 4, 3>, - Transform::template process_tile<1, 1, 4, 4>, - } - } - } -}; - -template struct WinogradGEMM<4, 4, 3, 3>::InputTransform; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp deleted file mode 100644 index 67e46499cd..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp +++ /dev/null @@ -1,226 +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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - - -namespace winograd -{ - -using Transform = WinogradGEMM<1, 6, 1, 3>::InputTransform; - -template <> -template <> -int Transform::ops_performed(const Tensor4DShape &input_shape) -{ - (void) input_shape; - return 0; // TODO -} - -template <> -template <> -template -void Transform::process_tile( - int n_channels, - const float* const input_base, - const int input_row_stride, - const int input_col_stride, - float* const matrix_base, - const int matrix_stride -) -{ - (void) input_row_stride; // No rows over which to stride - constexpr int inner_tile_j = 8; - constexpr int cells_j = inner_tile_j - pad_right; - - float *outptr = matrix_base; - - // Get pointers into the input tile - const float *x_ptrs[inner_tile_j]; - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[j] = input_base + xj*input_col_stride; - } - - // Vectors used/computed in this kernel. - float x[inner_tile_j]; - float U[inner_tile_j]; - - for (int j = 0; j < inner_tile_j; j++) - { - x[j] = 0.0f; - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __arm_any__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - float32x4_t x[inner_tile_j], U[inner_tile_j]; - for (int j = 0; j < inner_tile_cols; j++) - { - x[j] = vdupq_n_f32(0.0f); - } - - // Load x - for (int j = pad_left; j < cells_j; j++) - { - x[j] = vld1q_f32(x_ptrs[j]); - x_ptrs[j] += 4; - } - - // Compute U = x . X - U[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36); - U[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1); - U[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13); - U[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18); - U[4] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20); - U[5] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12); - U[6] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15); - U[7] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36); - - // Store the transformed vector - for (int j = 0; j < inner_tile_j; j++) - { - vst1q_f32(outptr + j*matrix_stride, U[j]); - } - outptr += 4; - } - - for (; channels_remaining >= 2; channels_remaining -= 2) - { - float32x2_t x[inner_tile_j], U[inner_tile_j]; - for (int j = 0; j < inner_tile_cols; j++) - { - x[j] = vdup_n_f32(0.0f); - } - - // Load x - for (int j = pad_left; j < cells_j; j++) - { - x[j] = vld1_f32(x_ptrs[j]); - x_ptrs[j] += 2; - } - - // Compute U = x . X - U[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36); - U[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1); - U[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13); - U[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18); - U[4] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20); - U[5] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12); - U[6] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15); - U[7] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36); - - // Store the transformed vector - for (int j = 0; j < inner_tile_j; j++) - { - vst1_f32(outptr + j*matrix_stride, U[j]); - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int j = pad_left; j < cells_j; j++) - { - x[j] = *(x_ptrs[j]++); - } - - // Compute U = x . X - U[0] = x[0]*-36 + x[4]*-14 + x[2]*49 + x[6]*1; - U[1] = x[5]*-1 + x[1]*-36 + x[4]*-13 + x[3]*13 + x[2]*36 + x[6]*1; - U[2] = x[3]*-13 + x[4]*-13 + x[1]*36 + x[2]*36 + x[5]*1 + x[6]*1; - U[3] = x[1]*-18 + x[4]*-10 + x[5]*-2 + x[2]*9 + x[3]*20 + x[6]*1; - U[4] = x[3]*-20 + x[4]*-10 + x[5]*2 + x[2]*9 + x[1]*18 + x[6]*1; - U[5] = x[1]*-12 + x[4]*-5 + x[5]*-3 + x[2]*4 + x[3]*15 + x[6]*1; - U[6] = x[3]*-15 + x[4]*-5 + x[5]*3 + x[2]*4 + x[1]*12 + x[6]*1; - U[7] = x[1]*-36 + x[5]*-14 + x[3]*49 + x[7]*1; - - // Store the transformed vector - for (int j = 0; j < inner_tile_j; j++) - { - *(outptr + j*matrix_stride) = U[j]; - } - outptr++; - } -} - -template <> -template <> -const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = -{ - { - { - { - Transform::template process_tile<0, 0, 0, 0>, - Transform::template process_tile<0, 0, 0, 1>, - Transform::template process_tile<0, 0, 0, 2>, - Transform::template process_tile<0, 0, 0, 3>, - Transform::template process_tile<0, 0, 0, 4>, - Transform::template process_tile<0, 0, 0, 5>, - Transform::template process_tile<0, 0, 0, 6>, - } - }, - { - { - Transform::template process_tile<0, 1, 0, 0>, - Transform::template process_tile<0, 1, 0, 1>, - Transform::template process_tile<0, 1, 0, 2>, - Transform::template process_tile<0, 1, 0, 3>, - Transform::template process_tile<0, 1, 0, 4>, - Transform::template process_tile<0, 1, 0, 5>, - Transform::template process_tile<0, 1, 0, 6>, - } - } - } -}; - -template -using TransformTransposed = typename WinogradGEMM::template InputTransform; - -template <> -template <> -const TransformTransposed<6, 3>::TileFn - TransformTransposed<6, 3>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {}; - -template <> -template <> -const TransformTransposed<4, 5>::TileFn - TransformTransposed<4, 5>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {}; - -template <> -template <> -const TransformTransposed<2, 7>::TileFn - TransformTransposed<2, 7>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {}; - - - -template struct WinogradGEMM<1, 6, 1, 3>::InputTransform; -template struct WinogradGEMM<6, 1, 3, 1>::InputTransform; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp new file mode 100644 index 0000000000..908613068a --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp @@ -0,0 +1,608 @@ +/* + * 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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp" +#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" +#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" + +namespace +{ + +template +void winograd_input_transform_6x6_fp32_process_tile( + int n_channels, + const float* const input_base, + const int input_row_stride, + const int input_col_stride, + float* const matrix_base, + const int matrix_stride +) +{ + constexpr int inner_tile_rows = 6; + constexpr int inner_tile_cols = 6; + constexpr int cells_i = inner_tile_rows - pad_bottom; + constexpr int cells_j = inner_tile_cols - pad_right; + + float *outptr = matrix_base; + + // Get pointers into the input tile + const float *x_ptrs[inner_tile_rows][inner_tile_cols]; + for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) + { + // Get a pointer into the row + const float* const row_ptr = input_base + xi*input_row_stride; + + for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) + { + x_ptrs[i][j] = row_ptr + xj*input_col_stride; + } + } + + // Matrices used/computed in this kernel. + float x[inner_tile_rows][inner_tile_cols]; + float XTx[inner_tile_rows][inner_tile_cols]; + float U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = XTx[i][j] = 0.0f; + } + } + + // Perform the Winograd input transformation for each channel in the input + // tensor. + int channels_remaining = n_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used/computed in this kernel + float32x4_t x[inner_tile_rows][inner_tile_cols]; + float32x4_t XTx[inner_tile_rows][inner_tile_cols]; + float32x4_t U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdupq_n_f32(0.0f); + XTx[i][j] = vdupq_n_f32(0.0f); + } + } + + // Read a 6x6 tile in the Winograd domain + for (int i = pad_top; i < cells_i; i++) + { + for (int j = pad_left; j < cells_j; j++) + { + x[i][j] = vld1q_f32(x_ptrs[i][j]); + x_ptrs[i][j] += 4; + } + } + + // Compute XT . x + for (int j = pad_left; j < cells_j; j++) + { + // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); + + // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f); + + // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f); + + // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); + + // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f); + + // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f); + + // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1q_f32(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 4; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 2; channels_remaining -= 2) + { + // Matrices used/computed in this kernel + float32x2_t x[inner_tile_rows][inner_tile_cols]; + float32x2_t XTx[inner_tile_rows][inner_tile_cols]; + float32x2_t U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdup_n_f32(0.0f); + XTx[i][j] = vdup_n_f32(0.0f); + } + } + + // Read a 6x6 tile in the Winograd domain + for (int i = pad_top; i < cells_i; i++) + { + for (int j = pad_left; j < cells_j; j++) + { + x[i][j] = vld1_f32(x_ptrs[i][j]); + x_ptrs[i][j] += 2; + } + } + + // Compute XT . x + for (int j = pad_left; j < cells_j; j++) + { + // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); + + // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); + + // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); + + // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); + + // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); + + // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); + + // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); + + // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); + + // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1_f32(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 2; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Load x + for (int i = pad_top; i < cells_i; i++) + { + for (int j = pad_left; j < cells_j; j++) + { + x[i][j] = *(x_ptrs[i][j]++); + } + } + + // Compute XT . x + for (int j = pad_left; j < cells_j; j++) + { + XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + *(outptr + m*matrix_stride) = U[i][j]; + } + } + outptr++; + } +} +} + +namespace winograd +{ +template +using Transform = InputTransformImpl; + +template <> +const Transform<3>::TileFn + Transform<3>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = +{ + { + { + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 0>, // No padding + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 1>, // Right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 4>, // " " + } + }, + { + { + winograd_input_transform_6x6_fp32_process_tile<0, 1, 0, 0>, // Left + winograd_input_transform_6x6_fp32_process_tile<0, 1, 0, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 0, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 0, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 0, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 1, 1, 0>, // Bottom left + winograd_input_transform_6x6_fp32_process_tile<0, 1, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 1, 2, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 1, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 1, 3, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 1, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 1, 4, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 1, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 1, 4, 4>, + } + } + }, + { + { + { + winograd_input_transform_6x6_fp32_process_tile<1, 0, 0, 0>, // Top + winograd_input_transform_6x6_fp32_process_tile<1, 0, 0, 1>, // Top right + winograd_input_transform_6x6_fp32_process_tile<1, 0, 0, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<1, 0, 0, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<1, 0, 0, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 0, 1, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 0, 2, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 0, 3, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 0, 4, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 0, 4, 4>, + }, + }, + { + { + winograd_input_transform_6x6_fp32_process_tile<1, 1, 0, 0>, // Top left + winograd_input_transform_6x6_fp32_process_tile<1, 1, 0, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 0, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 0, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 0, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 1, 1, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 1, 2, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 1, 3, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<1, 1, 4, 0>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<1, 1, 4, 4>, + } + } + } +}; + +template <> +const Transform<5>::TileFn + Transform<5>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = +{ + { + { + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 0>, // No padding + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 1>, // Right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 0, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 1, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 2, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 3, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 0>, // Bottom + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 1>, // Bottom right + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 0, 4, 4>, // " " + } + }, + { + { + winograd_input_transform_6x6_fp32_process_tile<0, 2, 0, 0>, // Left + winograd_input_transform_6x6_fp32_process_tile<0, 2, 0, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 0, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 0, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 0, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 2, 1, 0>, // Bottom left + winograd_input_transform_6x6_fp32_process_tile<0, 2, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 2, 2, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 2, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 2, 3, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 2, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<0, 2, 4, 0>, // " " + winograd_input_transform_6x6_fp32_process_tile<0, 2, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<0, 2, 4, 4>, + } + } + }, + { + { + { + winograd_input_transform_6x6_fp32_process_tile<2, 0, 0, 0>, // Top + winograd_input_transform_6x6_fp32_process_tile<2, 0, 0, 1>, // Top right + winograd_input_transform_6x6_fp32_process_tile<2, 0, 0, 2>, // " " + winograd_input_transform_6x6_fp32_process_tile<2, 0, 0, 3>, // " " + winograd_input_transform_6x6_fp32_process_tile<2, 0, 0, 4>, // " " + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 0, 1, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 0, 2, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 0, 3, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 0, 4, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 0, 4, 4>, + }, + }, + { + { + winograd_input_transform_6x6_fp32_process_tile<2, 2, 0, 0>, // Top left + winograd_input_transform_6x6_fp32_process_tile<2, 2, 0, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 0, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 0, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 0, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 2, 1, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 1, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 1, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 1, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 1, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 2, 2, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 2, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 2, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 2, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 2, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 2, 3, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 3, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 3, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 3, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 3, 4>, + }, + { + winograd_input_transform_6x6_fp32_process_tile<2, 2, 4, 0>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 4, 1>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 4, 2>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 4, 3>, + winograd_input_transform_6x6_fp32_process_tile<2, 2, 4, 4>, + } + } + } +}; + +template class InputTransform<3, 3, 6, 6, float>; +template class InputTransform<5, 5, 6, 6, float>; +} -- cgit v1.2.1