COMPMID-873: Integrate RSH NEON Depthwise Convolution routine

Change-Id: Ida1e9a836bc518bfe5563e16bf7f92bde5fc13f7
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118472
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

3 files changed, 0 insertions, 453 deletions

diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp
deleted file mode 100644
index 075765a513..0000000000
--- a/arm_compute/core/NEON/kernels/winograd/transforms/input.hpp
+++ /dev/null
@@ -1,195 +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.
- */
-
-#pragma once
-#include "../winograd_gemm.hpp"
-
-namespace winograd
-{
-  /***************************************************************************/
-  /* Instance-less API */
-  template <int output_tile_rows, int output_tile_cols,
-            int kernel_rows, int kernel_cols>
-  template <typename T>
-  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::execute(
-    const T *inptr,
-    const Tensor4DShape& input_shape,
-    const PaddingType padding_type,
-    const int tile_M,
-    const int tile_N,
-    T *outptr_base,
-    const int matrix_stride,
-    const int matrix_batch_stride,
-    const int matrix_row_stride
-  )
-  {
-    // Compute the padding required on each edge of the image
-    const bool base_padding = (padding_type == PADDING_SAME) ? 1 : 0;
-    const int pad_top = base_padding;
-    const int pad_left = base_padding;
-    const int tile_overlap = kernel_rows - 1;
-
-    // Compute striding values (assuming NHWC ordered data)
-    const int input_col_stride = input_shape.n_channels;
-    const int input_row_stride = input_shape.n_cols * input_col_stride;
-    const int input_batch_stride = input_shape.n_rows * input_row_stride;
-    const int output_col_stride = matrix_row_stride;
-    const int output_row_stride = tile_N * output_col_stride;
-
-    // Loop over batches
-    for (int batch = 0; batch < input_shape.n_batches; batch++)
-    {
-      // Pointer to the batch
-      const T* const input_base_batch = inptr + batch * input_batch_stride;
-      T* const outptr_base_batch = outptr_base + batch * matrix_batch_stride;
-
-      // Loop over rows of tiles
-      for (int tile_i = 0; tile_i < tile_M; tile_i++)
-      {
-        // Pointer to the row
-        const int row_offset = (tile_i == 0) ?
-          0 : ((padding_type == PADDING_VALID) ? 0 : 1);
-        const T* const input_base_row = (
-          input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*input_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 <= input_shape.n_rows) ? 0 : row_bottom - input_shape.n_rows;
-
-        // Process the row
-        process_tile_row(
-          tile_N, input_shape.n_channels,
-          input_base_row, input_row_stride, input_col_stride,
-          outptr_base_row, matrix_stride, matrix_row_stride,
-          row_pad_top, pad_left, row_pad_bottom, input_shape.n_cols
-        );
-      }
-    }
-  }
-
-  template <int output_tile_rows, int output_tile_cols,
-            int kernel_rows, int kernel_cols>
-  template <typename T>
-  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::process_tile_row(
-    const int tile_N,
-    int n_channels,
-    const T* const input_base,
-    const int input_row_stride,
-    const int input_col_stride,
-    T* const matrix_base,
-    const int matrix_stride,
-    const int matrix_row_stride,
-    const int pad_top,
-    const int row_pad_left,
-    const int pad_bottom,
-    const int n_cols
-  )
-  {
-    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_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 T* const input_base_col = (
-        input_base + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*input_col_stride
-      );
-      T* const outptr = matrix_base + tile_j*matrix_row_stride;
-
-      // Apply the specific tile processing function
-      tile_fns[pad_top][t_pad_left][pad_bottom][t_pad_right](
-        n_channels,
-        input_base_col,
-        input_row_stride,
-        input_col_stride,
-        outptr,
-        matrix_stride
-      );
-    }
-  }
-
-  /***************************************************************************/
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::InputTransform(
-    const T* const input,        /** Input tensor data */
-    const int n_batches,         /** Number of batches in input tensor. */
-    const int n_rows,            /** Number of rows in input tensor. */
-    const int n_cols,            /** Number of columns in input tensor. */
-    const int n_channels,        /** Number of channels in input tensor. */
-    const PaddingType padding,   /** Padding type. */
-    T* const output,             /** Base of output matrices. */
-    const int matrix_stride,     /** Stride between output matrices. */
-    const int matrix_row_stride  /** Stride within matrices. */
-  ) : _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 - 2, output_tile_rows)),
-      _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - 2, output_tile_cols)),
-      _padding_type(padding)
-  {
-  }
-
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  unsigned int WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::get_window() const
-  {
-    // TODO When the input transform supports multithreading, return the total
-    // number of tile rows (allowing for multiple batches). For now we return 1
-    // to indicate that the activations must be transformed as a single block.
-    return 1;  // TODO _tiles_M * _n_batches;
-  }
-
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  void WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::run(
-    const unsigned int start, const unsigned int stop
-  )
-  {
-    // TODO When the input transform supports multithreading call execute for a
-    // portion of the tile rows.
-    (void) start;
-    (void) stop;
-
-    // For now, just do all of the work.
-    const Tensor4DShape input_shape = {
-      _n_batches, _n_rows, _n_cols, _n_channels, NHWC
-    };
-    execute(
-      _inptr, input_shape, _padding_type, _tiles_M, _tiles_N, _outptr,
-      _matrix_stride, _matrix_row_stride * _tiles_M * _tiles_N, _matrix_row_stride
-    );
-  }
-}
diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp
deleted file mode 100644
index 4b54dfdf08..0000000000
--- a/arm_compute/core/NEON/kernels/winograd/transforms/kernel.hpp
+++ /dev/null
@@ -1,77 +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 "winograd_gemm.hpp"
-using namespace winograd;
-
-
-template <int otr, int otc, int kr, int kc>
-template <typename T>
-WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::WeightsTransform(
-  const T* const input,
-  T* const output,
-  const int matrix_stride,      /** Stride across matrices in the output. */
-  const int matrix_row_stride,  /** Stride across rows of the matrix. */
-  const int n_output_channels,
-  const int n_input_channels
-) : inptr(input), outptr(output),
-    matrix_stride(matrix_stride), matrix_row_stride(matrix_row_stride),
-    n_output_channels(n_output_channels), n_input_channels(n_input_channels)
-{
-}
-
-
-template <int otr, int otc, int kr, int kc>
-template <typename T>
-unsigned int WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::get_window() const
-{
-  // TODO When the weights transform supports multithreading, return the number
-  // of output channels. For now we return 1 to indicate that the weights must
-  // be transformed as a single block.
-  // return n_output_channels;
-  return 1;
-}
-
-
-template <int otr, int otc, int kr, int kc>
-template <typename T>
-void WinogradGEMM<otr, otc, kr, kc>::WeightsTransform<T>::run(
-  const unsigned int start, const unsigned int stop
-)
-{
-  // TODO When the weights transform supports multithreading call execute for a
-  // portion of the output channels.
-  (void) start;
-  (void) stop;
-
-  // For now, just do all of the work.
-  execute(
-    n_output_channels,
-    n_input_channels,
-    inptr,
-    outptr,
-    matrix_stride,
-    matrix_row_stride
-  );
-}
diff --git a/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp b/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp
deleted file mode 100644
index 0dd719751b..0000000000
--- a/arm_compute/core/NEON/kernels/winograd/transforms/output.hpp
+++ /dev/null
@@ -1,181 +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.
- */
-
-#pragma once
-#include "../winograd_gemm.hpp"
-
-namespace winograd
-{
-  template <int output_tile_rows, int output_tile_cols,
-            int kernel_rows, int kernel_cols>
-  template <typename T>
-  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::execute(
-    const Tensor4DShape &output_shape,
-    const T* const matrix_base,
-    const int matrix_stride,
-    const int matrix_row_stride,
-    const T* const biases,
-    T* const output
-  )
-  {
-    // Compute the number of tiles and hence the padding required on the bottom
-    // and right of the image.
-    const int tile_M = iceildiv(output_shape.n_rows, output_tile_rows);
-    const int tile_N = iceildiv(output_shape.n_cols, output_tile_cols);
-    const int pad_bottom = output_tile_rows*tile_M - output_shape.n_rows;
-    const int pad_right = output_tile_cols*tile_N - output_shape.n_cols;
-
-    const int matrix_tile_row_stride = tile_N * matrix_row_stride;
-    const int matrix_batch_stride = tile_M * matrix_tile_row_stride;
-    const int output_col_stride = output_shape.n_channels;
-    const int output_row_stride = output_shape.n_cols * output_col_stride;
-    const int output_batch_stride = output_shape.n_rows * output_row_stride;
-
-    // Perform the output transformation for each batch
-    for (int batch = 0; batch < output_shape.n_batches; batch++)
-    {
-      // Get batch offset for input and outputs.
-      const T* const matrix_batch = matrix_base + batch*matrix_batch_stride;
-      T* const outptr_batch = output + batch*output_batch_stride;
-
-      // Perform the output transformation for each row of the output tensor.
-      for (int tile_i = 0; tile_i < tile_M; tile_i++)
-      {
-        // Compute properties of this row of output tiles
-        const int row_pad_bottom = (tile_i < tile_M - 1) ? 0: pad_bottom;
-        const T* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride;
-        T* const outptr_row = outptr_batch + output_tile_rows*tile_i*output_row_stride;
-
-        // Process the row
-        process_tile_row(
-          tile_N, output_shape.n_channels, matrix_tile_row, matrix_stride,
-          matrix_row_stride, biases,
-          outptr_row, output_row_stride, output_col_stride, row_pad_bottom,
-          pad_right
-        );
-      }
-    }
-  }
-
-  template <int output_tile_rows, int output_tile_cols,
-            int kernel_rows, int kernel_cols>
-  template <typename T>
-  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::process_tile_row(
-    const int tile_N,
-    const int n_channels,
-    const T* const matrix_base,
-    const int matrix_stride,
-    const int matrix_row_stride,
-    const T* const biases,
-    T* const output,
-    const int output_row_stride,
-    const int output_col_stride,
-    const int row_pad_bottom,
-    const int row_pad_right
-  )
-  {
-    // Loop over columns of tiles
-    for (int tile_j = 0; tile_j < tile_N; tile_j++)
-    {
-      // Properties of this tile
-      const int tile_pad_right = (tile_j < tile_N - 1) ? 0 : row_pad_right;
-      const T* const matrix_row = matrix_base + tile_j * matrix_row_stride;
-      T* const outptr = output + output_tile_cols*tile_j*output_col_stride;
-
-      // Perform the output transformation
-      tile_fns[row_pad_bottom][tile_pad_right](
-        n_channels, matrix_row, matrix_stride, biases,
-        outptr, output_row_stride, output_col_stride
-      );
-    }
-  }
-
-  template <int output_tile_rows, int output_tile_cols, int kr, int kc>
-  template <typename T>
-  size_t WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::bytes_read(const Tensor4DShape &shape)
-  {
-    const int M = iceildiv(shape.n_rows, output_tile_rows) *
-                  iceildiv(shape.n_cols, output_tile_cols);
-    const int N = shape.n_channels;
-    return inner_tile_rows * inner_tile_cols * M * N * sizeof(T);
-  }
-
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  size_t WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::bytes_written(const Tensor4DShape &shape)
-  {
-    return shape.size() * sizeof(T);
-  }
-
-  template <int output_tile_rows, int output_tile_cols, int kr, int kc>
-  template <typename T>
-  WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::OutputTransform(
-    const T* const matrix_base,
-    const int matrix_stride,
-    const int matrix_row_stride,
-    const T* const biases,
-    T* const output,
-    const int n_batches,
-    const int n_rows,
-    const int n_cols,
-    const int n_channels
-  ) : _matrix_base(matrix_base), _biases(biases),
-      _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride),
-      _outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols),
-      _n_channels(n_channels), _tile_M(iceildiv(n_rows, output_tile_rows)),
-      _tile_N(iceildiv(n_cols, output_tile_cols))
-  {
-  }
-
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  unsigned int WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::get_window() const
-  {
-    // TODO When the output transform supports multithreading, return the total
-    // number of tile rows (allowing for multiple batches). For now we return 1
-    // to indicate that the activations must be transformed as a single block.
-    return 1;  // TODO _tile_M * _n_batches;
-  }
-
-  template <int otr, int otc, int kr, int kc>
-  template <typename T>
-  void WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::run(
-    const unsigned int start, const unsigned int stop
-  )
-  {
-    // TODO When the output transform supports multithreading call execute for a
-    // portion of the tile rows.
-    (void) start;
-    (void) stop;
-
-    // For now, just do all of the work.
-    const Tensor4DShape output_shape = {
-      _n_batches, _n_rows, _n_cols, _n_channels, NHWC
-    };
-    execute(
-      output_shape, _matrix_base, _matrix_stride, _matrix_row_stride, _biases,
-      _outptr
-    );
-  }
-}  // namespace winograd