COMPMID-2236: Move assembly implementation interfaces to src folder

Change-Id: I9d0493b64329e12120dce8cbe7cc19d90cea310a
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1536
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Matthew Bentham <matthew.bentham@arm.com>

2 files changed, 0 insertions, 799 deletions

diff --git a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp
deleted file mode 100644
index b102a24250..0000000000
--- a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp
+++ /dev/null
@@ -1,504 +0,0 @@
-/*
- * Copyright (c) 2018-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-/*
- * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- *
- *          NOTE: Header to be included by implementation files only.
- *
- * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- */
-
-#include <algorithm>
-#include <cstdint>
-#include "depthwise.hpp"
-#include "padding.hpp"
-#include "utils.hpp"
-
-#pragma once
-
-#define MEMBERFN(TOUT) template <\
-  unsigned int OutputTileRows, unsigned int OutputTileColumns,\
-  unsigned int KernelRows, unsigned int KernelColumns,\
-  unsigned int StrideRows, unsigned int StrideColumns,\
-  typename TIn, typename TBias, typename TOut,\
-  typename Derived\
-> TOUT DepthwiseConvolutionBase<\
-  OutputTileRows, OutputTileColumns,\
-  KernelRows, KernelColumns,\
-  StrideRows, StrideColumns,\
-  TIn, TBias, TOut, Derived\
->
-
-using namespace neon_convolution_kernels;
-
-namespace depthwise
-{
-
-template <unsigned int KernelRows, unsigned int KernelColumns, size_t WeightSize, size_t BiasSize>
-struct PackParameters
-{
-  static void execute(
-    unsigned int n_channels,
-    void *buffer,
-    const void *weights,
-    unsigned int weight_row_stride,
-    unsigned int weight_col_stride,
-    const void *biases
-  );
-};
-
-const unsigned int CHANNEL_BLOCK = 16;
-
-MEMBERFN(int)::get_output_size(
-  const int dim_size, const unsigned int padding_before, const unsigned int padding_after
-)
-{
-  return iceildiv(dim_size + padding_before + padding_after - KernelRows + 1, StrideRows);
-}
-
-MEMBERFN(int)::output_size(
-  const int dim_size, const unsigned int padding_before, const unsigned int padding_after
-) const
-{
-  return get_output_size(dim_size, padding_before, padding_after);
-}
-
-MEMBERFN()::DepthwiseConvolutionBase(
-  const int n_batches,
-  const int n_input_rows,
-  const int n_input_cols,
-  const int n_channels,
-  ActivationFunction activation,
-  const unsigned int padding_top,
-  const unsigned int padding_left,
-  const unsigned int padding_bottom,
-  const unsigned int padding_right
-) : DepthwiseConvolutionBase(
-      n_batches, n_input_rows, n_input_cols, n_channels,
-      get_output_size(n_input_rows, padding_top, padding_bottom),
-      get_output_size(n_input_cols, padding_left, padding_right),
-      activation,
-      padding_top, padding_left, padding_bottom, padding_right
-    )
-{
-}
-
-MEMBERFN()::DepthwiseConvolutionBase(
-  const int n_batches,
-  const int n_input_rows,
-  const int n_input_cols,
-  const int n_channels,
-  const int n_output_rows,
-  const int n_output_cols,
-  ActivationFunction activation,
-  const unsigned int padding_top,
-  const unsigned int padding_left,
-  const unsigned int padding_bottom,
-  const unsigned int padding_right
-) : _input(nullptr), _output(nullptr),
-    _packed_parameters(nullptr),
-    _working_space(nullptr),
-    _n_batches(n_batches),
-    _n_input_rows(n_input_rows),
-    _n_input_cols(n_input_cols),
-    _n_channels(n_channels),
-    _n_output_rows(n_output_rows),
-    _n_output_cols(n_output_cols),
-    _n_tile_rows(iceildiv(_n_output_rows, output_tile_rows)),
-    _n_tile_cols(iceildiv(_n_output_cols, output_tile_cols)),
-    _padding_top(padding_top),
-    _padding_left(padding_left),
-    _padding_bottom(padding_bottom),
-    _padding_right(padding_right),
-    _activation(activation),
-    _input_col_stride(0), _input_row_stride(0), _input_batch_stride(0),
-    _output_col_stride(0), _output_row_stride(0), _output_batch_stride(0)
-{
-}
-
-MEMBERFN(void)::set_input(const void* const inptr)
-{
-  set_input(inptr, _n_channels);
-}
-
-MEMBERFN(void)::set_input(const void* const inptr, const int ld_col)
-{
-  set_input(inptr, _n_input_cols * ld_col, ld_col);
-}
-
-MEMBERFN(void)::set_input(const void* const inptr, const int ld_row, const int ld_col)
-{
-  set_input(inptr, _n_input_rows * ld_row, ld_row, ld_col);
-}
-
-MEMBERFN(void)::set_input(const void* const inptr, const int ld_batch, const int ld_row, const int ld_col)
-{
-  _input = static_cast<const TIn *>(inptr);
-  _input_batch_stride = ld_batch;
-  _input_row_stride = ld_row;
-  _input_col_stride = ld_col;
-}
-
-MEMBERFN(void)::set_output(void* const outptr)
-{
-  set_output(outptr, _n_channels);
-}
-
-MEMBERFN(void)::set_output(void* const outptr, const int ld_col)
-{
-  set_output(outptr, _n_output_cols * ld_col, ld_col);
-}
-
-MEMBERFN(void)::set_output(void* const outptr, const int ld_row, const int ld_col)
-{
-  set_output(outptr, _n_output_rows * ld_row, ld_row, ld_col);
-}
-
-MEMBERFN(void)::set_output(void* const outptr, const int ld_batch, const int ld_row, const int ld_col)
-{
-  _output = static_cast<TOut *>(outptr);
-  _output_batch_stride = ld_batch;
-  _output_row_stride = ld_row;
-  _output_col_stride = ld_col;
-}
-
-MEMBERFN(size_t)::get_packed_params_size(void) const
-{
-  return _n_channels * (sizeof(TIn)*KernelRows*KernelColumns + sizeof(TBias));
-}
-
-MEMBERFN(void)::set_packed_params_buffer(void *buffer)
-{
-  _packed_parameters = buffer;
-}
-
-MEMBERFN(void)::pack_params(const void *weights, const void *biases) const
-{
-  static_cast<const Derived *>(this)->pack_params(_packed_parameters, weights, biases);
-}
-
-MEMBERFN(void)::pack_params(void *buffer, const void *weights, const void *biases) const
-{
-  const unsigned int weight_col_stride = _n_channels;
-  const unsigned int weight_row_stride = KernelColumns * weight_col_stride;
-  static_cast<const Derived *>(this)->pack_params(
-    buffer, weights, weight_row_stride, weight_col_stride, biases
-  );
-}
-
-MEMBERFN(void)::pack_params(
-  void * const buffer,
-  const void * const weights,
-  const unsigned int weight_row_stride,
-  const unsigned int weight_col_stride,
-  const void * const biases
-) const
-{
-  static_cast<const Derived *>(this)->_pack_params(
-    buffer, weights, weight_row_stride, weight_col_stride, biases
-  );
-}
-
-MEMBERFN(void)::_pack_params(
-  void * const buffer,
-  const void * const weights,
-  const unsigned int weight_row_stride,
-  const unsigned int weight_col_stride,
-  const void * const biases
-) const
-{
-  // Default implementation
-  PackParameters<KernelRows, KernelColumns, sizeof(TIn), sizeof(TOut)>::execute(
-    _n_channels, buffer, weights, weight_row_stride, weight_col_stride, biases
-  );
-}
-
-MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const
-{
-  return nthreads * (
-    _get_input_working_space_size() + _get_output_working_space_size()
-  );
-}
-
-MEMBERFN(void)::set_working_space(void *buffer)
-{
-  _working_space = buffer;
-}
-
-MEMBERFN(size_t)::_get_input_working_space_size(void) const
-{
-  return sizeof(TIn) * _n_channels;
-}
-
-MEMBERFN(size_t)::_get_output_working_space_size(void) const
-{
-  return sizeof(TOut) * _n_channels;
-}
-
-MEMBERFN(void *)::_get_input_working_space(const unsigned int threadid) const
-{
-  return static_cast<uint8_t*>(_working_space) + threadid * (
-    _get_input_working_space_size() + _get_output_working_space_size()
-  );
-}
-
-MEMBERFN(void *)::_get_output_working_space(const unsigned int threadid) const
-{
-  return static_cast<uint8_t*>(_get_input_working_space(threadid)) + _get_input_working_space_size();
-}
-
-MEMBERFN(unsigned int)::get_window() const
-{
-  // Parallelise over blocks of channels.
-  return iceildiv(_n_channels, CHANNEL_BLOCK);
-}
-
-MEMBERFN(void)::run(
-  const unsigned int start,
-  const unsigned int stop,
-  const unsigned int threadid
-)
-{
-  // Clear the input padding buffer
-  TIn *buf = static_cast<TIn *>(_get_input_working_space(threadid));
-  const TIn pad_value = static_cast<Derived *>(this)->_input_padding_value();
-  for (int n = 0; n < _n_channels; n++)
-  {
-    buf[n] = pad_value;
-  }
-
-  // Parallelise over blocks of channels
-  const auto start_channel = CHANNEL_BLOCK * start;
-  const auto stop_channel = std::min<unsigned int>(_n_channels, CHANNEL_BLOCK * stop);
-
-  // Compute top and bottom padding for input and output
-  const int input_pad_top = _padding_top;
-  const int input_pad_left = _padding_left;
-  constexpr int tile_overlap = kernel_rows - stride_rows;
-
-  // Perform the convolution by calling `process_tile_row` for each tile row in
-  // each batch.
-  for (int batch = 0; batch < _n_batches; batch++)
-  {
-    const TIn* const inptr_batch = _input + batch*_input_batch_stride;
-    TOut* const outptr_batch = _output + batch*_output_batch_stride;
-
-    // Loop over rows of tiles
-    for (int tile_i = 0; tile_i < _n_tile_rows; tile_i++)
-    {
-      // Pointer to the row
-      const int input_row_offset = (tile_i == 0) ? 0 : input_pad_top;
-      const TIn* const inptr_row = (inptr_batch + ((inner_tile_rows - tile_overlap)*tile_i - input_row_offset)*_input_row_stride);
-      TOut* const outptr_row = outptr_batch + output_tile_rows * tile_i * _output_row_stride;
-
-      // Input padding (top + bottom) for the row
-      const int input_row_top = tile_i*(inner_tile_rows - tile_overlap) - input_pad_top;
-      const int input_row_bottom = input_row_top + inner_tile_rows;
-      const int input_row_pad_top = (tile_i == 0) ? input_pad_top : 0;
-      const int input_row_pad_bottom = std::max(0, input_row_bottom - _n_input_rows);
-
-      // Output padding (bottom) for the row
-      const int output_row_bottom = (tile_i + 1)*output_tile_rows;
-      const int output_row_pad_bottom = std::max(0, output_row_bottom - _n_output_rows);
-
-      // Get the offset into the packed parameters
-      const auto params_ptr = static_cast<const uint8_t*>(_packed_parameters) +
-        start_channel*(sizeof(TIn)*KernelRows*KernelColumns + sizeof(TBias));
-
-      // Process the row
-      process_tile_row(
-        threadid,
-        stop_channel - start_channel,
-        params_ptr,
-        inptr_row + start_channel,
-        outptr_row + start_channel,
-        input_row_pad_top, input_pad_left, input_row_pad_bottom,
-        output_row_pad_bottom,
-        _n_tile_cols, _n_input_cols, _n_output_cols
-      );
-    }
-  }
-}
-
-MEMBERFN(void)::process_tile_row(
-  const unsigned int threadid,
-  const int n_channels,
-  const void* const packed_params,
-  const TIn* const inptr,
-  TOut* const outptr,
-  const int row_pad_in_top,
-  const int row_pad_in_left,
-  const int row_pad_in_bottom,
-  const int row_pad_out_bottom,
-  const int n_tiles,
-  const int n_input_cols,
-  const int n_output_cols
-)
-{
-  constexpr int tile_overlap = kernel_cols - stride_cols;
-
-  // Loop over columns of tiles
-  for (int tile_j = 0; tile_j < n_tiles; tile_j++)
-  {
-    // Input padding (left + right) for the tile
-    const int t_pad_in_left = (tile_j == 0) ? row_pad_in_left : 0;
-    const int t_in_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_in_left;
-    const int t_in_end = t_in_start + inner_tile_cols;
-    const int t_pad_in_right = std::max(0, t_in_end - n_input_cols);
-
-    // Output padding (right) for the tile
-    const int t_out_end = (tile_j + 1) * output_tile_cols;
-    const int t_pad_out_right = std::max(0, t_out_end - n_output_cols);
-
-    // Get pointers into the inputs and outputs
-    const int col_offset = (tile_j == 0) ? 0 : row_pad_in_left;
-    const TIn* const inptr_col = (inptr + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*_input_col_stride);
-    TOut* const outptr_col = outptr + tile_j * output_tile_cols * _output_col_stride;
-
-    // Process just this tile
-    process_tile(
-      threadid, n_channels, packed_params, inptr_col, outptr_col,
-      row_pad_in_top, t_pad_in_left, row_pad_in_bottom, t_pad_in_right,  // Input paddings
-      row_pad_out_bottom, t_pad_out_right  // Output paddings
-    );
-  }
-}
-
-MEMBERFN(TIn)::_input_padding_value(void) const
-{
-  return static_cast<TIn>(0);
-}
-
-MEMBERFN(void)::process_tile(
-  const unsigned int threadid,
-  const int n_channels,
-  const void* const packed_params,
-  const TIn* const inptr,
-  TOut* const outptr,
-  const int pad_in_top,
-  const int pad_in_left,
-  const int pad_in_bottom,
-  const int pad_in_right,
-  const int pad_out_bottom,
-  const int pad_out_right
-)
-{
-  Derived * dthis = static_cast<Derived *>(this);
-  const bool pad_input = pad_in_top || pad_in_left || pad_in_bottom || pad_in_right;
-  const bool pad_output = pad_out_bottom || pad_out_right;
-
-  if (!pad_input && !pad_output)
-  {
-    switch(_activation)
-    {
-      case ActivationFunction::ReLU:
-        dthis->template execute_tile<ActivationFunction::ReLU>(
-          n_channels, packed_params,
-          inptr, _input_row_stride, _input_col_stride,
-          outptr, _output_row_stride, _output_col_stride
-        );
-        break;
-      case ActivationFunction::ReLU6:
-        dthis->template execute_tile<ActivationFunction::ReLU6>(
-          n_channels, packed_params,
-          inptr, _input_row_stride, _input_col_stride,
-          outptr, _output_row_stride, _output_col_stride
-        );
-        break;
-      default:
-        dthis->template execute_tile<ActivationFunction::None>(
-          n_channels, packed_params,
-          inptr, _input_row_stride, _input_col_stride,
-          outptr, _output_row_stride, _output_col_stride
-        );
-        break;
-    }
-  }
-  else
-  {
-    // Create arrays of input and output pointers, pointing padded elements to
-    // the working space padding buffers provided.
-    const TIn *inptrs[inner_tile_rows][inner_tile_cols];
-    for (int i = 0; i < inner_tile_rows; i++)
-    {
-      for (int j = 0; j < inner_tile_cols; j++)
-      {
-        if (i < pad_in_top || (inner_tile_rows - pad_in_bottom) <= i ||
-            j < pad_in_left || (inner_tile_cols - pad_in_right) <= j)
-        {
-          // Padded input
-          inptrs[i][j] = static_cast<const TIn *>(_get_input_working_space(threadid));
-        }
-        else
-        {
-          inptrs[i][j] = inptr + (i - pad_in_top)*_input_row_stride + (j - pad_in_left)*_input_col_stride;
-        }
-      }
-    }
-
-    TOut *outptrs[output_tile_rows][output_tile_cols];
-    for (int i = 0; i < output_tile_rows; i++)
-    {
-      for (int j = 0; j < output_tile_cols; j++)
-      {
-        if (i < (output_tile_rows - pad_out_bottom) &&
-            j < (output_tile_cols - pad_out_right))
-        {
-          outptrs[i][j] = outptr + i*_output_row_stride + j*_output_col_stride;
-        }
-        else
-        {
-          outptrs[i][j] = static_cast<TOut *>(_get_output_working_space(threadid));
-        }
-      }
-    }
-
-    switch(_activation)
-    {
-      case ActivationFunction::ReLU:
-        dthis->template execute_tile<ActivationFunction::ReLU>(
-          n_channels, packed_params, inptrs, outptrs
-        );
-        break;
-      case ActivationFunction::ReLU6:
-        dthis->template execute_tile<ActivationFunction::ReLU6>(
-          n_channels, packed_params, inptrs, outptrs
-        );
-        break;
-      default:
-        dthis->template execute_tile<ActivationFunction::None>(
-          n_channels, packed_params, inptrs, outptrs
-        );
-        break;
-    }
-  }
-}
-
-MEMBERFN(int)::n_channels(void) const
-{
-  return _n_channels;
-}
-
-}  // namespace depthwise
diff --git a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
deleted file mode 100644
index 2ef965ba4b..0000000000
--- a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
+++ /dev/null
@@ -1,295 +0,0 @@
-/*
- * Copyright (c) 2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#include "depthwise_dilated.hpp"
-#include "utils.hpp"
-
-#define MEMBERFN(TOUT)                                                         \
-  template <unsigned int OutputTileRows, unsigned int OutputTileColumns,       \
-            unsigned int KernelRows, unsigned int KernelColumns,               \
-            unsigned int StrideRows, unsigned int StrideColumns, typename TIn, \
-            typename TBias, typename TOut>                                     \
-  TOUT DilatedDepthwiseConvolution<OutputTileRows, OutputTileColumns,          \
-                                   KernelRows, KernelColumns, StrideRows,      \
-                                   StrideColumns, TIn, TBias, TOut>
-
-namespace depthwise {
-
-MEMBERFN()
-::DilatedDepthwiseConvolution(const int n_batches, const int n_input_rows,
-                              const int n_input_cols, const int n_channels,
-                              const int dilation_factor,
-                              nck::ActivationFunction activation,
-                              const unsigned int padding_top,
-                              const unsigned int padding_left,
-                              const unsigned int padding_bottom,
-                              const unsigned int padding_right)
-    : DilatedDepthwiseConvolution(
-          n_batches, n_input_rows, n_input_cols, n_channels, dilation_factor,
-          DilatedDepthwiseConvolution::get_output_size(
-              n_input_rows, padding_top, padding_bottom, dilation_factor),
-          DilatedDepthwiseConvolution::get_output_size(
-              n_input_cols, padding_left, padding_right, dilation_factor),
-          activation, padding_top, padding_left, padding_bottom,
-          padding_right) {}
-
-MEMBERFN()
-::DilatedDepthwiseConvolution(const int n_batches, const int n_input_rows,
-                              const int n_input_cols, const int n_channels,
-                              const int dilation_factor,
-                              const int n_output_rows, const int n_output_cols,
-                              nck::ActivationFunction activation,
-                              const unsigned int padding_top,
-                              const unsigned int padding_left,
-                              const unsigned int, // padding_bottom
-                              const unsigned int  // padding_right
-                              )
-    : DilatedDepthwiseConvolution(
-          n_batches, n_input_rows, n_input_cols, n_channels, dilation_factor,
-          n_output_rows, n_output_cols, activation, padding_top, padding_left,
-          0, 0,
-          // Function which creates a new (standard) depthwise convolution
-          [](const int n_batches, const int n_input_rows,
-             const int n_input_cols, const int n_channels,
-             const int n_output_rows, const int n_output_cols,
-             const nck::ActivationFunction activation,
-             const unsigned int padding_top, const unsigned int padding_left,
-             const unsigned int padding_bottom,
-             const unsigned int padding_right) -> IDepthwiseConvolution * {
-            return new DepthwiseConvolution<
-                OutputTileRows, OutputTileColumns, KernelRows, KernelColumns,
-                StrideRows, StrideColumns, TIn, TBias, TOut>(
-                n_batches, n_input_rows, n_input_cols, n_channels,
-                n_output_rows, n_output_cols, activation, padding_top,
-                padding_left, padding_bottom, padding_right);
-          }) {}
-
-MEMBERFN()
-::DilatedDepthwiseConvolution(
-    const int n_batches, const int n_input_rows, const int n_input_cols,
-    const int n_channels, const int dilation_factor, const int n_output_rows,
-    const int n_output_cols, nck::ActivationFunction activation,
-    const unsigned int padding_top, const unsigned int padding_left,
-    const unsigned int, // padding_bottom
-    const unsigned int, // padding_right
-    std::function<IDepthwiseConvolution *(
-        int, int, int, int, int, int, nck::ActivationFunction, unsigned int,
-        unsigned int, unsigned int, unsigned int)>
-        subconvfn // Function to create a new convolution
-    )
-    : _dilation_factor(dilation_factor), _n_input_rows(n_input_rows),
-      _n_input_cols(n_input_cols), _n_channels(n_channels),
-      _padding_top(static_cast<int>(padding_top)),
-      _padding_left(static_cast<int>(padding_left)),
-      _n_output_rows(n_output_rows), _n_output_cols(n_output_cols),
-      _convs(_dilation_factor) {
-  // Instantiate the base convolutions
-  for (int i = 0; i < _dilation_factor; i++) {
-    // Compute properties of this row of base convolutions
-    const int row_top =
-        i * StrideRows - _padding_top; // -ve values are in the padding
-    const int row_pad_top =
-        row_top < 0 ? iceildiv(-row_top, dilation_factor) : 0;
-
-    const int _n_input_rows = iceildiv(n_input_rows - i, dilation_factor);
-    const int _n_output_rows = iceildiv(n_output_rows - i, dilation_factor);
-
-    for (int j = 0; j < _dilation_factor; j++) {
-      // Compute properties of the base convolution
-      const int col_left =
-          j * StrideColumns - padding_left; // -ve values are in the padding
-      const int col_pad_left =
-          col_left < 0 ? iceildiv(-col_left, dilation_factor) : 0;
-
-      const int _n_input_cols = iceildiv(n_input_cols - j, dilation_factor);
-      const int _n_output_cols = iceildiv(n_output_cols - j, dilation_factor);
-
-      // Create new depthwise convolution engine and include it in the vector
-      // of engines. The new depthwise convolution engine is created by calling
-      // the delegate function we received as an argument.
-      _convs[i].emplace_back(subconvfn(
-          n_batches, _n_input_rows, _n_input_cols, n_channels, _n_output_rows,
-          _n_output_cols, activation,
-          // Note: since we have computed the output tensor size we don't need
-          // to explicitly provide bottom and right padding values to the
-          // depthwise convolution.
-          row_pad_top, col_pad_left, 0, 0));
-    }
-  }
-}
-
-MEMBERFN(void)::set_input(const void *const inptr) {
-  set_input(inptr, _n_channels);
-}
-
-MEMBERFN(void)::set_input(const void *const inptr, const int ldcol) {
-  set_input(inptr, _n_input_cols * ldcol, ldcol);
-}
-
-MEMBERFN(void)
-::set_input(const void *const inptr, const int ldrow, const int ldcol) {
-  set_input(inptr, _n_input_rows * ldrow, ldrow, ldcol);
-}
-
-MEMBERFN(void)
-::set_input(const void *const inptr, const int ldbatch, const int ldrow,
-            const int ldcol) {
-  // Compute dilated strides
-  const int ldrow_dilated = ldrow * _dilation_factor;
-  const int ldcol_dilated = ldcol * _dilation_factor;
-
-  // Pass input parameters on to base convolutions
-  for (int i = 0; i < _dilation_factor; i++) {
-    const int top_pos =
-        i * StrideRows - _padding_top +
-        ((static_cast<int>(i * StrideRows) < _padding_top)
-             ? iceildiv(_padding_top - i * StrideRows, _dilation_factor) *
-                   _dilation_factor
-             : 0);
-    const TIn *const inptr_i =
-        static_cast<const TIn *>(inptr) + top_pos * ldrow;
-
-    for (int j = 0; j < _dilation_factor; j++) {
-      int left_pos = j * StrideColumns - _padding_left;
-      while (left_pos < 0)
-        left_pos += _dilation_factor;
-
-      // Modify the pointer to point to the first element of the dilated input
-      // tensor, then set the input for this convolution engine.
-      const void *const inptr_ij = inptr_i + left_pos * ldcol;
-      _convs[i][j]->set_input(inptr_ij, ldbatch, ldrow_dilated, ldcol_dilated);
-    }
-  }
-}
-
-MEMBERFN(void)::set_output(void *const outptr) {
-  set_output(outptr, _n_channels);
-}
-
-MEMBERFN(void)::set_output(void *const outptr, const int ldcol) {
-  set_output(outptr, _n_output_cols * ldcol, ldcol);
-}
-
-MEMBERFN(void)
-::set_output(void *const outptr, const int ldrow, const int ldcol) {
-  set_output(outptr, _n_output_rows * ldrow, ldrow, ldcol);
-}
-
-MEMBERFN(void)
-::set_output(void *const outptr, const int ldbatch, const int ldrow,
-             const int ldcol) {
-  // Compute dilated strides
-  const int ldrow_dilated = ldrow * _dilation_factor;
-  const int ldcol_dilated = ldcol * _dilation_factor;
-
-  // Pass input parameters on to base convolutions
-  for (int i = 0; i < _dilation_factor; i++) {
-    for (int j = 0; j < _dilation_factor; j++) {
-      // Modify the pointer to point to the first element of the dilated input
-      // tensor, then set the input for this convolution engine.
-      void *const outptr_ij =
-          static_cast<TOut *>(outptr) + i * ldrow + j * ldcol;
-      _convs[i][j]->set_output(outptr_ij, ldbatch, ldrow_dilated,
-                               ldcol_dilated);
-    }
-  }
-}
-
-MEMBERFN(int)
-::get_output_size(const int dim_size, const unsigned int padding_before,
-                  const unsigned int padding_after, const int dilation_factor) {
-  const int input_size =
-      dim_size + static_cast<int>(padding_before + padding_after);
-  const int window_size = (KernelRows - 1) * dilation_factor + 1;
-  return iceildiv(input_size - window_size + 1, StrideRows);
-}
-
-MEMBERFN(int)
-::output_size(const int dim_size, const unsigned int padding_before,
-              const unsigned int padding_after) const {
-  return get_output_size(dim_size, padding_before, padding_after,
-                         _dilation_factor);
-}
-
-MEMBERFN(size_t)::get_packed_params_size(void) const {
-  return _convs[0][0]->get_packed_params_size();
-}
-
-MEMBERFN(void)::set_packed_params_buffer(void *buffer) {
-  // Set the buffer for all convolution engines
-  for (auto &&row : _convs) {
-    for (auto &&conv : row) {
-      conv->set_packed_params_buffer(buffer);
-    }
-  }
-}
-
-MEMBERFN(void)
-::pack_params(const void *const weights, const void *const biases) const {
-  _convs[0][0]->pack_params(weights, biases);
-}
-
-MEMBERFN(void)
-::pack_params(void *const buffer, const void *const weights,
-              const void *const biases) const {
-  _convs[0][0]->pack_params(buffer, weights, biases);
-}
-
-MEMBERFN(void)
-::pack_params(void *const buffer, const void *const weights,
-              const unsigned int ldrow, const unsigned int ldcol,
-              const void *const biases) const {
-  _convs[0][0]->pack_params(buffer, weights, ldrow, ldcol, biases);
-}
-
-MEMBERFN(size_t)::get_working_space_size(unsigned int nthreads) const {
-  return _convs[0][0]->get_working_space_size(nthreads);
-}
-
-MEMBERFN(void)::set_working_space(void *const ws) {
-  // Use the same working space set for all contained depthwise engines.
-  for (auto &&row : _convs) {
-    for (auto &&conv : row) {
-      conv->set_working_space(ws);
-    }
-  }
-}
-
-MEMBERFN(unsigned int)::get_window(void) const {
-  return _convs[0][0]->get_window();
-}
-
-MEMBERFN(void)
-::run(const unsigned int start, const unsigned int stop,
-      const unsigned int threadid) {
-  // Run each contained convolution in turn
-  for (auto &&row : _convs) {
-    for (auto &&conv : row) {
-      conv->run(start, stop, threadid);
-    }
-  }
-}
-
-} // namespace depthwise