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, 799 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp
new file mode 100644
index 0000000000..b102a24250
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp
@@ -0,0 +1,504 @@
+/*
+ * 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/src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
new file mode 100644
index 0000000000..2ef965ba4b
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
@@ -0,0 +1,295 @@
+/*
+ * 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