src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp - ml/ComputeLibrary - Gitiles

 /*
  * 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 (uint32_t i = 0; i < static_cast<uint32_t>(_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 (uint32_t j = 0; j < static_cast<uint32_t>(_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 (uint32_t i = 0; i < static_cast<uint32_t>(_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 (uint32_t j = 0; j < static_cast<uint32_t>(_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 (uint32_t i = 0; i < static_cast<uint32_t>(_dilation_factor); i++) {
     for (uint32_t j = 0; j < static_cast<uint32_t>(_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
	/*
	* 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 (uint32_t i = 0; i < static_cast<uint32_t>(_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 (uint32_t j = 0; j < static_cast<uint32_t>(_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 (uint32_t i = 0; i < static_cast<uint32_t>(_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 (uint32_t j = 0; j < static_cast<uint32_t>(_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 (uint32_t i = 0; i < static_cast<uint32_t>(_dilation_factor); i++) {
	for (uint32_t j = 0; j < static_cast<uint32_t>(_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