src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2021-2023 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 "depthfirst_driver.hpp"
 #include "src/core/NEON/kernels/arm_conv/addressing.hpp"
 #include "utils.hpp"
 #if !defined(_WIN64) && !defined(__OpenBSD__)
 #include <alloca.h>
 #endif /* !defined(_WIN64) && !defined(__OpenBSD__) */
 #include <limits>

 namespace arm_conv {
 namespace pooling {

 template <typename TInput, typename TOutput>
 class DepthfirstStrategy : public IDepthfirstStrategy
 {
   unsigned int input_rows, input_cols, output_rows, output_cols;

   public:
   DepthfirstStrategy(unsigned int window_rows, unsigned int window_cols,
                      unsigned int stride_rows, unsigned int stride_cols,
                      unsigned int output_rows, unsigned int output_cols)
   : input_rows(output_rows + (window_rows - 1) * stride_rows),
     input_cols(output_cols + (window_cols - 1) * stride_cols),
     output_rows(output_rows), output_cols(output_cols)
   {
   }

   unsigned int get_input_rows() const override { return input_rows; }
   unsigned int get_input_cols() const override { return input_cols; }
   unsigned int get_output_rows() const override { return output_rows; }
   unsigned int get_output_cols() const override { return output_cols; }

   typedef void (*KernelType)(
     unsigned int n_channels,
     const TInput *const *,
     TOutput *const *,
     bool exclude_padding,
     unsigned int pad_left,
     unsigned int pad_top,
     unsigned int pad_right,
     unsigned int pad_bottom
   );
   virtual KernelType get_kernel(void) const = 0;
 };


 struct WorkingSpace
 {
   void *input_buffer;
   void *output_buffer;
 };


 template <typename TInput, typename TOutput=TInput, class OutputStage=Nothing>
 class PoolingDepthfirst : public DepthfirstDriver<TInput, TOutput>
 {
   size_t sizeof_input_buffer(void) const
   {
     return sizeof(TInput) * this->m_args.n_channels;
   }

   size_t sizeof_output_buffer(void) const
   {
     return sizeof(TOutput) * this->m_args.n_channels;
   }

   protected:
   /* Compute the amount of working space required for a single thread. */
   size_t get_working_size_per_thread(unsigned int n_channels) const override
   {
     return sizeof(WorkingSpace) + n_channels * (sizeof(TInput) + sizeof(TOutput));
   }

   /* Initialise the working space for a thread. */
   void initialise_working_space(void *raw_ws, unsigned int n_channels) const override
   {
     auto ws = reinterpret_cast<WorkingSpace *>(raw_ws);
     ws->input_buffer = ws + 1;
     ws->output_buffer = reinterpret_cast<char *>(ws + 1) + sizeof(TInput) * n_channels;

     // Fill the input buffer with an appropriate value
     TInput fill_val = 0;
     if (this->m_args.pool_type == PoolingType::MAX)
     {
       using limits = std::numeric_limits<TInput>;
       if (limits::has_infinity)
       {
         fill_val = -limits::infinity();
       }
       else
       {
         fill_val = limits::min();
       }
     }

     auto ptr = reinterpret_cast<TInput *>(ws->input_buffer);
     for (; n_channels; n_channels--)
     {
       *(ptr++) = fill_val;
     }
   }

   /* Compute a portion of the output tensor with padding. */
   void compute_tile_padded(
     unsigned int output_i, unsigned int output_j,
     unsigned int channel_start, unsigned int channel_end,
     const TensorSpec<const TInput *> &input,
     const TensorSpec<TOutput *> &output,
     void *working_space
   ) const override
   {
     const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>(
       this->m_strat.get())->get_kernel();

     // Get the working space, and some space on the stack for pointer arrays
     auto ws = reinterpret_cast<WorkingSpace *>(working_space);
     auto inptr_array = reinterpret_cast<const TInput **>(alloca(
         sizeof(TInput *) * this->m_strat->get_input_rows() * this->m_strat->get_input_cols()));
     auto outptr_array = reinterpret_cast<TOutput **>(alloca(
         sizeof(TOutput *) * this->m_strat->get_output_rows() * this->m_strat->get_output_cols()));

     // Prepare the input pointers
     const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top;
     const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0);
     const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii);

     const unsigned int end_ii = ii + this->m_strat->get_input_rows();
     const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows;

     const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left;
     const auto input_pad_left = static_cast<unsigned int>(ij < 0 ? -ij : 0);
     const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij);

     const unsigned int end_ij = ij + this->m_strat->get_input_cols();
     const auto input_pad_right = end_ij < this->m_args.input_cols ? 0 : end_ij - this->m_args.input_cols;

     fill_pointer_array<const TInput>(
       inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(),
       input.base + input_i*input.ld_row + input_j*input.ld_col + channel_start,
       input.ld_row, input.ld_col,
       reinterpret_cast<const TInput *>(ws->input_buffer),
       input_pad_top, this->m_args.input_rows - input_i,
       input_pad_left, this->m_args.input_cols - input_j
     );

     // Prepare the output pointers
     fill_pointer_array(
       outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
       output.base + output_i*output.ld_row + output_j*output.ld_col + channel_start,
       output.ld_row, output.ld_col,
       reinterpret_cast<TOutput *>(ws->output_buffer),
       0, this->m_args.output_rows - output_i, // Top padding, # valid rows
       0, this->m_args.output_cols - output_j  // Left padding, # valid columns
     );

     // Call the kernel
     kern(
       channel_end - channel_start, inptr_array, outptr_array,
       this->m_args.exclude_padding,
       input_pad_left, input_pad_top,
       input_pad_right, input_pad_bottom
     );
   }

   // Compute a portion of the work with only top/bottom padding.
   void compute_row_padded_tile_row(
     const unsigned int output_i, unsigned int output_j, unsigned int n_tile_cols,
     const unsigned int channel_start, const unsigned int channel_end,
     const TensorSpec<const TInput *> &input,
     const TensorSpec<TOutput *> &output,
     void *working_space
   ) const override
   {
     const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>(
       this->m_strat.get())->get_kernel();

     // Get the working space, and some space on the stack for pointer arrays
     auto ws = reinterpret_cast<WorkingSpace *>(working_space);
     auto inptr_array = reinterpret_cast<const TInput **>(alloca(
         sizeof(TInput *) * this->m_strat->get_input_rows() * this->m_strat->get_input_cols()));
     auto outptr_array = reinterpret_cast<TOutput **>(alloca(
         sizeof(TOutput *) * this->m_strat->get_output_rows() * this->m_strat->get_output_cols()));

     // Prepare the initial input pointers
     const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top;
     const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0);
     const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii);

     const unsigned int end_ii = ii + this->m_strat->get_input_rows();
     const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows;

     const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left;
     const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij);

     const auto end_oi = output_i + this->m_strat->get_output_cols();
     const auto output_pad_bottom = end_oi < this->m_args.output_rows ? 0 : end_oi - this->m_args.output_rows;

     fill_pointer_array<const TInput>(
       inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(),
       input.base + input_i*input.ld_row + input_j*input.ld_col + channel_start,
       input.ld_row, input.ld_col,
       reinterpret_cast<const TInput *>(ws->input_buffer),
       input_pad_top, this->m_args.input_rows - input_i,
       0, this->m_args.input_cols - input_j
     );

     // Prepare the initial output pointers
     fill_pointer_array(
       outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
       output.base + output_i*output.ld_row + output_j*output.ld_col + channel_start,
       output.ld_row, output.ld_col,
       reinterpret_cast<TOutput *>(ws->output_buffer),
       0, this->m_args.output_rows - output_i, // Top padding, # valid rows
       0, this->m_args.output_cols - output_j  // Left padding, # valid columns
     );

     // Call the kernel
     for (; n_tile_cols; n_tile_cols--)
     {
       kern(
         channel_end - channel_start, inptr_array, outptr_array,
         this->m_args.exclude_padding,
         0, input_pad_top,
         0, input_pad_bottom
       );

       // Progress the input and output pointer arrays
       const auto input_col_stride = input.ld_col * this->m_strat->get_output_cols() * this->m_args.pool_stride.cols;
       for (
         auto n = input_pad_top * this->m_strat->get_input_cols();
         n < (this->m_strat->get_input_rows() - input_pad_bottom) * this->m_strat->get_input_cols();
         n++
       )
       {
         inptr_array[n] += input_col_stride;
       }

       const auto output_col_stride = output.ld_col * this->m_strat->get_output_cols();
       for (
         auto n = 0u;
         n < (this->m_strat->get_output_rows() - output_pad_bottom) * this->m_strat->get_output_cols();
         n++
       )
       {
         outptr_array[n] += output_col_stride;
       }
     }
   }

   public:
   PoolingDepthfirst(const DepthfirstStrategy<TInput, TOutput> *strat,
                     const PoolingArgs &args, const OutputStage &os = {})
   : DepthfirstDriver<TInput, TOutput>(strat, args)
   {
     ARM_COMPUTE_UNUSED(os);
   }
 };

 }  // namespace pooling
 }  // namespace arm_conv
	/*
	* Copyright (c) 2021-2023 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 "depthfirst_driver.hpp"
	#include "src/core/NEON/kernels/arm_conv/addressing.hpp"
	#include "utils.hpp"
	#if !defined(_WIN64) && !defined(__OpenBSD__)
	#include <alloca.h>
	#endif /* !defined(_WIN64) && !defined(__OpenBSD__) */
	#include <limits>

	namespace arm_conv {
	namespace pooling {

	template <typename TInput, typename TOutput>
	class DepthfirstStrategy : public IDepthfirstStrategy
	{
	unsigned int input_rows, input_cols, output_rows, output_cols;

	public:
	DepthfirstStrategy(unsigned int window_rows, unsigned int window_cols,
	unsigned int stride_rows, unsigned int stride_cols,
	unsigned int output_rows, unsigned int output_cols)
	: input_rows(output_rows + (window_rows - 1) * stride_rows),
	input_cols(output_cols + (window_cols - 1) * stride_cols),
	output_rows(output_rows), output_cols(output_cols)
	{
	}

	unsigned int get_input_rows() const override { return input_rows; }
	unsigned int get_input_cols() const override { return input_cols; }
	unsigned int get_output_rows() const override { return output_rows; }
	unsigned int get_output_cols() const override { return output_cols; }

	typedef void (*KernelType)(
	unsigned int n_channels,
	const TInput const ,
	TOutput const ,
	bool exclude_padding,
	unsigned int pad_left,
	unsigned int pad_top,
	unsigned int pad_right,
	unsigned int pad_bottom
	);
	virtual KernelType get_kernel(void) const = 0;
	};


	struct WorkingSpace
	{
	void *input_buffer;
	void *output_buffer;
	};


	template <typename TInput, typename TOutput=TInput, class OutputStage=Nothing>
	class PoolingDepthfirst : public DepthfirstDriver<TInput, TOutput>
	{
	size_t sizeof_input_buffer(void) const
	{
	return sizeof(TInput) * this->m_args.n_channels;
	}

	size_t sizeof_output_buffer(void) const
	{
	return sizeof(TOutput) * this->m_args.n_channels;
	}

	protected:
	/* Compute the amount of working space required for a single thread. */
	size_t get_working_size_per_thread(unsigned int n_channels) const override
	{
	return sizeof(WorkingSpace) + n_channels * (sizeof(TInput) + sizeof(TOutput));
	}

	/* Initialise the working space for a thread. */
	void initialise_working_space(void *raw_ws, unsigned int n_channels) const override
	{
	auto ws = reinterpret_cast<WorkingSpace *>(raw_ws);
	ws->input_buffer = ws + 1;
	ws->output_buffer = reinterpret_cast<char >(ws + 1) + sizeof(TInput) n_channels;

	// Fill the input buffer with an appropriate value
	TInput fill_val = 0;
	if (this->m_args.pool_type == PoolingType::MAX)
	{
	using limits = std::numeric_limits<TInput>;
	if (limits::has_infinity)
	{
	fill_val = -limits::infinity();
	}
	else
	{
	fill_val = limits::min();
	}
	}

	auto ptr = reinterpret_cast<TInput *>(ws->input_buffer);
	for (; n_channels; n_channels--)
	{
	*(ptr++) = fill_val;
	}
	}

	/* Compute a portion of the output tensor with padding. */
	void compute_tile_padded(
	unsigned int output_i, unsigned int output_j,
	unsigned int channel_start, unsigned int channel_end,
	const TensorSpec<const TInput *> &input,
	const TensorSpec<TOutput *> &output,
	void *working_space
	) const override
	{
	const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>(
	this->m_strat.get())->get_kernel();

	// Get the working space, and some space on the stack for pointer arrays
	auto ws = reinterpret_cast<WorkingSpace *>(working_space);
	auto inptr_array = reinterpret_cast<const TInput **>(alloca(
	sizeof(TInput ) this->m_strat->get_input_rows() * this->m_strat->get_input_cols()));
	auto outptr_array = reinterpret_cast<TOutput **>(alloca(
	sizeof(TOutput ) this->m_strat->get_output_rows() * this->m_strat->get_output_cols()));

	// Prepare the input pointers
	const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top;
	const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0);
	const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii);

	const unsigned int end_ii = ii + this->m_strat->get_input_rows();
	const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows;

	const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left;
	const auto input_pad_left = static_cast<unsigned int>(ij < 0 ? -ij : 0);
	const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij);

	const unsigned int end_ij = ij + this->m_strat->get_input_cols();
	const auto input_pad_right = end_ij < this->m_args.input_cols ? 0 : end_ij - this->m_args.input_cols;

	fill_pointer_array<const TInput>(
	inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(),
	input.base + input_iinput.ld_row + input_jinput.ld_col + channel_start,
	input.ld_row, input.ld_col,
	reinterpret_cast<const TInput *>(ws->input_buffer),
	input_pad_top, this->m_args.input_rows - input_i,
	input_pad_left, this->m_args.input_cols - input_j
	);

	// Prepare the output pointers
	fill_pointer_array(
	outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
	output.base + output_ioutput.ld_row + output_joutput.ld_col + channel_start,
	output.ld_row, output.ld_col,
	reinterpret_cast<TOutput *>(ws->output_buffer),
	0, this->m_args.output_rows - output_i, // Top padding, # valid rows
	0, this->m_args.output_cols - output_j // Left padding, # valid columns
	);

	// Call the kernel
	kern(
	channel_end - channel_start, inptr_array, outptr_array,
	this->m_args.exclude_padding,
	input_pad_left, input_pad_top,
	input_pad_right, input_pad_bottom
	);
	}

	// Compute a portion of the work with only top/bottom padding.
	void compute_row_padded_tile_row(
	const unsigned int output_i, unsigned int output_j, unsigned int n_tile_cols,
	const unsigned int channel_start, const unsigned int channel_end,
	const TensorSpec<const TInput *> &input,
	const TensorSpec<TOutput *> &output,
	void *working_space
	) const override
	{
	const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>(
	this->m_strat.get())->get_kernel();

	// Get the working space, and some space on the stack for pointer arrays
	auto ws = reinterpret_cast<WorkingSpace *>(working_space);
	auto inptr_array = reinterpret_cast<const TInput **>(alloca(
	sizeof(TInput ) this->m_strat->get_input_rows() * this->m_strat->get_input_cols()));
	auto outptr_array = reinterpret_cast<TOutput **>(alloca(
	sizeof(TOutput ) this->m_strat->get_output_rows() * this->m_strat->get_output_cols()));

	// Prepare the initial input pointers
	const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top;
	const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0);
	const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii);

	const unsigned int end_ii = ii + this->m_strat->get_input_rows();
	const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows;

	const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left;
	const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij);

	const auto end_oi = output_i + this->m_strat->get_output_cols();
	const auto output_pad_bottom = end_oi < this->m_args.output_rows ? 0 : end_oi - this->m_args.output_rows;

	fill_pointer_array<const TInput>(
	inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(),
	input.base + input_iinput.ld_row + input_jinput.ld_col + channel_start,
	input.ld_row, input.ld_col,
	reinterpret_cast<const TInput *>(ws->input_buffer),
	input_pad_top, this->m_args.input_rows - input_i,
	0, this->m_args.input_cols - input_j
	);

	// Prepare the initial output pointers
	fill_pointer_array(
	outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(),
	output.base + output_ioutput.ld_row + output_joutput.ld_col + channel_start,
	output.ld_row, output.ld_col,
	reinterpret_cast<TOutput *>(ws->output_buffer),
	0, this->m_args.output_rows - output_i, // Top padding, # valid rows
	0, this->m_args.output_cols - output_j // Left padding, # valid columns
	);

	// Call the kernel
	for (; n_tile_cols; n_tile_cols--)
	{
	kern(
	channel_end - channel_start, inptr_array, outptr_array,
	this->m_args.exclude_padding,
	0, input_pad_top,
	0, input_pad_bottom
	);

	// Progress the input and output pointer arrays
	const auto input_col_stride = input.ld_col * this->m_strat->get_output_cols() * this->m_args.pool_stride.cols;
	for (
	auto n = input_pad_top * this->m_strat->get_input_cols();
	n < (this->m_strat->get_input_rows() - input_pad_bottom) * this->m_strat->get_input_cols();
	n++
	)
	{
	inptr_array[n] += input_col_stride;
	}

	const auto output_col_stride = output.ld_col * this->m_strat->get_output_cols();
	for (
	auto n = 0u;
	n < (this->m_strat->get_output_rows() - output_pad_bottom) * this->m_strat->get_output_cols();
	n++
	)
	{
	outptr_array[n] += output_col_stride;
	}
	}
	}

	public:
	PoolingDepthfirst(const DepthfirstStrategy<TInput, TOutput> *strat,
	const PoolingArgs &args, const OutputStage &os = {})
	: DepthfirstDriver<TInput, TOutput>(strat, args)
	{
	ARM_COMPUTE_UNUSED(os);
	}
	};

	} // namespace pooling
	} // namespace arm_conv