src/core/NEON/kernels/NEPermuteKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018 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 "arm_compute/core/NEON/kernels/NEPermuteKernel.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 namespace
 {
 #include "arm_compute/core/NEON/kernels/convolution/common/shims.hpp"
 } // namespace

 #include <cstddef>
 #include <cstdint>

 using namespace arm_compute;

 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
                                                          DataType::U16, DataType::S16, DataType::QS16,
                                                          DataType::U32, DataType::S32,
                                                          DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((perm.num_dimensions() == 3 && !(perm[0] == 2 && perm[1] == 0 && perm[2] == 1) && !(perm[0] == 1 && perm[1] == 2 && perm[2] == 0)),
                                     "Only [2, 0, 1] and [1, 2, 0] permutation is supported");

     const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input, perm);

     // Validate configured output
     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
     }

     return Status{};
 }
 } // namespace

 template <typename T>
 void NEPermuteKernel::run_permute(const Window &window)
 {
     // Input window
     Window window_in = window;
     window_in.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().end() - window.x().start()));
     window_in.set(Window::DimY, Window::Dimension(window.y().start(), window.y().end(), window.y().end() - window.y().start()));
     window_in.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), window.z().end() - window.z().start()));
     window_in.set(3, Window::Dimension(window[3].start(), window[3].end(), window[3].end() - window[3].start()));

     // Output window
     Window                  window_out(window);
     const Window::Dimension zero_window = Window::Dimension(0, 0, 0);
     for(size_t d = 0; d <= _perm.num_dimensions(); ++d)
     {
         window_out.set(d, zero_window);
     }

     // Create iterators
     Iterator in(_input, window_in);
     Iterator out(_output, window_out);

     // CHW -> HWC
     if((_perm.num_dimensions() == 3) && (_perm[0] == 2) && (_perm[1] == 0) && (_perm[2] == 1))
     {
         const int in_row_stride     = _input->info()->strides_in_bytes().y() / sizeof(T);
         const int in_channel_stride = _input->info()->strides_in_bytes().z() / sizeof(T);
         const int in_batch_stride   = _input->info()->strides_in_bytes()[3] / sizeof(T);

         const int out_channel_stride = _output->info()->strides_in_bytes().x() / sizeof(T);
         const int out_col_stride     = _output->info()->strides_in_bytes().y() / sizeof(T);
         const int out_row_stride     = _output->info()->strides_in_bytes().z() / sizeof(T);
         const int out_batch_stride   = _output->info()->strides_in_bytes()[3] / sizeof(T);

         const int n_cols     = _input->info()->tensor_shape().x();
         const int n_rows     = window_in.y().step();
         const int n_channels = _input->info()->tensor_shape().z();
         const int n_batches  = _input->info()->tensor_shape()[3];

         execute_window_loop(window_in, [&](const Coordinates & id)
         {
             const int idx = id[0] * out_col_stride + id[1] * out_row_stride + id[2] * out_channel_stride;
             reorder::nchw_to_nhwc(reinterpret_cast<const T *>(in.ptr()), reinterpret_cast<T *>(out.ptr()) + idx,
                                   n_batches, n_channels, n_rows, n_cols,
                                   in_batch_stride, in_channel_stride, in_row_stride,
                                   out_batch_stride, out_row_stride, out_col_stride);
         },
         in, out);
     }
     // HWC -> CHW
     else if((_perm.num_dimensions() == 3) && (_perm[0] == 1) && (_perm[1] == 2) && (_perm[2] == 0))
     {
         const int in_col_stride   = _input->info()->strides_in_bytes().y() / sizeof(T);
         const int in_row_stride   = _input->info()->strides_in_bytes().z() / sizeof(T);
         const int in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T);

         const int out_col_stride     = _output->info()->strides_in_bytes().x() / sizeof(T);
         const int out_row_stride     = _output->info()->strides_in_bytes().y() / sizeof(T);
         const int out_channel_stride = _output->info()->strides_in_bytes().z() / sizeof(T);
         const int out_batch_stride   = _output->info()->strides_in_bytes()[3] / sizeof(T);

         const int n_channels = _input->info()->tensor_shape().x();
         const int n_cols     = window_in.y().step();
         const int n_rows     = _input->info()->tensor_shape().z();
         const int n_batches  = _input->info()->tensor_shape()[3];

         execute_window_loop(window_in, [&](const Coordinates & id)
         {
             const int idx = id[0] * out_channel_stride + id[1] * out_col_stride + id[2] * out_row_stride;
             reorder::nhwc_to_nchw(reinterpret_cast<const T *>(in.ptr()), reinterpret_cast<T *>(out.ptr()) + idx,
                                   n_batches, n_rows, n_cols, n_channels,
                                   in_batch_stride, in_row_stride, in_col_stride,
                                   out_batch_stride, out_channel_stride, out_row_stride);
         },
         in, out);
     }
     else
     {
         ARM_COMPUTE_ERROR("Unsupported permutation vector");
     }
 }

 NEPermuteKernel::NEPermuteKernel()
     : _func(), _input(nullptr), _output(nullptr), _perm()
 {
 }

 void NEPermuteKernel::configure(const ITensor *input, ITensor *output, const PermutationVector &perm)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input->info(), perm);
     // Output auto inizialitation if not yet initialized
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm));

     _input  = input;
     _output = output;
     _perm   = perm;

     switch(input->info()->element_size())
     {
         case 1:
             _func = &NEPermuteKernel::run_permute<uint8_t>;
             break;
         case 2:
             _func = &NEPermuteKernel::run_permute<uint16_t>;
             break;
         case 4:
             _func = &NEPermuteKernel::run_permute<uint32_t>;
             break;
         default:
             ARM_COMPUTE_ERROR("Element size not supported");
             break;
     }

     // Configure kernel window
     Window win = calculate_max_window(*input->info(), Steps());

     // The NEPermute doesn't need padding so update_window_and_padding() can be skipped
     Coordinates coord;
     coord.set_num_dimensions(output->info()->num_dimensions());
     output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));

     ICPPKernel::configure(win);
 }

 Status NEPermuteKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, perm));
     return Status{};
 }

 void NEPermuteKernel::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window);

     if(_func != nullptr)
     {
         (this->*_func)(window);
     }
 }
	/*
	* Copyright (c) 2018 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 "arm_compute/core/NEON/kernels/NEPermuteKernel.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	namespace
	{
	#include "arm_compute/core/NEON/kernels/convolution/common/shims.hpp"
	} // namespace

	#include <cstddef>
	#include <cstdint>

	using namespace arm_compute;

	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, const PermutationVector &perm)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
	DataType::U16, DataType::S16, DataType::QS16,
	DataType::U32, DataType::S32,
	DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((perm.num_dimensions() == 3 && !(perm[0] == 2 && perm[1] == 0 && perm[2] == 1) && !(perm[0] == 1 && perm[1] == 2 && perm[2] == 0)),
	"Only [2, 0, 1] and [1, 2, 0] permutation is supported");

	const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input, perm);

	// Validate configured output
	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
	}

	return Status{};
	}
	} // namespace

	template <typename T>
	void NEPermuteKernel::run_permute(const Window &window)
	{
	// Input window
	Window window_in = window;
	window_in.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().end() - window.x().start()));
	window_in.set(Window::DimY, Window::Dimension(window.y().start(), window.y().end(), window.y().end() - window.y().start()));
	window_in.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), window.z().end() - window.z().start()));
	window_in.set(3, Window::Dimension(window[3].start(), window[3].end(), window[3].end() - window[3].start()));

	// Output window
	Window window_out(window);
	const Window::Dimension zero_window = Window::Dimension(0, 0, 0);
	for(size_t d = 0; d <= _perm.num_dimensions(); ++d)
	{
	window_out.set(d, zero_window);
	}

	// Create iterators
	Iterator in(_input, window_in);
	Iterator out(_output, window_out);

	// CHW -> HWC
	if((_perm.num_dimensions() == 3) && (_perm[0] == 2) && (_perm[1] == 0) && (_perm[2] == 1))
	{
	const int in_row_stride = _input->info()->strides_in_bytes().y() / sizeof(T);
	const int in_channel_stride = _input->info()->strides_in_bytes().z() / sizeof(T);
	const int in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T);

	const int out_channel_stride = _output->info()->strides_in_bytes().x() / sizeof(T);
	const int out_col_stride = _output->info()->strides_in_bytes().y() / sizeof(T);
	const int out_row_stride = _output->info()->strides_in_bytes().z() / sizeof(T);
	const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T);

	const int n_cols = _input->info()->tensor_shape().x();
	const int n_rows = window_in.y().step();
	const int n_channels = _input->info()->tensor_shape().z();
	const int n_batches = _input->info()->tensor_shape()[3];

	execute_window_loop(window_in, [&](const Coordinates & id)
	{
	const int idx = id[0] * out_col_stride + id[1] * out_row_stride + id[2] * out_channel_stride;
	reorder::nchw_to_nhwc(reinterpret_cast<const T >(in.ptr()), reinterpret_cast<T >(out.ptr()) + idx,
	n_batches, n_channels, n_rows, n_cols,
	in_batch_stride, in_channel_stride, in_row_stride,
	out_batch_stride, out_row_stride, out_col_stride);
	},
	in, out);
	}
	// HWC -> CHW
	else if((_perm.num_dimensions() == 3) && (_perm[0] == 1) && (_perm[1] == 2) && (_perm[2] == 0))
	{
	const int in_col_stride = _input->info()->strides_in_bytes().y() / sizeof(T);
	const int in_row_stride = _input->info()->strides_in_bytes().z() / sizeof(T);
	const int in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T);

	const int out_col_stride = _output->info()->strides_in_bytes().x() / sizeof(T);
	const int out_row_stride = _output->info()->strides_in_bytes().y() / sizeof(T);
	const int out_channel_stride = _output->info()->strides_in_bytes().z() / sizeof(T);
	const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T);

	const int n_channels = _input->info()->tensor_shape().x();
	const int n_cols = window_in.y().step();
	const int n_rows = _input->info()->tensor_shape().z();
	const int n_batches = _input->info()->tensor_shape()[3];

	execute_window_loop(window_in, [&](const Coordinates & id)
	{
	const int idx = id[0] * out_channel_stride + id[1] * out_col_stride + id[2] * out_row_stride;
	reorder::nhwc_to_nchw(reinterpret_cast<const T >(in.ptr()), reinterpret_cast<T >(out.ptr()) + idx,
	n_batches, n_rows, n_cols, n_channels,
	in_batch_stride, in_row_stride, in_col_stride,
	out_batch_stride, out_channel_stride, out_row_stride);
	},
	in, out);
	}
	else
	{
	ARM_COMPUTE_ERROR("Unsupported permutation vector");
	}
	}

	NEPermuteKernel::NEPermuteKernel()
	: _func(), _input(nullptr), _output(nullptr), _perm()
	{
	}

	void NEPermuteKernel::configure(const ITensor input, ITensor output, const PermutationVector &perm)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input->info(), perm);
	// Output auto inizialitation if not yet initialized
	auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm));

	_input = input;
	_output = output;
	_perm = perm;

	switch(input->info()->element_size())
	{
	case 1:
	_func = &NEPermuteKernel::run_permute<uint8_t>;
	break;
	case 2:
	_func = &NEPermuteKernel::run_permute<uint16_t>;
	break;
	case 4:
	_func = &NEPermuteKernel::run_permute<uint32_t>;
	break;
	default:
	ARM_COMPUTE_ERROR("Element size not supported");
	break;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input->info(), Steps());

	// The NEPermute doesn't need padding so update_window_and_padding() can be skipped
	Coordinates coord;
	coord.set_num_dimensions(output->info()->num_dimensions());
	output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));

	ICPPKernel::configure(win);
	}

	Status NEPermuteKernel::validate(const ITensorInfo input, const ITensorInfo output, const PermutationVector &perm)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, perm));
	return Status{};
	}

	void NEPermuteKernel::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window);

	if(_func != nullptr)
	{
	(this->*_func)(window);
	}
	}