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

 /*
  * Copyright (c) 2019-2020 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 "src/core/NEON/kernels/NEFFTDigitReverseKernel.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/Window.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include <set>

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
 {
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() != DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() > 2);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(idx, 1, DataType::U32);
     ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[config.axis] != idx->tensor_shape().x());

     // Checks performed when output is configured
     if((output != nullptr) && (output->total_size() != 0))
     {
         ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 2);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
 {
     ARM_COMPUTE_UNUSED(idx, config);

     auto_init_if_empty(*output, input->clone()->set_num_channels(2));

     Window win = calculate_max_window(*input, Steps());
     input->set_valid_region(ValidRegion(Coordinates(), input->tensor_shape()));

     return std::make_pair(Status{}, win);
 }
 } // namespace

 NEFFTDigitReverseKernel::NEFFTDigitReverseKernel()
     : _func(nullptr), _input(nullptr), _output(nullptr), _idx(nullptr)
 {
 }

 void NEFFTDigitReverseKernel::configure(const ITensor *input, ITensor *output, const ITensor *idx, const FFTDigitReverseKernelInfo &config)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, idx);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), idx->info(), config));

     _input  = input;
     _output = output;
     _idx    = idx;

     const size_t axis             = config.axis;
     const bool   is_conj          = config.conjugate;
     const bool   is_input_complex = (input->info()->num_channels() == 2);

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), output->info(), idx->info(), config);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     INEKernel::configure(win_config.second);

     if(axis == 0)
     {
         if(is_input_complex)
         {
             if(is_conj)
             {
                 _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<true, true>;
             }
             else
             {
                 _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<true, false>;
             }
         }
         else
         {
             _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<false, false>;
         }
     }
     else if(axis == 1)
     {
         if(is_input_complex)
         {
             if(is_conj)
             {
                 _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<true, true>;
             }
             else
             {
                 _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<true, false>;
             }
         }
         else
         {
             _func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<false, false>;
         }
     }
     else
     {
         ARM_COMPUTE_ERROR("Not supported");
     }
 }

 Status NEFFTDigitReverseKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, idx, config));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), idx->clone().get(), config).first);
     return Status{};
 }

 template <bool is_input_complex, bool is_conj>
 void NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0(const Window &window)
 {
     const size_t N = _input->info()->dimension(0);

     // Copy the look-up buffer to a local array
     std::vector<unsigned int> buffer_idx(N);
     std::copy_n(reinterpret_cast<unsigned int *>(_idx->buffer()), N, buffer_idx.data());

     // Input/output iterators
     Window slice = window;
     slice.set(0, Window::DimX);
     Iterator in(_input, slice);
     Iterator out(_output, slice);

     // Row buffers
     std::vector<float> buffer_row_out(2 * N);
     std::vector<float> buffer_row_in(2 * N);

     execute_window_loop(slice, [&](const Coordinates &)
     {
         if(is_input_complex)
         {
             // Load
             memcpy(buffer_row_in.data(), reinterpret_cast<float *>(in.ptr()), 2 * N * sizeof(float));

             // Shuffle
             for(size_t x = 0; x < 2 * N; x += 2)
             {
                 size_t idx            = buffer_idx[x / 2];
                 buffer_row_out[x]     = buffer_row_in[2 * idx];
                 buffer_row_out[x + 1] = (is_conj ? -buffer_row_in[2 * idx + 1] : buffer_row_in[2 * idx + 1]);
             }
         }
         else
         {
             // Load
             memcpy(buffer_row_in.data(), reinterpret_cast<float *>(in.ptr()), N * sizeof(float));

             // Shuffle
             for(size_t x = 0; x < N; ++x)
             {
                 size_t idx            = buffer_idx[x];
                 buffer_row_out[2 * x] = buffer_row_in[idx];
             }
         }

         // Copy back
         memcpy(reinterpret_cast<float *>(out.ptr()), buffer_row_out.data(), 2 * N * sizeof(float));
     },
     in, out);
 }

 template <bool is_input_complex, bool is_conj>
 void NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1(const Window &window)
 {
     const size_t Nx = _input->info()->dimension(0);
     const size_t Ny = _input->info()->dimension(1);

     // Copy the look-up buffer to a local array
     std::vector<unsigned int> buffer_idx(Ny);
     std::copy_n(reinterpret_cast<unsigned int *>(_idx->buffer()), Ny, buffer_idx.data());

     // Output iterator
     Window slice = window;
     slice.set(0, Window::DimX);
     Iterator out(_output, slice);

     // Row buffer
     std::vector<float> buffer_row(Nx);

     // Strides
     const size_t stride_z = _input->info()->strides_in_bytes()[2];
     const size_t stride_w = _input->info()->strides_in_bytes()[3];

     execute_window_loop(slice, [&](const Coordinates & id)
     {
         auto        *out_ptr    = reinterpret_cast<float *>(out.ptr());
         auto        *in_ptr     = reinterpret_cast<float *>(_input->buffer() + id.z() * stride_z + id[3] * stride_w);
         const size_t y_shuffled = buffer_idx[id.y()];

         if(is_input_complex)
         {
             // Shuffle the entire row into the output
             memcpy(out_ptr, in_ptr + 2 * Nx * y_shuffled, 2 * Nx * sizeof(float));

             // Conjugate if necessary
             if(is_conj)
             {
                 for(size_t x = 0; x < 2 * Nx; x += 2)
                 {
                     out_ptr[x + 1] = -out_ptr[x + 1];
                 }
             }
         }
         else
         {
             // Shuffle the entire row into the buffer
             memcpy(buffer_row.data(), in_ptr + Nx * y_shuffled, Nx * sizeof(float));

             // Copy the buffer to the output, with a zero imaginary part
             for(size_t x = 0; x < 2 * Nx; x += 2)
             {
                 out_ptr[x] = buffer_row[x / 2];
             }
         }
     },
     out);
 }

 void NEFFTDigitReverseKernel::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
     ARM_COMPUTE_UNUSED(info);
     (this->*_func)(window);
 }

 } // namespace arm_compute
	/*
	* Copyright (c) 2019-2020 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 "src/core/NEON/kernels/NEFFTDigitReverseKernel.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/Window.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include <set>

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() != DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() > 2);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(idx, 1, DataType::U32);
	ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0);
	ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[config.axis] != idx->tensor_shape().x());

	// Checks performed when output is configured
	if((output != nullptr) && (output->total_size() != 0))
	{
	ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 2);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output, ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
	{
	ARM_COMPUTE_UNUSED(idx, config);

	auto_init_if_empty(*output, input->clone()->set_num_channels(2));

	Window win = calculate_max_window(*input, Steps());
	input->set_valid_region(ValidRegion(Coordinates(), input->tensor_shape()));

	return std::make_pair(Status{}, win);
	}
	} // namespace

	NEFFTDigitReverseKernel::NEFFTDigitReverseKernel()
	: _func(nullptr), _input(nullptr), _output(nullptr), _idx(nullptr)
	{
	}

	void NEFFTDigitReverseKernel::configure(const ITensor input, ITensor output, const ITensor *idx, const FFTDigitReverseKernelInfo &config)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, idx);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), idx->info(), config));

	_input = input;
	_output = output;
	_idx = idx;

	const size_t axis = config.axis;
	const bool is_conj = config.conjugate;
	const bool is_input_complex = (input->info()->num_channels() == 2);

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), output->info(), idx->info(), config);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	INEKernel::configure(win_config.second);

	if(axis == 0)
	{
	if(is_input_complex)
	{
	if(is_conj)
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<true, true>;
	}
	else
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<true, false>;
	}
	}
	else
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0<false, false>;
	}
	}
	else if(axis == 1)
	{
	if(is_input_complex)
	{
	if(is_conj)
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<true, true>;
	}
	else
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<true, false>;
	}
	}
	else
	{
	_func = &NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1<false, false>;
	}
	}
	else
	{
	ARM_COMPUTE_ERROR("Not supported");
	}
	}

	Status NEFFTDigitReverseKernel::validate(const ITensorInfo input, const ITensorInfo output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, idx, config));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), idx->clone().get(), config).first);
	return Status{};
	}

	template <bool is_input_complex, bool is_conj>
	void NEFFTDigitReverseKernel::digit_reverse_kernel_axis_0(const Window &window)
	{
	const size_t N = _input->info()->dimension(0);

	// Copy the look-up buffer to a local array
	std::vector<unsigned int> buffer_idx(N);
	std::copy_n(reinterpret_cast<unsigned int *>(_idx->buffer()), N, buffer_idx.data());

	// Input/output iterators
	Window slice = window;
	slice.set(0, Window::DimX);
	Iterator in(_input, slice);
	Iterator out(_output, slice);

	// Row buffers
	std::vector<float> buffer_row_out(2 * N);
	std::vector<float> buffer_row_in(2 * N);

	execute_window_loop(slice, [&](const Coordinates &)
	{
	if(is_input_complex)
	{
	// Load
	memcpy(buffer_row_in.data(), reinterpret_cast<float >(in.ptr()), 2 N * sizeof(float));

	// Shuffle
	for(size_t x = 0; x < 2 * N; x += 2)
	{
	size_t idx = buffer_idx[x / 2];
	buffer_row_out[x] = buffer_row_in[2 * idx];
	buffer_row_out[x + 1] = (is_conj ? -buffer_row_in[2 * idx + 1] : buffer_row_in[2 * idx + 1]);
	}
	}
	else
	{
	// Load
	memcpy(buffer_row_in.data(), reinterpret_cast<float >(in.ptr()), N sizeof(float));

	// Shuffle
	for(size_t x = 0; x < N; ++x)
	{
	size_t idx = buffer_idx[x];
	buffer_row_out[2 * x] = buffer_row_in[idx];
	}
	}

	// Copy back
	memcpy(reinterpret_cast<float >(out.ptr()), buffer_row_out.data(), 2 N * sizeof(float));
	},
	in, out);
	}

	template <bool is_input_complex, bool is_conj>
	void NEFFTDigitReverseKernel::digit_reverse_kernel_axis_1(const Window &window)
	{
	const size_t Nx = _input->info()->dimension(0);
	const size_t Ny = _input->info()->dimension(1);

	// Copy the look-up buffer to a local array
	std::vector<unsigned int> buffer_idx(Ny);
	std::copy_n(reinterpret_cast<unsigned int *>(_idx->buffer()), Ny, buffer_idx.data());

	// Output iterator
	Window slice = window;
	slice.set(0, Window::DimX);
	Iterator out(_output, slice);

	// Row buffer
	std::vector<float> buffer_row(Nx);

	// Strides
	const size_t stride_z = _input->info()->strides_in_bytes()[2];
	const size_t stride_w = _input->info()->strides_in_bytes()[3];

	execute_window_loop(slice, [&](const Coordinates & id)
	{
	auto out_ptr = reinterpret_cast<float >(out.ptr());
	auto in_ptr = reinterpret_cast<float >(_input->buffer() + id.z() * stride_z + id[3] * stride_w);
	const size_t y_shuffled = buffer_idx[id.y()];

	if(is_input_complex)
	{
	// Shuffle the entire row into the output
	memcpy(out_ptr, in_ptr + 2 * Nx * y_shuffled, 2 * Nx * sizeof(float));

	// Conjugate if necessary
	if(is_conj)
	{
	for(size_t x = 0; x < 2 * Nx; x += 2)
	{
	out_ptr[x + 1] = -out_ptr[x + 1];
	}
	}
	}
	else
	{
	// Shuffle the entire row into the buffer
	memcpy(buffer_row.data(), in_ptr + Nx * y_shuffled, Nx * sizeof(float));

	// Copy the buffer to the output, with a zero imaginary part
	for(size_t x = 0; x < 2 * Nx; x += 2)
	{
	out_ptr[x] = buffer_row[x / 2];
	}
	}
	},
	out);
	}

	void NEFFTDigitReverseKernel::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
	ARM_COMPUTE_UNUSED(info);
	(this->*_func)(window);
	}

	} // namespace arm_compute